T01-Introduction.Pdf

Topic 1: Introduction 1 (Version of 20th September 2021)

Pierre Flener and Jean-Noel¨ Monette

Optimisation Group Department of Information Technology Uppsala University Sweden

Course 1DL441: Combinatorial Optimisation and Constraint Programming, whose part 1 is Course 1DL451: Modelling for Combinatorial Optimisation

1Based partly on material by Guido Tack Optimisation

Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain

Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

Optimisation is a science of service: to scientists, to engineers, to artists, and to society.

COCP/M4CO 1 - 2 - MiniZinc Challenge 2015: Some Winners Problem & Model Backend & Solver Technology Costas array Mistral CP capacitated VRP iZplus hybrid Constraint Problems GFD schedule Chuffed LCG Combinatorial grid colouring MiniSAT(ID) hybrid Optimisation instruction scheduling Chuffed LCG Modelling (in MiniZinc) large scheduling Google OR-Tools.cp CP Solving application mapping JaCoP CP The MiniZinc Toolchain multi-knapsack mzn-cplex MIP

Course portfolio design fzn-oscar-cbls CBLS Information Part 1: Modelling for open stacks Chuffed LCG Combinatorial Optimisation project planning Chuffed LCG Part 2: Combinatorial Optimisation and CP radiation mzn-gurobi MIP Contact satellite management mzn-gurobi MIP time-dependent TSP G12.FD CP zephyrus configuration mzn-cplex MIP

COCP/M4CO 1 - 3 - Outline

1. Constraint Problems Constraint Problems Combinatorial 2. Combinatorial Optimisation Optimisation

Modelling (in MiniZinc) 3. Modelling (in MiniZinc)

Solving

The MiniZinc 4. Solving Toolchain Course 5. The MiniZinc Toolchain Information Part 1: Modelling for Combinatorial Optimisation 6. Course Information Part 2: Combinatorial Optimisation and CP Part 1: Modelling for Combinatorial Optimisation Contact Part 2: Combinatorial Optimisation and CP Contact

COCP/M4CO 1 - 4 - Outline

1. Constraint Problems Constraint Problems Combinatorial 2. Combinatorial Optimisation Optimisation

Modelling (in MiniZinc) 3. Modelling (in MiniZinc)

Solving

COCP/M4CO 1 - 5 - Example (Agricultural experiment design) plot1 plot2 plot3 plot4 plot5 plot6 plot7 barley Constraint Problems corn

Combinatorial millet Optimisation oats Modelling rye (in MiniZinc) spelt Solving wheat The MiniZinc Toolchain

Course Constraints to be satisfied: Information Part 1: Modelling for 1 Equal growth load: Every plot grows 3 grains. Combinatorial Optimisation Part 2: Combinatorial 2 Equal sample size: Every grain is grown in 3 plots. Optimisation and CP Contact 3 Balance: Every grain pair is grown in 1 common plot. Instance: 7 plots, 7 grains, 3 grains/plot, 3 plots/grain, balance 1.

COCP/M4CO 1 - 6 - Example (Agricultural experiment design) plot1 plot2 plot3 plot4 plot5 plot6 plot7 barley ✓ ✓ ✓ – – – – Constraint Problems corn ✓ – – ✓ ✓ – –

Combinatorial millet ✓ – – – – ✓ ✓ Optimisation oats – ✓ – ✓ – ✓ – Modelling rye – ✓ – – ✓ – ✓ (in MiniZinc) spelt – – ✓ ✓ – – ✓ Solving wheat – – ✓ – ✓ ✓ – The MiniZinc Toolchain

COCP/M4CO 1 - 6 - Example (Doctor rostering) Mon Tue Wed Thu Fri Sat Sun Doctor A Doctor B Constraint Problems Doctor C Combinatorial Doctor D Optimisation Doctor E Modelling (in MiniZinc) Constraints to be satisfied: Solving 1 #on-call doctors / day = 1 The MiniZinc Toolchain 2 #operating drs / weekday ≤ 2 Course Information 3 #operating drs / week ≥ 7 Part 1: Modelling for Combinatorial Optimisation 4 #appointed drs / week ≥ 4 Part 2: Combinatorial Optimisation and CP 5 day off after operation day Contact 6 ... Objective function to be minimised: Cost: . . .

COCP/M4CO 1 - 7 - Example (Doctor rostering) Mon Tue Wed Thu Fri Sat Sun Doctor A call none oper none oper none none Doctor B appt call none oper none none call Constraint Problems Doctor C oper none call appt appt call none Combinatorial Doctor D appt oper none call oper none none Optimisation Doctor E oper none oper none call none none Modelling (in MiniZinc) Constraints to be satisfied: Solving 1 #on-call doctors / day = 1 The MiniZinc Toolchain 2 #operating drs / weekday ≤ 2 Course Information 3 #operating drs / week ≥ 7 Part 1: Modelling for Combinatorial Optimisation 4 #appointed drs / week ≥ 4 Part 2: Combinatorial Optimisation and CP 5 day off after operation day Contact 6 ... Objective function to be minimised: Cost: . . .

COCP/M4CO 1 - 7 - Example (Vehicle routing: parcel delivery) Given a depot with parcels for clients and a vehicle fleet, find which vehicle visits which client when.

Constraint Problems Constraints to be satisfied:

Combinatorial 1 All parcels are delivered on time. Optimisation 2 No vehicle is overloaded. Modelling (in MiniZinc) 3 Driver regulations are respected. Solving 4 ... The MiniZinc Toolchain Objective function to be minimised: Course Information Cost: the total fuel consumption and driver salary. Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact Example (Travelling salesperson: optimisation TSP) Given a map and cities, find a shortest route visiting each city once and returning to the starting city.

COCP/M4CO 1 - 8 - Applications in Air Traffic Management

Demand vs capacity Airspace sectorisation Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain Contingency planning Workload balancing Course Flow Time Span Hourly Rate Information From: Arlanda 00:00 – 09:00 3 Part 1: Modelling for Combinatorial To: west, south 09:00 – 18:00 5 Optimisation 18:00 – 24:00 2 Part 2: Combinatorial From: Arlanda 00:00 – 12:00 4 Optimisation and CP To: east, north 12:00 – 24:00 3 Contact ......

COCP/M4CO 1 - 9 - Example (Air-traffic demand-capacity balancing) Reroute flights, in height and speed, so as to balance the workload of air traffic controllers in a multi-sector airspace:

Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain

Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

COCP/M4CO 1 - 10 - Example (Airspace sectorisation) Given an airspace split Find a colouring of the cells into c cells, a targeted into s connected convex sec-

Constraint number s of sectors, and tors, with minimal imbalance Problems flight schedules. of the workloads of their air Combinatorial Optimisation traffic controllers.

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain

Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

There are sc possible colourings, but very few optimally satisfy the constraints: is intelligent search necessary?

COCP/M4CO 1 - 11 - gy

Applications in Biology and Medicine

Phylogenetic supertree Haplotype inference Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain

Course Medical image analysis Doctor rostering Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

COCP/M4CO 1 - 12 - Example (What supertree is maximally consistent with several given trees that share some species?)

Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain

Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

COCP/M4CO 1 - 13 - Example (Haplotype inference by pure parsimony) Given n child genotypes, with homo- & heterozygous sites:

··· Constraint A C/G T C A/T C Problems ··· Combinatorial Optimisation A/T G T C/G A C

Modelling ··· (in MiniZinc) Solving find a minimal set of (at most 2 · n) parent haplotypes: The MiniZinc Toolchain ··· Course A C T C T C Information ··· Part 1: Modelling for Combinatorial Optimisation A G T C A C Part 2: Combinatorial Optimisation and CP ··· Contact T G T G A C ···

so that each given genotype conflates 2 found haplotypes.

