Ahmed Ammeri is a PhD student at Faculty of Economics and Management of Sfax in Tunisia ( FSEG). He obtained his Master in Science of Transport and Logistic in 2009. He is a member of LOGIQ Unit. His research activities deal with the optimisation based simulation and supply chain management.
Abstract: This paper develops a simulation optimization approach for solving the Lot Sizing Problem (LSP) in make-to-order (MTO) supply chain. For this purpose, a discrete event simulation model was firstly implemented as a tool in estimating Order Mean Flow Time (OMFT) performance. Secondly, a multiple-objective optimization was achieved by applying Response Surface Methodology (RSM). A comprehensive case study is detailed which involves a multi-product, multi-stage, multi-location production planning with capacity-constrained and stochastic parameters such as lot arrivals order, transit time, set up time, processing time etc. The objective of the proposed approach is to determine the fixed optimal lot size for each manufacturing product type that will ensure OMFT target value for each finished product type. The study results illustrate that the LSP in MTO sector is viable and provide a prototype for further research on simulation optimization approaches.
Abstract: Simulation Optimization (SO) provides a structured approach to the system design and configuration
when analytical expressions for input/output relationships are unavailable. Several excellent surveys have been writtenon this topic. Each survey concentrates on only few classification criteria. This paper presents a literature survey with all classification criteria on techniques for SO according to the problem of characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter
spaces (discrete or continuous parameters). The survey focuses specifically on the SO problem that involves single performance
measure.
Abstract: Nowadays, many companies have been shifting its production strategy from the make-to-stock (MTS) to the make-to-order (MTO) sector. However, most of literature research on production planning concentrates on MTS systems. The MTO area has not received the same degree of attention. There are only some research papers which are based on queueing network models that explicitly talk about the Lot Sizing Problem (LSP) in MTO sector. This paper presents a case study in MTO sector for which analytical model is still extremely complex up to now (multi-stage, multi-product, multi-location, multi-resource with setup, capacity constraints and stochastic demand). The objective is to determine a fixed optimal lot size for each manufacturing product type that will ensure Order Mean Flow Time (OMFT) target value for each finished product type. The adopted approach is carried out in three steps. A Discrete Event Simulation (DES) model was firstly implemented as a tool in estimating (OMFT) performance. Secondly, Design of Experiment is applied to conduct simulation experiments. Finally, a multiple-objective optimization is achieved by applying desirability optimization methodology. The study results illustrate that the LSP in MTO sector is viable and provides a prototype for further research in supply chain coordination
Notes: Lot-Sizing Problem; Make to Order; Discrete Event Simulation; desirability optimization
Abstract: In recent years, several companies have been shifting its production strategy from the make-to-stock (MTS) to the make-to-order (MTO) sector. However, most of the literature research on production planning concentrates on MTS systems. The MTO area has not received the same degree of attention. There are only a handful of research papers that explicitly talk about the Lot sizing problem (LSP) in MTO sector. In this case, only methods based on queueing network models have been proposed in the literature. However, these existing analytical models, solved with or without using simulation technique, are not able to handle all the dynamically changing supply chain variables. Hence, discrete event simulation models have proved to be useful for analysis of different system configurations and for manage the stochastic behavior of supply chains.
After giving a brief literature review for LSP and for simulation using in supply chain, the aim of this research is to develop a simulation optimization method for solving the LSP in MTO supply chain. For this purpose, a discrete event simulation (DES) model was firstly implemented as a tool in estimating order mean flow time (OMFT) performance. Secondly, Design of Experiment and Analysis of Variance tools are applied to build the corresponding regression model for each response. Finally, a multiple-objective optimization is achieved by applying desirability optimization methodology. A comprehensive case study is fully detailed which involving a multi-product, multi-stage, multi-location production planning with capacity-constrained and stochastic parameters such as lot arrivals order, transit time, setup time, processing time etc. The objective is to determine the fixed lot sizes for each manufacturing product type that will ensure OMFT target value for each finished product type. The study results illustrate that the LSP in MTO sector is viable and provides a prototype for further research on simulation optimization methods.
