A Simulation-Based Optimization Framework for Parameter Optimization of Supply-Chain Networks

Abstract

This work presents a novel approach that addresses the management of chemical supply chains (SCs) under demand uncertainty. One of the main objectives is to overcome the numerical difficulties associated with solving the underlying large-scale mixed integer nonlinear problem (MINLP). The approach that is proposed relies on a simulation-based optimization strategy that uses a discrete-event system to model the SC. Within this framework, each SC entity is represented as an agent whose activity is described by a collection of states and transitions. The overall system is coupled with an optimization algorithm that is designed to improve its operation. This strategy is a very attractive alternative in the field of decision-making processes under uncertainty, the advantages of which are highlighted with some cases of SC networks that are composed of several plants, warehouses, distribution centers, and retailers.

The New Generation of Operations Research Methods in Supply Chain Optimization: A Review

“…A good example of this type is the study by Shahi et al [133], which is placed in optimization-based simulation frameworks according to our categorization, although the authors termed it as a simulation-based approach. In another relevant study, Mele et al [134] proposed the same idea by modelling the SC system in discrete-event simulation, where each entity is an agent. The model developed by Sahay et al [135] also applied optimization inside a nested structure to optimize the variables of each participating agent in an SC system.…”

Simulation-based optimization framework for multi-echelon inventory systems under uncertainty

“…Given that the simulation model provides black-box functions, metaheuristic methods can be applied which only require the input and output data (Tekin & Sabuncuoglu, 2004). Mele et al (2006) proposed a simulation-based optimization framework combining agent-based modeling and genetic algorithm. Mansouri (2006) developed a simulated annealing approach to solve a bi-criteria sequencing problem in a two-stage supply chain.…”

Supply Chain Network Optimization of the Canadian Forest Products Industry: A Critical Review

“…Simulation and optimization have also been combined for supply chain management in the manufacturing industries. In fact, simulation based optimization has become a popular approach, mainly because of its ability to incorporate uncertainty into optimization problems [14][15][16].…”

“…Each agent uses predefined characteristics, decision rules and objecttives in order to interact with each other, and tries to maximize its own utility, but does so in an environment where all other agents are present [2]. The main advantages of multi-agent systems are their ability to model decentralised complex systems easily, offering increased flexibility without losing efficiency, and providing learning systems that improve over time with better decisions [16]. ABM is being increasingly used for supply chain management in a number of manufacturing industries for production planning [52,[87][88][89][90][91][92][93][94].…”

“…However, these studies mostly deal with strategic decisions, for instance combinatorial operation research problems such as multi-stage facility location, rather than tactical or operational ones. Although, there are a few studies that used simulation-based optimization techniques in different industries [16,52,91,92] there are none to our knowledge that combine agent-based modeling with optimization techniques and also deal with uncertainty in the forest products industry. A summary of studies on forest product industries supply chain network integrated simulation-based optimization models is presented in Table 3.…”