Production sequence determination to minimize the required storage space for the multiple items production system

Titirat Vivithkeyoonvong, Wisut Supithak

Abstract


Purpose: The research studies the production system having multiple items being processed on the same production line. The objectives are to (1) investigate the influence of production sequence on the optimal value of production run size, (2) explore the effect of production sequence on the maximum inventory level, which can affect the storage space required, and (3) propose a method to determine the proper production sequence in order to minimize the required storage space.

Design/methodology/approach: Finding that the optimal production sequence, which yields the lowest storage space required, is independent of the production run size, the research problem is divided into two independent subproblems. The first subproblem is to determine the optimal production run size to minimize the total variable cost. Here, the solution obtained from the classical multiple items EPQ model still holds. The second subproblem is to explore the proper production sequence in order to minimize the storage space required. The relationship between the production sequence and the value of maximum inventory level is determined and formulated. To explore the proper production sequence, a genetic algorithm is developed. For the performance evaluation, two experimental studies are conducted. The first experiment is to compare the solution obtained from the proposed method with the optimal solution yielded from the enumeration method on 360 small size problems. The second experiment is conducted on 180 large size problems. The result obtained from the proposed method is compared with the result yielded from the Largest Rho First (LRF) heuristic constructed by arranging the production of each item according to the ratio between the demand rate and production rate.

Findings: It has been found that the optimal production sequence is independent of the production run size. Nonetheless, different production sequences yield different required storage spaces. With the proper production sequence, the manufacturer can reduce the total space required to keep its inventory. The proposed genetic algorithm can be applied to determine the proper production sequence in a reasonable amount of time. For the small size problem of 8 and 10 production items, the proposed genetic algorithm provides the solution with the maximum percentage deviation of 0.307 percent from the optimal solution. For the large size problem of 15 production items, the proposed genetic algorithm outperforms the LRF heuristic for all 180 problems. This result is more pronounced when the slack proportion is getting smaller.

Research limitations/implications: According to the research model, no shortages are allowed. Therefore, the model is applicable for the production system having the summation value of the ratio between demand rate and production rate for all items not greater than one.

Originality/value: Those traditional research involving the determination of optimal production run size and production sequence in the system having multiple items being produced on the same production line differs from each other in their production environments. However, most of them still have the objective function of minimizing the total system cost incurred. To the best of our literature searching, none of them discussed the influence of production sequence on the total inventory level, which directly affects the required storage space, one of the critical issues facing by many manufacturers. The originality of this work is to show that different production sequence yields different total storage space required and proposed the method to determine proper production sequence.

Keywords


Inventory management, production sequencing, genetic algorithm, multiple items

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DOI: http://dx.doi.org/10.3926/jiem.3715


Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2022

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience