Models and Algorithms for Solving Packing Problems in Logistics
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Selective Maximum Coverage and Set Packing
Selective Maximum Coverage and Set Packing Felix J. L. Willamowski1∗ and Bj¨ornF. Tauer2y 1 Lehrstuhl f¨urOperations Research RWTH Aachen University [email protected] 2 Lehrstuhl f¨urManagement Science RWTH Aachen University [email protected] Abstract. In this paper we introduce the selective maximum coverage and the selective maximum set packing problem and variants of them. Both problems are strongly related to well studied problems such as maximum coverage, set packing, and (bipartite) hypergraph matching. The two problems are given by a collection of subsets of a ground set and index subsets of the indices of these subsets. Additionally, there are weights either for each element of the ground set or each subset for each index subset. The goal is to find at most one index per index subset such that the total weight of covered elements or of disjoint subsets is maximum. Applications arise in transportation, e.g., dispatching for ride- sharing services. We prove strong intractability results for the problems and provide almost best possible approximation guarantees. 1 Introduction and Preliminaries We introduce the selective maximum coverage problem (smc) generalizing the weighted maximum k coverage problem (wmkc) which is given by a finite ground set X with weights w : X ! Q≥0, a finite collection S of subsets of X, and an 0 integer k 2 Z≥0. The goal is to find a subcollection S ⊆ S containing at most k P subsets and maximizing the total weight of covered elements x2X0 w(x) with 0 X = [S2S0 S. For unit weights, w ≡ 1, the problem is called maximum k coverage problem (mkc). -
Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model
Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model Kevin Sim Emma Hart Institute for Informatics and Digital Innovation Institute for Informatics and Digital Innovation Edinburgh Napier University Edinburgh Napier University Edinburgh, Scotland, UK Edinburgh, Scotland, UK [email protected] [email protected] ABSTRACT exhibit superior performance than similar human designed Novel deterministic heuristics are generated using Single Node heuristics and can be used to produce collections of heuris- Genetic Programming for application to the One Dimen- tics that collectively maximise the potential of selective HH. sional Bin Packing Problem. First a single deterministic The papers contribution is two fold. Single heuristics ca- heuristic was evolved that minimised the total number of pable of outperforming a range of well researched deter- bins used when applied to a set of 685 training instances. ministic heuristics are generated by combining features ex- Following this, a set of heuristics were evolved using a form tracted from those heuristics. Secondly an island model[19] of cooperative co-evolution that collectively minimise the is adapted to use multiple SNGP implementations to gener- number of bins used across the same set of problems. Re- ate diverse sets of heuristics which collectively outperform sults on an unseen test set comprising a further 685 prob- any of the single heuristics when used in isolation. Both ap- lem instances show that the single evolved heuristic out- proaches are trained and evaluated using equal divisions of a performs existing deterministic heuristics described in the large set of 1370 benchmark problem instances sourced from literature. -
13. Mathematics University of Central Oklahoma
Southwestern Oklahoma State University SWOSU Digital Commons Oklahoma Research Day Abstracts 2013 Oklahoma Research Day Jan 10th, 12:00 AM 13. Mathematics University of Central Oklahoma Follow this and additional works at: https://dc.swosu.edu/ordabstracts Part of the Animal Sciences Commons, Biology Commons, Chemistry Commons, Computer Sciences Commons, Environmental Sciences Commons, Mathematics Commons, and the Physics Commons University of Central Oklahoma, "13. Mathematics" (2013). Oklahoma Research Day Abstracts. 12. https://dc.swosu.edu/ordabstracts/2013oklahomaresearchday/mathematicsandscience/12 This Event is brought to you for free and open access by the Oklahoma Research Day at SWOSU Digital Commons. It has been accepted for inclusion in Oklahoma Research Day Abstracts by an authorized administrator of SWOSU Digital Commons. An ADA compliant document is available upon request. For more information, please contact [email protected]. Abstracts from the 2013 Oklahoma Research Day Held at the University of Central Oklahoma 05. Mathematics and Science 13. Mathematics 05.13.01 A simplified proof of the Kantorovich theorem for solving equations using scalar telescopic series Ioannis Argyros, Cameron University The Kantorovich theorem is an important tool in Mathematical Analysis for solving nonlinear equations in abstract spaces by approximating a locally unique solution using the popular Newton-Kantorovich method.Many proofs have been given for this theorem using techniques such as the contraction mapping principle,majorizing sequences, recurrent functions and other techniques.These methods are rather long,complicated and not very easy to understand in general by undergraduate students.In the present paper we present a proof using simple telescopic series studied first in a Calculus II class. -
