Ant colony optimization based algorithm for solving scheduling problems with setup times on parallel machines

In this paper, a production scheduling problem with sequence-dependent setup times on a set of unrelated parallel machines is addressed. The objective function is to minimize the total setup time . An algorithm based on ant colony optimization combined with a heuristic is proposed for solving large problems efficiently. It is shown that even a simpler version of the problem can not be tackled with MILP. ACO gives good results for the simpler problem version in a reasonable time. Even ACO can not give good results for the industrial problem. However, ACO combined with the heuristic can give us satisfactory results for the industrial problem in a reasonable time

Group sequencing around a common due date

Author name used in this publication: C. T. Ng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Scheduling unrelated parallel machines with resource-assignable sequence-dependent setup times

[EN] A novel scheduling problem that results from the addition of resource-assignable setups is presented in this paper. We consider an unrelated parallel machine problem with machine and job sequence-dependent setup times. The new characteristic is that the amount of setup time does not only depend on the machine and job sequence but also on the amount of resources assigned, which can vary between a minimum and a maximum. The aim is to give solution to real problems arising in several industries where frequent setup operations in production lines have to be carried out. These operations are indeed setups whose length can be reduced or extended according to the amount of resources assigned to them. The objective function considered is a linear combination of total completion time and the total amount of resources assigned. We present a mixed integer program (MIP) model and some fast dispatching heuristics. We carry out careful and comprehensive statistical analyses to study what characteristics of the problem affect the MIP model performance. We also study the effectiveness of the different heuristics proposed. © 2011 Springer-Verlag London Limited.The authors are indebted to the referees and editor for a close examination of the paper, which has increased its quality and presentation. This work is partially funded by the Spanish Ministry of Science and Innovation, under the project "SMPA-Advanced Parallel Multiobjective Sequencing: Practical and Theoretical Advances" with reference DPI2008-03511/DPI. The authors should also thank the IMPIVA-Institute for the Small and Medium Valencian Enterprise, for the project OSC with references IMIDIC/2008/137, IMIDIC/2009/198, and IMIDIC/2010/175.Ruiz García, R.; Andrés Romano, C. (2011). Scheduling unrelated parallel machines with resource-assignable sequence-dependent setup times. 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Hybrid Genetic Bees Algorithm applied to Single Machine Scheduling with Earliness and Tardiness Penalties

This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm's (GA's) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as â\u80\u9creinforced global searchâ\u80\u9d and â\u80\u9cjumping functionâ\u80\u9d strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs

Ordonnancement et contrôle avancé des procédés en fabrication de semi-conducteurs.

Dans cette thèse, nous avons examiné différentes possibilités d'intégration des décisions d'ordonnancement avec des informations provenant de systèmes avancés des contrôles des procédés dans la fabrication de semi-conducteurs. Nous avons développé des idées d'intégration et défini des nouveaux problèmes d'ordonnancement originales : Problème d'ordonnancement avec des contraintes de temps (PTC) et problème d'ordonnancement avec l'état de santé des équipement (PEHF). PTC et PEHF ont des fonctions objectives multicritères.PTC est un problème d'ordonnancement des familles de jobs sur des machines parallèles non identiques en tenant compte des temps de setup et des contraintes de temps. Les machines non identiques signifient que toutes les machines ne peuvent pas traités (qualifiés) tous les types de familles d'emplois. Les contraintes de temps nommés aussi Thresholds sont inspirées des besoins de l'APC. Elle est liée à l'alimentation régulière des boucles de contrôle de l'APC. L'objectif est de minimiser la somme des dates de fin et les pertes de qualification des machines lorsqu'une famille de jobs n'est pas ordonnancée sur la machine donnée avant un seuil de temps donné.D'autre part, PEHF est une extension de PTC. Il consiste d'intégrer les indices de santé des équipements (EHF). EHF est un indicateur associé à l'équipement qui donne l'état de la. L'objectif est d'ordonnancer des tâches de familles de jobs différents sur les machines tout en minimisant la somme des temps d'achèvement, les pertes de qualification de la machine et d'optimiser un rendement attendu. Ce rendement est défini comme une fonction d'EDH et de la criticité de jobs considérés.In this thesis, we discussed various possibilities of integrating scheduling decisions with information and constraints from Advanced Process Control (APC) systems in semiconductor Manufacturing. In this context, important questions were opened regarding the benefits of integrating scheduling and APC. An overview on processes, scheduling and Advanced Process Control in semiconductor manufacturing was done, where a description of semiconductor manufacturing processes is given. Two of the proposed problems that result from integrating bith systems were studied and analyzed, they are :Problem of Scheduling with Time Constraints (PTC) and Problem of Scheduling with Equipement health Factor (PEHF). PTC and PEHF have multicriteria objective functions.PTC aims at scheduling job in families on non-identical parallel machines with setup times and time constraints.Non-identical machines mean that not all miachines can (are qualified to) process all types of job families. Time constraints are inspired from APC needs, for which APC control loops must be regularly fed with information from metrology operations (inspection) within a time interval (threshold). The objective is to schedule job families on machines while minimizing the sum of completion times and the losses in machine qualifications.Moreover, PEHF was defined which is an extension of PTC where scheduling takes into account the equipement Health Factors (EHF). EHF is an indicator on the state of a machine. Scheduling is now done by considering a yield resulting from an assignment of a job to a machine and this yield is defined as a function of machine state and job state.ST ETIENNE-ENS des Mines (422182304) / SudocGARDANNE-Centre microélec. (130412301) / SudocSudocFranceF

