Performance evaluation of flexible manufacturing systems under uncertain and dynamic situations

Kumar, Vikas; Kumar, Shishir; Tiwari, Manoj Kumar; Chan, Felix T. S.

doi:10.1243/09544054jem950

Cited by 10 publications

(7 citation statements)

References 44 publications

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“…Nevertheless, industrial environments are highly dynamic and, therefore, the proposed schedule can rapidly become infeasible after the occurrence of an unexpected event. Despite the importance of revising the schedule after these unforeseen circumstances, few contributions have tackled the FMS rescheduling problem Jain and Foley, 2002;Kumar et al, 2008).…”

Section: Article In Pressmentioning

confidence: 99%

A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations

Zeballos

Quiroga

Henning

2010

Engineering Applications of Artificial Intelligence

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Section: Article In Pressmentioning

confidence: 99%

A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations

Zeballos

Quiroga

Henning

2010

Engineering Applications of Artificial Intelligence

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“…a in iteration f and inversely proportional to L a f . The global updating rule is shown in equations (5) and (6)…”

Section: Pheromone Updating Rule Of Aco Algorithmmentioning

confidence: 99%

“…Since the heuristic desirability is typically problem-dependent and static throughout the algorithm, limiting the pheromone amount on all paths in a certain range is adopted to achieve this goal. After obtaining t f + 1 by equations (5) and (6), MMACO algorithm imposes explicit limits t min f and t max f on the pheromone amount, given in equation (7). The detailed methods to update the range of pheromone amount are elaborated in the next section < :…”

Section: Pheromone Updating Rule Of Mmaco Algorithmmentioning

confidence: 99%

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An integrated ant colony optimization algorithm to solve job allocating and tool scheduling problem

Zhang

Wang

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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In this article, max-min ant colony optimization algorithm is proposed to determine how to allocate jobs and schedule tools with the objective of minimizing the makespan of processing plans in flexible manufacturing system. To expand the application range of max-min ant colony optimization algorithm, tool movement policy is selected as the running mode of flexible manufacturing system, which assumes that tools are shared among work centers and each operation is allowed to be machined by different kinds of tools. In the process of converting this scheduling problem into traveling salesman problem, disjunctive graph is modified to possess more than one path between each neighbor node. Besides providing practical methods of initializing pheromone, selecting node and calculating pheromone increment, max-min ant colony optimization algorithm employs the pheromone updating rule in max-min ant system to limit pheromone amount in a range, of which the upper and lower boundaries are updated after each iteration by formulations involving the current optimal makespan, the average number of optional tools and parameters. Finally, different sizes of processing plans are randomly generated, through which max-min ant colony optimization algorithm is proved effectively to tackle early stagnation and local convergence and thus obtains better solution than ant colony optimization algorithm and bidirectional convergence ant colony optimization algorithm.

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“…Scheduling is a decision-making process which concerns with the optimal allocation or assignment of resources to a set of tasks or activities over time, so it plays an important role in most manufacturing and production systems (Kumar, Kumar, Tiwari, and Chan 2008) as well as in most information-processing environments (Chen, Huang, Zheng, and Xiang 2008). Since the 1960s, modern scheduling theory has grown sharply with the strong support from academic, industry and government.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of rotation schedule and improved strategy for open shop problem to minimise makespan

Bai

Tang

2011

International Journal of Systems Science

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This article addresses the open shop scheduling problem with the objective to minimise the maximum completion time, or makespan. Both asymptotical analysis and worst case analysis are conducted for the heuristic, rotation schedule (RS) algorithm. In the asymptotical analysis, we prove that the RS algorithm is asymptotically equal to the optimal solution when the problem size is large enough. In the worst case analysis, we show that the tight worst case performance ratio of RS algorithm is equal to the machine number m. To accelerate the convergence of the RS algorithm for medium size problems, the improved version of the heuristic, the modified RS (MRS) algorithm is presented. At the end of this article, the asymptotical optimality of the RS algorithm and the practical effectiveness of the MRS algorithm are shown by numerical simulations.

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Performance evaluation of flexible manufacturing systems under uncertain and dynamic situations

Abstract: ABSTRACT

Cited by 10 publications

References 44 publications

A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations

A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations

An integrated ant colony optimization algorithm to solve job allocating and tool scheduling problem

Performance analysis of rotation schedule and improved strategy for open shop problem to minimise makespan

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