Purpose: The research studies the production system having multiple items being processed on the sameproduction line. The objectives are to (1) investigate the influence of production sequence on the optimalvalue of production run size, (2) explore the effect of production sequence on the maximum inventorylevel, which can affect the storage space required, and (3) propose a method to determine the properproduction sequence in order to minimize the required storage space.Design/methodology/approach: Finding that the optimal production sequence, which yields the loweststorage space required, is independent of the production run size, the research problem is divided into twoindependent subproblems. The first subproblem is to determine the optimal production run size tominimize the total variable cost. Here, the solution obtained from the classical multiple items EPQ modelstill holds. The second subproblem is to explore the proper production sequence in order to minimize thestorage space required. The relationship between the production sequence and the value of maximuminventory level is determined and formulated. To explore the proper production sequence, a geneticalgorithm is developed. For the performance evaluation, two experimental studies are conducted. The firstexperiment is to compare the solution obtained from the proposed method with the optimal solutionyielded from the enumeration method on 360 small size problems. The second experiment is conductedon 180 large size problems. The result obtained from the proposed method is compared with the resultyielded from the Largest Pi First (LPF) heuristic constructed by arranging the production of each itemaccording to its production rate in non-increasing order.Findings:It has been found that the optimal production sequence is independent of the production runsize. Nonetheless, different production sequences yield different required storage spaces. With the properproduction sequence, the manufacturer can reduce the total space required to keep its inventory. Theproposed genetic algorithm can be applied to determine the proper production sequence in a reasonableamount of time. For the small size problem of 8 and 10 production items, the 95% confidence interval onmean of the percentage deviation between the solution yielded from the proposed genetic algorithm andthe optimal solution is (0.0015, 0.0123).For the large size problem of 15 production items, the proposedgenetic algorithm provides the better solution than the LPF heuristic for 158 out of 180 problems with the95% confidence interval on mean of the percentage deviation of (5.5629, 7.0435). For those remaining 22problems, the two methods yield the same results. In comparison to the LPF heuristic, the benefit ofgenetic algorithm 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 ratiobetween demand rate and production rate for all items not greater than one Originality/value: Those traditional research involving the determination of optimal production run sizeand production sequence in the system having multiple items being produced on the same production linediffers from each other in their production environments. However, most of them still have the objectivefunction of minimizing the total system cost incurred. To the best of our literature searching, none ofthem discussed the influence of production sequence on the total inventory level, which directly affects therequired storage space,one of the critical issues facing by many manufacturers. The originality of this workis to show that different production sequence yields different total storage space required and proposed themethod to determine proper production sequence
