A heuristic algorithm for the distributed and flexible job-shop scheduling problem

Ziaee, Mohsen

doi:10.1007/s11227-013-0986-8

Cited by 43 publications

(18 citation statements)

References 11 publications

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“…There is a growing interest in the literature to study scheduling problems in cellular environment. In recent years, some researchers have studied the distributed and flexible job shop scheduling (DFJS), which involves the scheduling of jobs in a distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop (Giovanni and Pezzella, ; Ziaee, ; Chang and Liu, ; Lu et al, ). In this paper, the term FJCS is preferred to DFJS in introducing the cell scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Deliktaş

Torkul

Üstün

2017

Int Trans Operational Res

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In this paper, we propose novel mathematical models involving both single‐ and biobjective functions that deal with a flexible job shop scheduling problem in cellular manufacturing environment by taking into consideration exceptional parts, intercellular moves, intercellular transportation times, sequence‐dependent family setup times, and recirculation. The problem has been known as NP‐hard. The proposed models have been tested and solved using Lingo 11.0 with minimization of makespan for the problems involving about 4 cells, 4 part families, 15 parts, and 12 machines. The most suitable model among the proposed single‐objective models is determined using the test results. Then, another objective function as total tardiness is added to this model. The obtained biobjective model is solved using the scalarization methods, the weighted sum method, ɛ‐constraint method, and conic scalarization method (CSM), in order to convert the mathematical model's objectives into a single‐objective function. By utilizing these scalarization methods, the Pareto effective solutions are generated for a specific test problem. The advantages of the CSM are demonstrated by considering the Pareto effective solutions.

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Section: Introductionmentioning

confidence: 99%

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Deliktaş

Torkul

Üstün

2017

Int Trans Operational Res

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“…We compared the results with 11 algorithms from literature; HA, HTGA, GATS, TABC, Heuristic, AMMA, HHS, hGA, TS, and IACO. These algorithm were proposed by Li and Gao [1], Chang et al [50], Nouri et al [41], Gao et al [42], Ziaee [51], Zuo et al [52] and Yuan et al [43]. Results of AIA and TS are taken from Yuan et al [43].…”

Section: Methodsmentioning

confidence: 99%

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

Aqel

Gao

2019

Chin. J. Mech. Eng.

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The flexible job shop scheduling problem (FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them difficult to code and not easy to reproduce. This paper proposes a modified iterated greedy (IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an effective method that is also easy to apply and consumes less CPU time in solving the FJSP problem.

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“…Some of the very recent approaches include biogeography based optimization [227], firefly algorithm [228], heuristics [229], invasive weed optimization [230], and differential evolution [231]. However, GA remains the most used algorithm for the FJSSP [3,232].…”

Section: Fjssps Involving Gamentioning

confidence: 99%

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Amjad

Butt

Kousar

et al. 2018

Mathematical Problems in Engineering

114

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Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

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A heuristic algorithm for the distributed and flexible job-shop scheduling problem

Cited by 43 publications

References 11 publications

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

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