The Multi-Location Transshipment Problem with Positive Replenishment Lead Times

Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. With a single exception, research on transshipments overlooks replenishment lead times. The only approach for two-location inventory systems with non-negligible lead times could not be generalized to a multi-location setting, and the proposed heuristic method cannot guarantee to provide optimal solutions. This paper uses simulation optimization by combining an LP/network flow formulation with infinitesimal perturbation analysis to examine the multi-location transshipment problem with positive replenishment lead times, and demonstrates the computation of the optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys an elegant duality-based gradient computation method to improve computational efficiency. In test problems, our algorithm was also able to achieve better objective values than an existing algorithm.Transshipment;Infinitesimal Perturbation Analysis (IPA);Simulation Optimization

The Multi-Location Transshipment Problem with Positive Replenishment Lead Times

Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. With a single exception, research on transshipments overlooks replenishment lead times. The only approach for two-location inventory systems with non-negligible lead times could not be generalized to a multi-location setting, and the proposed heuristic method cannot guarantee to provide optimal solutions. This paper uses simulation optimization by combining an LP/network flow formulation with infinitesimal perturbation analysis to examine the multi-location transshipment problem with positive replenishment lead times, and demonstrates the computation of the optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys an elegant duality-based gradient computation method to improve computational efficiency. In test problems, our algorithm was also able to achieve better objective values than an existing algorithm

Production planning and inventory control of two-product recovery system in reverse logistics

Ph.DDOCTOR OF PHILOSOPH

Simulation-based Methods for Stochastic Control and Global Optimization

Ideas of stochastic control have found applications in a variety of areas. A subclass of the problems with parameterized policies (including some stochastic impulse control problems) has received significant attention recently because of emerging applications in the areas of engineering, management, and mathematical finance. However, explicit solutions for this type of stochastic control problems only exist for some special cases, and effective numerical methods are relatively rare. Deriving efficient stochastic derivative estimators for payoff functions with discontinuities arising in many problems of practical interest is very challenging. Global optimization problems are extremely hard to solve due to the typical multimodal properties of objective functions. With the increasing availability of computing power and memory, there is a rapid development in the merging of simulation and optimization techniques. Developing new and efficient simulation-based optimization algorithms for solving stochastic control and global optimization problems is the primary goal of this thesis. First we develop a new simulation-based optimization algorithm to solve a stochastic control problem with a parameterized policy that arises in the setting of dynamic pricing and inventory control. We consider a joint dynamic pricing and inventory control problem with continuous stochastic demand and model the problem as a stochastic control problem. An explicit solution is given when a special demand model is considered. For general demand models with a parameterized policy, we develop a new simulation-based method to solve this stochastic control problem. We prove the convergence of the algorithm and show the effectiveness of the algorithm by numerical experiments. In the second part of this thesis, we focus on the problem of estimating the derivatives for a class of discontinuous payoff functions, for which existing methods are either not valid or not efficient. We derive a new unbiased stochastic derivative estimator for performance functions containing indicator functions. One important feature of this new estimator is that it can be computed from a single sample path or simulation, whereas existing estimators in the literature require additional simulations. Finally we propose a new framework for solving global optimization problems by establishing a connection with evolutionary games, and show that a particular equilibrium set of the evolutionary game is asymptotically stable. Based on this connection, we propose a Model-based Evolutionary Optimization (MEO) algorithm, which uses probabilistic models to generate new candidate solutions and uses dynamics from evolutionary game theory to govern the evolution of the probabilistic models. MEO gives new insight into the mechanism of model updating in model-based global optimization algorithms from the perspective of evolutionary game theory. Furthermore, it opens the door to developing new algorithms by using various learning algorithms and analysis techniques from evolutionary game theory

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

"Rotterdam econometrics": publications of the econometric institute 1956-2005

This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005.

Optimal acquisition policy for a supply network with discount schemes and uncertain demands.

This study uses a mathematical programming approach in which a series of Mixed Integer Non-Linear Programming (MINLP) models are developed to represent a supply network for a manufacturer dealing with various quantity or volume discount schemes from suppliers, as well as incorporating uncertain product demands that follow Normal distributions. Furthermore, the manufacturer\u27s optimal acquisition policy and production level are obtained simultaneously by solving the models with an objective of maximizing the expected value of the manufacturer\u27s profit. Although complicated by the employment of an integration function, the mathematical models are solved by a GAMS program with integrated SBB, CONOPT, MINOS, and SNOPT solvers working in collaboration. This research is one of the few studies in this field to use commercial optimization software for solving such complex mathematical models. The MINLP models and the GAMS solution program are applied in two real-world cases, and the preliminary results justify the capabilities of both the mathematical models and the GAMS solution program. Numerical analysis supports the managerial implications regarding the acquisition policy, and the comparison between the quantity discount and the volume discount. (Abstract shortened by UMI.)Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .M3. Source: Masters Abstracts International, Volume: 45-01, page: 0438. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

Learning Algorithms for Stochastic Dynamic Pricing and Inventory Control.

This dissertation considers joint pricing and inventory control problems in which the customer's response to selling price and the demand distribution are not known a priori, and the only available information for decision-making is the past sales data. Data-driven algorithms are developed and proved to converge to the true clairvoyant optimal policy had decision maker known the demand processes a priori, and, for the first time in literature, this dissertation provides theoretical results on the convergence rate of these data-driven algorithms. Under this general framework, several problems are studied in different settings. Chapter 2 studies the classical joint pricing and inventory control problem with backlogged demand, and proposes a nonparametric data-driven algorithm that learns about the demand on the fly while making pricing and ordering decisions. The performance of the algorithm is measured by regret, which is the average profit loss compared with that of the clairvoyant optimal policy. It is proved that the regret vanishes at the fastest possible rate as the planning horizon increases. Chapter 3 studies the classical joint pricing and inventory control problem with lost-sales and censored demand. Major challenges in this study include the following: First, due to demand censoring, the firm cannot observe either the realized demand or realized profit in case of a stockout, therefore only biased data is accessible; second, the data-driven objective function is always multimodal, which is hard to solve and establish convergence for. Chapter 3 presents a data-driven algorithm that actively explores in the inventory space to collect more demand data, and designs a sparse discretization scheme to jointly learn and optimize the multimodal data-driven objective. The algorithm is shown to be very computationally efficient. Chapter 4 considers a constraint that only allows the firm to change prices no more than a certain number of times, and explores the impact of number of price changes on the quality of demand learning. In the data-driven algorithm, we extend the traditional maximum likelihood estimation method to work with censored demand data, and prove that the algorithm converges at the best possible rate for any data-driven algorithms.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120721/1/boxchen_1.pd

AICPA Professional Standards: U.S. Auditing Standards as of June 1, 2002

https://egrove.olemiss.edu/aicpa_prof/1160/thumbnail.jp