Simulation Tool for Inventory Models: SIMIN

Simulation Tool for Inventory Models: SIMIN

Pratiksha Saxen (Gautam Buddha University, Greater Noida, India) and Tulsi Kushwaha (Gautam Buddha University, Greater Noida, India)
Copyright: © 2014 |Pages: 16
DOI: 10.4018/ijsda.2014100106
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In this paper, an integrated simulation optimization model for the inventory system is developed. An effective algorithm is developed to evaluate and analyze the back-end stored simulation results. This paper proposes simulation tool SIMIN (Inventory Simulation) to simulate inventory models. SIMIN is a tool which simulates and compares the results of different inventory models. To overcome various practical restrictive assumptions, SIMIN provides values for a number of performance measurements. This tool is programmed in JAVA and is based on analytical approach to guide optimization strategy. Objective of this paper is to provide a user friendly simulation tool which gives optimized inventory model results. Simulation is carried out by providing the required values of input parameters and result is stored in the database for further comparison and study. Result is obtained in terms of the performance measurements of classical models of inventory system. Simulation results are stored in excel file and it also provides graphical results to compare the outcome. This simulation tool is interfaced with an optimization procedure based on classical models of inventory system. With the specified examples, the simulation results are obtained and analyzed rigorously. The result shows that input parameters, total system costs and capacity should be considered in the design of a practical system.
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Inventory modeling is very important for optimization of ordering quantity and total cost associated with it. Due to randomly changing demand, effective management of production and inventory control operations in dynamic and stochastic environments, a lot of efforts are needed to improve existing inventory models. The subject of inventory control is a major consideration because of its practical and economical importance. A number of models have been defined on the basis on deterministic and stochastic demands (Kleinrock, 1964). Silver has introduced realistic inventory models (Silver, 1992; Silver, 1993). Several benefits of simulation and modeling over the traditional and analytical modeling have been listed (Browne, 1994). It is mentioned that simulation models could provide greater accuracy, flexibility and more informative inputs. Inventory control is the second most frequent area of application of simulation after queuing systems (Banks et al., 1984). Banks and Spoerer provided a simulation procedure to solve and analyze the classical continuous review (Q, r) inventory system (Banks et al., 1986). Based on their simulation outputs, it was found that the form of the demand and lead-time distributions can greatly affect the stockout performance of the inventory system. Haddock and Bengu proposed a decision support system composed of a simulation generator, output analyis techniques, and optimization procedures to solve the classical (Q, r) inventory model with lost sales (Haddock et al., 1987). The dynamic behavior of production inventory systems was well addressed by research (Wikner et al., 1991). It is shown by previous research that system dynamics (SD) simulations have a role to play in supply chain design (Towill, 1996). SD modeling techniques have been adopted in research for modeling supply chain systems (Arnold et al., 1996). The application of simulation optimization to complex design and control problems started at Chemnitz University of Technology with a problem, where optimal order decisions could be found for an horizontal structured MLIM with lateral transshipments by coupling simulation with a simple search method (Eugene et al., 2007).

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