Stateline Shipping and Transport Company School of Business MAT 540 This paper was presented in submission for MAT 540 assignment four (Part 1 Only). Abstract This paper serves as a written response to the instructions and questions asked in assignment four. Assignment four instructed the writer to read the case problem Stateline Shipping and Transport Company from pages 273-274 in the text, Introduction to Management Science by Bernard W. Taylor. The assignment then directed the writer
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Megapop Inventory Model Assumptions: Lead time is constant Multi-period model Demand follows normal distribution (follow the histogram in excel sheet) Model with lost sale and discounted quantities Parameters: Q: order quantity Qo: optimal order quantity D: Annual demand H: holding cost per item per year Co: order cost C: cost per item n: number of orders per year r: order point ps : shortage cost p : lost profit per unit shortage B(r) : expected shortage L: lead time μ: average
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[Type the company name] SUBMITTED BY DEBASHREE MISHRA 211040 FMG 21(A) DMM PROJECT TRANSPORTATION PROBLEM [Type the author name] Acknowledgement I am grateful to Dr. Hitesh Arora, Professor , FORE School of Management to have helped me in the development of the project and provided me required guidelines for the same. I would also like to thank all my classmates , who in some way or the other, guided me to have a better understanding of the project and for their support to
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Inventory Management Systems Strayer University CIS 210 Systems Analysis and Development Abstract My sister owns a small clothing store. During dinner she mentioned her frustrations with having to manually track and reorder high demand items. She would like a small automated system but has a very small budget. I will be setting up a system for my sister that is tablet based. I will be using an inventory management system that runs on the apple iPad. Inventory management technology is a
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Q1. a. Torencia Products assembles two models, busterStop and Speed Luster, using the electronic components A and B. * The decision variables: unit quantities of each model to manufacture * Objective function maximize profits = $24BusterStop + $40SpeedLuster LaserStop Unit number to assemble LaserStop models. SpeedBuster Unit number to assemble SpeedBuster models . $24 the profit per unit of LaserStop model. $40 the profit per unit of SpeedBuster model. * Constraints:
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Sub Optimization Definition: “The result of different departments each attempting to reach a solution that is optimum for that department” (Stevenson, 2015). Unfortunately, the sub-optimization process does not always adhere to the principle “the whole is more important than the sum of its parts”. Sub optimization occurs when different departments each attempt to reach a solution that is optimal for that department, but that may not be optimum for the organization as a whole. This type of policy
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6. Is it advisable to purchase any of the available 100,000 lbs of grade A tomatoes? (use the sensitivity report from question 2). If so, (a) how much should we buy? (b) How will the additional tomatoes be used? In order to figure out if we should buy and by how much we should purchase, we made some modifications associated with our model. First of all, we need to add one more decision variable, Additional A, which is the amount of extra grade A tomatoes we decide to purchase. Then we need
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Executive Summary: The algorithmic dealer simulation is a simulation that places us in the shoes of a dealer trading in a pure dealer market. The objective is to create strategies that encourage and strengthen order flow. To do this we are asked to create trading strategies that cover a wide array of unique trading circumstances. Our goal is to provide strategies that maximize our average utility through all 60 problems. To design our strategies we took a broad approach and assigned each of
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0031tn © 2009 INFORMS I N F O R M S Transactions on Education Teaching Note Revenue Management at Harrah’s Entertainment, Inc. Narendra Agrawal Department of Operations and Management Information Systems, Leavey School of Business, Santa Clara University, Santa Clara, California 95053, nagrawal@scu.edu Morris A. Cohen, Noah Gans Operations and Information Management Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104 {cohen@wharton.upenn.edu, gans@wharton
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The advent of low-cost computing should not be seen as obviating the need for the ABC inventory classification scheme. Although the cost of computing has decreased considerably, the cost of data acquisition has not decreased in a similar fashion. Business organizations still have many items for which the cost of data acquisition for a “perpetual” inventory system is still considerably higher than the cost of the item. The standard EOQ model assumes instantaneous delivery (delivery of the entire lot
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