A resource-constrained assembly job shop scheduling problem with Lot Streaming technique

T.C. Wong, F.T.S. Chan, L.Y. Chan

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

To ensure effective shop floor production, it is vital to consider the capital investment. Among most of the operational costs, resource must be one of the critical cost components. Since each operation consumes resources, the determination of resource level is surely a strategic decision. For the first time, the application of Lot Streaming (LS) technique is extended to a Resource-Constrained Assembly Job Shop Scheduling Problem (RC_AJSSP). In general, AJSSP first starts with Job Shop Scheduling Problem (JSSP) and then appends an assembly stage for final product assembly. The primary objective of the model is the minimization of total lateness cost of all final products. To enhance the model usefulness, two more experimental factors are introduced as common part ratio and workload index. Hence, an innovative approach with Genetic Algorithm (GA) is proposed. To examine its goodness, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that GA can outperform PSO in terms of optimization power and computational effort for all test problems.
LanguageEnglish
Pages983-995
Number of pages13
JournalComputers and Industrial Engineering
Volume57
Issue number3
DOIs
Publication statusPublished - 1 Oct 2009

Fingerprint

Particle swarm optimization (PSO)
Genetic algorithms
Costs
Job shop scheduling

Keywords

  • particle swarm optimization
  • resource constraint
  • assembly job shop
  • genetic algorithm
  • lot streaming

Cite this

@article{9f38de03b42646c5bec1de2b97febf39,
title = "A resource-constrained assembly job shop scheduling problem with Lot Streaming technique",
abstract = "To ensure effective shop floor production, it is vital to consider the capital investment. Among most of the operational costs, resource must be one of the critical cost components. Since each operation consumes resources, the determination of resource level is surely a strategic decision. For the first time, the application of Lot Streaming (LS) technique is extended to a Resource-Constrained Assembly Job Shop Scheduling Problem (RC_AJSSP). In general, AJSSP first starts with Job Shop Scheduling Problem (JSSP) and then appends an assembly stage for final product assembly. The primary objective of the model is the minimization of total lateness cost of all final products. To enhance the model usefulness, two more experimental factors are introduced as common part ratio and workload index. Hence, an innovative approach with Genetic Algorithm (GA) is proposed. To examine its goodness, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that GA can outperform PSO in terms of optimization power and computational effort for all test problems.",
keywords = "particle swarm optimization, resource constraint, assembly job shop, genetic algorithm, lot streaming",
author = "T.C. Wong and F.T.S. Chan and L.Y. Chan",
year = "2009",
month = "10",
day = "1",
doi = "10.1016/j.cie.2009.04.002",
language = "English",
volume = "57",
pages = "983--995",
journal = "Computers and Industrial Engineering",
issn = "0360-8352",
number = "3",

}

A resource-constrained assembly job shop scheduling problem with Lot Streaming technique. / Wong, T.C.; Chan, F.T.S.; Chan, L.Y.

In: Computers and Industrial Engineering, Vol. 57, No. 3, 01.10.2009, p. 983-995.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A resource-constrained assembly job shop scheduling problem with Lot Streaming technique

AU - Wong, T.C.

AU - Chan, F.T.S.

AU - Chan, L.Y.

PY - 2009/10/1

Y1 - 2009/10/1

N2 - To ensure effective shop floor production, it is vital to consider the capital investment. Among most of the operational costs, resource must be one of the critical cost components. Since each operation consumes resources, the determination of resource level is surely a strategic decision. For the first time, the application of Lot Streaming (LS) technique is extended to a Resource-Constrained Assembly Job Shop Scheduling Problem (RC_AJSSP). In general, AJSSP first starts with Job Shop Scheduling Problem (JSSP) and then appends an assembly stage for final product assembly. The primary objective of the model is the minimization of total lateness cost of all final products. To enhance the model usefulness, two more experimental factors are introduced as common part ratio and workload index. Hence, an innovative approach with Genetic Algorithm (GA) is proposed. To examine its goodness, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that GA can outperform PSO in terms of optimization power and computational effort for all test problems.

AB - To ensure effective shop floor production, it is vital to consider the capital investment. Among most of the operational costs, resource must be one of the critical cost components. Since each operation consumes resources, the determination of resource level is surely a strategic decision. For the first time, the application of Lot Streaming (LS) technique is extended to a Resource-Constrained Assembly Job Shop Scheduling Problem (RC_AJSSP). In general, AJSSP first starts with Job Shop Scheduling Problem (JSSP) and then appends an assembly stage for final product assembly. The primary objective of the model is the minimization of total lateness cost of all final products. To enhance the model usefulness, two more experimental factors are introduced as common part ratio and workload index. Hence, an innovative approach with Genetic Algorithm (GA) is proposed. To examine its goodness, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that GA can outperform PSO in terms of optimization power and computational effort for all test problems.

KW - particle swarm optimization

KW - resource constraint

KW - assembly job shop

KW - genetic algorithm

KW - lot streaming

UR - http://www.scopus.com/inward/record.url?scp=69649103114&partnerID=8YFLogxK

U2 - 10.1016/j.cie.2009.04.002

DO - 10.1016/j.cie.2009.04.002

M3 - Article

VL - 57

SP - 983

EP - 995

JO - Computers and Industrial Engineering

T2 - Computers and Industrial Engineering

JF - Computers and Industrial Engineering

SN - 0360-8352

IS - 3

ER -