Technical Program

TECHNICAL PROGRAM Wednesday, 8:30-10:00 Wednesday, 10:30-12:30 WA-01 WB-02 Wednesday, 8:30-10:00 - AUDIMAX Wednesday, 10:30-12:30 - HS 7 Opening and EURO Plenary (Fischetti) Project Management and Scheduling I (i) Stream: Plenaries Stream: Scheduling and Project Management Chair: Gerhard Wäscher Chair: Richard Hartl 1 - Thin models for big data 1 - Resource-constrained project scheduling with over- Matteo Fischetti time Relevance and importance of the mathematical modeling and opti- Andre Schnabel, Carolin Kellenbrink mization tools has been widely accepted by professionals working in Jobs scheduled in the conventional resource-constrained project the field of business analytics. Predictive and prescriptive data analyt- scheduling problem (RCPSP) consume renewable resources during ics are nowadays impossible without efficient optimization tools capa- their execution. Thereby, it is often assumed that each of these re- ble of dealing with large amount of data. These recent synergies be- sources has a constant capacity throughout the planning horizon, which tween Operations Research and Business Analytics impose new chal- must not be exceeded. In practice, the usage of additional capacities lenges for the next generation of exact algorithms. Despite the huge can be part of the decision problem. For that reason, we extend the success of general purpose solvers in the last decade, finding opti- classical RCPSP by a decision on the usage of overtime with associ- mal solutions for Mixed-Integer Programming (MIP) models involv- ated penalty costs (RCPSP-OC). ing millions of variables still remains out of reach for many important optimization problems. In order to solve problem instances of practically relevant size, we de- The talk will be mainly focused on the methodological (very chal- velop heuristic solution methods. To prevent examination of unneces- lenging) issue of producing a highly scalable solution scheme for MIP sary schedules, a set containing optimal solutions for the RCPSP-OC models of very large size, as motivated by the nowadays applications. is derived and contrasted with the corresponding sets from different As a case study, we will address the development of an exact MIP- scheduling objectives. We present genetic algorithms using different based approach for one of the most famous and most studied problems solution encodings and schedule generation schemes. Some of these in the Operations Research literature: the Uncapacitated Facility Lo- genetic algorithms simultaneously minimize project duration and over- cation (UFL) problem, with linear or quadratic costs. UFL with lin- time costs. Others solve the RCPSP-OC by evaluating active schedules ear costs—together with its cardinality-constrained variant known as associated with different possible overtime patterns. Additionally, we the p-median problem—plays a fundamental role in business analyt- suggest solution methods for the RCPSP-OC, which minimize over- ics, and in particular in clustering and classification where it is used time for promising deadlines. For medium-sized problem instances, a for unsupervised learning. UFL with quadratic allocation costs, on the problem specific branch and bound procedure is developed in order to other hand, appears as an important subproblem in the design of energy obtain exact solutions. We conclude by evaluating the effectiveness of distribution networks where power loss is proportional to the square of solving the RCPSP-OC using the proposed heuristic and exact meth- the electric currents flowing in the system. ods in a comparative study. Our approach is based on the idea of working on a small subset of the 2 - Efficient CP-SAT approaches for the solution of the decision variables, using a sound Benders decomposition scheme. MRCPSP with GPRs Alexander Schnell, Richard Hartl Recent results from the literature have shown the power of exact approaches combining Constraint Programming (CP) and Boolean Sat- isfiability (SAT) Solving techniques for the solution of variants of the single-mode resource-constrained project scheduling problem (SR- CPSP). In our talk, we present extensions of these approaches to efficiently solve multi-mode RCPSP instances. Therefore, we introduce two constraint handlers gprecedencemm and cumulativemm which can be used within the optimization framework SCIP. With the above constraints one can model renewable resource constraints and generalized precedence relations (GPRs) in the context of multi-mode jobs. They both capture constraint propagation algorithms for the above problem characteristics. Moreover, the processed domain reductions are ex- plained to the SCIP-internal SAT Solving mechanism which is able to deduce nogoods and backjumps. We compare two formulations of the MRCPSP with GPRs within SCIP, one