COCP/M4CO 1 - 14 - Applications in Programming and Testing

Robot programming Sensor-net configuration Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving The MiniZinc Compiler design Base-station testing Toolchain

Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

COCP/M4CO 1 - 15 - Other Application Areas

School timetabling Sports tournament design

Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain

Course Security: SQL injection? Container packing Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

COCP/M4CO 1 - 16 - Definitions In a constraint problem, values have to be found for all the unknowns, called variables (in the mathematical sense) and ranging over given sets called domains, so that: Constraint Problems All the given constraints on the variables are satisfied. Combinatorial Optimisation Optionally: A given objective function on the variables Modelling has an optimal value: minimal cost or maximal profit. (in MiniZinc)

Solving

The MiniZinc Definitions Toolchain A candidate solution to a constraint problem maps each Course Information variable to a value within its domain; it is: Part 1: Modelling for Combinatorial Optimisation feasible if all the constraints are satisfied; Part 2: Combinatorial Optimisation and CP Contact optimal if the objective function takes an optimal value. The search space consists of all candidate solutions. A solution to a satisfaction problem is feasible. An optimal solution to an optimisation problem is feasible and optimal.

COCP/M4CO 1 - 17 - P =? NP (Cook, 1971; Levin, 1973)

This is one of the seven Millennium Prize problems Constraint of the Clay Mathematics Institute (Massachusetts, USA), Problems each worth 1 million US$. Combinatorial Optimisation Informally: Modelling (in MiniZinc) P = class of problems that need no search to be solved Solving NP = class of problems that might need search to solve The MiniZinc Toolchain P = class of problems with easy-to-compute solutions Course Information NP = class of problems with easy-to-check solutions Part 1: Modelling for Combinatorial Thus: Can search always be avoided (P = NP), Optimisation Part 2: Combinatorial Optimisation and CP or is search sometimes necessary (P ̸= NP)? Contact Problems that are solvable in polynomial time (in the input size) are considered tractable, or easy. Problems requiring super-polynomial time are considered intractable, or hard.

COCP/M4CO 1 - 18 - NP Completeness: Examples

Given a digraph (V , E):

Constraint Examples Problems

Combinatorial Finding a shortest path takes O(V · E) time and is in P. Optimisation

Modelling Determining the existence of a simple path (which has (in MiniZinc) distinct vertices), from a given single source, that has Solving at least a given number ℓ of edges is NP-complete. The MiniZinc Toolchain Hence finding a longest path seems hard: increase ℓ

Course starting from a trivial lower bound, until answer is ‘no’. Information Part 1: Modelling for Combinatorial Optimisation Examples Part 2: Combinatorial Optimisation and CP Contact Finding an Euler tour (which visits each edge once) takes O(E) time and is thus in P. Determining the existence of a Hamiltonian cycle (which visits each vertex once) is NP-complete.

COCP/M4CO 1 - 19 - NP Completeness: More Examples

Examples Constraint 2-SAT: Determining the satisfiability of a conjunction of Problems disjunctions of 2 Boolean literals is in P. Combinatorial Optimisation 3-SAT: Determining the satisfiability of a conjunction of Modelling (in MiniZinc) disjunctions of 3 Boolean literals is NP-complete. Solving SAT: Determining the satisfiability of a formula over The MiniZinc Toolchain Boolean literals is NP-complete. Course Clique: Determining the existence of a clique (complete Information Part 1: Modelling for Combinatorial subgraph) of a given size in a graph is NP-complete. Optimisation Part 2: Combinatorial Vertex Cover: Determining the existence of a vertex Optimisation and CP Contact cover (a vertex subset with at least one endpoint for all edges) of a given size in a graph is NP-complete. Subset Sum: Determining the existence of a subset, of a given set, that has a given sum is NP-complete. COCP/M4CO 1 - 20 - Search spaces are often larger than the universe!

Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain

Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Many important real-life problems are NP-hard or worse: Contact their real-life instances can only be solved exactly and fast enough by intelligent search, unless P = NP. NP-hardness is not where the fun ends, but where it begins!

COCP/M4CO 1 - 21 - Example (Optimisation TSP over n cities) A brute-force algorithm evaluates all n! candidate routes: A computer of today evaluates 106 routes / second: n time Constraint Problems 11 40 seconds Combinatorial 14 1 day Optimisation 18 203 years Modelling 20 77k years (in MiniZinc) −44 Solving Planck time is shortest useful interval: ≈ 5.4 · 10 s; 43 The MiniZinc a Planck computer would evaluate 1.8 · 10 routes / s: Toolchain n time Course Information 37 0.7 seconds Part 1: Modelling for Combinatorial 41 20 days Optimisation Part 2: Combinatorial 48 1.5 · age of universe Optimisation and CP Contact The dynamic program by Bellman-Held-Karp “only” takes O(n2 · 2n) time: a computer of today takes a day for n = 27, a year for n = 35, the age of the universe for n = 67, and it beats the O(n!) algo on the Planck computer for n ≥ 44.

COCP/M4CO 1 - 22 - Intelligent Search upon NP-Hardness

Do not give up but try to stay ahead of the curve: there is an instance size until which an exact algorithm is fast enough! Constraint Problems n! (today) n! (Planck) Combinatorial 18 Optimisation 10 age of universe Modelling 13 (in MiniZinc) 10 n2 · 2n (today) Solving 108 1 year The MiniZinc 5 Toolchain 10 1 day Course Information 100 Part 1: Modelling for time (s): log scale! n Combinatorial Optimisation 10 14 27 35 44 48 Part 2: Combinatorial Optimisation and CP Contact The Concorde TSP Solver beats the Bellman-Held-Karp exact algo: it uses approximation & local-search algorithms, but it can sometimes prove the exactness (optimality) of its solutions. The largest instance it has solved exactly, in 136 CPU years in 2006, has 85,900 cities! ☞ Let the fun begin! COCP/M4CO 1 - 23 - Outline

1. Constraint Problems Constraint Problems Combinatorial 2. Combinatorial Optimisation Optimisation

Modelling (in MiniZinc) 3. Modelling (in MiniZinc)

Solving

COCP/M4CO 1 - 24 - A solving technology offers methods and tools for:

what: Modelling constraint problems in declarative language.

and / or Constraint Problems

Combinatorial how: Solving constraint problems intelligently: Optimisation • Modelling Search: Explore the space of candidate solutions. (in MiniZinc) • Inference: Reduce the space of candidate solutions. Solving The MiniZinc • Relaxation: Exploit solutions to easier problems. Toolchain

Course Information A solver is a program that takes a model & data as Part 1: Modelling for Combinatorial input and tries to solve the modelled problem instance. Optimisation Part 2: Combinatorial Optimisation and CP Combinatorial (= discrete) optimisation covers satisfaction Contact and optimisation problems, for variables over discrete sets. The ideas in this course extend to continuous optimisation, to soft optimisation, and to stochastic optimisation.

COCP/M4CO 1 - 25 - Examples (Solving technologies) With general-purpose solvers, taking model&data as input: Boolean satisfiability (SAT)

Constraint SAT (resp. optimisation) modulo theories (SMT & OMT) Problems (Mixed) integer linear programming (IP & MIP) Combinatorial Optimisation Constraint programming (CP) ☞ part 2 of 1DL441 Modelling (in MiniZinc) ... Solving Hybrid technologies (LCG = CP + SAT, . . . ) The MiniZinc Toolchain Methodologies, usually without modelling and solvers: Course Information Dynamic programming (DP) Part 1: Modelling for Combinatorial Optimisation Greedy algorithms Part 2: Combinatorial Optimisation and CP Contact Approximation algorithms Local search (LS) Genetic algorithms (GA) ... COCP/M4CO 1 - 26 - Outline

1. Constraint Problems Constraint Problems Combinatorial 2. Combinatorial Optimisation Optimisation

Modelling (in MiniZinc) 3. Modelling (in MiniZinc)

Solving

COCP/M4CO 1 - 27 - What vs How

Example Constraint Problems Consider the problem of sorting an array A of n numbers

Combinatorial into an array S of increasing-or-equal numbers. Optimisation Modelling A formal specification is: (in MiniZinc) Solving sort(A, S) ≡ permutation(A, S) ∧ increasing(S) The MiniZinc Toolchain

Course saying S must be a permutation of A in increasing order. Information Part 1: Modelling for Combinatorial Seen as a generate-and-test algorithm, it takes O(n!) time, Optimisation Part 2: Combinatorial Optimisation and CP but it can be refined into the existing O(n log n) algorithms. Contact A specification is a declarative description of what problem is to be solved. An algorithm is an imperative description of how to solve the problem (efficiently).

