The quadratic multiknapsack problem consists of packing a set of items of various weights into knapsacks of limited capacities with profits being associated with pairs of items packed into the same knapsack. This prob...
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The quadratic multiknapsack problem consists of packing a set of items of various weights into knapsacks of limited capacities with profits being associated with pairs of items packed into the same knapsack. This problem has been solved by various heuristics since its inception, and more recently it has also been solved with an exact method. We introduce a generalization of this problem that includes pairwise conflicts as well as balance constraints, among other particularities. We present and compare constraint programming and integer programming approaches for solving this generalized problem. Summary of Contribution: The quadratic multiknapsack problem consists of packing a set of items of various weights into knapsacks of limited capacities - with profits being associated with pairs of items packed into the same knapsack. This problem has been solved by various heuristics since its inception, and more recently it has also been solved with an exact method. We introduce a generalization of this problem which includes pairwise conflicts as well as balance constraints, among other particularities. We present and compare constraint programming and integer programming approaches for solving this generalized problem. The problem we address is clearly in the core of the operations research applications in which subsets have to be built and, in particular, we add the concept of fairness to the modeling and solution process by computationally evaluating techniques to take fairness into account. This is clearly at the core of computational evaluation of algorithms.
Due to the incorporation of heterogeneous cores in modern multi-core systems, the exploitation of their full potential strongly depends on the proper mapping of an application to the platform. This work presents an ap...
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ISBN:
(数字)9781665490054
ISBN:
(纸本)9781665490054
Due to the incorporation of heterogeneous cores in modern multi-core systems, the exploitation of their full potential strongly depends on the proper mapping of an application to the platform. This work presents an approach to map static applications on heterogeneous platforms minimizing their makespan based on the Benders decomposition principle combined with an Integer Linear programming (ILP) model. The proposed approach adopts a three-stage decomposition scheme, finding permutations of infeasible solutions to generate multiple cuts in every iteration. The first stage deals with the assignment of the tasks to the cores and the last one with their scheduling, whereas the second stage propagates new bounds based on the current assignment and provides an explanation of the infeasibility in the form of subsets of assignment variables. Based on that, other infeasible combinations are computed by checking their permutations and more Benders cuts are produced. The proposed method is compared with a two-stage decomposition approach and an ILP model and exhibits better performance in terms of solution time and number solved instances to optimality.
Industry 4.0 promises sustainable and more efficient manufacturing through new digital technologies. However, existing methodologies like Lean Manufacturing have been tested and proven, and could benefit greatly from ...
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ISBN:
(纸本)9789811661280;9789811661273
Industry 4.0 promises sustainable and more efficient manufacturing through new digital technologies. However, existing methodologies like Lean Manufacturing have been tested and proven, and could benefit greatly from increased digitalisation. In this paper, we claim that the enhancement of existing Lean and related methodologies with digital technology is a necessary step to fulfil Industry 4.0's promise. To demonstrate this claim, we introduce a framework for integrating raw material and finished product inventories and production scheduling. We validate the framework by developing a proof-of-concept system that combines constraint programming (CP) and inventory management to address a combined reactive scheduling and inventory management problem. The production model we use is a Resource-Constrained Project Scheduling Problem (RCPSP) with three finished products and four raw materials, and the two-bin or (s, Q) inventory policy. The system enables coordination of raw material, finished product and work-in-progress inventories through optimal scheduling (minimal makespan). The results inform operators of expected stock-outs and consumption and production rates, while allowing for modifications in case of disruption.
Codac (Catalog Of Domains And Contractors) is a C++/Python library providing tools for constraint programming over reals, trajectories and sets. It has many applications in parameter estimation, guaranteed integration...
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Codac (Catalog Of Domains And Contractors) is a C++/Python library providing tools for constraint programming over reals, trajectories and sets. It has many applications in parameter estimation, guaranteed integration or robot localization and provides reliable outputs by computing sets of feasible solutions according to the constraints defining the problem. This paper provides a brief overview of the library and its Contractor Network approach, illustrated on a convincing robotic application.
Translating time expression into absolute time points or durations is a challenge for natural languages processing such as text mining and text understanding in general. We present a constraint logic language CLP(Time...
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An optimization formulation is presented for timed Petri nets, based on a recently developed optimization solver where a satisfiability solver is integrated with constraint programming. The solver, called CP-SAT, is a...
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An optimization formulation is presented for timed Petri nets, based on a recently developed optimization solver where a satisfiability solver is integrated with constraint programming. The solver, called CP-SAT, is a part of Google's OR-Tools. The first optimization formulation includes an arbitrary number of concurrent sequences of operations, with shared, alternative, and local resources. A benchmark shows how much faster CP-SAT is compared to both an alternative SAT optimization solver and an A* implementation. The optimization formulation is then generalized to mixed alternative and concurrent sequences. A comparison with a recent MILP formulation for timed Petri nets is presented, showing the strength of the proposed optimization formulation. Finally, an evaluation of an industrial-sized flexible manufacturing system, including uncontrollable events, demonstrates how efficient and easy to implement the proposed strategy is compared to existing results.
