Scheduling job families on non-identical parallel machines with time constraints

Obeid, Ali; Dauzère‐Pérès, Stéphane; Yugma, Claude

doi:10.1007/s10479-012-1107-4

Cited by 23 publications

(20 citation statements)

References 6 publications

(5 reference statements)

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“…The ILP model in [10] is an improvement of two existing models introduced in [12]. The first ILP model of [12] relies on a job-based formulation. Indeed, in this model, a variable x m j,t is defined for each job j, each machine m and each time t. This variable is then equal to 1 if and only if job j starts at time t on machine m. However, in a solution, there is no need to know which job start at which time on which machine.…”

Section: Ilp Modelmentioning

confidence: 99%

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A New CP-Approach for a Parallel Machine Scheduling Problem with Time Constraints on Machine Qualifications

Malapert¹,

Nattaf²

2019

Integration of Constraint Programming, Artificial Intelligence, and Operations Research

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This paper considers the scheduling of job families on parallel machines with time constraints on machine qualifications. In this problem, each job belongs to a family and a family can only be executed on a subset of qualified machines. In addition, machines can lose their qualifications during the schedule. Indeed, if no job of a family is scheduled on a machine during a given amount of time, the machine lose its qualification for this family. The goal is to minimize the sum of job completion times, i.e. the flow time, while maximizing the number of qualifications at the end of the schedule. The paper presents a new Constraint Programming model taking more advantages of the CP feature to model machine disqualifications. This model is compared with two existing models: an Integer Linear Programming (ILP) model and a Constraint Programming (CP) model. The experiments show that the new CP model outperforms the other model when the priority is given to the number of disqualifications objective. Furthermore, it is competitive with the other model when the flow time objective is prioritized.

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Section: Ilp Modelmentioning

confidence: 99%

“…Indeed, only the family of the job is important. Hence, a family-based model is developed in [12](IP2) and improved in [10](IP3).…”

Section: Ilp Modelmentioning

confidence: 99%

A New CP-Approach for a Parallel Machine Scheduling Problem with Time Constraints on Machine Qualifications

Malapert¹,

Nattaf²

2019

Integration of Constraint Programming, Artificial Intelligence, and Operations Research

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“…In addition to proposing MILP and a Tabu search for the same problem, Bektur and Sarac [13] aimed to minimize the total weighted tardiness of a scheduling problem of an unrelated parallel machine. Obeid et al [14] developed two mixed-integer linear programming models to solve a problem for job families on parallel machines. Zeidi and Hosseini [15] presented a mathematical model to solve the problem of scheduling jobs on unrelated parallel machines with sequence-dependent setup times under due-date constraints.…”

Section: Introductionmentioning

confidence: 99%

Modified Harmony Search Algorithm for Resource-Constrained Parallel Machine Scheduling Problem with Release Dates and Sequence-Dependent Setup Times

et al. 2021

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This research focuses on the problem of scheduling a set of jobs on unrelated parallel machines subject to release dates, sequence-dependent setup times, and additional renewable resource constraints. The objective is to minimize the maximum completion time (makespan). To optimize the problem, a modified harmony search (MHS) algorithm was proposed. The parameters of MHS are regulated using full factorial analysis. The MHS algorithm is examined, evaluated, and compared to the best methods known in the literature. Four algorithms were represented from similar works in the literature. A benchmark instance has been established to test the sensitivity and behavior of the problem parameters of the different algorithms. The computational results of the MHS algorithm were compared with those of other metaheuristics. The competitive performance of the developed algorithm is verified, and it was shown to provide a 42% better solution than the others.

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“…This problem, called scheduling Problem with Time Constraints (PTC), is introduced in [11]. It comes from the semiconductor industries.…”

Section: Introductionmentioning

confidence: 99%

Filtering Rules for Flow Time Minimization in a Parallel Machine Scheduling Problem

Nattaf¹,

Malapert²

2020

Lecture Notes in Computer Science

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This paper studies the scheduling of jobs of different families on parallel machines with qualification constraints. Originating from semiconductor manufacturing, this constraint imposes a time threshold between the execution of two jobs of the same family. Otherwise, the machine becomes disqualified for this family. The goal is to minimize both the flow time and the number of disqualifications. Recently, an efficient constraint programming model has been proposed. However, when priority is given to the flow time objective, the efficiency of the model can be improved. This paper uses a polynomial-time algorithm which minimize the flow time for a single machine relaxation where disqualifications are not considered. Using this algorithm one can derived filtering rules on different variables of the model. Experimental results are presented showing the effectiveness of these rules. They improve the competitiveness with the mixed integer linear program of the literature.

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Scheduling job families on non-identical parallel machines with time constraints

Cited by 23 publications

References 6 publications

A New CP-Approach for a Parallel Machine Scheduling Problem with Time Constraints on Machine Qualifications

A New CP-Approach for a Parallel Machine Scheduling Problem with Time Constraints on Machine Qualifications

Modified Harmony Search Algorithm for Resource-Constrained Parallel Machine Scheduling Problem with Release Dates and Sequence-Dependent Setup Times

Filtering Rules for Flow Time Minimization in a Parallel Machine Scheduling Problem

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