COCP/M4CO 1 - 28 - Modelling vs Programming

problem Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc) specification Solving

The MiniZinc what? (declarative) how? (imperative) Toolchain

Course Information Part 1: Modelling for Combinatorial model algorithm Optimisation Part 2: Combinatorial Optimisation and CP Contact automatic! manual!

program program

COCP/M4CO 1 - 29 - Example (Sudoku)

8 8 1 2 7 5 3 6 4 9 3 6 9 4 3 6 8 2 1 7 5

Constraint 7 9 2 6 7 5 4 9 1 2 8 3 Problems 5 7 1 5 4 2 3 7 8 9 6 Combinatorial 4 5 7 3 6 9 8 4 5 7 2 1 Optimisation 1 3 2 8 7 1 6 9 5 3 4 Modelling 1 6 8 5 2 1 9 7 4 3 6 8 (in MiniZinc) 8 5 1 4 3 8 5 2 6 9 1 7 Solving 9 4 7 9 6 3 1 8 4 5 2 The MiniZinc Toolchain A Sudoku is a 9-by-9 array of integers in the range 1..9. Course Information Some of the elements are provided as parameters. Part 1: Modelling for Combinatorial The remaining elements are unknowns Optimisation Part 2: Combinatorial that have to satisfy the following constraints: Optimisation and CP Contact 1 the elements in each row are all different; 2 the elements in each column are all different; 3 the elements in each 3-by-3 block are all different.

COCP/M4CO 1 - 30 - Example (Sudoku) Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain

Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

COCP/M4CO 1 - 31 - Example (Sudoku)

8 8 1 2 7 5 3 6 4 9 3 6 9 4 3 6 8 2 1 7 5 7 9 2 6 7 5 4 9 1 2 8 3 Constraint Problems 5 7 1 5 4 2 3 7 8 9 6 4 5 7 3 6 9 8 4 5 7 2 1 Combinatorial Optimisation 1 3 2 8 7 1 6 9 5 3 4

Modelling 1 6 8 5 2 1 9 7 4 3 6 8 (in MiniZinc) 8 5 1 4 3 8 5 2 6 9 1 7 Solving 9 4 7 9 6 3 1 8 4 5 2 The MiniZinc Toolchain -2 array[1..9,1..9] of var 1..9: Sudoku; Course -1 Information Part 1: Modelling for 0 solve satisfy; Combinatorial Optimisation 1 constraint forall(row in 1..9) Part 2: Combinatorial Optimisation and CP (alldifferent(Sudoku[row,..])); Contact 2 constraint forall(col in 1..9) (alldifferent(Sudoku[..,col])); 3 constraint forall(i,j in {0,3,6}) (alldifferent(Sudoku[i+1..i+3,j+1..j+3]));

COCP/M4CO 1 - 32 - Example (Agricultural experiment design, AED) plot1 plot2 plot3 plot4 plot5 plot6 plot7 barley ✓ ✓ ✓ – – – – corn ✓ – – ✓ ✓ – – Constraint Problems millet ✓ – – – – ✓ ✓ Combinatorial oats – ✓ – ✓ – ✓ – Optimisation rye – ✓ – – ✓ – ✓ Modelling (in MiniZinc) spelt – – ✓ ✓ – – ✓ ✓ ✓ ✓ Solving wheat – – – –

The MiniZinc Toolchain Constraints to be satisfied: Course 1 Information Equal growth load: Every plot grows 3 grains. Part 1: Modelling for Combinatorial 2 Equal sample size: Every grain is grown in 3 plots. Optimisation Part 2: Combinatorial Optimisation and CP 3 Balance: Every grain pair is grown in 1 common plot. Contact Instance: 7 plots, 7 grains, 3 grains/plot, 3 plots/grain, balance 1.

General term: balanced incomplete block design(BIBD).

COCP/M4CO 1 - 33 - Example (Agricultural experiment design, AED) plot1 plot2 plot3 plot4 plot5 plot6 plot7 barley 1 1 1 0 0 0 0 corn 1 0 0 1 1 0 0 Constraint Problems millet 1 0 0 0 0 1 1 Combinatorial oats 0 1 0 1 0 1 0 Optimisation rye 0 1 0 0 1 0 1 Modelling (in MiniZinc) spelt 0 0 1 1 0 0 1

Solving wheat 0 0 1 0 1 1 0

General term: balanced incomplete block design(BIBD).

COCP/M4CO 1 - 33 - In a BIBD, the plots are blocks and the grains are varieties:

Example (BIBD integer model: ✓ ⇝ 1 and – ⇝ 0)

Constraint -3 enum Varieties; enum Blocks; Problems -2 int: blockSize; int: sampleSize; int: balance; Combinatorial -1 array[Varieties,Blocks] of var 0..1: BIBD; Optimisation 0 solve satisfy; Modelling 1 constraint forall(b in Blocks) (in MiniZinc) (blockSize = sum(BIBD[..,b]));

Solving 2 constraint forall(v in Varieties) (sampleSize = sum(BIBD[v,..])); The MiniZinc constraint forall(v, w in Varieties where v < w) Toolchain 3 (balance = sum([BIBD[v,b]*BIBD[w,b] | b in Blocks])); Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Example (Instance data for our AED) Optimisation and CP Contact -3 Varieties = {barley,...,wheat}; Blocks = {plot1,...,plot7}; -2 blockSize = 3; sampleSize = 3; balance = 1;

COCP/M4CO 1 - 34 - Using the count abstraction instead of sum:

Example (BIBD integer model: ✓ ⇝ 1 and – ⇝ 0)

Solving 2 constraint forall(v in Varieties) (sampleSize = count(BIBD[v,..], 1)); The MiniZinc constraint forall(v, w in Varieties where v < w) Toolchain 3 (balance = count([BIBD[v,b]*BIBD[w,b] | b in Blocks], 1)); Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Example (Instance data for our AED) Optimisation and CP Contact -3 Varieties = {barley,...,wheat}; Blocks = {plot1,...,plot7}; -2 blockSize = 3; sampleSize = 3; balance = 1;

COCP/M4CO 1 - 35 - Using the count abstraction over linear expressions:

Example (BIBD integer model: ✓ ⇝ 1 and – ⇝ 0)

Solving 2 constraint forall(v in Varieties) (sampleSize = count(BIBD[v,..], 1)); The MiniZinc constraint forall(v, w in Varieties where v < w) Toolchain 3 (balance = count([BIBD[v,b]+BIBD[w,b] | b in Blocks], 2)); Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Example (Instance data for our AED) Optimisation and CP Contact -3 Varieties = {barley,...,wheat}; Blocks = {plot1,...,plot7}; -2 blockSize = 3; sampleSize = 3; balance = 1;

COCP/M4CO 1 - 36 - Reconsider the model fragment:

2 constraint forall(v in Varieties) (sampleSize = count(BIBD[v,..], 1)); This constraint is declarative (and by the way non-linear), Constraint Problems so read it using only the verb “to be” or synonyms thereof:

Combinatorial Optimisation for all varieties v, Modelling the count of occurrences of 1 in row v of BIBD (in MiniZinc) must equal sampleSize Solving

The MiniZinc Toolchain The constraint is not procedural: Course Information v Part 1: Modelling for for all varieties , Combinatorial Optimisation we first count the occurrences of 1 in row v Part 2: Combinatorial Optimisation and CP and then check if that count equals sampleSize Contact

The latter reading is appropriate for solution checking, but solution finding performs no such procedural counting.

