The role of simulation in advanced planning and scheduling

Abstract

The tasks of planning and scheduiing in manufacturing have evolved from simplistic Material Requirements Planning systems to today's sophisticated Advanced Planning and Scheduling systems. While planning is concerned with the long-range determination of what needs to be manufactured, typically over a relatively long time period, scheduling is the task of deciding how that manufachlring is to be accomplished, typically over a relatively short time period. Simulation is well suited to the scheduling task since it can handle as much detail as is necessary to capture the subtleties of the manufacturing process. It is desirable for a simulation-based scheduling function to be integrated with an Enterprise Resource Planning system, which maintains the system data suitable for driving a simulation of the current system load and thereby producing a feasible schedule. This paper describes such an integrated system and the role of simulation within it.

Advanced Supply Chain Planning Systems (APS) Today and Tomorrow

“…It allows for scenario analysis in stochastic and complex contexts. Basically, as explained by Musselman et al (2002), this kind of simulation is mainly composed of experiments where one or more parameters or data of the APS are changed so that different scenario results can be compared. For example, the demand forecast can be changed manually and the master planning be executed in a 'simulated mode', so that different demand scenarios are generated.…”

Multi-agent based beam search for intelligent production planning and scheduling

“…Therefore, these algorithms usually cannot be applied directly to real-world problems, which are difficult to model in a rigorous mathematical approach. In this connection, other approaches have been developed, such as simulation-based approach (Marcus 1990, Harmonosky 1995, Musselman et al 2002, expert system based approach (Bruno et al 1986, Bruno andMorisio 1987), multi-agent based approach (Shen 2002, Caridi and Cavalieri 2004, Shen et al 2006, etc. These approaches consider some practical factors, but they often cannot effectively address the NP-hard property and cannot provide high quality solutions (Smith 1992, Shen 2002, Caridi and Cavalieri 2004.…”

Simulation-based finite scheduling at Albany International

“…Unlike tactical simulation models used for policy formulation, operative simulation models are usually deterministic. If a random event such as a machine failure occurs, a new schedule can be quickly generated and evaluated (Gupta and Sivakumar, 2002;Musselman et al, 2002). The way of generating the preliminary schedule varies.…”

“…and Pate1 (1998) and Andersson and Olsson (1998) the schedule is generated using heuristic rules. Musselman et al (2002) choose the orders that can be completed within the given timeframe. The multi-model based system of Artiba and Riane (1998) includes expert system techniques, discrete event simulation, optimization algorithms and heuristics to support decisionmaking for complex production planning and scheduling problems.…”

“…The interest has since grown considerably and Winter Simulation Conference 2002 had an entire track on Teemu Ruohola 1% Mattila Delfoi Ltd. V 2 " j a 2 Espoo, FIN-02600, FINLAND simulation-based scheduling approaches. According to Musselman et al (2002) simulation is well suited to the scheduling task since it can handle as much detail as is necessary to capturc the subtleties of the manufacturing process. While the manufacturing simulation software domain has huge future, at present, however, there does not appear to exist a robust market like ERP (Bansal, 2002).…”