View 3 excerpts, cites background and methods. Safety Inventory under a Periodic Review System. The objective of this paper is to derive and verify a formula for calculating safety inventory that satisfies a desired cycle service level in a periodic review system. Stochastic variables, … Expand.
View 1 excerpt, cites results. This research aims to improve the operating delivery system of water-treatment chemicals by establishing a chemicals inventory policy for planning appropriate chemicals delivery to five customers in … Expand. View 1 excerpt, cites methods. Additive manufacturing in the spare parts supply chain. Engineering, Computer Science. Supply chain analysis of e-tailing versus retailing operation — a case study. View 1 excerpt, cites background.
Computer Science, Mathematics. The objective of any controller design is to maintain the set point value despite its variation, with a good rejection of various disturbances that can infect the system to be controlled and to … Expand. Highly Influential. View 5 excerpts, references methods and background. Operations Research Models and Methods. Every once in a while, a clever text comes along that breathes a nice dose of fresh air into the mature field of Operations Research OR.
The Jensen and Bard book is one such welcome example. Its … Expand. View 12 excerpts, references methods and background. The Extend simulation environment. Proceeding of the Winter Simulation Conference Cat. View 8 excerpts, references background and methods.
Fuzzy fractal dimensions and fuzzy modeling. Mathematics, Computer Science. Granular prototyping in fuzzy clustering. A model of granular data: a design problem with the Tchebyschev FCM. Soft Comput. In our paper, we will study the issues related There are two decision variables: order quantity and only to independent demand. The main purpose of policy is evaluated according to total inventory cost simulation runs is to try out various schemes of order over certain time.
Total inventory cost is composed of quantities and reorder point and to find to minimize holding costs also including insurance cost, handling the smallest total inventory cost. That means that etc. The main purpose of model analysis is The elements of pull system model are as follows: generally to minimize the total costs. One can assume - Daily demands for items, collected from observation sometimes that the demand is known or constant but of past days.
For example, - Lead time in days , collected from observations of during applying ANOVA test we obtained critical value past orders. According to definition of Monte Carlo simulation Lawrence and Pasternack , simulated events take place randomly and match the description of the theoretical probabilities derived from acquired experiences. The process of fundamental importance in Monte Carlo simulation is called random number mapping and consists in matching the random number with simulated events when they occur and how long they last.
See Heizer and The key formulas of our spreadsheet are as follows Render , Jensen and Bard and Lawrence starting from the cells in 4th row, from column B : and Pasternack Figure 2 shows one of day simulation runs. Second random number: environment. Then, two models were compared and validated by confronting the outcomes. Modelling in Extend Krahl consists in using pre-built modelling components to build and analyse system.
This approach is also applied in other software like Arena, Flexim and others. As we see on Figure 4 , results from this model implementation is similar average After introductory validation of our Extend model, we think that discrete event model built in Extend is more flexible, because it is possible to easily prolong simulated time and the number of replications runs , to see the animation of objects during the run and to optimise model variables minimization of total cost.
We will try to prove it in the next section. Guidelines of the Inventory Management Business Game with Cost Accounting We prepared the equivalent model to the previous one using the Extend software, but we extended it by cost coefficient taken from the example of inventory system simulation given in Jensen and Bard Next, we validated it by comparing the optimal values found in our simulations with these presented in the book.
The set of assumptions are as follows: the daily Figures 3: Tabularization of Simulation Outcomes of 60 demand oscillates between 6 and 16 units, lead time Runs.
The Optimisation calculations took about 4 minutes. See Figure 6. This paper is an excerpt of research in the field of Results of day simulation of inventory system one application of simulation tools in supply chain of the replications described in Jensen and Bard management, beginning from The results of replications given in Heizer, J. Operations Management, Jensen and Bard are very near to our outcome: sixth edition. Upper Saddle River, N.
Operations Research. Models and Methods. Krahl, D. IEEE, changed values of reorder point and order quantity. We Piscataway, N. Applied evolutionary optimisation that is approachable in Management Science. Seila, A. All rights reserved. He obtained his degree in and then his Ph. His e-mail address is: jacek. She works as an assistant minimized.
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