Abstract
Dynamic lotsizing and scheduling on multiple lines to meet the customer due dates is significant in multi-line production environments. Therefore, this study investigates dynamic lotsizing and scheduling problems in multiple flexible machining lines considering mixed products. In addition, uncertainty in demand and machine failure is considered. A mathematical model is proposed for the considered problem with an aim to maximize the probability of completion of product models from different customer orders. A constructive heuristic method (CHLP) is proposed to solve the current problem. The proposed heuristic involves the steps to distribute different customer order demands among multiple lines and schedule them considering balancing of makespan between the lines. The performance of CHLP is measured with famous heuristics from the literature, based on the test problem instances. Results indicate that CHLP gives better results in terms of quality of results as compared to other famous literature heuristics.
Highlights
Manufacturers are striving to establish an effective and efficient production system, while facing changes in demand, fierce competition environments and uncontrollable production conditions
The comparison of results is performed by measuring the values of objective (Obj), makespan (MS), makespan deviation from the average value of makespan of all lines (MSD) and run time (RT) of the proposed constructive heuristic for the lotsizing problem (CHLP) for all problem instances with the corresponding values obtained from AGB and NEHedd heuristics
These results indicate that as the problem complexity increases with the increase in the number of customer orders, the performance of the proposed CHLP heuristic decreases slightly when compared with the NEHedd heuristic results
Summary
Manufacturers are striving to establish an effective and efficient production system, while facing changes in demand, fierce competition environments and uncontrollable production conditions. Based on the variety and volume of products, manufacturing industries use different kinds of production lines. CPS-based intelligent factories represent a form of future industrial network, while Industry 4.0 represents the concept of smart and intelligent manufacturing networks, that is, networks where machines, orders and products can interact without human control. The dynamic characteristics of a manufacturing system, such as product changes, demand fluctuation and the uncertainty of machine failure, pose challenges to the structure of manufacturing systems. This requires a dynamic manufacturing structure and scheduling scheme which can quickly respond to environmental changes, and this provides a good opportunity for the current research
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