Stochastic scheduling to minimize expected maximum lateness

This paper is concerned with the problems in scheduling a set of jobs associated with random due dates on a single machine so as to minimize the expected maximum lateness in stochastic environment. This is a difficult problem and few efforts have been reported on its solution in the literature. In t...

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Bibliographic Details
Published in:European journal of operational research Vol. 190; no. 1; pp. 103 - 115
Main Authors: Wu, Xianyi, Zhou, Xian
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.10.2008
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Applied sciences
Due dates
Dynamic programming
Dynamic programming algorithms
Exact sciences and technology
Expected maximum lateness
Heuristic
Job shops
Operational research and scientific management
Operational research. Management science
Scheduling algorithms
Scheduling, sequencing
Solutions
Stochastic models
Stochastic scheduling
Studies
Dynamic programming algorithms
Stochastic scheduling
Expected maximum lateness
Due dates
Probabilistic approach
Processing time
Scheduling
Due date
Stochastic programming
Heuristic method
Lateness
Dynamic programming
Single machine
Execution time
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:This paper is concerned with the problems in scheduling a set of jobs associated with random due dates on a single machine so as to minimize the expected maximum lateness in stochastic environment. This is a difficult problem and few efforts have been reported on its solution in the literature. In this paper, we first derive a deterministic equivalent to the expected maximum lateness and then propose a dynamic programming algorithm to obtain the optimal solutions. The procedures to compute optimal solutions are initially developed in the case of deterministic processing times, and then extended to stochastic processing times following arbitrary probability distributions. Moreover, several heuristic rules are suggested to compute near-optimal solutions, which are shown to be highly efficient and accurate by computer-based experiments.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2007.06.015