Many critical real world problems, including problems in areas such as logistics, routing and scheduling are very difficult to solve computationally (often NP-hard). Various programming and algorithmic paradigms have ...
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Many critical real world problems, including problems in areas such as logistics, routing and scheduling are very difficult to solve computationally (often NP-hard). Various programming and algorithmic paradigms have been developed to deal with these problems, including constraint programming (CP), Integer programming (IP) and Local Search (LS). These technologies are largely declarative in nature and rely on vastly different underlying mathematical and algorithmic approaches. Hence, each paradigm has inherent strengths and weaknesses making it more or less suitable to a given problem. For particularly difficult problems, it can often be beneficial to leverage a sophisticated “hybrid solver” technique. Such techniques include, for example, combining CP and IP solvers in a cooperative fashion or iteratively solving problem relaxations such as in Lagrangian Relaxation, Column Generation or Logic-Based Benders Decomposition. The development of such hybrids, however, is often technically difficult and requires a great deal of trial and error. This thesis introduces a new high-level framework for automating the generation of several important classes of hybrid solvers as well as proposing a new set of theoretical abstractions allowing high-level model descriptions to be transformed and combined into hybrids while maintaining semantic soundness. Among the new theoretical abstractions is a proposal for `Generic Lagrangian Relaxation', allowing a well-known Integer programming technique to be generalized and applied to other technologies such as CP. Experimental results demonstrate the practical benefits of this new framework.
Optimization plays an important role in various disciplines of engineering. Multi-objective optimization is usually characterized by a Pareto front. In large scale multi-objective optimization problems, determining an...
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ISBN:
(纸本)9781509018970
Optimization plays an important role in various disciplines of engineering. Multi-objective optimization is usually characterized by a Pareto front. In large scale multi-objective optimization problems, determining an optimal Pareto front consumes large time. Thus, parallel computing is used to speed up the search. constraint programming is one of the logic-based optimization techniques for solving combinatorial optimization problems. In our previous study, we proposed the multi-objective embarrassingly parallel search (MO-EPS) for multi-objective constraint optimization, which combines two strategies: a constraint programming-based strategy to determine Pareto front and a parallel search for constraint programming. In this study, we propose the MO-EPS with upper bound constraints, an extended algorithm of the MO-EPS.
This paper focuses on devising a configurable resource management technique for use in clouds for processing batches of MapReduce jobs associated with Service Level Agreements (SLAs). The proposed technique permits cl...
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ISBN:
(纸本)9781509014453
This paper focuses on devising a configurable resource management technique for use in clouds for processing batches of MapReduce jobs associated with Service Level Agreements (SLAs). The proposed technique permits cloud service providers to make resource management decisions which consider client quality of service requirements, system performance, and energy consumption which directly affects data center operation costs. This research models the resource management problem as an optimization problem using constraint programming (CP). A simulation-based performance analysis that demonstrates the effectiveness of the approach is provided.
In this paper, we are interested in one hand to review a set of problems encountered in logistics, and in a second hand to highlight the contribution of constraint programming, Metaheuristics and numerical solvers usi...
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ISBN:
(纸本)9781467385718
In this paper, we are interested in one hand to review a set of problems encountered in logistics, and in a second hand to highlight the contribution of constraint programming, Metaheuristics and numerical solvers using non-linear inequalities in solving them. One of the objectives is to address a panorama of bin packing applications in logistics and their embedding in cloud computing.
the problem studied in this paper is to allocate and to sequence the elective operation on operating rooms (ORs). We develop a mixed integer linear programming (MILP) model to solve this problem. Decisions in this mod...
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ISBN:
(纸本)9781509018970
the problem studied in this paper is to allocate and to sequence the elective operation on operating rooms (ORs). We develop a mixed integer linear programming (MILP) model to solve this problem. Decisions in this model include the allocation of operations to material resources and human resources, the starting time of them and the starting time for each surgeon. To show the efficiency of this model, we decide to compare it with a constraints programming (CP) approach. The performance of these models is tested using a benchmark of the literature. The results indicate the efficiency of the MILP model compared with the CP model in terms of computational time.
Business Process compliance is an important issue in control-flow and data-flow perspectives. Control-flow correctness can be analysed at design time, whereas data-flow accuracy should be verified at run-time, since d...