Three-Dimensional Bin-Packing Approaches, Issues, and Solutions
Three Dimensional Bin-Packing Issues and Solutions Seth Sweep University of Minnesota, Morris [email protected] Abstract This paper reviews the three-dimensional version of the classic NP-hard bin-packing, optimization problem along with its theoretical relevance and practical importance. Also, new approximation solutions are introduced based on prior research. The three-dimensional bin-packing optimization problem concerns placing box-shaped objects of arbitrary size and number into a box-shaped, three-dimensional space efficiently. Approximation algorithms become a guide that attempts to place objects in the least amount of space and time. As a NP-hard problem, three-dimensional bin- packing holds much academic interest to computer scientists. However, this problem also has relevance in industrial settings such as shipping cargo and efficiently designing machinery with replaceable parts, such as automobiles. Industry relies on algorithms to provide good solutions to versions of this problem. This research project adapted common approaches to one-dimensional bin-packing, such as next fit and best fit, to three dimensions. Adaptation of these algorithms is possible by dividing the space of a bin. Then, by paying attention strictly to the width of each object, the one-dimensional approaches attempt to efficiently pack into the divided compartments of the original bin. Through focusing entirely on width, these divisions effectively become multiple one-dimensional bins. However, entirely disregarding the volume of the bin by ignoring height and depth may generate inefficient packings. As this paper details in more depth, generally cubical objects may pack well but exceptionally high or long objects leave large gaps of empty space unpacked. -
Exact Algorithms and APX-Hardness Results for Geometric Packing and Covering Problems∗
Exact Algorithms and APX-Hardness Results for Geometric Packing and Covering Problems∗ Timothy M. Chan† Elyot Grant‡ March 29, 2012 Abstract We study several geometric set cover and set packing problems involv- ing configurations of points and geometric objects in Euclidean space. We show that it is APX-hard to compute a minimum cover of a set of points in the plane by a family of axis-aligned fat rectangles, even when each rectangle is an ǫ-perturbed copy of a single unit square. We extend this result to several other classes of objects including almost-circular ellipses, axis-aligned slabs, downward shadows of line segments, downward shad- ows of graphs of cubic functions, fat semi-infinite wedges, 3-dimensional unit balls, and axis-aligned cubes, as well as some related hitting set prob- lems. We also prove the APX-hardness of a related family of discrete set packing problems. Our hardness results are all proven by encoding a highly structured minimum vertex cover problem which we believe may be of independent interest. In contrast, we give a polynomial-time dynamic programming algo- rithm for geometric set cover where the objects are pseudodisks containing the origin or are downward shadows of pairwise 2-intersecting x-monotone curves. Our algorithm extends to the weighted case where a minimum-cost cover is required. We give similar algorithms for several related hitting set and discrete packing problems. 1 Introduction In a geometric set cover problem, we are given a range space (X, )—a universe X of points in Euclidean space and a pre-specified configurationS of regions or geometric objects such as rectangles or half-planes. -
Algorithmic Combinatorial Game Theory∗
Playing Games with Algorithms: Algorithmic Combinatorial Game Theory∗ Erik D. Demaine† Robert A. Hearn‡ Abstract Combinatorial games lead to several interesting, clean problems in algorithms and complexity theory, many of which remain open. The purpose of this paper is to provide an overview of the area to encourage further research. In particular, we begin with general background in Combinatorial Game Theory, which analyzes ideal play in perfect-information games, and Constraint Logic, which provides a framework for showing hardness. Then we survey results about the complexity of determining ideal play in these games, and the related problems of solving puzzles, in terms of both polynomial-time algorithms and computational intractability results. Our review of background and survey of algorithmic results are by no means complete, but should serve as a useful primer. 1 Introduction Many classic games are known to be computationally intractable (assuming P 6= NP): one-player puzzles are often NP-complete (as in Minesweeper) or PSPACE-complete (as in Rush Hour), and two-player games are often PSPACE-complete (as in Othello) or EXPTIME-complete (as in Check- ers, Chess, and Go). Surprisingly, many seemingly simple puzzles and games are also hard. Other results are positive, proving that some games can be played optimally in polynomial time. In some cases, particularly with one-player puzzles, the computationally tractable games are still interesting for humans to play. We begin by reviewing some basics of Combinatorial Game Theory in Section 2, which gives tools for designing algorithms, followed by reviewing the relatively new theory of Constraint Logic in Section 3, which gives tools for proving hardness. -
Exactly Solving Packing Problems with Fragmentation
