A Hybrid Algorithm Based on Particle Swarm Optimization and Artificial Immune for an Assembly Job Shop Scheduling Problem

Du, Hui Bin; Liu, Dacheng; Mian-hao, Zhang

doi:10.1155/2016/3938679

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…Generally speaking, two types of two-stage production systems can be distinguished. The first type is the assembly job shop scheduling problem (AJSSP) (Chan et al , 2009; Thiagarajan and Rajendran, 2005; Pathumnakul and Egbelu, 2006; Wong and Ngan, 2013; Du et al , 2016). The second type is the job shop with assembly scheduling problem (Cheng, 1990; Dimyati, 2007; Zhang, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wong and Ngan (2013) studied the assembly job shop scheduling with lot streaming, using a proposed hybrid genetic algorithm (HGA) and a hybrid particle swarm optimization (HPSO). Du et al (2016) proposed a hybrid algorithm based on PSO and artificial immune systems to solve an assembly job shop scheduling problem. The PSO algorithm is highly regarded because of its global search capability in continuous space.…”

Section: Literature Reviewmentioning

confidence: 99%

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A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

Fattahi

Rad

Daneshamooz

et al. 2020

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Purpose The purpose of this paper is to present a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations. In this problem, each product is produced by assembling a set of several different parts. At first, the parts are processed in a flexible job shop system, and then at the second stage, the parts are assembled and products are produced. Design/methodology/approach As the problem is non-deterministic polynomial-time-hard, a new hybrid particle swarm optimization and parallel variable neighborhood search (HPSOPVNS) algorithm is proposed. In this hybrid algorithm, particle swarm optimization (PSO) algorithm is used for global exploration of search space and parallel variable neighborhood search (PVNS) algorithm for local search at vicinity of solutions obtained in each iteration. For parameter tuning of the metaheuristic algorithms, Taguchi approach is used. Also, a statistical test is proposed to compare the ability of metaheuristics at finding the best solution in the medium and large sizes. Findings Numerical experiments are used to evaluate and validate the performance and effectiveness of HPSOPVNS algorithm with hybrid particle swarm optimization with a variable neighborhood search (HPSOVNS) algorithm, PSO algorithm and hybrid genetic algorithm and Tabu search (HGATS). The computational results show that the HPSOPVNS algorithm achieves better performance than competing algorithms. Practical implications Scheduling of manufacturing parts and planning of assembly operations are two steps in production systems that have been studied independently. However, with regard to many manufacturing industries having assembly lines after manufacturing stage, it is necessary to deal with a combination of these problems that is considered in this paper. Originality/value This paper proposed a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

Fattahi

Rad

Daneshamooz

et al. 2020

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“…A genetic algorithm for the generic assembly problem is presented by Pongcharoen et al (2002), taking into account penalties for earliness and for tardiness. Also Du et al (2016) propose a metaheuristic for a general-layout assembly problem, although only a sample instance is solved. Kyparisis and Koulamas (2002) address a general problem consisting of r-serial stages, with stage k containing m k machines in charge of carrying out the operations of the jobs.…”

Section: General Layout (Job-shop) Assembly Modelsmentioning

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

102

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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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“…As is evident in the existing literature, all studies about the two-stage scheduling problems with considering job shop at the first stage presented a metaheuristic algorithm to solve the problem (Thiagarajan and Rajendran, 2005;Pathumnakul and Egbelu, 2006;Dimyati, 2007;Chan et al, 2009;Zhang, 2011;Wong and Ngan, 2013;Du et al, 2016). However, some recent works have tackled this problem with different approaches.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling and two efficient branch and bound algorithms for job shop scheduling problem followed by an assembly stage

Daneshamooz

Hosseini

2021

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Purpose Two-stage production systems including a processing shop and an assembly stage are widely used in various manufacturing industries. These two stages are usually studied independently which may not lead to ideal results. This paper aims to deal with a two-stage production system including a job shop and an assembly stage. Design/methodology/approach Some exact methods are proposed based on branch and bound (B&B) approach to minimize the total completion time of products. As B&B approaches are usually time-consuming, three efficient lower bounds are developed for the problem and variable neighborhood search is used to provide proper upper bound of the solution in each branch. In addition, to create branches and search new nodes, two strategies are applied including the best-first search and the depth-first search (DFS). Another feature of the proposed algorithms is that the search space is reduced by releasing the precedence constraint. In this case, the problem becomes equivalent to a parallel machine scheduling problem, and the redundant branches that do not consider the precedence constraint are removed. Therefore, the number of nodes and computational time are significantly reduced without eliminating the optimal solution. Findings Some numerical examples are used to evaluate the performance of the proposed methods. Comparison result to mathematical model (mixed-integer linear programming) validates the performance accuracy and efficiency of the proposed methods. In addition, computational results indicate the superiority of the DFS strategy with regard to CPU time. Originality/value Studies about the scheduling problems for two-stage production systems including job shop followed by an assembly stage traditionally present approximate method and metaheuristic algorithms to solve the problem. This is the first study that introduces exact methods based on (B&B) approach.

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A Hybrid Algorithm Based on Particle Swarm Optimization and Artificial Immune for an Assembly Job Shop Scheduling Problem

Cited by 14 publications

References 22 publications

A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

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

Mathematical modeling and two efficient branch and bound algorithms for job shop scheduling problem followed by an assembly stage

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