246 research outputs found

    Quadratic Approximation of the Newsvendor Problem with Imperfect Quality

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    The paper presents a newsvendor problem in a fuzzy environment by introducing product quality as a fuzzy variable, and product demand as a probability distribution in an economic and supply chain management environment. In order to determine the optimal order quantity, a methodology is developed where the solution is achieved using a fuzzy ranking method combined with a quadratic programming problem approximation. Numerical examples are provided and compared in both situations, namely fuzzy and crisp. The results of these numerical examples show that the decision maker has to order a higher quantity when product quality is a fuzzy variable. The model can be useful for real world problems when historical data are not available

    Essays in Operations Management: Applications in Health Care and the Operations-Finance Interface

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    I present three essays pertaining to the management of supply chain risks in this dissertation. The first essay and the second essay analyze supply chain risks from a financial perspective, while the third essay analyzes supply chain risk with the objective of maximizing societal benefits in health care. In my first essay, I consider a firm facing inventory decisions under the influence of the financial market. With stochastic analytical methods, the purpose of this essay is to examine the optimal inventory decisions under a variety of conditions. I have identified the relevant factors impacting such decisions and the firm's value. Moreover, I have studied the benefits brought by efforts to improve the random capacity of the firm. I conclude that the financial market can significantly impact both a firm's inventory decisions and process improvement incentives. In my second essay, I model a stylized supply chain managed by a base-stock inventory policy where the decision maker holds concerns about the down-side risk of the supply chain cost. With stochastic analytical methods, the purpose of this essay is to obtain solutions of the problem of minimizing Conditional Value-at-Risk under various supply chain scenarios. I find that various supply chain parameters may influence the optimal solution and the optimality of a stock-less operation. I conclude that operating characteristics of a supply chain can shape its inventory policy when down-side risks are taken into account. For my third essay, the purpose of this essay is to investigate the operational decisions of a medical center specializing in bone marrow transplants. Using the queuing system method, I formulate the medical center as a queuing system with random patient arrivals and departures. I find optimal decisions and efficient frontiers regarding waiting room size and the number of transplant rooms with the objective of maximizing patient health benefits. I conclude that the design of a health care delivery system is crucial for health care institutions to sustain and improve their social impacts. In each of the three essays, I use analytical and numerical approaches to optimize managers' decisions with respect to various sources of risk

    Supply chain flexibility in the special purpose vehicle industry.

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    Supply chains of the Special Purpose Vehicle (SPV) industry are complex and with many constraints. Since the SPV industry is a special field of operation, there is no classical supply chain strategy which is appropriate. It is possible to apply concepts of industries with similar requirements but there is a high loss of time and money because these classical concepts do not fit to the SPV industry. Even strategies of the conventional automobile industry cannot be transferred. Therefore, there is the need to develop a supply chain concept for companies of the SPV industry. As a first step, basic knowledge about supply chain management is provided. Based on this, special supply chain characteristics of the SPV industry are analyzed in detail. A profound research shows that the focus of the developed supply chain should be on flexibility. High supply chain flexibility addresses the specific difficulties related to the SPV industry. These are for example individual customer requirements and uncertain demand. Therefore appropriate flexibility methods are derived which are called variant, volume and time flexibility. For the implementation, several formulas and strategies are derived. This supply chain concept is a basic concept. It can be adapted to the environment of different SPV companies. For the application of the derived formulas, MATLAB codes are provided. These MATLAB scripts and functions are also used for a performance evaluation. Therefore, economic parameters, which are same important for all companies, are used. Thus, all improvements and strategies in this research are evaluated mathematically. A performance evaluation with realistic input values shows that the following savings are expected for the three flexibility types: · volume flexibility: 47% · variant flexibility: 42% · time flexibility: 42% A comprehensive example with all the flexibility types shows that overall savings of about 18% can be realized. This comprehensive example includes further new approaches like an asymmetrical flexibility and a method to order the optimal quantity at the posterior point of time which is explained and introduced. The savings due to the individual flexibility types, which are mentioned above, are related to costs and thus very high at first glance. Furthermore, these results depend on input variables, which reflect realistic examples. Thus, these values can be different in other example. They are however appropriate indicators to show that the new supply chain strategy for the SPV industry is profitable, reliable, stable and flexible. Thus, the new approach is a research contribution, which leads to clear benefits in reality

    Dynamic Stochastic Inventory Management in E-Grocery Retailing: The Value of Probabilistic Information

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    Inventory management optimisation in a multi-period setting with dependent demand periods requires the determination of replenishment order quantities in a dynamic stochastic environment. Retailers are faced with uncertainty in demand and supply for each demand period. In grocery retailing, perishable goods without best-before-dates further amplify the degree of uncertainty due to stochastic spoilage. Assuming a lead time of multiple days, the inventory at the beginning of each demand period is determined jointly by the realisations of these stochastic variables. While existing contributions in the literature focus on the role of single components only, we propose to integrate all of them into a joint framework, explicitly modelling demand, supply shortages, and spoilage using suitable probability distributions learned from historic data. As the resulting optimisation problem is analytically intractable in general, we use a stochastic lookahead policy incorporating Monte Carlo techniques to fully propagate the associated uncertainties in order to derive replenishment order quantities. We develop a general inventory management framework and analyse the benefit of modelling each source of uncertainty with an appropriate probability distribution. Additionally, we conduct a sensitivity analysis with respect to location and dispersion of these distributions. We illustrate the practical feasibility of our framework using a case study on data from a European e-grocery retailer. Our findings illustrate the importance of properly modelling stochastic variables using suitable probability distributions for a cost-effective inventory management process

    Open source solution approaches to a class of stochastic supply chain problems

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    This research proposes a variety of solution approaches to a class of stochastic supply chain problems, with normally distributed demand in a certain period of time in the future. These problems aim to provide the decisions regarding the production levels; supplier selection for raw materials; and optimal order quantity. The typical problem could be formulated as a mixed integer nonlinear program model, and the objective function for maximizing the expected profit is expressed in an integral format. In order to solve the problem, an open source solution package BONMIN is first employed to get the exact optimum result for small scale instances; then according to the specific feature of the problem a tailored nonlinear branch and bound framework is developed for larger scale problems through the introduction of triangular approximation approach and an iterative algorithm. Both open source solvers and commercial solvers are employed to solve the inner problem, and the results to larger scale problems demonstrate the competency of introduced approaches. In addition, two small heuristics are also introduced and the selected results are reported

    Agribusiness supply chain risk management: A review of quantitative decision models

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    Supply chain risk management is a large and growing field of research. However, within this field, mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due to challenges associated with seasonality, supply spikes, long supply lead-times, and perishability. This paper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as two key techniques for managing risk. Since these terms are not used consistently in the literature, we propose clear definitions and metrics for these terms; we then use these definitions to classify the agricultural supply chain risk management literature. Implications are given for both practice and future research on agricultural supply chain risk management
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