COCP/M4CO 1 - 37 - , plot4, plot5, plot6, plot7 plot2, plot3, , plot6, plot7 plot2, plot3, plot4, plot5, plot1, plot3, plot5, , plot7 plot1, plot3, plot4, plot6, plot1, plot2, plot5, plot6, plot1, plot2, plot4, , plot7

Example (Idea for another BIBD model) barley {plot1, plot2, plot3 } Constraint corn {plot1, plot4, plot5 } Problems

Combinatorial millet {plot1, plot6, plot7} Optimisation oats { plot2, plot4, plot6 } Modelling (in MiniZinc) rye { plot2, plot5, plot7}

Solving spelt { plot3, plot4, plot7} The MiniZinc wheat { plot3, plot5, plot6 } Toolchain Course Constraints to be satisfied: Information Part 1: Modelling for Combinatorial 1 Equal growth load: Every plot grows 3 grains. Optimisation Part 2: Combinatorial Optimisation and CP 2 Equal sample size: Every grain is grown in 3 plots. Contact 3 Balance: Every grain pair is grown in 1 common plot.

COCP/M4CO 1 - 38 - Example (BIBD set model: a block set per variety)

-3 enum Varieties; enum Blocks; Constraint -2 int: blockSize; int: sampleSize; int: balance; Problems -1 array[Varieties] of var set of Blocks: BIBD; Combinatorial 0 solve satisfy; Optimisation 1 constraint forall(b in Blocks) Modelling (blockSize = sum(v in Varieties)(b in BIBD[v])); (in MiniZinc) 2 constraint forall(v in Varieties)

Solving (sampleSize = card(BIBD[v])); 3 constraint forall(v, w in Varieties where v < w) The MiniZinc (balance = card(BIBD[v] intersect BIBD[w])); Toolchain

Course Information Part 1: Modelling for Combinatorial Example (Instance data for our AED) Optimisation Part 2: Combinatorial Optimisation and CP -3 Varieties = {barley,...,wheat}; Blocks = {plot1,...,plot7}; Contact -2 blockSize = 3; sampleSize = 3; balance = 1;

COCP/M4CO 1 - 39 - Example (Doctor rostering) Mon Tue Wed Thu Fri Sat Sun Doctor A call none oper none oper none none Doctor B appt call none oper none none call Constraint Problems Doctor C oper none call appt appt call none Combinatorial Doctor D appt oper none call oper none none Optimisation Doctor E oper none oper none call none none Modelling (in MiniZinc) Constraints to be satisfied: Solving 1 #on-call doctors / day = 1 The MiniZinc Toolchain 2 #operating drs / weekday ≤ 2 Course Information 3 #operating drs / week ≥ 7 Part 1: Modelling for Combinatorial Optimisation 4 #appointed drs / week ≥ 4 Part 2: Combinatorial Optimisation and CP 5 day off after operation day Contact 6 ... Objective function to be minimised: Cost: . . .

COCP/M4CO 1 - 40 - Example (Doctor rostering)

-4 set of int: Days;%d mod7=1 iffd isa Monday -3 enum Doctors; -2 enum ShiftTypes = {appt, call, oper, none}; Constraint -1 array[Doctors,Days] of var ShiftTypes: Roster; Problems 0 solve minimize ...;% plug in an objective function Combinatorial 1 constraint forall(d in Days) Optimisation (count(Roster[..,d],call) = 1); Modelling 2 constraint forall(d in Days whered mod7 in 1..5) (in MiniZinc) (count(Roster[..,d],oper) <= 2); Solving 3 constraint count(Roster,oper) >= 7; The MiniZinc 4 constraint count(Roster,appt) >= 4; Toolchain 5 constraint forall(d in Doctors) Course (regular(Roster[d,..],"((oper none)|appt|call|none) *")); Information 6 ...% other constraints Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact Example (Instance data for our small hospital unit)

-4 Days = 1..7; -3 Doctors = {Dr_A, Dr_B, Dr_C, Dr_D, Dr_E};

COCP/M4CO 1 - 41 - Using variables as indices within arrays: black magic?!

Constraint Problems Example (Job allocation at minimal salary cost) Combinatorial Optimisation Given jobs Jobs and the salaries of work applicants Apps, Modelling (in MiniZinc) find a work applicant for each job

Solving such that some constraints (on the qualifications of the

The MiniZinc work applicants for the jobs, on workload distribution, etc) Toolchain are satisfied and the total salary cost is minimal: Course Information 1 array[Apps] of 0..1000: Salary;% Salary[a]/job bya Part 1: Modelling for Combinatorial 2 array[Jobs] of var Apps: Worker;% jobj by Worker[j] Optimisation Part 2: Combinatorial 3 solve minimize sum(j in Jobs)(Salary[Worker[j]]); Optimisation and CP Contact 4 constraint ...;% qualifications, workload, etc

COCP/M4CO 1 - 42 - 128.8 162.6 BRU LUX

1 array[Cities,Cities] of float: Dist;% instance data 2 array[Cities] of var Cities: Next;% fromc to Next[c] 3 solve minimize sum(c in Cities)(Dist[c,Next[c]]); 4 constraint circuit(Next); 5 constraint ...;% side constraints, if any

Using variables as indices within arrays: black magic?!

Example (Vehicle routing: backbone model) Constraint Problems

Combinatorial BRU AMS Optimisation enum Cities = {AMS,BRU,LUX,CDG} Modelling (in MiniZinc) AMS BRU LUX CDG Solving Next: The MiniZinc Toolchain CDG LUX

Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

COCP/M4CO 1 - 43 - 128.8 162.6

Using variables as indices within arrays: black magic?!

COCP/M4CO 1 - 43 - 1 array[Cities,Cities] of float: Dist;% instance data 2 array[Cities] of var Cities: Next;% fromc to Next[c] 3 solve minimize sum(c in Cities)(Dist[c,Next[c]]); 4 constraint circuit(Next); 5 constraint ...;% side constraints, if any

Using variables as indices within arrays: black magic?!

COCP/M4CO 1 - 43 - Using variables as indices within arrays: black magic?!

Example (Vehicle routing: backbone model) Constraint Problems 85.2 Combinatorial BRU AMS Optimisation enum Cities = {AMS,BRU,LUX,CDG} Modelling (in MiniZinc) AMS BRU LUX CDG 128.8 162.6 Solving Next: BRU CDG AMS LUX The MiniZinc circuit(Next) Toolchain Let us use instead: CDG LUX 146.7 Course Information 1 array[Cities,Cities] of float: Dist;% instance data Part 1: Modelling for Combinatorial Optimisation 2 array[Cities] of var Cities: Next;% fromc to Next[c] Part 2: Combinatorial 3 solve minimize sum(c in Cities)(Dist[c,Next[c]]); Optimisation and CP Contact 4 constraint circuit(Next); 5 constraint ...;% side constraints, if any

COCP/M4CO 1 - 43 - Toy Example: 8-Queens

Constraint Problems Can one place 8 queens onto an 8 × 8 chessboard so that Combinatorial all queens are in distinct rows, columns, and diagonals? Optimisation

Modelling

(in MiniZinc)

Solving

The MiniZinc

Toolchain Course

Information

Part 1: Modelling for

Combinatorial

Optimisation

Part 2: Combinatorial

Optimisation and CP

Contact

COCP/M4CO 1 - 44 - An 8-Queens Model

One of the many models, with one variable per queen: Row[c] Constraint Let variable , Problems of domain 1..8, represent the

Combinatorial

Optimisation row of the queen in column c, for Modelling c in a..h, renamed into 1..8. (in MiniZinc) Example: Row[3] = 4 means Solving

the queen of column 3 is in row 4. The MiniZinc

Toolchain The constraint that all queens

Course be in distinct columns is satis-

Information

Part 1: Modelling for fied by the choice of variables!

