Hybrid meta-heuristics for minimizing the total weighted completion time on uniform parallel machines

Zaidi, Makram; Jarboui, Bassem; Kacem, Imed; Loukil, Taïcir

doi:10.1016/j.endm.2010.05.069

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Other researchers (e.g. [30,39,44,47]) have proposed heuristics and meta-heuristics for scheduling problems with unrelated parallel machines and total flowtime minimization To our knowledge, the work on problems with flexible or variable release dates is quite limited. One work that explicitly treats release dates as flexible is [38].…”

Section: Related Workmentioning

confidence: 99%

Single and Parallel Machine Scheduling with Variable Release Dates

Mohr,

Mejía,

Yuraszeck

2021

Preprint

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In this paper we study a simple extension of the total weighted flowtime minimization problem for single and identical parallel machines. While the standard problem simply defines a set of jobs with their processing times and weights and assumes that all jobs have release date 0 and have no deadline, we assume that the release date of each job is a decision variable that is only constrained by a single global latest arrival deadline. To our knowledge, this simple yet practically highly relevant extension has never been studied. Our main contribution is that we show the NPcompleteness of the problem even for the single machine case and provide an exhaustive empirical study of different typical approaches including genetic algorithms, tree search, and constraint programming.

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Section: Related Workmentioning

confidence: 99%

Single and Parallel Machine Scheduling with Variable Release Dates

Mohr,

Mejía,

Yuraszeck

2021

Preprint

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An agent-based intelligent algorithm for uniform machine scheduling to minimize total completion time

Leung

Cheng

2014

Applied Soft Computing

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Multi-objective mathematical models to resolve parallel machine scheduling problems with multiple resources

et al. 2023

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Mathematical programming, and above all, the multi-objective scheduling problems stand as remarkably versatile tools, highly useful for optimizing the health care services. In this context, the present work is designed to put forward two-fold multi-objective mixed integer linear programs, simultaneously integrating the objectives of minimizing the patients? total waiting and flow time, while minimizing the doctors' work-load variations. For this purpose, the three major health-care system intervening actors are simultaneously considered, namely, the patients, doctors and machines. To the best of our knowledge, such an issue does not seem to be actually addressed in the relevant literature. To this end, we opt for implementing an appropriate lexicographic method, whereby, effective solutions enabling to minimize the performance of two-objective functions could be used to solve randomly generated small cases. Mathematical models of our study have been resolved using the CPLEX software. Then, results have been comparatively assessed in terms of both objectives and CPU times. A real laser-treatment case study, involving a set of diabetic retinopathy patients in the ophthalmology department in Habib Bourguiba Hospital in Sfax, Tunisia, helps in illustrating the effective practicality of our advanced approach. To resolve the treated problem, we use three relevant heuristics which have been compared to the first-come first-served rule. We find that the program based on our second formulation with time-limit provided the best solution in terms of total flow time.

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Hybrid meta-heuristics for minimizing the total weighted completion time on uniform parallel machines

Cited by 3 publications

References 8 publications

Single and Parallel Machine Scheduling with Variable Release Dates

Single and Parallel Machine Scheduling with Variable Release Dates

An agent-based intelligent algorithm for uniform machine scheduling to minimize total completion time

Multi-objective mathematical models to resolve parallel machine scheduling problems with multiple resources

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