A simulated annealing with multiple‐search paths and parallel computation for a comprehensive flowshop scheduling problem

Defersha, Fantahun M.

doi:10.1111/itor.12105

Cited by 14 publications

(4 citation statements)

References 44 publications

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“…Based on the lessons learnt from the real‐world applications, we are currently constructing an intelligent decision support system, which can automatically generate importance weights of rescue tasks, perform parallel computation and multiple‐path search (Defersha, ), and extract important features from the scheduling solutions, and thus provide a much more effective decision support to emergency response. We believe that further extensions and applications of our approach can substantially contribute to the development of disaster management.…”

Section: Discussionmentioning

confidence: 99%

Emergency scheduling of engineering rescue tasks in disaster relief operations and its application in China

Zheng

Ling

et al. 2015

Int Trans Operational Res

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Disaster relief operations typically involve a large number of engineering rescue tasks, the efficient completion of which is vital to the success of the operations. The paper establishes a model of emergency scheduling of engineering rescue tasks in disaster relief operations, which involves multiple rescue teams and tasks, different and, perhaps fuzzy, processing times, as well as different importance weights of the tasks. We then propose a method based on biogeography‐based optimization, develop effective migration and mutation operators, and employ a multiobjective optimization approach for providing a set of candidate solutions for decision support. Computational experiments demonstrate that our approach exhibits competitive performance on a set of test problems. The proposed model and method have been successfully applied to the recent 2013 Dingxi earthquake that occurred in China.

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Section: Discussionmentioning

confidence: 99%

Emergency scheduling of engineering rescue tasks in disaster relief operations and its application in China

Zheng

Ling

et al. 2015

Int Trans Operational Res

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“…Tabu search [Rudek, 2014, Jin et al, 2012, Bozejko et al, 2017, Bozejko et al, 2013, Czapinski and Barnes, 2011, James et al, 2009, Czapiński, 2013, Bukata et al, 2015, Cordeau and Maischberger, 2012, Wei et al, 2017, Janiak et al, 2008, Shylo et al, 2011, Jin et al, 2014, Bożejko et al, 2016, Jin et al, 2011, Maischberger and Cordeau, 2011, Van Luong et al, 2013, Dai et al, 2009] Simulated annealing [Thiruvady et al, 2016, Rudek, 2014, Defersha, 2015, Mu et al, 2016, Ferreiro et al, 2013, Lou and Reinitz, 2016, Banos et al, 2016, 2016, Lazarova and Borovska, 2008 Variable neigborhood search [Yazdani et al, 2010, Lei and Guo, 2015, Davidović and Crainic, 2012, Quan and Wu, 2017, Menendez et al, 2017, Eskandarpour et al, 2013, Coelho et al, 2016, Polat, 2017, Tu et al, 2017, Aydin and Sevkli, 2008, Polacek et al, 2008 (Greedy randomized)…”

Section: Algorithm Typementioning

confidence: 99%

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Schryen

2020

European Journal of Operational Research

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Solving optimization problems with parallel algorithms has a long tradition in OR. Its future relevance for solving hard optimization problems in many fields, including finance, logistics, production and design, is leveraged through the increasing availability of powerful computing capabilities. Acknowledging the existence of several literature reviews on parallel optimization, we did not find reviews that cover the most recent literature on the parallelization of both exact and (meta)heuristic methods. However, in the past decade substantial advancements in parallel computing capabilities have been achieved and used by OR scholars so that an overview of modern parallel optimization in OR that accounts for these advancements is beneficial. Another issue from previous reviews results from their adoption of different foci so that concepts used to describe and structure prior literature differ. This heterogeneity is accompanied by a lack of unifying frameworks for parallel optimization across methodologies, application fields and problems, and it has finally led to an overall fragmented picture of what has been achieved and still needs to be done in parallel optimization in OR. This review addresses the aforementioned issues with three contributions: First, we suggest a new integrative framework of parallel computational optimization across optimization problems, algorithms and application domains. The framework integrates the perspectives of algorithmic design and computational implementation of parallel optimization. Second, we apply the framework to synthesize prior research on parallel optimization in OR, focusing on computational studies published in the period 2008-2017. Finally, we suggest research directions for parallel optimization in OR.Keywords computing science · parallel optimization · computational optimization · literature review * I am grateful for the support provided by Abdullah Burak, Philip Empl, Constanze Hilmer, Gerhard Rauchecker, Richard Schuster, Henning Siemes, and Melih Yilmaz, who supported me substantially in searching and coding research articles.2 Impressive computational results of applying parallelization to the traveling salesman problem (TSP) are reported by Crainic et al. [2006, p.2]. arXiv:1910.03028v1 [cs.DC] 3 Oct 2019Parallel computational optimization in operations research is challenging in general from both the algorithmic and the computational perspective, and ii) a viable alternative to parallelizing algorithms has been the exploitation of ongoing increases of clock speed of single CPUs of modern microprocessors. But this growth process reached a limit already several years ago due to heat dissipation and energy consumption issues . This development makes parallelization efforts (not only in optimization) much more important than it was in earlier times.Fortunately, the need for parallelization has been acknowledged and accompanied by an increased availability of parallel computing resources. This availability is rooted in two phenomena: a) the rapid development of para...

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“…Numerous papers have been published in the last few years on different variants of the permutation flowshop scheduling problem (Dhouib et al., ; Juan et al., ; , Bai, ; Defersha, ; Ren et al., 2015; Wang and Zhang, ; Wang et al., , to name just a few). A particular interest has been devoted to the two‐machine flowshop with various job settings, including release dates (Xie et al., ), time delays (Msakni et al., ), deteriorating jobs (Cheng et al., ), setup times (Detienne et al., ), and learning‐based processing times (Lai et al.…”

Section: Introductionmentioning

confidence: 99%

Enhanced lower bounds and exact procedures for total completion time minimization in a two‐machine permutation flowshop with release dates

Mrad

Chalghoumi²,

Ladhari

et al. 2017

Int Trans Operational Res

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We consider the problem of minimizing the sum of completion times in a two‐machine permutation flowshop subject to release dates. New procedures are proposed for effectively bounding the completion time of a given job that is processed at a given position. New assignment‐based lower bounds are derived as well as an enhanced mathematical programming formulation. Our computational analysis shows a consistent tightness of the proposed lower bounds and a high outperformance of the enhanced mathematical formulation with respect to the classical one.

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A simulated annealing with multiple‐search paths and parallel computation for a comprehensive flowshop scheduling problem

Cited by 14 publications

References 44 publications

Emergency scheduling of engineering rescue tasks in disaster relief operations and its application in China

Emergency scheduling of engineering rescue tasks in disaster relief operations and its application in China

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Enhanced lower bounds and exact procedures for total completion time minimization in a two‐machine permutation flowshop with release dates

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