Combinatorial Optimisation Part 2: Combinatorial The remaining constraints to be satisfied are: Optimisation and CP Contact • All queens are in distinct rows: the variables Row[c] take distinct values for all c. • All queens are in distinct diagonals: the expressions Row[c]+c take distinct values for all c; the expressions Row[c]-c take distinct values for all c. COCP/M4CO 1 - 45 - An 8-Queens Model in MiniZinc

Consider the following model in a file 8-queens.mzn:

Constraint 1 % Model of the 8-queens problem Problems 2 include"globals.mzn"; Combinatorial 3 % parameter: Optimisation 4 int: n = 8;%n denotes the given number of queens Modelling (in MiniZinc) 5 % Row[c] denotes the row of the queen in columnc: 6 array[1..n] of var 1..n: Row;% variables and domains Solving 7 % constraints: The MiniZinc Toolchain 8 constraint alldifferent( Row );

Course 9 constraint alldifferent([Row[c]+c | c in 1..n]); Information 10 constraint alldifferent([Row[c]-c | c in 1..n]); Part 1: Modelling for Combinatorial 11 % objective: Optimisation Part 2: Combinatorial 12 solve satisfy;% solve to satisfaction Optimisation and CP Contact 13 % pretty-printing of solutions: 14 output[show(Row)]; The alldifferent constraint predicate requires that all its argument expressions take different values.

COCP/M4CO 1 - 46 - Modelling Concepts

A variable, also called a decision variable, Constraint Problems is an existentially quantified unknown of a problem.

Combinatorial Optimisation The domain of a variable x, here denoted by dom(x), is Modelling the set of values in which x must take its value, if any. (in MiniZinc) Solving A variable expression takes a value that depends on The MiniZinc Toolchain the value of one or more decision variables.

Course Information A parameter has a value from a problem description. Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Variables, parameters, and expressions are typed. Optimisation and CP Contact MiniZinc types are (arrays and sets of) Booleans, integers, floating-point numbers, enumerations, and strings, but not all these types can serve as types for variables.

COCP/M4CO 1 - 47 - Variables, Parameters, and Identifiers

Decision variables and parameters in a model are Constraint concepts very different from programming variables in Problems

Combinatorial an imperative or object-oriented program. Optimisation

Modelling A variable in a model is like a variable in mathematics: (in MiniZinc) it is not given a value in a model or a formula, and Solving its value is only fixed in a solution, if a solution exists. The MiniZinc Toolchain A parameter in a model must be given a value, but only Course Information once: we say that it is instantiated. Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial A variable or parameter is referred to by an identifier. Optimisation and CP Contact An index identifier of an array comprehension takes on all its possible values in turn. Example: the index c in the 8-queens model.

COCP/M4CO 1 - 48 - Parametric Models

A parameter need not be instantiated inside a model. Constraint Problems Ex: drop “=8” from “int: n=8” in the 8-queens model Combinatorial in order to make it an n-queens model, and Optimisation

Modelling rename the file 8-queens.mzn into n-queens.mzn. (in MiniZinc) Solving Data are values for parameters given outside a model, The MiniZinc Toolchain either in a datafile( .dzn suffix), or at the command Course line, or interactively in the integrated development Information Part 1: Modelling for environment (IDE). Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact A parametric model has uninstantiated parameters.

An instance is a pair of a parametric model and data.

COCP/M4CO 1 - 49 - Modelling Concepts (end)

A constraint is a restriction on the values that its Constraint Problems variables can take conjointly; equivalently, it is a Combinatorial Boolean-valued variable expression that must be true. Optimisation

Modelling (in MiniZinc) An objective function is a numeric variable expression

Solving whose value is to be minimised or maximised.

The MiniZinc Toolchain An objective states what is being asked for: Course • Information find a first solution Part 1: Modelling for • Combinatorial find a solution minimising an objective function Optimisation • Part 2: Combinatorial find a solution maximising an objective function Optimisation and CP • Contact find all solutions • count the number of solutions • prove that there is no solution • ...

COCP/M4CO 1 - 50 - Constraint-Based Modelling

MiniZinc is a high-level constraint-based modelling

Constraint language (not a solver): Problems

Combinatorial There are several types for variables: int, enum, Optimisation float, bool, string, and set, possibly as elements Modelling (in MiniZinc) of multidimensionsal matrices (array). Solving There is a nice vocabulary of predicates (<, <=, =, !=, The MiniZinc Toolchain >=, >, alldifferent, circuit, regular, . . . ), Course functions (+, −, , card, count, intersect, sum, Information * Part 1: Modelling for not /\ \/ -> <- <-> Combinatorial . . . ), and connectives ( , , , , , , . . . ). Optimisation Part 2: Combinatorial Optimisation and CP There is support for both constraint satisfaction Contact (satisfy) and constrained optimisation (minimize and maximize). Most modelling languages are (much) lower-level than this!

COCP/M4CO 1 - 51 - Correctness Is Not Enough for Models

Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain

Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

COCP/M4CO 1 - 52 - Modelling is an Art!

There are good & bad models for each constraint problem: Constraint Problems Different models of a problem may take different time Combinatorial Optimisation on the same solver for the same instance. Modelling (in MiniZinc) Different models of a problem may scale differently Solving

The MiniZinc on the same solver for instances of growing size. Toolchain Course Different solvers may take different time Information Part 1: Modelling for on the same model for the same instance. Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact Good modellers are worth their weight in gold! Use solvers: based on decades of cutting-edge research, they are very hard to beat on exact solving.

COCP/M4CO 1 - 53 - Outline

1. Constraint Problems Constraint Problems Combinatorial 2. Combinatorial Optimisation Optimisation

Modelling (in MiniZinc) 3. Modelling (in MiniZinc)

Solving

COCP/M4CO 1 - 54 - Solutions to a problem instance can be found by running a MiniZinc backend, that is a MiniZinc wrapper for a particular solver, on a file containing a model of the problem. Example (Solving the 8-queens instance) Constraint Problems Let us run the solver Gecode, of CP technology, from the Combinatorial command line: Optimisation

Modelling (in MiniZinc) minizinc --solver gecode 8-queens.mzn

Solving

The MiniZinc The result is printed on stdout: Toolchain

Course [4, 2, 7, 3, 6, 8, 5, 1] Information ------Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial This means that the queen of column 1 is in row 4, Optimisation and CP Contact the queen of column 2 is in row 2, and so on. Use the command-line flag -a to ask for all solutions: the line ------is printed after each solution, but the line ======is printed after the last (92nd) solution.

COCP/M4CO 1 - 55 - How Do Solvers Work?

Definition (Solving = Search + Inference + Relaxation) Constraint Search: Explore the space of candidate solutions. Problems

Combinatorial Inference: Reduce the space of candidate solutions. Optimisation

Modelling Relaxation: Exploit solutions to easier problems. (in MiniZinc) Solving Definition (Systematic Search) The MiniZinc Toolchain Progressively build a solution, and backtrack if necessary. Course Use inference and relaxation to reduce the search effort. Information Part 1: Modelling for It is used in most SAT, SMT, OMT, CP, LCG, & MIP solvers. Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Definition (Local Search) Contact Start from a candidate solution and iteratively modify it. It is the basic idea behind LS and GA technologies. For details, see Topic 7: Solving Technologies. COCP/M4CO 1 - 56 - There Are So Many Solving Technologies

Constraint No technology universally dominates all the others. Problems

Combinatorial Optimisation One should test several technologies on each problem. Modelling (in MiniZinc)

Solving Some technologies have no modelling languages: The MiniZinc LS, DP, and GA are rather methodologies. Toolchain

Course Information Some technologies have standardised modelling Part 1: Modelling for Combinatorial Optimisation languages across all solvers: SAT, SMT, OMT, & (M)IP. Part 2: Combinatorial Optimisation and CP Contact Some technologies have non-standardised modelling languages across their solvers: CP and LCG.

