Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic

Armas, Jésica de; Juan, Ángel A.; Marquès, Joan Manuel; Pedroso, João Pedro

doi:10.1057/s41274-016-0155-6

Cited by 76 publications

(36 citation statements)

References 64 publications

(61 reference statements)

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“…() present simheuristic algorithms for solving inventory routing problems with stochastic demands; De Armas et al. () propose a simheuristic algorithm for the uncapacitated facility location problem with stochastic costs; in a similar way, Onggo et al. () use simheuristics to study agri‐food supply chains with stochastic demands. Computer Networks : Cabrera et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

Raba

Estrada‐Moreno

Panadero

et al. 2020

Int Trans Operational Res

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In the context of a supply chain for the animal‐feed industry, this paper focuses on optimizing replenishment strategies for silos in multiple farms. Assuming that a supply chain is essentially a value chain, our work aims at narrowing this chasm and putting analytics into practice by identifying and quantifying improvements on specific stages of an animal‐feed supply chain. Motivated by a real‐life case, the paper analyses a rich multi‐period inventory routing problem with homogeneous fleet, stochastic demands, and maximum route length. After describing the problem and reviewing the related literature, we introduce a reactive heuristic, which is then extended into a biased‐randomized simheuristic. Our reactive approach is validated and tested using a series of adapted instances to explore the gap between the solutions it provides and the ones generated by existing nonreactive approaches.

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Section: Literature Reviewmentioning

confidence: 99%

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

Raba

Estrada‐Moreno

Panadero

et al. 2020

Int Trans Operational Res

Self Cite

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“…BRAs have been applied successfully to solve both capacitated and uncapacitated FLPs. The latter has been tackled mainly considering hard constraints [50]. In Correia and Melo [51], the authors considered a multi-period FLP in which customers are sensitive to delivery lead times (i.e., some flexibility is allowed regarding the delivery dates).…”

Section: Applications In Logisticsmentioning

confidence: 99%

On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems

et al. 2019

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Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines.

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“…At each step, however, higher probabilities of being selected are assigned to those elements that are more "promising," that is, those that are likely to contribute to a larger improvement of the objective function. BR techniques have been successfully employed in solving VRPs (Dominguez et al, 2016), arc routing problems (González-Martín et al, 2012), flow-shop problems (Juan et al, 2014b), and even facility-location problems (de Armas et al, 2016).…”

Section: The Br-vns Metaheuristic Algorithmmentioning

confidence: 99%

Using horizontal cooperation concepts in integrated routing and facility‐location decisions

Quintero-Araújo

Gruler

Faulín

2017

Int Trans Operational Res

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In a global and competitive economy, efficient supply networks are essential for modern enterprises. Horizontal cooperation (HC) concepts represent a promising strategy to increase the performance of supply chains. HC is based on sharing resources and making joint decisions among different agents at the same level of the supply chain. This paper analyzes different cooperation scenarios concerning integrated routing and facility‐location decisions in road transportation: (a) a noncooperative scenario in which all decisions are individually taken (each enterprise addresses its own vehicle routing problem [VRP]); (b) a semicooperative scenario in which route‐planning decisions are jointly taken (facilities and fleets are shared and enterprises face a joint multidepot VRP); and (c) a fully cooperative scenario in which route‐planning and facility‐location decisions are jointly taken (also customers are shared, and thus enterprises face a general location routing problem). Our analysis explores how this increasing level of HC leads to a higher flexibility and, therefore, to a lower total distribution cost. A hybrid metaheuristic algorithm, combining biased randomization with a variable neighborhood search framework, is proposed to solve each scenario. This allows us to quantify the differences among these scenarios, both in terms of monetary and environmental costs. Our solving approach is tested on a range of benchmark instances, outperforming previously reported results.

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Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic

Cited by 76 publications

References 64 publications

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems

Using horizontal cooperation concepts in integrated routing and facility‐location decisions

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