Evaluation of a new decision-aid parameter for job shop scheduling under uncertainties
RAIRO - Operations Research - Recherche Opérationnelle, Tome 53 (2019) no. 2, pp. 593-608.

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.

Reçu le :
Accepté le :
DOI : 10.1051/ro/2017073
Classification : 90B35, 90B36, 68M20
Mots-clés : Scheduling, decision-aid system, lower bounds, Job shop, makespan
Yahouni, Zakaria 1 ; Mebarki, Nasser 1 ; Sari, Zaki 1

1
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Yahouni, Zakaria; Mebarki, Nasser; Sari, Zaki. Evaluation of a new decision-aid parameter for job shop scheduling under uncertainties. RAIRO - Operations Research - Recherche Opérationnelle, Tome 53 (2019) no. 2, pp. 593-608. doi : 10.1051/ro/2017073. http://www.numdam.org/articles/10.1051/ro/2017073/

[1] M.A. Alloulou and C. Artigues , Worst-case evaluation of flexible solutions in disjunctive scheduling problems. ICCSA’07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III. In Vol. 1205 of Lecture Notes in Computer Science (2007) 1027–1036.

[2] C. Artigues , J.C. Billaut and C. Esswein , Maximization of solution flexibility for robust shop scheduling. Eur. J. Operat. Res. 165 (2005) 314–328. | Zbl

[3] Ch. Artigues , J.-Ch. Billaut , A. Cherif , N. Mebarki , Z. Yahouni , Robust machine scheduling based on group of permutable jobs. In: Robustness Analysis in Decision Aiding, Optimization, and Analytics, edited by M. Doumpos , C. Zopounidis , E. Grigoroudis International Series in Operations Research & Management Science, vol. 241. Springer, Cham (2016).

[4] P. Baptiste and C. Le Pape , Edge-finding constraint propagation algorithms for disjunctive and cumulative scheduling. In Proceedings 15th Workshop of the U.K. Planning Special Interest Group (1996).

[5] J.C. Bean , J.R. Birge , J. Mittenthal and C.E. Noon , Match-up scheduling with multiple resources, release dates and disruptions. Option Res. 39 (1991) 470–483. | Zbl

[6] J. Beasley , Job shop benchmark instances (2004).

[7] D. Behnke and M.J. Geiger , Test instances for the flexible job shop scheduling problem with work centers. Technical report, Universitat Der Bundewehr Hamburg (2012).

[8] J. Bidot , Th. Vidal , Ph. Laborie and J.Ch. Beck , A theoretic and practical framework for scheduling in a stochastic environment. J. Scheduling 12 (2008) 315–344. | Zbl

[9] J.C. Billaut , A. Moukrim and E. Sanlaville , Flexibility and Robustness in Scheduling. Wiley-ISTE (2008).

[10] J.R. Birge , M.A. Dempster , H.I. Gassman , E.A. Gunn , A.J. King and S.W. Wallace , A standard input format for multi-period stochastic linear programs. Math. Program. Soc. 17 (1987) 1–21.

[11] P. Brucker , B. Jurisch and B. Sievers , A branch and bound algorithm for the job-shop scheduling problem. Discrete Appl. Math. 49 (1994) 107–127. | Zbl

[12] P. Brucker and S. Knust , Complexity results for scheduling, problems (2007).

[13] O. Cardin , N. Mebarki and G. Pinot , A study of the robustness of the group scheduling method using an emulation of a complex fms. Inter. J. Production Econ. 146 (2013) 199–207.

[14] J. Carlier , The one machine problem. Eur. J. Oper. Res. 42–47 (1982). | Zbl

[15] J. Carlier and E. Pinson , An algorithm for solving the job-shop problem. Manag. Sci. 35 (1989) 164–176. | Zbl

[16] J. Carlier and E. Pinson , A practical use of jackson’s preemptive schedule for solving the job shop problem. Ann. Oper. Res. 26 (1991) 269–287. | Zbl

[17] J. Carlier and E. Pinson , Adjustment of heads and tails for the job-shop problem. Eur. J. Oper. Res. 78 (1994) 146–161. | Zbl

[18] F.T.S. Chan , H.K. Chan and H.C.W. Lau , The state of the art in simulation study on fms scheduling: a comprehensive survey. Inter. J. Adv. Manuf. Techn. 19 (2002) 830–849.

[19] E.B. Da Silva , M.G. Costa , M.F. Da Silva and F.H.I. Pereira , Simulation study of dispatching rules in stochastic job shop dynamic scheduling. World J. Model. Simul. 10 (2014) 231–240.

[20] A.J. Davenport and J.C. Beck , A survey of techniques for scheduling with uncertainty (2000).

[21] Erschler and Roubellat , An approach for real time scheduling for activities with time and resource constraints Advances in project scheduling. Edited by R. Slowinski and J. Weglarz . Elsevier (1989).