Exactly solving packing problems with fragmentation M. Casazza, A. Ceselli, Università degli Studi di Milano, Dipartimento di Informatica, OptLab – Via Bramante 65, 26013 – Crema (CR) – Italy {marco.casazza, alberto.ceselli}@unimi.it April 13, 2015 In packing problems with fragmentation a set of items of known weight is given, together with a set of bins of limited capacity; the task is to find an assignment of items to bins such that the sum of items assigned to the same bin does not exceed its capacity. As a distinctive feature, items can be split at a price, and fractionally assigned to different bins. Arising in diverse application fields, packing with fragmentation has been investigated in the literature from both theoretical, modeling, approximation and exact optimization points of view. We improve the theoretical understanding of the problem, and we introduce new models by exploiting only its combinatorial nature. We design new exact solution algorithms and heuristics based on these models. We consider also variants from the literature arising while including different objective functions and the option of handling weight overhead after splitting. We present experimental results on both datasets from the literature and new, more challenging, ones. These show that our algorithms are both flexible and effective, outperforming by orders of magnitude previous approaches from the literature for all the variants considered. By using our algorithms we could also assess the impact of explicitly handling split overhead, in terms of both solutions quality and computing effort. Keywords: Bin Packing, Item Fragmentation, Mathematical Programming, Branch&Price 1 1 Introduction Logistics has always been a benchmark for combinatorial optimization methodologies, as practitioners are traditionally familiar with the competitive advantage granted by optimized systems. -
Solving Packing Problems with Few Small Items Using Rainbow Matchings
Solving Packing Problems with Few Small Items Using Rainbow Matchings Max Bannach Institute for Theoretical Computer Science, Universität zu Lübeck, Lübeck, Germany [email protected] Sebastian Berndt Institute for IT Security, Universität zu Lübeck, Lübeck, Germany [email protected] Marten Maack Department of Computer Science, Universität Kiel, Kiel, Germany [email protected] Matthias Mnich Institut für Algorithmen und Komplexität, TU Hamburg, Hamburg, Germany [email protected] Alexandra Lassota Department of Computer Science, Universität Kiel, Kiel, Germany [email protected] Malin Rau Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France [email protected] Malte Skambath Department of Computer Science, Universität Kiel, Kiel, Germany [email protected] Abstract An important area of combinatorial optimization is the study of packing and covering problems, such as Bin Packing, Multiple Knapsack, and Bin Covering. Those problems have been studied extensively from the viewpoint of approximation algorithms, but their parameterized complexity has only been investigated barely. For problem instances containing no “small” items, classical matching algorithms yield optimal solutions in polynomial time. In this paper we approach them by their distance from triviality, measuring the problem complexity by the number k of small items. Our main results are fixed-parameter algorithms for vector versions of Bin Packing, Multiple Knapsack, and Bin Covering parameterized by k. The algorithms are randomized with one-sided error and run in time 4k · k! · nO(1). To achieve this, we introduce a colored matching problem to which we reduce all these packing problems. The colored matching problem is natural in itself and we expect it to be useful for other applications. -
Reductions and Satisfiability
Reductions and Satisfiability 1 Polynomial-Time Reductions reformulating problems reformulating a problem in polynomial time independent set and vertex cover reducing vertex cover to set cover 2 The Satisfiability Problem satisfying truth assignments SAT and 3-SAT reducing 3-SAT to independent set transitivity of reductions CS 401/MCS 401 Lecture 18 Computer Algorithms I Jan Verschelde, 30 July 2018 Computer Algorithms I (CS 401/MCS 401) Reductions and Satifiability L-18 30 July 2018 1 / 45 Reductions and Satifiability 1 Polynomial-Time Reductions reformulating problems reformulating a problem in polynomial time independent set and vertex cover reducing vertex cover to set cover 2 The Satisfiability Problem satisfying truth assignments SAT and 3-SAT reducing 3-SAT to independent set transitivity of reductions Computer Algorithms I (CS 401/MCS 401) Reductions and Satifiability L-18 30 July 2018 2 / 45 reformulating problems The Ford-Fulkerson algorithm computes maximum flow. By reduction to a flow problem, we could solve the following problems: bipartite matching, circulation with demands, survey design, and airline scheduling. Because the Ford-Fulkerson is an efficient algorithm, all those problems can be solved efficiently as well. Our plan for the remainder of the course is to explore computationally hard problems. Computer Algorithms I (CS 401/MCS 401) Reductions and Satifiability L-18 30 July 2018 3 / 45 imagine a meeting with your boss ... From Computers and intractability. A Guide to the Theory of NP-Completeness by Michael R. Garey and David S. Johnson, Bell Laboratories, 1979. Computer Algorithms I (CS 401/MCS 401) Reductions and Satifiability L-18 30 July 2018 4 / 45 what you want to say is From Computers and intractability. -
