Distributed simulation with incorporated APS procedures for high-fidelity supply chain optimization

Abstract

Tactical and operational planning for manufacturing enterprises are more important today than ever before as their supply chains span the globe. Two state-of-the-art technologies that are critical to success are Discrete Event Simulation and Advanced Planning and Scheduling. They are commonly applied in designing and executing operations at each site within the supply chain. However, as supply chains become leaner and more responsive, operational constraints and stochastic influences within the manufacturing sites and the logistics network require a combination of both technologies applied to the entire supply chain. This paper describes a novel framework for advanced distributed simulation with integrated APS procedures for collaborative supply chain optimization. The framework can be used for fast optimization of both planning procedures and execution policies and also provides a base for easy implementation of simulation results. A prototype of a distributed semiconductor supply chain simulation has been developed and is currently being refined.

Advanced Supply Chain Planning Systems (APS) Today and Tomorrow

“…For example, Landeghem & Vanmaele (2002) developed a tactical planning method embedded with a Monte Carlo simulation approach for allowing the assessment of uncertainties in supply chains. Additionally, the integration of a traditional APS system could be made with some discrete-event simulation approaches, such as the one proposed by Lendermann et al (2001). Within their simulation framework, APS procedures represent the decision system and a discrete-event simulation approach is used to represent the manufacturing and logistics operations.…”

Agent-based supply-chain planning in the forest products industry

“…In a context where supply-chain members use complex optimization software such as APS systems to plan and synchronize their operations, it is difficult to build accurate models of such complex decision-making behaviors. That is why some authors have undertaken the design of simulation environments that include such optimization tools (Lendermann et al 2001(Lendermann et al , 2003Baumgaertel and John 2003).…”

Distributed Simulation with COTS Simulation Packages: A Case Study in Health Care Supply Chain Simulation

“…The time taken to execute the conventional, single computer NBS model rises exponentially as more hospitals are added to the model (see section 6). Thus, it is argued that (a) as the size of the supply chain increases it is beyond the capability of a single computer to simulate it, and (b) distributed simulation offers a potential solution to this (Taylor et al 2002;Lendermann, Gan, and McGinnis 2001;Gan et al 2000). It allows us to execute a single simulation model, perhaps composed of several distinct models, over multiple computers that are connected through a network (Fujimoto 2001).…”