In this paper, the optimization of Capacitated Vehicle Routing Problem with Alternative Delivery, Pick-up and Time windows is considered. The development of this problem was motivated by analysis of postal and courier...
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In this paper, the optimization of Capacitated Vehicle Routing Problem with Alternative Delivery, Pick-up and Time windows is considered. The development of this problem was motivated by analysis of postal and courier delivery issues. In some generalization, the problem examined can be classified as a combination of many variants of the classical VRP (Vehicle Routing Problem), such as CVRP (Capacitated Vehicle Routing Problem), VRPPD (Vehicle Routing Problem with Pickup and Delivery), VRPTW (Vehicle Routing Problem with Time Windows), etc. What distinguishes the presented problem from known variants of VRPs is the introduction of alternative delivery points and parcel lockers incorporated into the distribution network and the ability to take into account logical constraints. The problem model was formulated in the form of BIP (Binary Integer programming). Moreover, the original hybrid approach integrating CP (constraint programming), GA (Genetic Algorithm) and MP (Mathematical programming) was proposed for the model implementation and optimization. Numerous computational experiments verifying the correctness of the model and the effectiveness of the hybrid approach were also presented. (C) 2020 Elsevier B.V. All rights reserved.
We presentConArgLib, a C++ library implemented to help programmers solve some of the most important problems related to extension-based abstract Argumentation. The library is based on ConArg, which exploitsconstraint ...
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We presentConArgLib, a C++ library implemented to help programmers solve some of the most important problems related to extension-based abstract Argumentation. The library is based on ConArg, which exploitsconstraint programmingand, in particular, Gecode, a toolkit for developing constraint-based systems and applications. Given a semantics, such problems consist, for example, in enumerating all the extensions, and checking the credulous or sceptical acceptance of an argument passed as parameter. The goal is to let programmers use the library to quickly develop programs on top of it, as, for instance, implementing decision-making procedures based on the strongest arguments, or comparing two frameworks by looking at the differences between their (e.g., stable) semantics. The library features the possibility to use different branching strategies, which we all test and compare on a set of frameworks taken from theInternational Competition on Computational Models of Argumentation(ICCMA17). Moreover, for some of the tasks, it is possible to perform a parallel search using several workers at the same time: we test the speed-up between using from 1 to 16 threads on a set of ICCMA17 frameworks.
Specific government regulations for waste products and increasing environmental awareness are concerns for companies. Disassembly operations have become essential tools for green manufacturing and for reducing ecologi...
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Specific government regulations for waste products and increasing environmental awareness are concerns for companies. Disassembly operations have become essential tools for green manufacturing and for reducing ecological hazards from waste electrical and electronic equipment. An effective and efficient line design may be vital to encourage companies to establish a disassembly center. The line-balancing problem is a critical issue in a disassembly center. Large products may allow simultaneous disassembly of parts using more than one operator at a workstation. However, multi-manned disassembly line balancing creates a more complicated problem. Thus, an efficient solution technique based on a constraint programming (CP) approach is proposed for multi-manned disassembly line balancing with AND/OR precedence relations to minimize cycle time as a primary objective and the total number of workers as a secondary objective. A mixed-integer linear programming (MILP) model based on previous studies was proposed to define the problem. A CP approach was developed for the first time to solve this problem. A genetic algorithm was used to show the relative performance of the CP method for large problems. The proposed CP approach demonstrates superior performance. (c) 2021 Elsevier Inc. All rights reserved.
This paper schedules capacitated parallel machines of a real production system by considering different quantities of production and processing times required to complete customer orders. A new mixed linear programmin...
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This paper schedules capacitated parallel machines of a real production system by considering different quantities of production and processing times required to complete customer orders. A new mixed linear programming model is developed according to the concept of constrained vehicle routing problems to have a complete schedule for machines by determining the sequence of both jobs and idle times for each machine. The optimisation model minimises the total cost of the production system, including tardiness, earliness and sequence-dependent setup costs. A constraint programming (CP) model and a meta-heuristic hybrid algorithm are also developed to compare their results with the mixed linear programming model. The numerical findings show that the total cost estimated by the mixed integer programming model is 10%-13% better (lower) than the ones estimated by the CP model and the meta-heuristic algorithm when small instances of the scheduling problem are solved. By increasing the size of the scheduling problem, the meta-heuristic algorithm shows the best computational performance estimating 11% better (lower) total cost compared with the CP model.
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