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|Title: ||Problems in Supply Chain Location and Inventory under Uncertainty|
|Authors: ||Hajizadeh Saffar, Iman|
|Advisor: ||Berman, Oded|
|Keywords: ||Operations Management|
Supply Chain Management
|Issue Date: ||13-Aug-2010|
|Abstract: ||We study three problems on supply chain location and inventory under uncertainty. In Chapter 2, we study the inventory purchasing and allocation problem in a movie rental chain under demand uncertainty. We formulate this problem as a newsvendor-like problem with multiple rental opportunities. We study several demand and return forecasting models based on comparable films using iterative maximum likelihood estimation and Bayesian estimation via Markov chain Monte Carlo simulation. Test results on data from a large movie rental firm reveal systematic under-buying of movies purchased through revenue sharing contracts and over-buying of movies purchased through standard ones. For the movies considered, the model estimates an increase in the average profit per title for new movies by 15.5% and 2.5% for revenue sharing and standard titles, respectively. We discuss the implications of revenue sharing on the profitability of both the rental firm and the studio.
In Chapter 3, we focus on the effect of travel time uncertainty on the location of facilities that provide service within a given coverage radius on the transportation network. Three models - expected covering, robust covering and expected p-robust covering - are studied; each appropriate for different types of facilities. Exact and approximate algorithms are developed. The models are used to analyze the location of fire stations in the city of Toronto. Using real traffic data we show that the current system design is quite far from optimality and provide recommendations for improving the performance.
In Chapter 4, we continue our analysis in Chapter 3 to study the trade-off between adding new facilities versus relocating some existing facilities. We consider a multi-objective problem that aims at minimizing the number of facility relocations while maximizing expected and worst case network coverage. Exact and approximate algorithms are developed to solve three variations of the problem and find expected--worst case trade-off curves for any given number of relocations. The models are used to analyze the addition of four new fire stations to the city of Toronto. Our results suggest that the benefit of adding four new stations is achievable, at a lower cost, by relocating 4-5 stations.|
|Appears in Collections:||Doctoral|
Joseph L. Rotman School of Management - Doctoral theses
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