COCP/M4CO 1 - 57 - Model and Solve

Advantages: Constraint Problems + Declarative model of a problem. Combinatorial Optimisation Modelling + Easy adaptation to changing problem requirements. (in MiniZinc) Solving + Use of powerful solving technologies that are based on The MiniZinc Toolchain decades of cutting-edge research.

Course Information Part 1: Modelling for Disadvantages: Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP − Need to learn several modelling languages? No! Contact − Need to understand the used solving technologies in order to get the most out of them? Yes, but . . . !

COCP/M4CO 1 - 58 - Outline

1. Constraint Problems Constraint Problems Combinatorial 2. Combinatorial Optimisation Optimisation

Modelling (in MiniZinc) 3. Modelling (in MiniZinc)

Solving

COCP/M4CO 1 - 59 - MiniZinc

MiniZinc is a declarative language (not a solver) for the

Constraint constraint-based modelling of constraint problems: Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving

The MiniZinc Toolchain Course At Monash University, Australia Information Part 1: Modelling for Combinatorial Introduced in 2007; version 2.0 in 2014 Optimisation Part 2: Combinatorial Optimisation and CP Homepage: https://www.minizinc.org Contact Integrated development environment (IDE) Annual MiniZinc Challenge for solvers, since 2008 There are also courses at Coursera, also in Chinese

COCP/M4CO 1 - 60 - MiniZinc Features

Declarative language for modelling what the problem is Constraint Problems Separation of problem model and instance data Combinatorial Optimisation Open-source toolchain Modelling (in MiniZinc) Much higher-level language than those of (M)IP & SAT Solving

The MiniZinc Solver-independent language Toolchain

Course Solving-technology-independent language Information Part 1: Modelling for Combinatorial Vocabulary of predefined types, predicates & functions Optimisation Part 2: Combinatorial Optimisation and CP Contact Support for user-defined predicates and functions Support for annotations with hints on how to solve Ever-growing number of users, solvers, and other tools

COCP/M4CO 1 - 61 - Backends installed on IT dept’s ThinLinc hardware are red. The commercial Gurobi Optimizer is under a free academic license: you may not use it for non-academic purposes.

Solvers with MiniZinc Backends

SAT = Boolean satisfiability: Plingeling via PicatSAT, ... OMT = optimisation modulo theories: Constraint Problems OptiMathSAT via emzn2fzn + fzn2omt Combinatorial MIP = mixed integer programming: Cbc, FICO Xpress, Optimisation

COCP/M4CO 1 - 62 - Solvers with MiniZinc Backends

Modelling Gurobi Optimizer, IBM ILOG CPLEX Optimizer, . . . (in MiniZinc) CP = constraint programming: Solving Choco, Gecode, JaCoP, Mistral, SICStus Prolog, . . . The MiniZinc Toolchain CBLS = constraint-based LS (local search): Course OscaR.cbls via fzn-oscar-cbls, Yuck, . . . Information Part 1: Modelling for LCG = lazy clause generation = CP + SAT: Combinatorial Optimisation Chuffed, Google OR-Tools, Opturion CPX, . . . Part 2: Combinatorial Optimisation and CP Contact Other hybrid technos: iZplus, MiniSAT(ID), SCIP, . . . Portfolios of solvers: sunny-cp, . . . Backends installed on IT dept’s ThinLinc hardware are red. The commercial Gurobi Optimizer is under a free academic license: you may not use it for non-academic purposes. COCP/M4CO 1 - 62 - MiniZinc Challenge 2015: Some Winners Problem & Model Backend & Solver Technology Costas array Mistral CP capacitated VRP iZplus hybrid Constraint GFD schedule Chuffed LCG Problems grid colouring MiniSAT(ID) hybrid Combinatorial Optimisation instruction scheduling Chuffed LCG Modelling large scheduling Google OR-Tools.cp CP (in MiniZinc)

Solving application mapping JaCoP CP

The MiniZinc multi-knapsack mzn-cplex MIP Toolchain portfolio design fzn-oscar-cbls CBLS Course Information open stacks Chuffed LCG Part 1: Modelling for Combinatorial project planning Chuffed LCG Optimisation Part 2: Combinatorial Optimisation and CP radiation mzn-gurobi MIP Contact satellite management mzn-gurobi MIP time-dependent TSP G12.FD CP zephyrus configuration mzn-cplex MIP (portfolio and parallel categories omitted)

COCP/M4CO 1 - 63 - MiniZinc: Model Once, Solve Everywhere!

Constraint instance Problems data Combinatorial Optimisation

Modelling (in MiniZinc) flat backend Solving model flattening model & solver The MiniZinc Toolchain

Course Information Part 1: Modelling for Combinatorial techno & solver Optimisation (optimal) Part 2: Combinatorial capabilities Optimisation and CP solution Contact From a single language, one has access transparently to a wide range of solving technologies from which to choose.

COCP/M4CO 1 - 64 - There Is No Need to Reinvent the Wheel!

Before solving, each variable of a type that is non-native to Constraint the targeted solver is replaced by variables of native types, Problems

Combinatorial using some well-known linear / clausal / . . . encoding. Optimisation

Modelling (in MiniZinc) Example (SAT) Solving The order encoding of integer variable var 4..6: x is The MiniZinc Toolchain array[4..7] of var bool: B;%B[i] denotes truth ofx >=i Course constraint B[4];% lower bound onx Information constraint not B[7];% upper bound onx Part 1: Modelling for Combinatorial constraint B[4] \/ not B[5];% consistency Optimisation constraint B[5] \/ not B[6];% consistency Part 2: Combinatorial Optimisation and CP constraint B[6] \/ not B[7];% consistency Contact For an integer variable with n domain values, there are n + 1 Boolean variables and n clauses, all 2-ary.

COCP/M4CO 1 - 65 - Before solving, each use of a non-native predicate or function is replaced by either: its MiniZinc-provided default definition, stated in terms of a kernel of imposed predicates;

Constraint Problems Example (default; not to be used for IP and MIP) Combinatorial alldifferent([x,y,z]) x!=y/\y!=z/\z!=x Optimisation gives .

Modelling (in MiniZinc) or: a backend-provided solver-specific definition, Solving using some well-known linear / clausal / . . . encoding. The MiniZinc Toolchain Example (IP and MIP) Course Information A compact linearisation of x != y is Part 1: Modelling for Combinatorial Optimisation var 0..1: p;%p=1 denotes thatxy andp=0 One cannot naturally model graph colouring in IP, but the problem has integer variables (ranging over the colours).

COCP/M4CO 1 - 66 - Benefits of Model-and-Solve with MiniZinc

+ Try many solvers of many technologies from 1 model. Constraint Problems + A model improves with the state of the art of backends: Combinatorial Optimisation • Variable type: native representation or encoding. Modelling (in MiniZinc) • Predicate: inference, relaxation, and definition. Solving • Implementation of a solving technology. The MiniZinc Toolchain More on this in Topic 7: Solving Technologies. Course Information Part 1: Modelling for + For most managers, engineers, and scientists, it is Combinatorial Optimisation easier with such a model-once-&-solve-everywhere Part 2: Combinatorial Optimisation and CP toolchain to achieve good solution quality and high Contact solving speed, including for harder and bigger data, and without knowing (deeply) how the solvers work, compared to programming from first principles.

COCP/M4CO 1 - 67 - • Understand the problem • Choose the decision variables and their domains • Choose predicates to model the constraints • Model the objective function, if any • Make sure the model really represents the problem • Iterate!

• Choose a solving technology • Choose a backend • Choose a search strategy, if not black-box search • Improve the model • Run the model and interpret the (lack of) solution(s) • Debug the model, if need be • Iterate!

How to Solve a Constraint Problem?

1 Model the problem

Constraint Problems

Combinatorial Optimisation

Modelling (in MiniZinc)

Solving The MiniZinc 2 Solve the problem Toolchain

Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Contact

Easy, right?

COCP/M4CO 1 - 68 - How to Solve a Constraint Problem?

