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

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 sequencePeer Reviewe

A memetic algorithm to minimize the total sum of earliness tardiness and sequence dependent setup costs for flow shop scheduling problems with job distinct due windows

The research considers the flow shop scheduling problem under the Just-In-Time (JIT) philosophy. There are n jobs waiting to be processed through m operations of a flow shop production system. The objective is to determine the job schedule such that the total cost consisting of setup, earliness, and tardiness costs, is minimized. To represent the problem, the Integer Linear Programming (ILP) mathematical model is created. A Memetic Algorithm (MA) is developed to determine the proper solution. The evolutionary procedure, worked as the global search, is applied to seek for the good job sequences. In order to conduct the local search, an optimal timing algorithm is developed and inserted in the procedure to determine the best schedule of each job sequence. From the numerical experiment of 360 problems, the proposed MA can provide optimal solutions for 355 problems. It is obvious that the MA can provide the good solution in a reasonable amount of time

Determination of inventory replenishment policy with the open vehicle routing concept in a multi-depot and multi-retailer distribution system

The Inventory Open Vehicle Routing Problem (IOVRP) involves decisions in inventory replenishment and vehicle routing of a system having multiple depots and multiple retailers. The research objective of this work is to develop practical replenishment decisions by applying meta-heuristics of an Ant Colony Algorithm. The routing solutions applying IOVRP and an Inventory Routing Problem (IRP) that are solved and compared to measure the methods’ performance. The result shows that the IOVRP gives 24.66% better solutions in term of total costs than the IRP. Additionally, sensitivity analysis of related factors, i.e., inventory holding costs, ordering cost and vehicle capacity, was performed on the percentage deviation of total costs. Based on the analysis of variance, there is an advantage of IOVRP over IRP when the problem involves small vehicle capacity, low ordering costs, and high holding costs

นโยบายเติมเต็มวัสดุคงคลังอะไหล่สำหรับการซ่อมบำรุงเชิงแก้ไขในหลายเครื่องจักรที่มีอัตราการเกิดชำรุดแตกต่างกันInventory Replenishment Policy for Corrective Maintenance Spare Part Inventory of Multiple Machines with Different Failure Rates

งานวิจัยทำการกำหนดนโยบายเติมเต็มที่เหมาะสมสำหรับวัสดุคงคลังอะไหล่ในงานซ่อมบำรุงเชิงแก้ไขของระบบที่มีหลายเครื่องจักรซึ่งมีค่าเฉลี่ยการเกิดชำรุดแตกต่างกัน การชำรุดของแต่ละเครื่องจักรมีการใช้วัสดุคงคลังอะไหล่ชนิดเดียวกันด้วยจำนวนที่แตกต่างกัน นโยบายเติมเต็มที่นำเสนอเป็นการคำนวณปริมาณเติมเต็มที่เหมาะสมสำหรับแต่ละรอบเวลาเติมเต็มซึ่งถูกกำหนดไว้ โดยมีวัตถุประสงค์เพื่อลดต้นทุนรวมของระบบ ซึ่งประกอบด้วย ต้นทุนวัสดุ ต้นทุนถือครอง ต้นทุนเสื่อมสภาพ และต้นทุนขาดแคลนวัสดุ จากลักษณะของนโยบายการสั่งซื้อตามรอบเวลา ปริมาณเติมเต็มวัสดุคงคลังมีค่าเท่ากับผลต่างของระดับวัสดุคงคลังสูงสุดและระดับวัสดุคงคลังคงเหลือที่ช่วงเวลาสั่งเติมเต็ม ระดับวัสดุคงคลังสูงสุดถูกกำหนดจากความน่าจะเป็นของการขาดแคลนวัสดุที่เหมาะสมซึ่งคำนวณโดยการหาอนุพันธ์ของสมการต้นทุนรวม คำตอบที่ได้จากตัวแบบการคำนวณที่นำเสนอถูกนำไปเปรียบเทียบกับคำตอบที่ได้จากการจำลองสถานการณ์มอนติคาร์โลที่ระดับแตกต่างกันของสามปัจจัย ประกอบด้วย จำนวนเครื่องจักร (2 ระดับ) ต้นทุนการขาดแคลนวัสดุ (3 ระดับ) และ อัตราชำรุด (3 ระดับ) จากผลการทดสอบปัญหา 18 กรณี ตัวแบบการคำนวณที่นำเสนอให้คำตอบเดียวกับการจำลองสถานการณ์ทุกกรณี โดยระดับวัสดุคงคลังสูงสุดมีแนวโน้มเพิ่มขึ้นที่ระดับสูงขึ้นของแต่ละปัจจัยThe research is to determine the proper replenishment policy for corrective maintenance spare part inventory of multiple machines with different failure rates. All failure machines require the same type of spare part but with different quantities. The objective is to evaluate proper replenishment quantity, at a predetermined time between orders, such that the total cost composed of item cost, holding cost, depreciation cost, and shortage cost is minimized. From the characteristic of fixed order interval model, the replenishment quantity is the difference between the maximum inventory level and the inventory stock position at the time of placing the order. The maximum inventory level is ascertained according to the optimal probability of stock out determined by taking the derivative of the total system cost function. The result obtained from the proposed model is compared to the result yielded from the Monte Carlo simulation model at different levels of three factors: the number of machines (2 levels), the shortage costs (3 levels), and the failure rates (3 levels). According to the study result, both proposed calculation model and Monte Carlo simulation model suggest the same values of maximum inventory level for all 18 different problem cases. The maximum inventory level tends to increase at higher level of each factor