Genetic algorithm to production planning and scheduling problems for manufacturing systems

Ying LI; Kim Fung MAN; Kit Sang TANG; Sam KWONG; W. H. IP

doi:10.1080/09537280050051942

Genetic algorithm to production planning and scheduling problems for manufacturing systems

Ying LI, Kim Fung MAN, Kit Sang TANG, Sam KWONG, W. H. IP

Research output: Journal Publications › Journal Article (refereed) › peer-review

37 Citations (Scopus)

Abstract

Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.

Original language	English
Pages (from-to)	443-458
Journal	Production Planning and Control
Volume	11
Issue number	5
DOIs	https://doi.org/10.1080/09537280050051942
Publication status	Published - Jul 2000
Externally published	Yes

Keywords

Earliness/tardiness production scheduling and planning (ETPSP)
Genetic algorithms (GAs)
Multi-objective (MO)
Optimization
Production/inventory management and control (PIMC)

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10.1080/09537280050051942

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title = "Genetic algorithm to production planning and scheduling problems for manufacturing systems",

abstract = "Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.",

keywords = "Earliness/tardiness production scheduling and planning (ETPSP), Genetic algorithms (GAs), Multi-objective (MO), Optimization, Production/inventory management and control (PIMC)",

author = "Ying LI and MAN, {Kim Fung} and TANG, {Kit Sang} and Sam KWONG and IP, {W. H.}",

year = "2000",

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T1 - Genetic algorithm to production planning and scheduling problems for manufacturing systems

AU - LI, Ying

AU - MAN, Kim Fung

AU - TANG, Kit Sang

AU - KWONG, Sam

AU - IP, W. H.

PY - 2000/7

Y1 - 2000/7

N2 - Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.

AB - Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.

KW - Earliness/tardiness production scheduling and planning (ETPSP)

KW - Genetic algorithms (GAs)

KW - Multi-objective (MO)

KW - Optimization

KW - Production/inventory management and control (PIMC)

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U2 - 10.1080/09537280050051942

DO - 10.1080/09537280050051942

M3 - Journal Article (refereed)

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VL - 11

SP - 443

EP - 458

JO - Production Planning and Control

JF - Production Planning and Control

IS - 5

ER -

Genetic algorithm to production planning and scheduling problems for manufacturing systems

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Keywords

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