1 Model the problem

Constraint • Understand the problem Problems • Choose the decision variables and their domains Combinatorial • Choose predicates to model the constraints Optimisation • Model the objective function, if any Modelling (in MiniZinc) • Make sure the model really represents the problem Solving • Iterate! The MiniZinc 2 Solve the problem Toolchain • Course Choose a solving technology Information • Choose a backend Part 1: Modelling for Combinatorial • Optimisation Choose a search strategy, if not black-box search Part 2: Combinatorial • Optimisation and CP Improve the model Contact • Run the model and interpret the (lack of) solution(s) • Debug the model, if need be • Iterate! Easy, right?

COCP/M4CO 1 - 68 - How to Solve a Constraint Problem?

1 Model the problem

COCP/M4CO 1 - 68 - Outline

1. Constraint Problems Constraint Problems Combinatorial 2. Combinatorial Optimisation Optimisation

Modelling (in MiniZinc) 3. Modelling (in MiniZinc)

Solving

COCP/M4CO 1 - 69 - Outline

1. Constraint Problems Constraint Problems Combinatorial 2. Combinatorial Optimisation Optimisation

Modelling (in MiniZinc) 3. Modelling (in MiniZinc)

Solving

COCP/M4CO 1 - 70 - Content of Part 1 = M4CO (course 1DL451)

Constraint Problems The use of tools for solving a combinatorial problem, by Combinatorial Optimisation

Modelling (in MiniZinc)

Solving 1 first modelling it in a solving-technology-independent The MiniZinc Toolchain constraint-based modelling language, and Course Information Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP 2 then running the model on an off-the-shelf solver. Contact

COCP/M4CO 1 - 71 - Learning Outcomes of Part 1 = M4CO

In order to pass, the student must be able to: define the concept of combinatorial problem; Constraint Problems explain the concept of constraint, as used in a Combinatorial constraint-based modelling language; Optimisation

Modelling model a combinatorial problem in a constraint-based (in MiniZinc) solving-technology-independent modelling language; Solving compare empirically several models, say by introducing The MiniZinc Toolchain redundancy or by detecting and breaking symmetries; Course describe and compare solving technologies that can be Information Part 1: Modelling for used by the backends to a constraint-based modelling Combinatorial Optimisation language, including CP, LS, SAT, SMT, and MIP; Part 2: Combinatorial Optimisation and CP Contact choose suitable solving technologies for a new combinatorial problem, and motivate this choice; present & discuss topics related to the course content, orally and in writing, with a skill appropriate for the level of education. ☞ written reports & oral resubmissions! COCP/M4CO 1 - 72 - Organisation and Time Budget of Part 1

Period 1: late August to late October, budget = 133.3 h:

Constraint 1 student-chosen project, to be done in student-chosen Problems duo team, and peer review of other team’s initial report: Combinatorial Optimisation budget = 45 hours / student (2 credits) Modelling 12 lectures, plus 3 mandatory project presentation (in MiniZinc)

Solving sessions: budget = 22.5 hours The MiniZinc No textbook: slides, MiniZinc documentation, Coursera Toolchain

Course 1 warm-up session for learning the MiniZinc toolchain Information Part 1: Modelling for 3 teacher-chosen assignments with 3 help sessions, Combinatorial Optimisation Part 2: Combinatorial 1 grading session, and 1 solution session each, Optimisation and CP Contact to be done in student-chosen duo team: budget = avg 22 hours / assignment / student (3 credits) Prerequisites: basic algebra, combinatorics, logic, graph theory, set theory, and search algorithms

COCP/M4CO 1 - 73 - Lecture Topics of Part 1 = M4CO

Topic 1: Introduction

Constraint Problems Topic 2: Basic Modelling

Combinatorial Optimisation Topic 3: Constraint Predicates

Modelling (in MiniZinc) Topic 4: Modelling (for CP & LCG) Solving Topic 5: Symmetry The MiniZinc Toolchain Topic 6: Case Studies Course Information Topic 7: Solving Technologies Part 1: Modelling for Combinatorial Optimisation Topic 8: Inference & Search in CP & LCG Part 2: Combinatorial Optimisation and CP Contact (Topic 9: Modelling for CBLS) (Topic 10: Modelling for SAT, SMT, and OMT) (Topic 11: Modelling for MIP)

COCP/M4CO 1 - 74 - Project (2 credits) in Part 1 = M4CO

Topic:

Constraint Model and solve a combinatorial problem that you are Problems interested in, say for research, a course, a hobby, . . . Combinatorial Optimisation Ask us, or see sites like Google Hash Code or CSPlib Modelling for problems (with no published MiniZinc or OPL (in MiniZinc) models) and third-party instance data. Solving

The MiniZinc Deadlines in 2021 (overlap with Assignments 2 and 3): Toolchain Wed 15 Sep at 15:00: upload proposal Course Information Wed 22 Sep at 17:00: secure our approval Part 1: Modelling for Combinatorial Optimisation Tue 12 Oct at 13:00: upload initial report Part 2: Combinatorial Optimisation and CP Contact Wed 13 & Thu 14 Oct: present, oppose, upload slides Fri 15 Oct at 13:00: upload peer review Wed 27 Oct at 13:00: upload report; score p ∈ 0..10 The length & order of presentations will be fixed in due time. COCP/M4CO 1 - 75 - 3 Assignment Cycles of 2–3 Weeks in Part 1

Let Di be the deadline of Assignment i, with i ∈ 1..3:

Constraint Problems Di − 14: publication & all needed material taught: start!

Combinatorial Optimisation Di − 8: help session a: attendance recommended

Modelling (in MiniZinc) Di − 4: help session b: attendance recommended Solving Di − 2: help session c: attendance recommended The MiniZinc Toolchain Di ± 0: submission, by 13:00 Swedish time on a Friday Course Information Part 1: Modelling for Di + 5 by 16:00: initial score ai ∈ 0..5 points Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Di + 6: teamwise oral grading session Contact for some ai ∈ {1, 2}: possibility of earning 1 extra point for final score; otherwise final score = initial score

Di + 6 = Di+1 − 8: solution session& help sessiona

COCP/M4CO 1 - 76 - Assignments (3 c) & Overall Grade in Part 1

The final score on Assignment 1 is actually “pass” or “fail”. Constraint Problems Let ai ∈ 0..5 be final score on Assignment i, with i ∈ 2..3: Combinatorial Optimisation 20% threshold: ∀i ∈ 2..3 : a ≥ 20% · 5 = 1 Modelling i (in MiniZinc) No catastrophic failure on individual assignments Solving The MiniZinc 50% threshold: a = a + a ≥ 50% · (5 + 5) = 5 Toolchain 2 3 ☞ Course the formulae for the modelling assignment grade Information and project grade in 3..5 are at the course homepage Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Optimisation and CP Worth going full-blast: An assignment sum a ∈ 5..10 Contact is combined with a project score p ∈ 5..10 to determine the overall grade in 3..5 for 1DL451 or Part 1 of 1DL441 according to a formula at the course homepage

COCP/M4CO 1 - 77 - Assignment and Project Rules

Constraint Duo team: Two consenting partners sign up Problems Solo team: Apply to head teacher, who rarely agrees Combinatorial Optimisation Random partner? Assent to TAs, else you’re bounced Modelling (in MiniZinc) Other considerations: Solving Why (not) like this? Why no email reply? See FAQ The MiniZinc Toolchain Partner swapping: Allowed, but to be declared to TAs Course Partner scores may differ if no-show or passivity Information Part 1: Modelling for Combinatorial No freeloader: Implicit honour declaration in reports Optimisation Part 2: Combinatorial that each partner can individually explain everything; Optimisation and CP Contact random checks will be made by us No plagiarism: Implicit honour declaration in reports; extremely powerful detection tools will be used by us; suspected cases of using or providing will be reported

COCP/M4CO 1 - 78 - Outline

1. Constraint Problems Constraint Problems Combinatorial 2. Combinatorial Optimisation Optimisation

Modelling (in MiniZinc) 3. Modelling (in MiniZinc)

Solving

COCP/M4CO 1 - 79 - Learning Outcomes of Part 2 = CO and CP

In order to pass, the student must be able to: describe how a CP solver works, by giving its Constraint Problems architecture and explaining the principles it is based on; Combinatorial Optimisation augment a CP solver with a propagator for a new

Modelling constraint predicate, and evaluate empirically whether (in MiniZinc) the propagator is better than a definition based on the Solving existing constraint predicates of the solver; The MiniZinc Toolchain devise empirically a (problem-specific) search strategy Course Information that can be used by a CP solver; Part 1: Modelling for Combinatorial design and compare empirically several constraint Optimisation Part 2: Combinatorial programs (with model and search parts) for a Optimisation and CP Contact combinatorial problem; present & discuss topics related to the course content, orally and in writing, with a skill appropriate for the level of education. ☞ written reports & oral resubmissions!

