Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms

Chang, Hao‐Chin; Liu, Tung-Kuan

doi:10.1007/s10845-015-1084-y

Cited by 114 publications

(36 citation statements)

References 32 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

View full text Add to dashboard Cite

show abstract

“…Chang et al, 2007;Luo et al, 2009;Damodaran et al, 2009;Ventura and Yoon, 2013;Li et al, 2014;Chang and Liu, 2015 This scheme of GA has been implemented using both codi cations presented in the previous section: permutation and binary codi cation denoted GA(P) and GA(B) respectively . Both algorithms are identical, except with respect to the tness computation, since the way to calculate the owtime is simpler in the binary codi cation than in the permutation codi cation.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Single machine interfering jobs problem with flowtime objective

González

2015

J Intell Manuf

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)>IJH=?J Interfering jobs problems (or multi agents scheduling problems) are an emergent topic in the scheduling literature. In these decision problems, two or more sets of jobs have to be scheduled, each one with its own criteria. More speci cally, we focus on a problem in which jobs belonging to two sets have to be scheduled in a single machine in order to minimize the total owtime of the jobs in one set, while the total owtime of the jobs in the other set should not exceed a given constant ϵ. This problem is known to be weakly NP-hard, and a Dynamic Programming (DP) algorithm has been proposed to nd optimal solutions.In this paper, we rst analyse the distribution of solutions of the problem in order to establish its empirical hardness. Next, a novel encoding scheme and a set of properties associated to the neighbourhood of this scheme are presented. These properties are used to develop both exact and approximate methods, i.e. a branch and bound (B&B) method, several constructive heuristics, and di erent versions of a genetic algorithm (GA). The computational experience carried out shows that the proposed B&B is more e cient than the existing DP algorithm. The results also show the advantages of the proposed encoding scheme, as the approximate methods yield close-to-optimum solutions for big-sized instances where exact methods are not feasible.Scheduling interfering jobs two-agent scheduling problem total owtime single machine

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“…Chang et al [8] proposed a hybrid genetic algorithm (HGA), which used the Taguchi method to optimize the parameters of the genetic algorithm, and in the paper, a novel coding mechanism was proposed to solve the problem of invalid work assignment. Yi et al [9] proposed an effective memetic algorithm (MA), which combines the local search ability of TS with the global search capability of GA to provide an effective way to "explore" and "exploit."…”

Section: Introductionmentioning

confidence: 99%

An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

Huang

2019

Algorithms

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For solving the complex flexible job-shop scheduling problem, an improved genetic algorithm with adaptive variable neighborhood search (IGA-AVNS) is proposed. The improved genetic algorithm first uses a hybrid method combining operation sequence (OS) random selection with machine assignment (MA) hybrid method selection to generate the initial population, and it then groups the population. Each group uses an improved genetic operation for global search, then the better solutions from each group are stored in the elite library, and finally, the adaptive local neighborhood search is used in the elite library for detailed local searches. The simulation experiments are carried out by three sets of international standard examples. The experimental results show that the IGA-AVNS algorithm is an effective algorithm for solving flexible job-shop scheduling problems.

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Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms

Cited by 114 publications

References 32 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

Single machine interfering jobs problem with flowtime objective

An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

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