Introduction to Flexibility and Robustness in Scheduling

Billaut, Jean‐Charles; Moukrim, Aziz; Sanlaville, Éric

doi:10.1002/9780470611432.ch1

Cited by 36 publications

(56 citation statements)

References 20 publications

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“…Several reactive/proactive techniques have been developed in the literature to manage incidences in scheduling problems [3]. Thus, computing a new solution from scratch after each problem change is possible (reactive technique), but it has two important drawbacks: inefficiency and instability of the successive solutions [15].…”

Section: Modeling and Solving A Jso(n P) In Three Steps: Csop+ppmentioning

confidence: 99%

Robust Solutions to Job-Shop Scheduling Problems with Operators

Escamilla

Rodriguez-Molins

Salido

et al. 2012

2012 IEEE 24th International Conference on Tools With Artificial Intelligence

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Abstract-The job-shop scheduling problem with operators is an extension of the classical job-shop scheduling problem where each operation has to be assisted by one operator from a limited set of them. We confront this problem with the objective of obtaining robust schedules and minimizing the makespan. In this way the problem becomes more difficult but more interesting from a practical point of view. We propose to solve the problem in three steps. In the first one, the JSP relaxation (without operators) is modeled and solved using a CSOP solver with the objective of minimizing the makespan. Then, the solution is modified to include operators, in this step robustness is introduced by means of a set of buffers in the operators sequences. Finally, these buffers are uniformly distributed among the operations that are not involved in a critical path. We have conducted an experimental study showing that the proposed method reaches a good trade-off between robustness and optimality.

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Section: Modeling and Solving A Jso(n P) In Three Steps: Csop+ppmentioning

confidence: 99%

Robust Solutions to Job-Shop Scheduling Problems with Operators

Escamilla

Rodriguez-Molins

Salido

et al. 2012

2012 IEEE 24th International Conference on Tools With Artificial Intelligence

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“…In some cases, exibility is thus considered as a measure of deviation from a given target value (Billaut et al, 2013). This target may be the xed value considered for regular policies.…”

Section: Fixed Polices Flexible Policesmentioning

confidence: 99%

Scheduling policies for multi-period services

Nunez-del-Toro

Fernández

Kalcsics

et al. 2016

European Journal of Operational Research

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This paper discusses a multi-period service scheduling problem. In this problem, a set of customers is given who periodically require service over a finite time horizon. To satisfy the service demands, a set of operators is given, each with a fixed capacity in terms of the number of customers an operator can serve per period. The task is to determine for each customer the periods in which he will be visited by an operator such that the periodic service requests of the customers are adhered to and the total number of operators used over the time horizon is minimal. Two alternative policies for scheduling customer visits are considered. In the first one, a customer is visited just on time, i.e., in the period where he or she has a demand for service. The second policy allows service visits ahead of time. The rationale behind this policy is that allowing irregular visits may reduce the overall number of operators needed throughout the time horizon. To solve the problem, integer linear programming formulations are proposed for both policies and numerical experiments are presented that show the reduction in the number of operators used when visits ahead of time are allowed. As only small instances can be solved optimally, a heuristic algorithm is introduced in order to obtain good quality solutions and shorter computing times.Peer ReviewedPostprint (author's final draft

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“…However, in practice, manufacturing systems are not so deterministic. During the execution of the offline generated schedule, many disturbances may occur, like arrival of new jobs, uncertain task duration, machine breakdown, etc, [9,20,31]. These disturbances will in most cases, deteriorate the expected performances.…”

Section: Introductionmentioning

confidence: 99%

“…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]. This method was created by LAAS-CNRS laboratory of Toulouse, France [21] and is implemented in an industrial software [41].…”

Section: Introductionmentioning

confidence: 99%

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

2019

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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.

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Introduction to Flexibility and Robustness in Scheduling

Cited by 36 publications

References 20 publications

Robust Solutions to Job-Shop Scheduling Problems with Operators

Robust Solutions to Job-Shop Scheduling Problems with Operators

Scheduling policies for multi-period services

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

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