COCP/M4CO 1 - 80 - Organisation and Time Budget of Part 2

Period 2: late October to mid January(!), budget = 133.3 h:

Constraint Problems 12 lectures, including a mandatory guest lecture: Combinatorial Optimisation budget = 19.5 hours

Modelling (in MiniZinc) No textbook: slides and MiniCP teaching materials Solving

The MiniZinc 1 warm-up session for learning the MiniCP toolchain Toolchain Course 3 teacher-chosen assignments, with 3 help sessions, Information Part 1: Modelling for 1 grading session, and 1 solution session each, Combinatorial Optimisation Part 2: Combinatorial to be done in student-chosen duo team: Optimisation and CP Contact budget = avg 38 hours / assignment / student (5 credits)

Prerequisites: Java; basic algebra, combinatorics, logic, graph theory, set theory, and search algorithms

COCP/M4CO 1 - 81 - Lecture Topics of Part 2

Part 1: CP, Filtering, Search, Consistency, Fixpoint

Constraint Problems Part 2: Domains, Variables, Constraints

Combinatorial Optimisation Part 3: Memory Management (Trail + Copy) & Search

Modelling (in MiniZinc) Part 4: Sum and Element Constraints Solving Part 5: Circuit Constraint, TSP, and LNS The MiniZinc Toolchain Part 6: AllDifferent Constraint Course Information Part 7: Table Constraints Part 1: Modelling for Combinatorial Optimisation Part 8: Search Part 2: Combinatorial Optimisation and CP Contact Part 9: Cumulative Scheduling Part 10: Disjunctive Scheduling Topic 18: Conclusion

COCP/M4CO 1 - 82 - 3 Assignment Cycles of 2–3 Weeks in Part 2

Let Di be the deadline day of Assignment i, with i ∈ 4..6:

Constraint Problems Di − 14: publication & all needed material taught: start!

Combinatorial Optimisation Di − 7: help session a: attendance recommended

Di + 6 = Di+1 − 8: solution session& help sessiona

COCP/M4CO 1 - 83 - Assignments (5 c) in Part 2 & Overall Grade

The final score on Assignment 4 is actually “pass” or “fail”.

Constraint Problems Let ai ∈ 0..5 be final score on Assignment i, with i ∈ 5..6: Combinatorial Optimisation 20% threshold: ∀i ∈ 5..6 : ai ≥ 20% · 5 = 1 Modelling No catastrophic failure on individual assignments (in MiniZinc)

Solving 50% threshold: a5 + a6 ≥ ⌈50% · (5 + 5)⌉ = 5 The MiniZinc Toolchain ☞ the formula for the programming assignment grade Course in 3..5 is at the course homepage Information Part 1: Modelling for Combinatorial Optimisation Worth going full-blast: An overall grade m ∈ 3..5 for Part 2: Combinatorial Optimisation and CP Part 1 is combined with a programming assignment Contact grade c ∈ 3..5 for Part 2 in order to determine the overall course grade in 3..5 for 1DL441 according to a formula at the course homepage

COCP/M4CO 1 - 84 - Assignment Rules

Constraint Duo team: Two consenting partners write to TAs Problems

Combinatorial Solo team: Apply to head teacher, who rarely agrees Optimisation Other considerations: Modelling (in MiniZinc) Why (not) like this? Why no email reply? See FAQ Solving

The MiniZinc Partner swapping: Allowed, but to be declared to TAs Toolchain Partner scores may differ if no-show or passivity Course Information Part 1: Modelling for No freeloader: Implicit honour declaration in reports Combinatorial Optimisation that each partner can individually explain everything; Part 2: Combinatorial Optimisation and CP Contact random checks will be made by us No plagiarism: Implicit honour declaration in reports; extremely powerful detection tools will be used by us; suspected cases of using or providing will be reported

COCP/M4CO 1 - 85 - Outline

1. Constraint Problems Constraint Problems Combinatorial 2. Combinatorial Optimisation Optimisation

Modelling (in MiniZinc) 3. Modelling (in MiniZinc)

Solving

COCP/M4CO 1 - 86 - How To Communicate by Email or Studium?

If you have a question about the lecture material

Constraint or course organisation, then contact the head Problems teacher. An immediate answer will be given right before Combinatorial Optimisation and after lectures, as well as during their breaks. Modelling If you have a question about the assignments (in MiniZinc)

Solving or infrastructure, then contact the assistants at a help

The MiniZinc session or solution session for an immediate answer. Toolchain Short clarification questions (that is: not about Course Information modelling or programming issues) that are emailed Part 1: Modelling for Combinatorial (see address at course website) or posted (at Studium Optimisation Part 2: Combinatorial discussion) to the COCP helpdesk are answered as Optimisation and CP Contact soon as possible during working days and hours. No answer means that you should go to a help session: almost all the assistants’ budgeted time is allocated to grading and to the help, grading, and solution sessions.

COCP/M4CO 1 - 87 - What Has Changed Since Last Time?

Changes wanted by the head teacher and assistants: Constraint Problems

Combinatorial Assignments 1 to 3: set variables are allowed again Optimisation

Modelling (in MiniZinc) Assignments 1 to 3: grading rules made more precise

Solving

The MiniZinc Assignment 6: 2nd help session is now in December Toolchain

Course Information Changes also triggered by the course evaluations: Part 1: Modelling for Combinatorial Optimisation Part 2: Combinatorial Assignment 3: skeleton code trimmed: more fun! Optimisation and CP Contact Part 2 of 1DL441: switch from the industry-strength Gecode (in C++) to the pedagogical MiniCP (in Java)

COCP/M4CO 1 - 88 - What To Do Now in Part 1?

Bookmark & read course website, especially FAQs. Constraint Problems

Combinatorial Optimisation Read Sections 1 to 2.2 of the MiniZinc Handbook.

Modelling (in MiniZinc) Get started on Assignment 1 and have questions ready Solving for the first help session, which is on Tue 31 Aug 2021. The MiniZinc Toolchain Course Register a duo team by Sun 5 Sep 2021 at 23:59, Information Part 1: Modelling for possibly upon advertising for a teammate at a course Combinatorial Optimisation Part 2: Combinatorial event or the discussion at Studium, and requesting a Optimisation and CP Contact random teammate from the TAs as a last resort.

Install the MiniZinc toolchain on your hardware, if any.

COCP/M4CO 1 - 89 - What To Do Now in Part 2?

Constraint Bookmark & re-read course website, especially FAQs. Problems

Combinatorial Optimisation Get started on Assignment 4 and have questions ready Modelling for the first help session, which is on Fri 5 Nov 2021. (in MiniZinc)

Solving The MiniZinc Possibly inform us of a new duo team by Sun 31 Oct Toolchain at 23:59, possibly upon advertising for a teammate at a Course Information course event or the discussion at Studium, and Part 1: Modelling for Combinatorial Optimisation requesting a random teammate from the TAs as a last Part 2: Combinatorial Optimisation and CP resort. Contact

Install MiniCP on your hardware, if any.

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