Solving a flexible job shop lot sizing problem with shared operations using a self-adaptive COA

• Published in: International journal of production research Vol. 59; no. 2; pp. 483 - 515
• Main Authors: Abdollahzadeh Sangroudi, Hadi, Ranjbar-Bourani, Mehdi
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 17.01.2021; Taylor & Francis LLC
• Subjects: Adaptive algorithms; Adaptive systems; assembly operations; Cuckoo optimisation algorithm; Decisions; flexible job shop; Job shops; Lot sizing; Mathematical models; Optimization; order-based scheduling; Safety stocks; self-adaptive mechanism; Setup times
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2019.1696492

This paper deals with lot sizing decisions in a flexible job shop manufacturing system considering dependencies between lot sizing decisions. A novel mathematical model is developed to optimise lot sizing and scheduling simultaneously using a product-oriented approach as opposed to the job-oriented approach which has been prevalently considered in the literature. Moreover, the proposed model considers further underlying assumptions such as assembly operations, sequence-dependent setup times, initial inventory and safety stock levels, as well as lots with unequal and variable sizes. The objective is to minimise the total production, setup, and tardiness penalty costs of the system. To solve the formulated problem, a self-adaptive Cuckoo Optimisation Algorithm (COA) embedded with three new immigration mechanisms is developed. Numerical experiments are conducted to demonstrate the validity of the model and investigate the efficiency and effectiveness of the employed optimisation algorithm.