Genetic algorithm for short-term scheduling of make-and-pack batch production process

Wuthichai Wongthatsanekorn; Busaba Phruksaphanrat

doi:10.1016/j.cjche.2015.04.021

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Genetic algorithm for short-term scheduling of make-and-pack batch production process

过程系统工程与过程安全 | Updated：2026-01-06

- Genetic algorithm for short-term scheduling of make-and-pack batch production process
- Genetic algorithm for short-term scheduling of make-and-pack batch production process
- 中国化学工程学报（英文版） 2015年23卷第9期页码：1475-1483
- Affiliations：
  
  Industrial Engineering Department, Faculty of Engineering, Thammasat University, Pathum-thani,Thailand,12120
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2015.04.021
  中图分类号：
- 纸质出版：2015
- Accepted：
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Wuthichai Wongthatsanekorn, Busaba Phruksaphanrat. Genetic algorithm for short-term scheduling of make-and-pack batch production process[J]. 中国化学工程学报（英文版）, 2015,23(9):1475-1483.

Wuthichai Wongthatsanekorn, Busaba Phruksaphanrat. Genetic algorithm for short-term scheduling of make-and-pack batch production process[J]. , 2015, 23(9): 1475-1483.
Wuthichai Wongthatsanekorn, Busaba Phruksaphanrat. Genetic algorithm for short-term scheduling of make-and-pack batch production process[J]. 中国化学工程学报（英文版）, 2015,23(9):1475-1483. DOI： 10.1016/j.cjche.2015.04.021.

Wuthichai Wongthatsanekorn, Busaba Phruksaphanrat. Genetic algorithm for short-term scheduling of make-and-pack batch production process[J]. , 2015, 23(9): 1475-1483. DOI： 10.1016/j.cjche.2015.04.021.

摘要

Abstract

This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time

multipurpose storage units with limited capacity

storage time

batch splitting

partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problemis NP-hard and the problemsize is exponentially large for a realistic-sized problem. Therefore

we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition

design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics

local search is applied for the packed product sequence to improve makespan. In an experimental analysis

all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances

genetic algorithmaveragely outperforms ant colony optimization and Tabu search with slightly longer computational time.

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