Metaheuristics for minimizing the makespan of the dynamic shop scheduling problem

Liu, S. Q.; Ong, H. L.; Ng, Kien Ming

doi:10.1016/j.advengsoft.2004.10.002

Cited by 53 publications

(21 citation statements)

References 9 publications

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“…Over the past decade, many approaches have been proposed to solve the job insertion problem. These included heuristics (Chiang and Hau 1996;Unal et al 1997;Subramaniam and Raheja 2003;Subramaniam et al 2005), scheduling rulesbased heuristics (Ourari and Bouzouia 2003;Hall and Potts 2004), disjunctive graphs Roubellat 2000, 2002), Genetic Algorithms (GA) (Jain and ElMaraghy 1997;Mesghouni and Rabenasolo 2002;Branke and Mattfeld 2005), and Tabu search (Liu et al 2005). As for total rescheduling, Raheja and Subramaniam (2002) stated that, in general total rescheduling can result in considerable system nervousness and take more time to implement than other repair approaches.…”

Section: Related Workmentioning

confidence: 98%

Analysis of reactive deadlock-free scheduling in flexible job shops

Fahmy

ElMekkawy

Balakrishnan

2007

Int J Flex Manuf Syst

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Reactive scheduling is a procedure followed in production systems to react to unforeseen events that disturb the normal operation of the system. In this paper, a novel operations insertion heuristic is proposed to solve the deadlock-free reactive scheduling problem in flexible job shops, upon the arrival of new jobs. The heuristic utilizes rank matrices (Latin rectangles) to insert new jobs in schedules, while preventing the occurrence of deadlocks or resolving them using the available buffer space (if any). Jobs with alternative processing routes through the system are also considered. The heuristic can be employed to execute two reactive scheduling approaches in a timely efficient manner; to insert the new jobs in the already existing schedule (job insertion) or to reschedule all the jobs in the system (total rescheduling). Using experimental design and analysis of variance (ANOVA), the relative performance of the two approaches is studied and analyzed to provide some measures and guidelines for selecting the appropriate reactive scheduling approach for different problem settings. Three measures of performance are considered in the analysis; efficiency of the revised schedules in terms of the mean flow time, resulting system nervousness, and the required solution time. The results show that, on average, job insertion obtains revised schedules featuring significantly lower system nervousness and slightly higher mean flow time than total rescheduling. However, depending on the system size, number and processing times of the new

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Section: Related Workmentioning

confidence: 98%

Analysis of reactive deadlock-free scheduling in flexible job shops

Fahmy

ElMekkawy

Balakrishnan

2007

Int J Flex Manuf Syst

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“…Liu, Ong, and Ng [13] showed that dynamic mixed shop problems with machine breakdowns and dynamic job arrivals can be modeled as equivalent static group shop problems. They combined standard static job shop benchmark problems to generate unusual dynamic problems that combined characteristics of job shops, flow shops, and open shops.…”

Section: Static and Dynamic Job Shop Problemsmentioning

confidence: 99%

“…Our notion of a multi-contextual method is intended to improve on Ying and Lin's randomly selected SCHs. A randomly chosen SCH might be inappropriate to the neighborhood structure of the problem; by contrast, methods that make calculations about the search state [13,14] can be designed to select appropriate SCHs.…”

Section: Problems Other Than Traditional Job Shopsmentioning

confidence: 99%

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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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“…Thus, no dynamic stage is tested in our study, Liu, Ong, and Ng (2005), Fattahi and Fallahi (2010) and Adibi, Zandieh, and Amiri (2010) can be cited to justify this choice. These authors' approaches are based on pure adaption of the OR methods to the dynamic behavior or on hybridization with artificial intelligence, such as agent systems.…”

Section: Data Preparationmentioning

confidence: 99%

Benchmarking flexible job-shop scheduling and control systems

Trentesaux

Pach

Bekrar

et al. 2013

Control Engineering Practice

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a b s t r a c tBenchmarking is comparing the output of different systems for a given set of input data in order to improve the system's performance. Faced with the lack of realistic and operational benchmarks that can be used for testing optimization methods and control systems in flexible systems, this paper proposes a benchmark system based on a real production cell. A three-step method is presented: data preparation, experimentation, and reporting. This benchmark allows the evaluation of static optimization performances using traditional operation research tools and the evaluation of control system's robustness faced with unexpected events.

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Metaheuristics for minimizing the makespan of the dynamic shop scheduling problem

Cited by 53 publications

References 9 publications

Analysis of reactive deadlock-free scheduling in flexible job shops

Analysis of reactive deadlock-free scheduling in flexible job shops

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

Benchmarking flexible job-shop scheduling and control systems

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