Makespan minimization of a flowshop sequence-dependent group scheduling problem

Salmasi, Nasser; Logendran, Rasaratnam; Skandari, M. Reza

doi:10.1007/s00170-011-3206-9

Cited by 34 publications

(25 citation statements)

References 14 publications

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“…The comparisons in the two procedures are developed on the basis of a proper benchmark of problems using the scheme provided by Salmasi et al (2011). In reference to the parameters of the mathematical model described in the paragraph 3, the test cases are generated as shown in Tables 2.…”

Section: Experimental Computationsmentioning

confidence: 99%

“…In particular they are the Hybrid Particle Swarm Optimization (hereinafter coded as PSO) devised by Salmasi et al (2011) and the metaheuristic procedure hybridizing Genetic and Simulated Annealing algorithms (hereinafter coded as GSA) proposed by Naderi et al (2012). The optimization procedures are executed on a 2GB RAM virtual machine embedded on a workstation powered by two quad-core 2.39 GHz processors.…”

Section: Comparison Of Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneous workers with learning ability assignment in a cellular manufacturing system

Fichera¹,

Costa²,

Cappadonna³

2017

10.5267/j.ijiec

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This paper deals with Flow-shop Sequence-Dependent Group Scheduling and worker assignment problem. Flow-shop allows the process of a set of families of products applying the group technology concept to reduce setup costs, lead times, and work-in-process inventory costs. The worker assignment problem deals with assigning workers to workstations considering their different abilities and learning effect. The proposed model in this paper considers different objectives. The decision problems in this cellular manufacturing system are the jobs scheduling within of own group, the group scheduling and the workers assignment to the machines. The aim of this paper is to consider a more realistic profile of heterogeneous workers introducing the learning effect in the joint group scheduling and workers assignment problem. A mathematical model and an evolutionary procedure has been developed to solve this problem. A benchmark of test cases having different numbers of machines, groups, jobs, worker skills and learning index, has been taken into account to compare the efficiency of the proposed algorithm with two well known procedures.

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Section: Experimental Computationsmentioning

confidence: 99%

Section: Comparison Of Algorithmsmentioning

confidence: 99%

Heterogeneous workers with learning ability assignment in a cellular manufacturing system

Fichera¹,

Costa²,

Cappadonna³

2017

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…Moreover, pheromone trail intensities were limited between dynamically changing lower and upper bounds. Salmasi N et al [14] developed a hybrid ant colony optimization (HACO) algorithm to solve the makespan minimization problem of a flow shop sequence-dependent group. A lowerbounding technique based on relaxation of four constraints of the mathematical model developed for the original problem was proposed to evaluate the quality of the HACO algorithm.…”

Section: Meanings Of Abbreviations Inmentioning

confidence: 99%

A novel approach for product makespan prediction in production life cycle

Chang

Kong

Yin

2015

Int J Adv Manuf Technol

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A novel self-adaptive immune genetic algorithm (SAIGA)-dynamic back propagation neural network (DBPNN) model was developed to solve the difficulty of making maximum completion time prediction (makespan prediction). By analyzing history data of makespan and its related factors in production life cycle, a prediction model based on back propagation (BP) neural network was established, weight values and threshold values of the BP neural network model were improved dynamically, and the DBPNN model was further optimized via SAIGA, thus obtaining the SAIGA-DBPNN model. The proposed SAIGA-DBPNN model was applied to an aviation enterprise's makespan prediction. The application case suggested that the novel prediction method can yield accurate results. The usefulness of accurate makespan prediction in production life cycle as a tool for improving production efficiency was highlighted.

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“…Salmasi, Logendran, and Skandari [28] applied a hybrid ACO to flow shop sequence-dependent group shops. In that problem, each machine's group setup time depends on the previous work; all jobs and groups must be processed on the same sequence on all machines.…”

Section: Computational Requirements Of Aco With Local Searchmentioning

confidence: 99%

“…Furthermore, such a flexible program could include some form of local search to improve performance. Future research is intended to integrate MCAC with other techniques such as agents [23], alternate algorithms [24-26, 29, 30], and various local search methods [25,26,[28][29][30].…”

Section: Limitations and Directions For Future Researchmentioning

confidence: 99%

Multi-contextual ant colony optimization of intermediate dynamic job shop problems

Romanowski

2011

Int J Adv Manuf Technol

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Myopic scheduling uses information without correct context. Because job shops with dynamic job arrival are NP-hard, real-world planners often resort to myopic dispatching rules that produce schedules with unsatisfactory makespans and mean flow times. If metaheuristic scheduling does not use context properly, it can produce unsatisfactory schedules. This study proposes a new theory of context-dependent and multi-contextual scheduling functions in dynamic job shops. Three multi-contextual ant colony (MCAC) scheduling methods, SPT_MIT_JWT, MRT_MIT, and LRT_MIT_JWT, were designed to combine several context-dependent functions so that each ant had an independent, artificially intelligent model of what might happen in the future. These three MCACs scheduled 27 dynamic job shops. Each job shop had three parameters (number of operations per job, processing time per operation, and utilization level) at one of three levels. The results were compared to schedules from dispatching rules. Schedules from MRT_MIT had the best makespans for all 27 combinations of factors. For five cases in which utilization and operations per job were both high, schedules from longest processing time had the best mean flow time; in the other 22 cases, either SPT_MIT_JWT or LRT_MIT_JWT produced the schedule with the best mean flow time. These results indicate that the new theory can be used to design multi-contextual methods that produce effective schedules.

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Makespan minimization of a flowshop sequence-dependent group scheduling problem

Cited by 34 publications

References 14 publications

Heterogeneous workers with learning ability assignment in a cellular manufacturing system

Heterogeneous workers with learning ability assignment in a cellular manufacturing system

A novel approach for product makespan prediction in production life cycle

Multi-contextual ant colony optimization of intermediate dynamic job shop problems

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