A Matheuristic Approach Combining Local Search and Mathematical Programming

Lagos, Carolina; Guerrero, Guillermo; Cabrera, Enrique; Niklander, Stefanie; Johnson, Franklin; Paredes, Fernando; Moreno-Vega, J. Marcos

doi:10.1155/2016/1506084

Cited by 3 publications

(6 citation statements)

References 27 publications

(32 reference statements)

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“…, 1000 × 1000). For each problem class, 10 instances are randomly generated using the procedure proposed in [30] and that was also used in [5,23]. We do this in order to minimise any instance dependant effect.…”

Section: Methodsmentioning

confidence: 99%

“…The variety of the tools and search principles introduced and described in [21] are such that the TS can be considered as the seed of a general framework for modern heuristic search [22]. We include TS as it has Input: (portion of the resources included in I) Output: (̂,̂) (locally optimal solution) (1) begin (2) = 0; (3) I = selectBinaryVariablesRandomly( ); (4) (̂,̂) = solve MIP I ( , ); (5) repeat (6) localOptimum = true; been applied to several combinatorial optimisation problems (see, e.g., [5,[23][24][25][26][27]) including, of course, mixed integer programming problems as the one we consider in this study.…”

Section: Tabu Search Algorithmmentioning

confidence: 99%

“…The CFLP has been shown to be NP-hard [28]. The problem consists of selecting specific sites at which to install plants, warehouses, and distribution centres while assigning customers to service facilities and interconnecting facilities using flow assignment decisions [23]. In this study we consider the CFLP problem to evaluate the performance of a very simple matheuristic algorithm.…”

Section: The Capacitated Facility Location Problemmentioning

confidence: 99%

“…The mathematical model for the CFLP is It consists of one central plant, a set of potential warehouses, and a set of customers or retailers [23].…”

Section: The Capacitated Facility Location Problemmentioning

confidence: 99%

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Parameter Tuning for Local-Search-Based Matheuristic Methods

et al. 2017

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Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers' attention. These methods, also known as matheuristics, have been shown to perform especially well for large, complex optimisation problems that include both integer and continuous decision variables. One common strategy used by matheuristic methods to solve such optimisation problems is to divide the main optimisation problem into several subproblems. While heuristics are used to seek for promising subproblems, exact methods are used to solve them to optimality. In general, we say that both mixed integer (non)linear programming problems and combinatorial optimisation problems can be addressed using this strategy. Beside the number of parameters researchers need to adjust when using heuristic methods, additional parameters arise when using matheuristic methods. In this paper we focus on one particular parameter, which determines the size of the subproblem. We show how matheuristic performance varies as this parameter is modified. We considered a well-known NP-hard combinatorial optimisation problem, namely, the capacitated facility location problem for our experiments. Based on the obtained results, we discuss the effects of adjusting the size of subproblems that are generated when using matheuristics methods such as the one considered in this paper.

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Section: Methodsmentioning

confidence: 99%

Section: Tabu Search Algorithmmentioning

confidence: 99%

Section: The Capacitated Facility Location Problemmentioning

confidence: 99%

“…The mathematical model for the CFLP is It consists of one central plant, a set of potential warehouses, and a set of customers or retailers [23].…”

Section: The Capacitated Facility Location Problemmentioning

confidence: 99%

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Parameter Tuning for Local-Search-Based Matheuristic Methods

et al. 2017

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“…Once the metaheuristic search is finished, a more computationally intensive simulation stage can be added to increase the accuracy of the estimated values. At some steps of the solving process we use exact methods, and thus the algorithm could be considered as matheuristic (Lagos et al., ).…”

Section: Our Simheuristic Approach For the Stochastic Cflpmentioning

confidence: 99%

Designing e‐commerce supply chains: a stochastic facility–location approach

Pagès‐Bernaus

Ramalhinho

Juan

et al. 2017

Int Trans Operational Res

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e‐Commerce activity has been increasing during recent years, and this trend is expected to continue in the near future. e‐Commerce practices are subject to uncertainty conditions and high variability in customers’ demands. Considering these characteristics, we propose two facility–location models that represent alternative distribution policies in e‐commerce (one based on outsourcing and another based on in‐house distribution). These models take into account stochastic demands as well as more than one regular supplier per customer. Two methodologies are then introduced to solve these stochastic versions of the well‐known capacitated facility–location problem. The first is a two‐stage stochastic‐programming approach that uses an exact solver. However, we show that this approach is not appropriate for tackle large‐scale instances due to the computational effort required. Accordingly, we also introduce a “simheuristic” approach that is able to deal with large‐scale instances in short computing times. An extensive set of benchmark instances contribute to illustrate the efficiency of our approach, as well as its potential utility in modern e‐commerce practices.

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