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ISBN:
(纸本)9783319394299;9783319394282
Business Process compliance is an important issue in control-flow and data-flow perspectives. Control-flow correctness can be analysed at design time, whereas data-flow accuracy should be verified at run-time, since data is accessed and modified during execution. Compliance validation should consider the conformance of data to business rules. Business compliance rules are policies or statements that govern corporate behaviour. Since business compliance rules and data change during process execution, faults can appear due to the erroneous inclusion of rules and/or data in the process. A hybrid diagnosis therefore needs to be performed regarding the likelihood of faults in data vs. business rules. In order to achieve the correct diagnosis, it is fundamental to attain the best assumption concerning the degree of likelihood. In this paper, we present an automatic process to diagnose possible faults that simultaneously combines business rules and data of multiple process instances. This process is based on constraint programming paradigm to efficiently ascertain a minimal diagnosis. Furthermore, a methodology for calculation of the most appropriate degree of likelihood of faults in data vs. business rules is proposed.
This paper deals with declarative decision support framework for scheduling groups of orders. All orders in a group should be delivered at the same time after processing. The authors present a novel declarative approa...
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ISBN:
(纸本)9788360810903
This paper deals with declarative decision support framework for scheduling groups of orders. All orders in a group should be delivered at the same time after processing. The authors present a novel declarative approach to modeling and solving scheduling problems as a declarative decision support framework. The proposed framework makes it possible to ask different types of questions (general, specific, logical, etc.). It also allows, scheduling emerging orders or groups of orders without changing the existing schedules. To implement was used CLP (constraint Logic programming) environment. To increase the efficiency of the framework, particularly in the area of optimization made its integration with MP (Mathematical programming) environment. The paper also presents the implementation of illustrative model, using the proposed framework. In addition, an efficiency analysis of the presented solution in relation to the application of mathematical programming have been conducted.
Recent work in model combinators, as well as projects like G12 and SIMPL, achieved significant progress in automating the generation of complex and hybrid solvers from high-level model specifications. This paper exten...
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ISBN:
(数字)9783319339542
ISBN:
(纸本)9783319339542;9783319339535
Recent work in model combinators, as well as projects like G12 and SIMPL, achieved significant progress in automating the generation of complex and hybrid solvers from high-level model specifications. This paper extends model combinators into the scheduling domain. This is of particular interest as, today, both constraint programming (CP) and Mixed-Integer programming (MIP) perform well on scheduling problems providing different capabilities and trade-offs. The ability to construct hybrid scheduling solvers to leverage the strengths of both technologies as well as multiple problem encodings through high-level model combinators provides new opportunities. Complex parallel hybrids can be synthesized with minimal effort on the part of the user and provide substantial performance benefits over standalone solvers.
The problem of personnel and interventions scheduling faced by a container ship maintenance service provider (MSPC), commonly the manufacturer of a main ship subsystem such as engines, is analysed. Clients can make a ...
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The problem of personnel and interventions scheduling faced by a container ship maintenance service provider (MSPC), commonly the manufacturer of a main ship subsystem such as engines, is analysed. Clients can make a request for a maintenance service of a containership at a given harbour with a given number of days in advance to the desired date, as established in the service contract. The MSPC is allowed to delay the intervention to any future stop of the route within a specified time window depending on its urgency, as set in the contract. The MSPC technicians can be divided into different categories of skills and further distinguished as belonging to the MSPC main company, to the MSP network of subsidiaries, or hired on demand, with different availability constraints, personnel costs, and transport costs in relation to harbour proximity. Delays on planned arrival dates to harbours as well as changes in the duration of stay are common due to bad meteorological conditions, congestions at harbours, or other issues arisen during sailing or previous stops, so a rolling planning horizon should be adopted to face such a dynamic environment. A constraint programming optimisation model hybridized with Large Neighborhood Search is proposed in order to address the problem and its performance compared to actual plans from a world-wide known MSPC. The model has been developed to perform also as a decision making tool;a factorial design of experiment is adopted in order to analyse the impact of a change in some contractual features, such as the minimum time allowed to clients for requiring a service, or the maximum delay allowed to the MSPC to satisfy a service request. How granting clients more flexibility while preserving efficacy and efficiency of the service can so be investigated. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
constraint satisfaction and data validation problems can be expressed very elegantly in state-based formal methods such as B. However, is B suited for developing larger applications and are there existing tools that s...
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ISBN:
(纸本)9783319336008;9783319335995
constraint satisfaction and data validation problems can be expressed very elegantly in state-based formal methods such as B. However, is B suited for developing larger applications and are there existing tools that scale for these projects? In this paper, we present our experiences on two real-world data validation projects from different domains which are based on the B language and use ProB as the central validation tool. The first project is the validation of university timetables, and the second project is the validation of railway topologies. Based on these two projects, we present a general structure of a data validation project in B and outline common challenges along with various solutions. We also discuss possible evolutions of the B language to make it (even) more suitable for such projects.
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