Scheduling parallel machines to minimize total weighted and unweighted tardiness

Alidaee, Bahram; Rosa, Duane J.

doi:10.1016/s0305-0548(96)00080-9

Cited by 65 publications

(42 citation statements)

References 21 publications

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“…In 1969 Pritsker et al, (1969) have done the formulation with linear programming. Alidaee & Rosa, (1997) have proposed a method based on the modified due date method of Baker K.R. & Bertrand J.W., (1982).…”

Section: Parallel Machinementioning

confidence: 99%

Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion

Dugardin¹,

Chehade²,

Yalaoui³

et al. 2007

Multiprocessor Scheduling, Theory and Applications

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Section: Parallel Machinementioning

confidence: 99%

Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion

Dugardin¹,

Chehade²,

Yalaoui³

et al. 2007

Multiprocessor Scheduling, Theory and Applications

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“…Schutten & Leussink (1996) proposed a branch-andbound algorithm to minimize the maximum lateness. Alidaee & Rosa (1997) developed a "modified-due-date" algorithm to minimize total weighted tardiness. Azizoglu & Kirca (1998) developed a branch-and-bound algorithm to minimize total tardiness.…”

Section: Previously Related Studies On Parallel Machine Schedulingmentioning

confidence: 99%

Heuristics for Unrelated Parallel Machine Scheduling with Secondary Resource Constraints

Chen¹

2007

Multiprocessor Scheduling, Theory and Applications

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“…Shim and Kim (2007) have developed some dominance properties and a lower bound for the same problem, and a branch-and-bound method is adopted to solve it. Moreover, many heuristics have been developed, such that of Pritsker et al (1969), Alidaee and Rosa (1997), Baker and Bertrand (1982).…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics and exact methods to solve a multiobjective parallel machines scheduling problem

Yalaoui

Chehade

2010

J Intell Manuf

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This paper deals with a multiobjective parallel machines scheduling problem. It consists in scheduling n independent jobs on m identical parallel machines. The job data such as processing times, release dates, due dates and sequence dependent setup times are considered. The goal is to optimize two different objectives: the makespan and the total tardiness. A mixed integer linear program is proposed to model the studied problem. As this problem is NPhard in the strong sense, a metaheuristic method which is the second version of the non dominated sorting genetic algorithm (NSGA-II) is proposed to solve this problem. Since the parameters setting of a genetic algorithm is difficult, a fuzzy logic controller coupled with the NSGA-II (FLC-NSGA-II) is therefore proposed. The role of the fuzzy logic is to better set the crossover and the mutation probabilities in order to update the search ability. After that, an exact method based on the two phase method is also developed. We have used four measuring criteria to compare these methods. The experimental results show the advantages and the efficiency of FLC-NSGA-II.

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Scheduling parallel machines to minimize total weighted and unweighted tardiness

Cited by 65 publications

References 21 publications

Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion

Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion

Heuristics for Unrelated Parallel Machine Scheduling with Secondary Resource Constraints

Metaheuristics and exact methods to solve a multiobjective parallel machines scheduling problem

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