Scheduling model for systems with complex alternative behaviour

In this paper we propose a flexible model for scheduling problems, which allows the modeling of systems with complex alternative behaviour. This model could for example facilitate the step from process planning model to optimization model. We show how automatic constraint generation can be performed for both Constraint Programming and Mixed Integer Linear Programming (MILP) models. Also, for the MILP case, a new formulation for mutual exclusion of resources is proposed. This new formulation works well for proving optimality in systems with multiple capacity resources. Some benchmarks for such job shop scheduling problems as well as systems with a large number of alternatives are also presented

Constraint Programming and Local Search Heuristic: A Matheuristic Approach for Routing and Scheduling Feeder Vessels in Multi-Terminal Ports

International audienceIn the liner shipping business, shipping ports represent the main nodes in the maritime transportation network. These ports have a collection of terminals where container vessels can load and discharge containers. However, the logistics and planning of operations differ depending on the vessel size. Large container vessels visit a single terminal, whereas smaller container vessels, or feeder vessels, visit several terminals to transport all containers within the multiple terminals of the port. In this paper, we study the Port Scheduling Problem, the problem of scheduling the operations of feeder vessels in multi-terminal ports. The resulting problem can be identified as a version of the General Shop Scheduling Problem. We consider a Constraint Programming formulation of the problem, and we propose a math-heuristic solution approach for solving large instances. The proposed math-heuristic is a hybrid solution method that combines Constraint Programming with a local search heuristic. The solution approach benefits from the fast search capabilities of local search heuristics to explore the solution space using an Adaptive Large Neighbourhood Search heuristic. During the search, we further use the Constraint Programming model as an intensification technique, every time a new best-known solution is found. We conduct detailed computational experiments on the PortLib instances, showing that the incorporation of Constraint Programming within the heuristic search can result in significant benefits. The high instability in solution quality obtained by local search heuristics can be lowered by a simple combination of both methods

An Optimal Constraint Programming Approach to the Open-Shop Problem

International audienceThis paper presents an optimal constraint programming approach for the open-shop scheduling problem, which integrates recent constraint propagation and branching techniques with new upper bound heuristics. Randomized restart policies combined with nogood recording allow us to search diversification and learning from restarts. This approach is compared with the best-known metaheuristics and exact algorithms, and it shows better results on a wide range of benchmark instances

Four decades of research on the open-shop scheduling problem to minimize the makespan

One of the basic scheduling problems, the open-shop scheduling problem has a broad range of applications across different sectors. The problem concerns scheduling a set of jobs, each of which has a set of operations, on a set of different machines. Each machine can process at most one operation at a time and the job processing order on the machines is immaterial, i.e., it has no implication for the scheduling outcome. The aim is to determine a schedule, i.e., the completion times of the operations processed on the machines, such that a performance criterion is optimized. While research on the problem dates back to the 1970s, there have been reviving interests in the computational complexity of variants of the problem and solution methodologies in the past few years. Aiming to provide a complete road map for future research on the open-shop scheduling problem, we present an up-to-date and comprehensive review of studies on the problem that focuses on minimizing the makespan, and discuss potential research opportunities

Ansatz zur dreidimensionalen Layoutplanung bei heterogener Raumgröße

Layoutplanungsprobleme können in zahlreichen Bereichen beobachtet werden, wobei Beziehungen zwischen einzelnen Komponenten zu beachten sind. Für die Vielzahl an unterschiedlichen Anwendungsformen existiert eine ebenso große Zahl an unterschiedlichen Optimierungsmodellen. In dieser Arbeit liegt der Fokus auf der Layoutplanung großtechnischer Anlagen. Als Charakteristika werden dabei die variierende Größe der Komponenten, sowie technische und wirtschaftliche Nebenbedingungen berücksichtigt

Identifying sources of global contention in constraint satisfaction search

Much work has been done on learning from failure in search to boost solving of combinatorial problems, such as clause-learning and clause-weighting in boolean satisfiability (SAT), nogood and explanation-based learning, and constraint weighting in constraint satisfaction problems (CSPs). Many of the top solvers in SAT use clause learning to good effect. A similar approach (nogood learning) has not had as large an impact in CSPs. Constraint weighting is a less fine-grained approach where the information learnt gives an approximation as to which variables may be the sources of greatest contention. In this work we present two methods for learning from search using restarts, in order to identify these critical variables prior to solving. Both methods are based on the conflict-directed heuristic (weighted-degree heuristic) introduced by Boussemart et al. and are aimed at producing a better-informed version of the heuristic by gathering information through restarting and probing of the search space prior to solving, while minimizing the overhead of these restarts. We further examine the impact of different sampling strategies and different measurements of contention, and assess different restarting strategies for the heuristic. Finally, two applications for constraint weighting are considered in detail: dynamic constraint satisfaction problems and unary resource scheduling problems