Heuristics for parallel-machine flexible-resource scheduling problems with unspecified job assignment

Richard L. Daniels, Stella Y. Hua, Scott Webster

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

This paper considers a parallel-machine scheduling problem in which the operational impact of resource flexibility is explored through machine flexibility as well as labor flexibility. The main contribution of this paper is the identification of the existence of the problem in practice, the development of two efficient heuristics, and the comparison and analysis of the proposed heuristics. In this paper, we examine the parallel-machine flexible-resource scheduling problem in which job assignment to machines are not specified (UPMFRS). The UPMFRS problem is NP-hard, motivating the development of effective heuristics that approximately determine the job assignment to machines and the allocation of resources to jobs. The paper compares a decomposition heuristic and a tabu-search heuristic, and concludes that the tabu-search heuristic is cost and quality effective in locating the near-optimal solutions.

Original languageEnglish (US)
Pages (from-to)143-155
Number of pages13
JournalComputers and Operations Research
Volume26
Issue number2
DOIs
StatePublished - Feb 1999
Externally publishedYes

Fingerprint

Resource Scheduling
Tabu search
Parallel Machines
scheduling
Scheduling Problem
heuristics
Assignment
Scheduling
Heuristics
resources
Computational complexity
tabu
flexibility
Flexibility
Personnel
Tabu Search
Decomposition
Costs
Parallel Machine Scheduling
Resources

Keywords

  • Flexible resource allocation
  • Heuristics
  • Production scheduling

ASJC Scopus subject areas

  • Information Systems and Management
  • Management Science and Operations Research
  • Applied Mathematics
  • Modeling and Simulation
  • Transportation

Cite this

Heuristics for parallel-machine flexible-resource scheduling problems with unspecified job assignment. / Daniels, Richard L.; Hua, Stella Y.; Webster, Scott.

In: Computers and Operations Research, Vol. 26, No. 2, 02.1999, p. 143-155.

Research output: Contribution to journalArticle

@article{56e21e7cdc5345d88bb1a7d9ceba245c,
title = "Heuristics for parallel-machine flexible-resource scheduling problems with unspecified job assignment",
abstract = "This paper considers a parallel-machine scheduling problem in which the operational impact of resource flexibility is explored through machine flexibility as well as labor flexibility. The main contribution of this paper is the identification of the existence of the problem in practice, the development of two efficient heuristics, and the comparison and analysis of the proposed heuristics. In this paper, we examine the parallel-machine flexible-resource scheduling problem in which job assignment to machines are not specified (UPMFRS). The UPMFRS problem is NP-hard, motivating the development of effective heuristics that approximately determine the job assignment to machines and the allocation of resources to jobs. The paper compares a decomposition heuristic and a tabu-search heuristic, and concludes that the tabu-search heuristic is cost and quality effective in locating the near-optimal solutions.",
keywords = "Flexible resource allocation, Heuristics, Production scheduling",
author = "Daniels, {Richard L.} and Hua, {Stella Y.} and Scott Webster",
year = "1999",
month = "2",
doi = "10.1016/S0305-0548(98)00054-9",
language = "English (US)",
volume = "26",
pages = "143--155",
journal = "Surveys in Operations Research and Management Science",
issn = "0305-0548",
publisher = "Elsevier Limited",
number = "2",

}

TY - JOUR

T1 - Heuristics for parallel-machine flexible-resource scheduling problems with unspecified job assignment

AU - Daniels, Richard L.

AU - Hua, Stella Y.

AU - Webster, Scott

PY - 1999/2

Y1 - 1999/2

N2 - This paper considers a parallel-machine scheduling problem in which the operational impact of resource flexibility is explored through machine flexibility as well as labor flexibility. The main contribution of this paper is the identification of the existence of the problem in practice, the development of two efficient heuristics, and the comparison and analysis of the proposed heuristics. In this paper, we examine the parallel-machine flexible-resource scheduling problem in which job assignment to machines are not specified (UPMFRS). The UPMFRS problem is NP-hard, motivating the development of effective heuristics that approximately determine the job assignment to machines and the allocation of resources to jobs. The paper compares a decomposition heuristic and a tabu-search heuristic, and concludes that the tabu-search heuristic is cost and quality effective in locating the near-optimal solutions.

AB - This paper considers a parallel-machine scheduling problem in which the operational impact of resource flexibility is explored through machine flexibility as well as labor flexibility. The main contribution of this paper is the identification of the existence of the problem in practice, the development of two efficient heuristics, and the comparison and analysis of the proposed heuristics. In this paper, we examine the parallel-machine flexible-resource scheduling problem in which job assignment to machines are not specified (UPMFRS). The UPMFRS problem is NP-hard, motivating the development of effective heuristics that approximately determine the job assignment to machines and the allocation of resources to jobs. The paper compares a decomposition heuristic and a tabu-search heuristic, and concludes that the tabu-search heuristic is cost and quality effective in locating the near-optimal solutions.

KW - Flexible resource allocation

KW - Heuristics

KW - Production scheduling

UR - http://www.scopus.com/inward/record.url?scp=0033080067&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033080067&partnerID=8YFLogxK

U2 - 10.1016/S0305-0548(98)00054-9

DO - 10.1016/S0305-0548(98)00054-9

M3 - Article

AN - SCOPUS:0033080067

VL - 26

SP - 143

EP - 155

JO - Surveys in Operations Research and Management Science

JF - Surveys in Operations Research and Management Science

SN - 0305-0548

IS - 2

ER -