In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which ...
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ISBN:
(纸本)9783030192129;9783030192112
In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests has to be performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Our approaches are evaluated using CP solvers and a MIP solver on a set of generated instances of different sizes. With our best approach we could find feasible and several optimal solutions for instances that are generated based on real-world test laboratory problems.
The Electric Vehicle Routing Problem with Time Windows (EVRPTW) extends traditional vehicle routing to address the recent development of electric vehicles (EVs). In addition to traditional VRP problem components, the ...
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ISBN:
(数字)9783030192129
ISBN:
(纸本)9783030192129;9783030192112
The Electric Vehicle Routing Problem with Time Windows (EVRPTW) extends traditional vehicle routing to address the recent development of electric vehicles (EVs). In addition to traditional VRP problem components, the problem includes consideration of vehicle battery levels, limited vehicle range due to battery capacity, and the presence of vehicle recharging stations. The problem is related to others in emissions-conscious routing such as the Green Vehicle Routing Problem (GVRP). We propose the first constraint programming (CP) approaches for modeling and solving the EVRPTW and compare them to an existing mixed-integer linear program (MILP). Our initial CP model follows the alternative resource approach previously applied to routing problems, while our second CP model utilizes a single resource transformation. Experimental results on various objectives demonstrate the superiority of the single resource transformation over the alternative resource model, for all problem classes, and over MILP, for the majority of medium-to-large problem classes. We also present a hybrid MILP-CP approach that outperforms the other techniques for distance minimization problems over long scheduling horizons, a class that CP struggles with on its own.
Using constraint programming (CP) to explore a local-search neighbourhood was first tried in the mid 1990s. The advantage is that constraint propagation can quickly rule out uninteresting neighbours, sometimes greatly...
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ISBN:
(纸本)9783030300487
Using constraint programming (CP) to explore a local-search neighbourhood was first tried in the mid 1990s. The advantage is that constraint propagation can quickly rule out uninteresting neighbours, sometimes greatly reducing the number actually probed. However, a CP model of the neighbourhood has to be handcrafted from the model of the problem: this can be difficult and tedious. That research direction appears abandoned since large-neighbourhood search (LNS) and constraint-based local search (CBLS) arose as alternatives that seem easier to use. Recently, the notion of declarative neighbourhood was added to the technology-independent modelling language MiniZinc, for use by any backend to MiniZinc, but currently only used by a CBLS backend. We demonstrate that declarative neighbourhoods are indeed technology-independent by using the old idea of CP-based neighbourhood exploration: we explain how to encode automatically a declarative neighbourhood into a CP model of the neighbourhood. This enables us to lift any CP solver into a local-search backend to MiniZinc. Our prototype is competitive with CP, CBLS, and LNS backends to MiniZinc.
The social golfer problem (SGP) has received plenty of attention in constraint satisfaction problem (CSP) research as a standard benchmark for symmetry breaking. However, the constraint satisfaction approach has stagn...
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ISBN:
(纸本)9789897583506
The social golfer problem (SGP) has received plenty of attention in constraint satisfaction problem (CSP) research as a standard benchmark for symmetry breaking. However, the constraint satisfaction approach has stagnated for solving larger SGP instances over the last decade. We improve the existing model of the SGP by introducing more constraints that effectively reduce the search space, particularly for instances of special form. Furthermore, we present a search space splitting method to solve the SGP in parallel through data-level parallelism. Our implementation of the presented techniques allows us to attain solutions for eight instances with maximized weeks, in which six of them were open instances for the constraint satisfaction approach, and two of them are computed for the first time. Besides, super-linear speedups are observed for all the instances solved in parallel.
The Agile Earth Observation Satellite (AEOS) is equipped with onboard optical instruments. They take image of the Earth's surface according to the requests of customers. Each imaging request which is called as a t...
