Wafik Hachicha

A comparative study of lot sizing problem in MTO supply chain based on simulation optimization approach

This paper deals with the lot sizing problem in Make To Order (MTO) supply chain solved by simulation optimization (SO) approach. A comprehensive case study which presented in detail involves a multi-stage, multi-product, multi-location, multi-resource with setup, capacity constraints and stochastic demand. The case study objective is to determine a fixed optimal lot size for each manufactured product type that will ensure Order Mean Flow Time (OMFT) target value for each finished product type. For a comparative purpose, three SO methods are used: (1) A metamodel method based on Response Surface Methodology (RSM), (2) A metamodel method based on Factorial Design, and (3) Global Search method based simultaneously on Tabu Search, Neural Networks, and Scatter Search. Methods (1) and (2) have been treated in the literature with a manual based approach, while the method (3) using the OptQuest Software will be more addressed in this paper,. The results of the comparative study show that the first method which is based on RSM is very effective while the third one is very practical and gives a good satisfactory solution.

A neural network-based simulation metamodel for a process parameters optimization: A case study

Simulation is a widely accepted tool in complex systems design and analysis. However, it is essentially a trial-and-error approach, and is therefore, time-consuming and does not provide a method for optimization. Metamodeling techniques have been recently pursued in order to tackle these drawbacks. The main objective has been to provide robust, fast decision support aids to enhance the overall effectiveness of decision-making processes. This paper proposes an application of simulation metamodeling through artificial neural networks (ANNs). The proposed approach is composed of two main steps assisted successively by the ©Neuro Software. The first one consists of building the appropriate ANN model over second-order linear regression model. Based on this model, the second step is a reverse simulation metamodeling that will be used as an optimization tool. To validate the proposed approach, a real case study is adopted from literature (Yang and Tseng [1]). It concerns an anonymous integrated-circuit (IC) packaging company in Taiwan. The case study problem is to maximize throughput performance. The comparative results with Response Surface Methodology (RSM) based metamodel results; illustrate the efficiency and effectiveness of the proposed approach.