[22] C. Esswein , Un apport de flexibilité séquentielle pour l’ordonnancement robuste. Ph.D. thesis, Université François Rabelais Tours, France (2003).

[23] C. Fang , R. Kolisch , L. Wang and C. Mu , An estimation of distribution algorithm and new computational results for the stochastic resource-constrained project scheduling problem. Flexible Services and Manufacturing J. 27 (2015) 585–605.

[24] H. Fisher and G.L. Thomson , Probabilistic learning combinations of local job-shop scheduling rules, Industrial Scheduling (1963) 225–251.

[25] N. Fu , P. Varakantham and H.Ch. Lau , Towards finding robust execution strategies for rcpsp/max with durational uncertainty. In Proc. 20th Inter. Confer. Automated Planning and Scheduling, ICAPS 2010, Toronto, Ontario, Canada. 12–16 2010 (2010) 73–80.

[26] G. Gallego , Linear control policies for scheduling a single facility after an initial disruption. Technical report, School of operations research and industrial engineering, College of Engineering, Cornell univeresity, ITHACA, New york 14853 (1988).

[27] G. Gallego , Produce-up-to policies for scheduling a single facility after an initial disruption. Technical report. School of operations research and industrial engineering, College of Engineering, Cornell univeresity, ITHACA, New york 14853, 1988.

[28] E.M Goldratt , Critical Chain. North River Press (1997).

[29] Graham , Lawler , Lenstra and K. Rinnooy , Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discrete Math. 5 (1979) 287–326. | Zbl

[30] W. Herroelen and R. Leus , Project scheduling under uncertainty: Survey and research potentials. Europ. J. Oper. Res. 165 (2005) 289–306. | Zbl

[31] P. Kouvelis and G. Yu , Robust Discrete Optimization and Its Applications, Kluwer Academic Publishers (1997). | Zbl

[32] O. Lambrechts , E. Demeulemeester and W. Herroelen , Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. J. Scheduling 11 (2008) 121–136. | Zbl

[33] E.L. Lawler , Optimal sequencing of a single machine subject to precedence constraints. Manag. Sci. 19 (1973) 544–546. | Zbl

[34] S. Lawrence , Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (supplement) (1984).

[35] N. Mebarki and A. Shahzad , Correlation among tardiness-based measures for scheduling using priority dispatching rules. Inter. J. Produc. Res. 51 (2013) 3688–3697.

[36] K. Morikawa and K. Takahashi , A flexible branch and bound method for the job shop scheduling problem. Industrial Eng. Manag. Syst. 8 (2009) 239–246.

[37] Ch. Muise , J.Ch. Beck and Sh.A. Mcilraith , Flexible execution of partial order plans with temporal constraints. In Inter. Joint Confer. Artificial Intell. (2013) 2328–2335.

[38] W. Nuijten and C. Le Pape , Constraint-based job shop scheduling with ilog sheduler. J. Heuristics 3 (1998) 271–286. | Zbl

[39] G. Pinot and N. Mebarki , Best-case lower bounds in a group sequence for the job shop problem. In 17th IFAC World Congress (2008) 14876–14881.

[40] N. Policella , A. Oddi , S.F. Smith and A. Cesta , Generating Robust Partial Order Schedules. Springer, Berlin Heidelberg, Berlin, Heidelberg (2004) 496–511. | Zbl

[41] F. Roubellat , J.-C. Billaut and M. Villaumié , Ordonnancement d’atelier en temps réel: d’orabaid à ordo. Revue Automat. Productique Appl. 8 (1995) 683–713.

[42] B. Roy and B. Sussmann , Les problèmes d’ordonnancement avec contraintes disjonctives. Rapport de recherches no 9, SEMA (1964).

[43] I. Sabuncuoglu and S. Goren , Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research. Inter. J. Comput. Integrated Manufacturing 22 (2009) 138–157.

[44] F. Sourd and W. Nuijten , Multiple-machine lower bounds for shop-scheduling problems. INFORMS J. comput. 12 (2000) 341–352. | Zbl

[45] K. Swanson , J. Bresina and M. Drummond , Just-in-case scheduling. In AAAI conference, Seattle (1994).

[46] K. Szczesny and M. Knig , Reactive scheduling based on actual logistics data by applying simulation-based optimization. Visualiz. Eng. 3 (2015).

[47] R. Tadei , F. Della Croce and G. Menga , Advanced search techniques for the job shop problem: a comparison. RAIRO: OR 29 (1995) 179–194. | Zbl

[48] S. Van De Vonder , E. Demeulemeester and W. Herroelen , A classication of predictive-reactive project scheduling procedures. J. Scheduling 10 (2007) 195–207. | Zbl

[49] P. Wojakowski and D. Waroek , The classification of scheduling problems under production uncertainty. Res. Logistics Prod. 4 (2014) 245–255.

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