with and one without gprecedencemm. Our computational results on instances from the literature show that the integration of gprecedencemm immensely strengthens the original formulation. Moreover, our SCIP-approach outperforms the state-of-the-art exact algorithm for the MRCPSP with GPRs on instances with 50 activities. In total, our results are highly promising, i.e. we can close 289 open instances with 30, 50 and 100 activities from the literature. WB-03 Wednesday, 10:30-12:30 - HS 16 Scheduling Applications (i) Stream: Scheduling and Project Management Chair: Jens Brunner 1 WB-04 OR 2015 - Vienna 1 - Integrated task planning and shift scheduling of lo- edge-weights. The goal is to minimize the total added weight. We gistics assistants in hospitals give a combinatorial polynomial time algorithm to solve the problem Jonas Volland, Andreas Fügener, Jens Brunner in factor-critical graphs. For general graphs, we show the existence of a half-integral optimal stabilizer, and exploit the half-integrality to Effective and efficient logistics management in hospitals is of contin- show that the problem is strongly NP-hard in general graphs. More- uously increasing importance. Nurses usually spend a significant part over, we present an algorithm to compute an optimal additive stabilizer of their time for logistics activities in the areas of material supply, food whose running time is exponential only in the size of the Tutte set in preparation and disposal, and cleaning. In order to release nurses from the Gallai-Edmonds decomposition. those non-patient-care related activities, a case hospital employs logistics assistants who take over the tasks. In this context, the case hospital 2 - Robust flows in path-based interdiction models is faced with two optimization problems, i.e., first how to schedule the Jannik Matuschke, Thomas S. McCormick, Gianpaolo Oriolo, logistics tasks and second, how to plan and design the shifts for the logistics assistants. We propose an integrated optimization model, which Britta Peis, Martin Skutella simultaneously performs both optimization problems. We therefore develop a model that combines resource-constrained project schedul- Due to the importance of robustness in many real-world optimization ing (RCPSP) and shift scheduling, and propose a column generation- problems, the field of robust optimization has gained a lot of attention based approach to solve the problem. Computational results are pre- over the past decade. We concentrate on max flow problems and in- sented and we show that cost reductions can be achieved. troduce a novel robust optimization model which, compared to known models from the literature, features several advantageous properties: 2 - Heuristics for the part inventory model sequencing (i) We consider a general class of path-based flow problems which can problem be used to model a large variety of network routing problems (and other Martin Wirth, Florian Jaehn, Michael Schneider packing problems). (ii) We aim at solutions that are robust against interdiction by a potent adversary who has the power to attack any flow Just-in-time supply for mixed-model assembly lines out of third-party path of his choice on any edge of the network. (iii) In contrast to pre- consignment stocks is currently employed in several industries, like, vious robust max flow models, which are intractable, optimal robust e.g., car manufacturing. The manufacturer only keeps a small interme- flows for the most important basic variants of our model can be found diate storage and replenishments are made from a consignment ware- efficiently in polynomial time. We also consider generalizations where house (closely aligned to the assembly line) whenever the inventory the flow player can spend a budget to protect the network against the at the manufacturer’s site is depleted. Boysen et al. (2007) described interdictor. a short-term planning problem that aims at minimizing the inventory holding cost of the manufacturer depending on the model sequence of 3 - k-Delete Recoverable Robust Combinatorial Opti- the assembly line and the consequential order release dates. The result- ing part inventory model sequencing problem is NP-hard. In this paper, mization we present several construction heuristics and an adaptive large neigh- Christina Büsing borhood search to address the problem. Furthermore, we compare the performance of the methods to existing results from the literature in We consider combinatorial optimization problems and their k-Delete numerical studies. recoverable robust (k-del RR) version. K-del RR is a two stage pro- cess to deal with cost-uncertainties. In the first stage, a solution of the 3 - Partially-Concurrent Open Shop Scheduling with underlying problem is fixed. In the second

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