Multi-objective meta-heuristics to solve three-stage assembly flow shop scheduling problem with machine availability constraints

Shoaardebili, Niloofar

doi:10.1080/00207543.2014.948575

Cited by 32 publications

(11 citation statements)

References 18 publications

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“…Finally, a different version of the APFSP—in which the assembly stage has also parallel machines—has been studied by Mozdgir et al. () and Shoaardebili and Fattahi (). These works consider minimization of the makespan and minimization of the sum of weighted tardiness and earliness, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

Ferone

Hatami

González-Neira

et al. 2019

Int Trans Operational Res

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Modern production systems require multiple manufacturing centers—usually distributed among different locations—where the outcomes of each center need to be assembled to generate the final product. This paper discusses the distributed assembly permutation flow‐shop scheduling problem, which consists of two stages: the first stage is composed of several production factories, each of them with a flow‐shop configuration; in the second stage, the outcomes of each flow‐shop are assembled into a final product. The goal here is to minimize the makespan of the entire manufacturing process. With this objective in mind, we present an efficient and parameter‐less algorithm that makes use of a biased‐randomized iterated local search metaheuristic. The efficiency of the proposed method is evaluated through the analysis of an extensive set of computational experiments. The results show that our algorithm offers excellent performance when compared with other state‐of‐the‐art approaches, obtaining several new best solutions.

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Section: Literature Reviewmentioning

confidence: 99%

A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

Ferone

Hatami

González-Neira

et al. 2019

Int Trans Operational Res

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“…In the first assumption, some researchers considered one maintenance period in the horizon, for example the authors of [15][16][17] focused on scheduling resumable jobs in two-machine flow shops and the authors of [18,19] focused on the non-resumable jobs. Some researchers considered that the intervals are periodically fixed, for example the authors of [20][21][22] investigated the two-machine flow shops and the authors of [23,24] studied the multi-machine flow shops. In the second assumption, some researchers considered that the continuous working time of machine must be smaller than a threshold, for example the authors of [25] compared the difference between fixed constraint and flexible constraint.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

Cui

2020

Sustainability

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With the growing concern of energy shortage and environment pollution, the energy aware operation management problem has emerged as a hot topic in industrial engineering recently. An integrated model consisting of production scheduling, preventive maintenance (PM) planning, and energy controlling is established for the flow shops with the PM constraint and peak demand constraint. The machine’s on/off and the speed level selection are considered to save the energy consumption in this problem. To minimize the makespan and the total energy consumption simultaneously, a multi-objective algorithm founded on NSGA-II is designed to solve the model effectively. The key decision variables are coded into the chromosome, while the others are obtained heuristically using the proposed decoding method when evaluating the chromosome. Numerical experiments were conducted to validate the effectiveness and efficiency by comparing the proposed algorithm and the traditional rules in manufacturing plant. The impacts of constraints on the Pareto frontier are also shown when analyzing the tradeoff between two objectives, which can be used to explicitly assess the energy consumption.

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“…• The problem DP m → F 2|a i , pmtn−semi|F l ( C j , T j , E j ) is tackled by Shoaardebili and Fattahi (2015), who present multi-objective metaheuristics.…”

Section: Flowshop-type In the Assembly Stage: Dp M → F M And Dp M → Hmentioning

confidence: 99%

Deterministic assembly scheduling problems: A review and classification of concurrent-type scheduling models and solution procedures

Framiñán

2019

European Journal of Operational Research

101

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Many activities in industry and services require the scheduling of tasks that can be concurrently executed, the most clear example being perhaps the assembly of products carried out in manufacturing. Although numerous scientific contributions have been produced on this area over the last decades, the wide extension of the problems covered and the lack of a unified approach have lead to a situation where the state of the art in the field is unclear, which in turn hinders new research and makes translating the scientific knowledge into practice difficult. In this paper we propose a unified notation for assembly scheduling models that encompass all concurrent-type scheduling problems. Using this notation, the existing contributions are reviewed and classified into a single framework, so a comprehensive, unified picture of the field is obtained. In addition, a number of conclusions regarding the state of the art in the topic are presented, as well as some opportunities for future research.

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Multi-objective meta-heuristics to solve three-stage assembly flow shop scheduling problem with machine availability constraints

Cited by 32 publications

References 18 publications

A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

Deterministic assembly scheduling problems: A review and classification of concurrent-type scheduling models and solution procedures

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