Production Scheduling by Reachability Analysis - A Case Study

Behrmann, Gerd; Brinksma, Ed; Hendriks, Martijn; Mader, Angelika

doi:10.1109/ipdps.2005.363

Cited by 16 publications

(18 citation statements)

References 12 publications

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“…Many specialized exact algorithms and heuristics have been developed to deal with o ine and online optimization of job shop and ow shop variants. A plethora of optimization objectives have been de ned [10], among which are minimizing makespan [1,6,13,15], total tardiness [11], maximum lateness [7] mean ow time [14], and total completion time [5,12]. Some approaches can deal only with processing times, others include set-up times, or due dates.…”

Section: Related Workmentioning

confidence: 99%

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Online Scheduling of 2-Re-entrant Flexible Manufacturing Systems

Pinxten

Waqas

Geilen

et al. 2017

ACM Trans. Embed. Comput. Syst.

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Online scheduling of operations is essential to optimize productivity of exible manufacturing systems (FMSs) where manufacturing requests arrive on the y. An FMS processes products according to a particular ow through processing stations. This work focusses on online scheduling of re-entrant FMSs with ows using processing stations where products pass twice and with limited bu ering between processing stations. This kind of FMS is modelled as a re-entrant ow shop with due dates and sequence-dependent set-up times. Such ow shops can bene t from minimization of the time penalties incurred from set-up times. On top of an existing greedy scheduling heuristic we apply a meta-heuristic that simultaneously explores several alternatives considering trade-o s between the used metrics by the scheduling heuristic. We identify invariants to e ciently remove many infeasible scheduling options so that the running time of online implementations is improved. The resulting algorithm is much faster than the state of the art and produces schedules with on average 4.6% shorter makespan.

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Section: Related Workmentioning

confidence: 99%

“…Some approaches can deal only with processing times, others include set-up times, or due dates. Other approaches deal with claiming (multiple) shared resources and simultaneously solve a mapping problem and a sequencing problem [1]. Use cases such as production lines for mirrors, lacquer [1] and textile [13] have been researched.…”

Section: Related Workmentioning

confidence: 99%

Online Scheduling of 2-Re-entrant Flexible Manufacturing Systems

Pinxten

Waqas

Geilen

et al. 2017

ACM Trans. Embed. Comput. Syst.

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“…an optimal schedule. However, it was shown that this technique, extended by suitable heuristics, allows to find good schedules in very short time [5,6].…”

Section: Uppaal and Uppaal Coramentioning

confidence: 99%

“…In earlier work [5,6] we experimented with several heuristics. In the current context, we used only greediness as strategy and restrict the number of orders that may be processed at any point in time.…”

Section: Uppaal Modelsmentioning

confidence: 99%

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Synthesis and stochastic assessment of cost-optimal schedules

Mader

Bohnenkamp

Usenko

et al. 2009

Int J Softw Tools Technol Transfer

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We present a novel approach to synthesize good schedules for a class of scheduling problems that is slightly more general than the scheduling problem F Jm, a|gpr, r j , d j |early/tardy. The idea is to prime the schedule synthesizer with stochastic information more meaningful than performance factors with the objective to minimize the expected cost caused by storage or delay. The priming information is obtained by stochastic simulation of the system environment. The generated schedules are assessed again by simulation. The approach is demonstrated by means of a non-trivial scheduling problem from lacquer production. The experimental results show that our approach achieves in all considered scenarios better results than the extended processing times approach.

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Using Statistical-Model-Checking-Based Simulation for Evaluating the Robustness of a Production Schedule

Himmiche

Aubry

Marangé

et al. 2018

Service Orientation in Holonic and Multi-Agent Manufacturing

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Industry 4.0 implies new scheduling problems linked to the optimal using of flexible resources and to mass customisation of products. In this context, first research results show that Discrete Event Systems models and tools are a relevant alternative to the classical approaches for modelling scheduling problems and for solving them. Moreover, the challenges of the industry 4.0 mean taking into account the uncertainties linked to the mass customisation (volume and mix of the demand) but also to the states of the resources (failures, operation durations,. . .). The goal of this paper is to show how it is possible to use the simulation based on statistical model checking for taking into account these uncertainties and for evaluating the robustness of a given schedule.

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Production Scheduling by Reachability Analysis - A Case Study

Abstract: Abstract

Cited by 16 publications

References 12 publications

Online Scheduling of 2-Re-entrant Flexible Manufacturing Systems

Online Scheduling of 2-Re-entrant Flexible Manufacturing Systems

Synthesis and stochastic assessment of cost-optimal schedules

Using Statistical-Model-Checking-Based Simulation for Evaluating the Robustness of a Production Schedule

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