Heuristical job allocation in a flexible manufacturing system

Chan, T. S.; Pak, H. A.

doi:10.1007/bf02601447

Cited by 25 publications

(15 citation statements)

References 11 publications

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“…Most of these algorithms include enumerative procedures [9], mathematical programming and approximation techniques viz. Linear Programming, Integer Programming [10,11], Goal Programming [12], Dynamic Programming [13], Transportation and Network Analysis, Branch and Bound [14,15], Lagrangian Relaxation [16], priority rule based Heuristics [17,18,19] and Local search algorithms (Simulated Annealing, Tabu Search) [20,19] etc. Among these techniques few are specific to a particular objective and few are specific to particular problem instances and few are inefficient with respect to the computational time needed.…”

Section: Earlier Research Workmentioning

confidence: 99%

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A multiobjective genetic algorithm for scheduling a flexible manufacturing system

Sankar

Ponnanbalam

Rajendran

2003

The International Journal of Advanced Manufacturing Technology

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Though the designers of Flexible Manufacturing Systems (FMS) strive to ensure the maximum flexibility in the system, in practice, after the implementation of such systems the operational executives often find it hard to accommodate frequent variations in the part designs of incoming jobs. This difficulty can very well be overcome by scheduling the variety of incoming parts into the system efficiently. In this work an appropriate scheduling mechanism is designed to generate a nearerto-optimum schedule using Genetic Algorithm (GA) with two different GA Coding Schemes. Two contradictory objectives of the system were achieved simultaneously by the scheduling mechanism. The results are compared with those obtained by different scheduling rules and conclusions are presented.

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Section: Earlier Research Workmentioning

confidence: 99%

“…Fig. 2 A flow chart of the scheduling procedure (E.g) String before mutation: 11,07,31,40,12,13,43,10,38,21,02,29,37,42,36,09,33,03,22,41,14,25,30,01,15,39,04,18,17,24,05,28,27,19,34,26,08,35,23,20,32,16,06 String after mutation: 11,07,31,40,12,13,43,10,38,21,02,29,37,26,36,…”

Section: Mutationmentioning

confidence: 99%

A multiobjective genetic algorithm for scheduling a flexible manufacturing system

Sankar

Ponnanbalam

Rajendran

2003

The International Journal of Advanced Manufacturing Technology

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“…Select the job with 'first in, first out' (Bell and Bilalis 1982, Carrie and Petsopoulos 1985, Chan and Pak 1986, Elmaraghy 1981, Lin and Lu 1986, Nof et al 1979, Schriber and Stecke 1987, Wang 1986). Select the job with 'last in, first out' (Denzler and Boe 1987c, Kanet 1987, Kimemia and Gershwin 1983.…”

Section: Number Of Operations (Nop)mentioning

confidence: 99%

A review of scheduling rules in flexible manufacturing systems

Gupta

Bector

1989

International Journal of Computer Integrated Manufacturing

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In this paper, the literature dealing with the parts scheduling problem in flexible manufacturing systems (FMS) has been reviewed. Although the parts scheduling problem is only part of a much larger decision-making process, it has fundamental implications on the overall performance of the system: internally by affecting the utilization of expensive resources (c.g, machine tools and fixtures), and externally by affecting its responsiveness to meet the changing customer demands. This paper has two objectives: (1) to develop a framework within which to discuss the current literature on dispatching rules; and (2) to compare the developed list of dispatching rules and performance criteria from the surveyed literature with that desired by the FMS operators. The research findings arc critically reviewed and. analysed according to various two-dimcnsicnal classifications. The results from reallife case studies of FMS. are also discussed. Finally, the results arc summarized to present suggestions for future research efforts.

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“…Chan and Pak [10] proposed two heuristic algorithms for solving the scheduling problem with the goal of minimising the total cost of tardiness in a statically loaded FMS. He and Kusiak [11] addressed three different industrial scheduling problems, with heuristic algorithms for each problem.…”

Section: Earlier Researchmentioning

confidence: 99%

Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm

Jerald

Asokan

Prabaharan

et al. 2004

Int J Adv Manuf Technol

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The increased use of flexible manufacturing systems (FMS) to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and automated guided vehicle (AGV) routings. The FMS scheduling problem has been tackled by various traditional optimisation techniques. While these methods can give an optimal solution to small-scale problems, they are often inefficient when applied to larger-scale problems. In this work, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimising the idle time of the machine and minimising the total penalty cost for not meeting the deadline concurrently. The memetic algorithm presented here is essentially a genetic algorithm with an element of simulated annealing. The results of the different optimisation algorithms (memetic algorithm, genetic algorithm, simulated annealing, and particle swarm algorithm) are compared and conclusions are presented.

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Heuristical job allocation in a flexible manufacturing system

Cited by 25 publications

References 11 publications

A multiobjective genetic algorithm for scheduling a flexible manufacturing system

A multiobjective genetic algorithm for scheduling a flexible manufacturing system

A review of scheduling rules in flexible manufacturing systems

Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm

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