A theoretic and practical framework for scheduling in a stochastic environment

Bidot, Julien; Vidal, Thierry; Laborie, Philippe; Beck, J. Christopher

doi:10.1007/s10951-008-0080-x

Cited by 78 publications

(54 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As [52] puts it, "Intuitively, a solution can be considered as robust if it behaves "well" or "not too bad" in all the scenarios.". This is the idea underlying a definition of -robustness given in [53] for stochastic scheduling which can be adapted to the fuzzy flexible job shop as follows.…”

Section: Robust Schedulesmentioning

confidence: 99%

Coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

Palacios

González-Rodríguez

Vela

et al. 2015

Fuzzy Sets and Systems

View full text Add to dashboard Cite

In this paper we tackle a variant of the flexible job shop scheduling problem with uncertain task durations modelled as fuzzy numbers. To minimise the schedule's makespan, we consider different ranking methods for fuzzy numbers. We then propose a cooperative co-evolutionary algorithm with two different populations evolving the two components of a solution: machine assignment and task relative order. Additionally, we incorporate a specific local search method for each population. The resulting hybrid algorithm is then evaluated on existing benchmark instances, comparing favourably with the state-of-theart methods. The experimental results also serve to analyse the influence in the robustness of the resulting schedules of the chosen ranking method.

show abstract

Section: Robust Schedulesmentioning

confidence: 99%

Coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

Palacios

González-Rodríguez

Vela

et al. 2015

Fuzzy Sets and Systems

View full text Add to dashboard Cite

show abstract

“…Consider a problem of 3 jobs and 2 machines with the following matrix for fuzzy processing times: Here p 21 = (2, 3, 3) is the processing time of task o 21 , the task of job J 2 to be processed in machine M 1 . Figure 1 shows the Gantt chart adapted to TFNs of the schedule given by the priority vector π= (1,4,6,3,5,2). It represents the partial schedules on each machine obtained from this decision variable.…”

Section: Fuzzy Open Shop Schedulingmentioning

confidence: 99%

“…As [19] puts it, "Intuitively, a solution can be considered as robust if it behaves "well" or "not too bad" in all the scenarios.". This is the idea underlying a definition of ϵ-robustness given in [2] for stochastic scheduling which can be adapted to the fuzzy open shop as follows.…”

Section: Robust Schedulesmentioning

confidence: 99%

Robust swarm optimisation for fuzzy open shop scheduling

et al. 2014

View full text Add to dashboard Cite

In this paper we consider a variant of the open shop problem where task durations are allowed to be uncertain and where uncertainty is modelled using fuzzy numbers. Solutions to this problem are fuzzy schedules, which we argue should be seen as predictive schedules, thus establishing links with the concept of robustness and a measure thereof. We propose a particle swarm optimization (PSO) approach to minimise the schedule's expected makespan, using priorities to represent particle position, as well as a decoding algorithm to generate schedules in a subset of possibly active ones. Our proposal is evaluated on a varied set of several benchmark problems. The experimental study includes a parametric analysis, results of the PSO compared with the state-of-the-art, and an empirical study of the robustness of taking into account uncertainty along the scheduling process.

show abstract

“…These approaches focus on the proper combination between a proactive flexible method and a reactive algorithm capable of revising the flexible schedule without deteriorating too much the expected performances. Some of these methods can be found in [8,25,32,37,40,48]. Groups of Permutable Operations (GoPO) is one of the most studied proactive-reactive methods [2,3,9,13,22].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a new decision-aid parameter for job shop scheduling under uncertainties

2019

View full text Add to dashboard Cite

Abstract. This paper addresses the groups of permutable operations method. This method is a flexible scheduling approach to hedge against uncertainties and is composed of a proactive/reactive phase. The proactive phase consists of computing a set of solutions (schedule) to a scheduling problem, leaving the choice of executing one of these solutions during the reactive phase according to the current state of the shop floor. During the reactive phase, the remaining decisions have to be made in real-time. The worst-case evaluation of the remaining solutions is a decision-aid parameter used during the reactive phase in order to control the final schedule from exceeding a worst-case performance. While the existing literature only tackles the worst-case evaluation of the groups of permutable operations, this paper deals with its best-case evaluation. For solving this problem, a new lower bound calculating this parameter in polynomial time is proposed. The computational efficiency of this parameter in a reactive algorithm exhibits very good performance. Moreover, the experiments show the robustness of this evaluation allowing this parameter to be used in an unstable job shop environment.

show abstract

A theoretic and practical framework for scheduling in a stochastic environment

Cited by 78 publications

References 20 publications

Coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

Coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

Robust swarm optimisation for fuzzy open shop scheduling

Evaluation of a new decision-aid parameter for job shop scheduling under uncertainties

Contact Info

Product

Resources

About