Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems

Wu, Muh-Cherng; Lin, Chi Shiuan; Lin, Chia Hui; Chen, Chen Fu

doi:10.1016/j.cor.2016.11.021

Cited by 48 publications

(18 citation statements)

References 32 publications

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“…By using GA, Lu et al [19] also proposed an incomplete decoding representation with only a job sequence, and all the three sub-problems were decided in the decoding process by heuristic rules. Then, Wu et al [15] proposed another incomplete encoding representation that only took operations sequence into consideration. Moreover, a novel GA with this encoding scheme was designed [15].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Then, Wu et al [15] proposed another incomplete encoding representation that only took operations sequence into consideration. Moreover, a novel GA with this encoding scheme was designed [15]. Except for GA, Ziaee [20] designed an efficient heuristic; Marzouki et al [21] proposed a chemical reaction optimization (CRO) algorithm, and Wu et al [22] proposed an improved differential evolution (IDE) algorithm for solving DFJSP with minimizing makespan or earliness/tardiness.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint sets (15)- (17) are the constraints for Turn Off/On strategy. More specifically, constraint set (15) assures that if a Turn Off/On strategy is implemented between two adjacent positions of a machine, the idle time is no less than the break-even of the machine. Constraint set (21) defines the makespan of each factory, and constraint (22) defines the overall makespan.…”

Section: ) Decision Variablementioning

confidence: 99%

“…Although DFJSP has attracted a large number of concerns, the existing research mainly focuses on minimizing makespan [14,15], and there is no published work about FJSP with considering energy consumption in multi-factory production environment. In order to make up this gap, this paper addresses the energy-efficient DFJSP (EE-DFJSP) with minimizing total energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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MILP Modeling and Optimization of Energy- Efficient Distributed Flexible Job Shop Scheduling Problem

et al. 2020

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With the global warming problem and increasing energy cost, manufacturing firms are paying more and more attention to reducing energy consumption. This paper addresses the distributed flexible job shop scheduling problem (DFJSP) with minimizing energy consumption. To solve the problem, firstly, a novel mixed integer linear programming (MILP) model is developed to solve small-scaled problems to optimality. Due to the NP-hardness of DFJSP, we then propose an efficient hybrid shuffled frog-leaping algorithm (HSFLA) for solving DFJSP, particularly for large-sized problems. HSFLA combines the shuffled frog-leaping algorithm (SFLA) with powerful global search ability and variable neighborhood search (VNS) with good local search ability. Moreover, in HSFLA, the encoding method, the decoding method, the initialization method and the memetic evolution process are specifically designed. Finally, numerical experiments are conducted to evaluate the performance of the proposed MILP model and HFSLA. INDEX TERMS Distributed flexible job shop scheduling problem, mixed integer linear programming, shuffled frog-leaping algorithm, variable neighborhood search, energy consumption.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: ) Decision Variablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MILP Modeling and Optimization of Energy- Efficient Distributed Flexible Job Shop Scheduling Problem

et al. 2020

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“…(i) Dual-resource constrained FJSSP, for example, [52,162] (ii) Sequence dependent setup times, for example, [88,207] (iii) Distributed and flexible JSSP, for example, [79,99] (iv) Just-In-Time dynamic scheduling, for example, [80,83] (v) Overlapping in operations, for example, [73,98] (vi) Random machine breakdowns, for example, [91,184] (vii) Dynamic FJSSP, for example, [94,96].…”

Section: Software Toolsmentioning

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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Optimal Design of a Fuzzy System with a Real-Coded Genetic Algorithm for Diabetes Classification

Monica

Melín

Sánchez

2021

Hybrid Intelligent Systems

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Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems

Cited by 48 publications

References 32 publications

MILP Modeling and Optimization of Energy- Efficient Distributed Flexible Job Shop Scheduling Problem

MILP Modeling and Optimization of Energy- Efficient Distributed Flexible Job Shop Scheduling Problem

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

Optimal Design of a Fuzzy System with a Real-Coded Genetic Algorithm for Diabetes Classification

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