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

City University of Hong Kong

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › 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

Research 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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This output contributes to the following UN Sustainable Development Goals (SDGs)

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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)",

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language = "English",

volume = "11",

pages = "443--458",

journal = "Production Planning and Control",

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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 - RGC 21 - Publication in refereed journal

SN - 0953-7287

VL - 11

SP - 443

EP - 458

JO - Production Planning and Control

JF - Production Planning and Control

IS - 5

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Genetic algorithm to production planning and scheduling problems for manufacturing systems

Abstract

Research Keywords

UN SDGs

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