Jigsaw Puzzles, Edge Matching, and Polyomino Packing: Connections and Complexity∗
Jigsaw Puzzles, Edge Matching, and Polyomino Packing: Connections and Complexity∗ Erik D. Demaine, Martin L. Demaine MIT Computer Science and Artificial Intelligence Laboratory, 32 Vassar St., Cambridge, MA 02139, USA, {edemaine,mdemaine}@mit.edu Dedicated to Jin Akiyama in honor of his 60th birthday. Abstract. We show that jigsaw puzzles, edge-matching puzzles, and polyomino packing puzzles are all NP-complete. Furthermore, we show direct equivalences between these three types of puzzles: any puzzle of one type can be converted into an equivalent puzzle of any other type. 1. Introduction Jigsaw puzzles [37,38] are perhaps the most popular form of puzzle. The original jigsaw puzzles of the 1760s were cut from wood sheets using a hand woodworking tool called a jig saw, which is where the puzzles get their name. The images on the puzzles were European maps, and the jigsaw puzzles were used as educational toys, an idea still used in some schools today. Handmade wooden jigsaw puzzles for adults took off around 1900. Today, jigsaw puzzles are usually cut from cardboard with a die, a technology that became practical in the 1930s. Nonetheless, true addicts can still find craftsmen who hand-make wooden jigsaw puzzles. Most jigsaw puzzles have a guiding image and each side of a piece has only one “mate”, although a few harder variations have blank pieces and/or pieces with ambiguous mates. Edge-matching puzzles [21] are another popular puzzle with a similar spirit to jigsaw puzzles, first appearing in the 1890s. In an edge-matching puzzle, the goal is to arrange a given collection of several identically shaped but differently patterned tiles (typically squares) so that the patterns match up along the edges of adjacent tiles. -
8. NP and Computational Intractability
Chapter 8 NP and Computational Intractability CS 350 Winter 2018 1 Algorithm Design Patterns and Anti-Patterns Algorithm design patterns. Ex. Greedy. O(n log n) interval scheduling. Divide-and-conquer. O(n log n) FFT. Dynamic programming. O(n2) edit distance. Duality. O(n3) bipartite matching. Reductions. Local search. Randomization. Algorithm design anti-patterns. NP-completeness. O(nk) algorithm unlikely. PSPACE-completeness. O(nk) certification algorithm unlikely. Undecidability. No algorithm possible. 2 8.1 Polynomial-Time Reductions Classify Problems According to Computational Requirements Q. Which problems will we be able to solve in practice? A working definition. [von Neumann 1953, Godel 1956, Cobham 1964, Edmonds 1965, Rabin 1966] Those with polynomial-time algorithms. Yes Probably no Shortest path Longest path Matching 3D-matching Min cut Max cut 2-SAT 3-SAT Planar 4-color Planar 3-color Bipartite vertex cover Vertex cover Primality testing Factoring 4 Classify Problems Desiderata. Classify problems according to those that can be solved in polynomial-time and those that cannot. Provably requires exponential-time. Given a Turing machine, does it halt in at most k steps? (the Halting Problem) Given a board position in an n-by-n generalization of chess, can black guarantee a win? Frustrating news. Huge number of fundamental problems have defied classification for decades. This chapter. Show that these fundamental problems are "computationally equivalent" and appear to be different manifestations of one really hard problem. 5 Polynomial-Time Reduction Desiderata'. Suppose we could solve X in polynomial-time. What else could we solve in polynomial time? don't confuse with reduces from Reduction. -
NP-Hard Triangle Packing Problems
NP-Hard Triangle Packing Problems Amy Chou January 20, 2016 Abstract In computational geometry, packing problems ask whether a set of rigid pieces can be placed inside a target region such that no two pieces overlap. The triangle packing problem is a packing problem that involves triangular pieces, and it is crucial for algorithm design in many areas, including origami design, cutting industries, and warehousing. Previous works in packing algorithms have conjectured that triangle packing is NP-hard. In this paper, we mathematically prove this conjecture. We prove the NP-hardness of three distinct triangle packing problems: (i) packing right triangles into a rectangle, (ii) packing right triangles into a right triangle, and (iii) packing equilateral triangles into an equilateral triangle. We construct novel reductions from the known NP-complete problems 3-partition and 4-partition. Furthermore, we generalize that packing arbitrary triangles into an arbitrary target region is strongly NP-hard. Because triangle packing is NP-hard, triangle packing must be determined by approximation or heuristic algorithms rather than exact algorithms. 1 1 Introduction In a packing problem, we wish to determine whether a set of objects can be placed into a container such that no two objects overlap. These problems have been studied extensively and are motivated by a number of applications, including warehousing, origami design, newspaper paging, and cutting industries. In wood, glass, or steel industries, for example, packing is involved when determining how to cut pieces from large sheets of material. Complexity theory involves the classification of decision problems | problems where the answer is either yes or no | by computational hardness.