A simulated annealing for hybrid flow shop scheduling with multiprocessor tasks to minimize makespan

Wang, Huimei; Chou, Fuh-Der; Wu, Feng‐Chin

doi:10.1007/s00170-010-2868-z

Cited by 47 publications

(28 citation statements)

References 27 publications

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“…The compared algorithms include genetic algorithm (GA) given by Şerifoglu and Ulusoy [35] and simulated annealing (SA) by Wang et al [18]. The problem sizes are involved in three different numbers of jobs (i.e., = 20, 60, and 100) and three different numbers of production stages (i.e., = 5, 10, 15).…”

Section: Comparison With Several Existing Algorithmsmentioning

confidence: 99%

“…Kahraman et al [17] developed an efficient GA for hybrid flow-shop scheduling problems with the objective of minimizing the makespan. Wang et al [18] presented the SAA to study a hybrid flowshop scheduling problem with multiprocessor tasks under the makespan criterion. Mirsanei et al [19] provided a novel SAA algorithm with a new neighborhood function to obtain a better result of the makespan in a hybrid flow-shop scheduling problem with identical parallel machines.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Genetic-Simulated Annealing Algorithm Based on a Hormone Modulation Mechanism for a Flexible Flow-Shop Scheduling Problem

Dai

Tang

Zheng

et al. 2013

Advances in Mechanical Engineering

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A flexible flow-shop scheduling (FFS) with nonidentical parallel machines for minimizing the maximum completion time or makespan is a well-known combinational problem. Since the problem is known to be strongly NP-hard, optimization can either be the subject of optimization approaches or be implemented for some approximated cases. In this paper, an improved geneticsimulated annealing algorithm (IGAA), which combines genetic algorithm (GA) based on an encoding matrix with simulated annealing algorithm (SAA) based on a hormone modulation mechanism, is proposed to achieve the optimal or near-optimal solution. The novel hybrid algorithm tries to overcome the local optimum and further to explore the solution space. To evaluate the performance of IGAA, computational experiments are conducted and compared with results generated by different algorithms. Experimental results clearly demonstrate that the improved metaheuristic algorithm performs considerably well in terms of solution quality, and it outperforms several other algorithms.

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Section: Comparison With Several Existing Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Genetic-Simulated Annealing Algorithm Based on a Hormone Modulation Mechanism for a Flexible Flow-Shop Scheduling Problem

Dai

Tang

Zheng

et al. 2013

Advances in Mechanical Engineering

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show abstract

“…Different kinds of methods (e.g., exact methods, heuristics and metaheuristics) were proposed to minimize a variety of objectives, which include the maximum completion time, the maximum flow time, the number of late jobs [16][17][18][19]. In terms of minimizing the maximum completion time, i.e., makespan, Mirsanei et al [20] proposed a simulated annealing algorithm to solve the HFS scheduling problem featured with sequence-dependent setup times.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Regulatory Network-Based Method for Dynamic Hybrid Flow Shop Scheduling with Uncertain Processing Times

Zhang

Qin

2017

Applied Sciences

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Abstract:The hybrid flow shop is a typical discrete manufacturing system. A novel method is proposed to solve the shop scheduling problem featured with uncertain processing times. The rolling horizon strategy is adopted to evaluate the difference between a predictive plan and the actual production process in terms of job delivery time. The genetic regulatory network-based rescheduling algorithm revises the remaining plan if the difference is beyond a specific tolerance. In this algorithm, decision variables within the rolling horizon are represented by genes in the network. The constraints and certain rescheduling rules are described by regulation equations between genes. The rescheduling solutions are generated from expression procedures of gene states, in which the regulation equations convert some genes to the expressed state and determine decision variable values according to gene states. Based on above representations, the objective of minimizing makespan is realized by optimizing regulatory parameters in regulation equations. The effectiveness of this network-based method over other ones is demonstrated through a series of benchmark tests and an application case collected from a printed circuit board assembly shop.

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“…In [26], sets of benchmark problems were designed and a genetic algorithm (GA) was proposed for the HFSPMT. Later, a new GA [15], an ant colony system (ACS) algorithm [27], a particle swarm optimization (PSO) algorithm [28], and a simulated annealing (SA) [29] were developed. In solving the HFSPMT, almost all the existing heuristics and metaheuristics only determine the processing order of the jobs at the first stage and then a decoding method is employed to determine the processing orders at other stages.…”

Section: Introductionmentioning

confidence: 99%

A novel decoding method for the hybrid flow-shop scheduling problem with multiprocessor tasks

Zhou

Wang

et al. 2011

Int J Adv Manuf Technol

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As the strongly NP-hard problem, the hybrid flow-shop problem with multiprocessor tasks (HFSPMT) has gained increasing attention due to its academic significance and wide application background. For many heuristics and meta-heuristics to solve the HFSPMT, decoding method is the key element to decode sequences to schedules, which has great effect on the solution quality. To narrow the idle time between the consecutive operations in the processor and to increase the flexibility in selecting processors to schedule the following operations, several rules are proposed to adjust sequences to minimize the makespan of the HFSPMT. Based on the rules, a novel and effective decoding method named forward scheduling (FS) is proposed in this paper for solving the HFSPMT. At each stage, it first decodes the solution according to the nondecreasing order of the jobs' completion times at the previous stage, and then it adjusts the processing order of the jobs dynamically according to the rules to obtain potentially better schedule. Numerical results based on the well-known benchmarks and comparisons with some existing decoding methods demonstrate the effectiveness of the proposed FS decoding method.

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A simulated annealing for hybrid flow shop scheduling with multiprocessor tasks to minimize makespan

Cited by 47 publications

References 27 publications

An Improved Genetic-Simulated Annealing Algorithm Based on a Hormone Modulation Mechanism for a Flexible Flow-Shop Scheduling Problem

An Improved Genetic-Simulated Annealing Algorithm Based on a Hormone Modulation Mechanism for a Flexible Flow-Shop Scheduling Problem

A Genetic Regulatory Network-Based Method for Dynamic Hybrid Flow Shop Scheduling with Uncertain Processing Times

A novel decoding method for the hybrid flow-shop scheduling problem with multiprocessor tasks

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