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ISBN:
(纸本)9781538694480
The Agile Earth Observation Satellite (AEOS) is equipped with onboard optical instruments. They take image of the Earth's surface according to the requests of customers. Each imaging request which is called as a task generates a profit but it may not be possible to perform all tasks, due to the presence of several constraints. In this paper we consider the AEOS scheduling problem, in which a subset of requests from a given set of tasks is selected to maximize profit. We propose a constraint programming (CP) model to solve this NP-hard problem and test the performance of the CP model by solving a set of generated test instances involving 35 to 55 requests. The results show that our model is competitive and can find optimum solutions in reasonable computation times.
Examination timetabling problems is the allocation of exams into feasible slots and rooms subject to a set of constraints. constraints can be categorized into hard and soft constraints where hard constraints must be s...
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ISBN:
(纸本)9789811326226;9789811326219
Examination timetabling problems is the allocation of exams into feasible slots and rooms subject to a set of constraints. constraints can be categorized into hard and soft constraints where hard constraints must be satisfied while soft constraints are not necessarily to satisfy but be minimized as much as possible in order to produce a good solution. Generally, UMSLIC produces exam timetable without considering soft constraints. Therefore, this paper proposes the application of two algorithms which are constraint programming and Simulated Annealing to produce a better solution. constraint programming is used to generate feasible solution while Simulated Annealing is applied to improve the quality of solution. Experiments have been conducted with two datasets and the results show that the proposed algorithm managed to improve the solution regardless the different problem instances.
Unexpected events can compromise the execution of the production schedule in low-volume assembly lines. When a disruption occurs because of a delayed part supply, a quality problem or an operator absence, a reactive s...
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Unexpected events can compromise the execution of the production schedule in low-volume assembly lines. When a disruption occurs because of a delayed part supply, a quality problem or an operator absence, a reactive scheduling approach should be used in a short time in order to prevent significant deviations in final performances. In this study, we propose a new approach based on constraint programming to deal with disruptions. It is tested on a large dataset of problem instances and the obtained results are discussed. (C) 2019 The Authors. Published by Elsevier Ltd.
A configuration design problem in mechanical engineering involves finding an optimal assembly of components and joints that realizes some desired performance criteria. Such a problem is a discrete, constrained, and bl...
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ISBN:
(纸本)9781728121536
A configuration design problem in mechanical engineering involves finding an optimal assembly of components and joints that realizes some desired performance criteria. Such a problem is a discrete, constrained, and black-box optimization problem. A novel method is developed to solve the problem by applying Bivariate Marginal Distribution Algorithm (BMDA) and constraint programming (CP). BMDA is a type of Estimation of Distribution Algorithm (EDA) that exploits the dependency knowledge learned between design variables without requiring too many fitness evaluations, which tend to be expensive for the current application. BMDA is extended with adaptive chi-square testing to identify dependencies and Gibbs sampling to generate new solutions. Also, repair operations based on CP are used to deal with infeasible solutions found during search. The method is applied to a vehicle suspension design problem and is found to be more effective in converging to good solutions than a genetic algorithm and other EDAs. These contributions are significant steps towards solving the difficult problem of configuration design in mechanical engineering with evolutionary computation.
Diagnostic reasoning is often based on abduction. Abductive inference consists in generation of hypotheses which explain the current behavior of the system under investigation. Such a reasoning is based on accessible ...
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ISBN:
(纸本)9783319644745;9783319644738
Diagnostic reasoning is often based on abduction. Abductive inference consists in generation of hypotheses which explain the current behavior of the system under investigation. Such a reasoning is based on accessible background knowledge and the results must be consistent with all auxiliary observations. Efficient abductive diagnosis is carried out as Model-Based Reasoning. The knowledge about the model defines the search-space for diagnostic hypotheses. Unfortunately, use of classical consistency-based reasoning leads to rough, qualitative results only, even if good knowledge of the correct model is available. In this paper and attempt to use constraint programming as a tool for diagnostic reasoning is presented. The ultimate goal is to provide more precise diagnoses. Two case studies, one concerning fault parameter evaluation, and the second concerning structural fault localization are presented.
In this work, the online printing shop scheduling problem is considered. This challenging real problem, that appears in the nowadays printing industry, can be seen as a flexible job shop scheduling problem with sequen...
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