Match-Up Strategies for Job Shop Rescheduling

Moratori, Patrick; Petrović, Sanja; Vazquez, Antonio J.

doi:10.1007/978-3-540-69052-8_13

Cited by 10 publications

(12 citation statements)

References 11 publications

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“…The predictive-reactive approaches are commonly used scheduling methods under uncertainties [30,31]. Two main steps are included: (1) a predictive schedule is generated over the time horizon and (2) the resulted schedule is then modified to respond to uncertainties.…”

Section: Related Workmentioning

confidence: 99%

New meta-heuristic for dynamic scheduling in permutation flowshop with new order arrival

Liu

Jin

Price

2018

Int J Adv Manuf Technol

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Permutation flowshop scheduling problem (PFSP) has attracted lots of attention from both academia and industry as it finds many applications especially for today's mass customization. The PFSP is proved NP-hard, and the dynamic uncertainties such as stochastic new order arrivals significantly increase the problem complexity and difficulty. Many enterprises often struggle to make decisions on accepting new orders and setting due dates for them due to lack of effective scheduling methods. To fill in the knowledge gap, this paper is to propose a new meta-heuristic algorithm which is based on a new enhanced destruction and construction method and a novel repair method while adopting the architecture of the iterated greedy algorithm. Statistical tests were conducted and results show that the new algorithm outperforms existing ones. The maximum completion time of the new order

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Section: Related Workmentioning

confidence: 99%

New meta-heuristic for dynamic scheduling in permutation flowshop with new order arrival

Liu

Jin

Price

2018

Int J Adv Manuf Technol

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“…In predictivereactive scheduling, an optimal predictive schedule that is based on certain criteria and constraints is provided at the first stage, and then the schedule is revised during the implementation stage to adapt changes in the shop floor. This revision can be performed in several ways, either by generating a new schedule [7], [8] or by repairing the existing schedule [9]- [11]. Repairing the existing schedule may involve partial or total rescheduling methods.…”

Section: Introductionmentioning

confidence: 99%

“…There are many techniques that have been used for solving predictive-reactive scheduling such as knowledgebased or expert systems [12], [13], genetic algorithm (GA) [7], [11] and filtered beam search [8]. GA is amongst the very popular heuristic search methods [14]- [18] as it often provides a good performance solution for many types of combinatorial optimisation problems.…”

Section: Introductionmentioning

confidence: 99%

Non-Reshuffle-Based Approach for Rescheduling of Flexible Manufacturing System

Zakaria

Deris

Othman

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Scheduling and rescheduling play vital roles in ensuring the effectiveness of the production control in flexible manufacturing system (FMS). The failure of these systems may interrupt the efficiency of the production activities and thus may lessen the profit to be gained by the company. The FMS scheduling problem is considered as dynamic as new orders may get in every day. The new orders need to be immediately desegregated with the existing production schedule by preserving the efficiency and stability of the existing schedule. This research applies the non-reshuffle-based genetic match-up algorithms which admit new orders by manipulating available machine idle times to address rescheduling problem in a FMS that practises the pull strategy. The idea of the match-up approach is to update only a part of the initial schedule and genetic algorithms used to optimise the solution within the rescheduling horizon in such a way in order to preserve the efficiency and stability of the shop floor. The proposed methodology has been tested using different rescheduling parameters. The experiments show that the rescheduling method improves efficiency and stability of the new schedule.

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“…However, a hospital emergency department is a dynamic place, with great uncertainty about the future course of the execution of any realistic process. Uncertainties such as the sudden arrival of new patients, unexpectedly slow task performance, and unplanned lack of resources [11,20] all change the execution environment creating the potential for consequent schedule disruptions [12].…”

Section: Introductionmentioning

confidence: 99%

Dynamic scheduling of emergency department resources

Xiao

Osterweil

Wang

2010

Proceedings of the 1st ACM International Health Informatics Symposium

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The processes carried out in a hospital emergency department can be thought of as structures of activities that require resources in order to execute. Costs are reduced when resource levels are kept low, but this can lead to competition for resources and poor system performance. Careful allocation can improve performance by enabling more efficient use of resources. This paper proposes that resource scheduling be done in a series of dynamic reschedulings that use precise, detailed information about emergency department processes and available department resources to improve the quality of scheduling results. Rescheduling is done over a small set of activities, and uses a genetic algorithm. Simulations are used to evaluate this approach, and results indicate that it can be effective.

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Match-Up Strategies for Job Shop Rescheduling

Cited by 10 publications

References 11 publications

New meta-heuristic for dynamic scheduling in permutation flowshop with new order arrival

New meta-heuristic for dynamic scheduling in permutation flowshop with new order arrival

Non-Reshuffle-Based Approach for Rescheduling of Flexible Manufacturing System

Dynamic scheduling of emergency department resources

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