Document Type : Article
Authors
1
Department of Industrial Engineering, Faculty of Engineering, Khatam University,Tehran, Iran
2
Department of Industrial Engineering, Faculty of Engineering, Khatam University, Tehran, Iran
10.24200/j65.2023.61050.2318
Abstract
Today, for success in the competitive market, lead time is an important issue, and its control increases customer satisfaction and improves service levels. In the latency between the initiation and completion of orders, a series of activities are performed, such as order preparation, waiting time, combining orders, purchasing raw materials, setup time, assembling, packing, sending, etc. These activities are defined by characteristics such as precedence relationships, execution modes, and their corresponding time and cost. Each activity can be controlled at the expense of extra cost, which can lead to shortened lead time and increased service levels.
In this paper, we investigate the service level-cost trade-off where orders' entry, demand and execution times are assumed to be stochastic. The main goal is to determine activities’ execution modes to simultaneously optimize the service level and costs. In this regard, the decision-maker’s main questions are (a) what is the maximum reachable service level with a maximum of 10% increase in product cost? (b) to increase the service level to 70%, how much will the product cost increase?
We propose a hybrid approach of Monte Carlo simulation and Non-dominant Sorting Genetic Algorithm (NSGA II) to identify the Pareto solutions. The effectiveness of the proposed algorithm was shown by applying it to a medical equipment supplier company as a case study. After implementing the proposed algorithm, 9 Pareto solutions were identified for the problem. It was found that the maximum reachable service level is 66.7% with a 7.7% increase in product cost; and, to increase the service level to 70%, the product cost increases by 20.7%. The sensitivity analysis results showed that the service level is very sensitive to reducing the execution time of orders in the range 71 to 100, and the answers to research questions may undergo changes; therefore, the decision-maker can focus on reducing the execution time of this interval.
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