Minimizing weighted earliness-tardiness on a single machine with a common due date using quadratic models

In this paper we study the problem of minimizing weighted earliness and tardiness on a single machine when all the jobs share the same due date. We propose two quadratic integer programming models for solving both cases of unrestricted and restricted due dates, an auxiliary model based on unconstrained quadratic integer programming and an algorithmic scheme for solving each instance, according to its size and characteristics, in the most efficient way. The scheme is tested on a set of well-known test problems. By combining the solutions of the three models we prove the optimality of the solutions obtained for most of the problems. For large instances, although optimality cannot be proved, we actually obtain optimal solutions for all the tested instances.This study has been partially supported by the Spanish Ministry of Science and Technology, DPI2008-02700, cofinanced by FEDER funds, and by Project PCI08-0048-8577, Consejeria de Ciencia y Tecnologia, Junta de Comunidades de Castilla-La Mancha, and Generalitat Valenciana ACOMP/2009/264.Alvarez-Valdes Olaguibel, R.; Crespo, E.; Manuel Tamarit, J.; Villa Juliá, MF. (2012). Minimizing weighted earliness-tardiness on a single machine with a common due date using quadratic models. TOP. 20(3):754-767. https://doi.org/10.1007/s11750-010-0163-7S754767203Alidaee B, Kochenberger G, Ahmadian A (1994) 0–1 Quadratic programming approach for optimum solutions of two scheduling problems. Int J Syst Sci 25:401–408Baker KR, Scudder GD (1990) Sequencing with earliness and tardiness penalties: a review. Oper Res 38:22–36Billionnet A, Elloumi S (2007) Using a mixed integer quadratic programming solver for the unconstrained quadratic 0–1 problem. Math Program 109:55–68Billionnet A, Elloumi S, Plateau MC (2009) Improving the performance of standard solvers for quadratic 0–1 programs by a tight convex reformulation: the QCR method. Discrete Appl Math 157:1185–1197Biskup D, Feldman M (2001) Benchmarks for scheduling on a single machine against restrictive and unrestrictive common due dates. Comput Oper Res 28:787–801Cheng TCE, Kahlbacher HG (1991) A proof for the longest-job-first in one-machine scheduling. Nav Res Log 38:715–720De P, Gosh JB, Wells CE (1990) CON due-date determination and sequencing. Comput Oper Res 17:333–342Feldman M, Biskup D (2003) Single machine scheduling for minimizing earliness and tardiness penalties by meta-heuristic approaches. Comput Ind Eng 44:307–323Józefowska J (2007) Just-in-time scheduling. Springer, BerlinKanet JJ (1981) Minimizing the average deviation of job completion times about a common due date. Nav Res Log 28:643–651Kedad-Sidhoum S, Sourd F (2010) Fast neighborhood search for the single machine earliness–tardiness problem. Comput Oper Res 37:1464–1471Hall NG, Posner ME (1991) Earliness–tardiness scheduling problem, I: Weighted deviation of completion times about a common due date. Oper Res 39:836–846Hall NG, Kubiak W, Sethi SP (1991) Earliness–tardiness scheduling problem, II: Deviation of completion times about a restrictive common due date. Oper Res 39:847–856Hino CM, Ronconi DP, Mendes AB (2005) Minimizing earliness and tardiness penalties in a single-machine problem with a common due date. Eur J Oper Res 55:190–201Hoogeveen JA, van de Velde SL (1991) Scheduling around a small common due date. Eur J Oper Res 55:237–242Liao CJ, Cheng CC (2007) A variable neighbourhood search for minimizing single machine weighted earliness and tardiness with common due date. Comput Ind Eng 52:404–413Lin S-W, Chou S-Y, Ying K-C (2007) A sequential exchange approach for minimizing earliness–tardiness penalties of single-machine scheduling with a common due date. Eur J Oper Res 177:1294–1301Nearchou AC (2008) A differential evolution approach for the common due date early/tardy job scheduling problem. Comput Oper Res 35:1329–1343Oral M, Kettani O (1987) Equivalent formulations of nonlinear integer problems for efficient optimization. Manag Sci 36:115–119Panwalkar SS, Smith ML, Seidman A (1982) Common due date assignment to minimize total penalty for the one machine scheduling problem. Oper Res 30:391–399Plateau MC, Rios-Solis Y (2010) Optimal solutions for unrelated parallel machines scheduling problems using convex quadratic reformulations. Eur J Oper Res 201:729–736Skutella M (2001) Convex quadratic and semidefinite programming relaxations in scheduling. J ACM 48:206–242Sourd F (2009) New exact algorithms for on-machine earliness–tardiness scheduling. INFORMS J Comput 21:167–175Sourd F, Kedad-Sidhoum S (2003) The one machine problem with earliness and tardiness penalties. J Sched 6:533–549Sourd F, Kedad-Sidhoum S (2008) A faster branch-and-bound algorithm for the earliness–tardiness scheduling problem. J Sched 11:49–58Tanaka S, Fujikuma S, Araki M (2009) An exact algorithm for single-machine scheduling without machine idle time. J Sched 12:575–593Webster ST (1997) The complexity of scheduling job families about a common due date. Oper Res Lett 20:65–7