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With increased globalization and competition in the current market, supply chain has become longer and more complicated than ever before. An effective and efficient supply chain is crucial and essential to a successful firm. In a supply chain, inventories are a very important component as the investment in inventories is enormous. Inventory management is always coupled with other functions, for example purchasing, production and marketing. In this dissertation, we study inventory management for both single-stage and multi-echelon systems. Two main streams of research work are summarized. The first is the joint optimization of pricing and inventory control for continuous/periodic review single-stage inventory/production systems. We characterize the optimal policies and further develop efficient computational algorithms to find the optimal control parameters. We also provide insights on the pricing and inventory relationship. The second is the analysis of multi-echelon inventory systems, in which we derive the optimal inventory control policies for several different systems that have not yet been studied in the current literature. Moreover, simple bounds and heuristics for the optimal policies are developed for the serial systems with and without expedited shipping so that the implementability of the optimal policies is improved.
With increased globalization and competition in the current market, supply chain has become longer and more complicated than ever before. An effective and efficient supply chain is crucial and essential to a successful firm. In a supply chain, inventories are a very important component as the investment in inventories is enormous. Inventory management is always coupled with other functions, for example purchasing, production and marketing. In this dissertation, we study inventory management for both single-stage and multi-echelon systems. Two main streams of research work are summarized. The first is the joint optimization of pricing and inventory control for continuous/periodic review single-stage inventory/production systems. We characterize the optimal policies and further develop efficient computational algorithms to find the optimal control parameters. We also provide insights on the pricing and inventory relationship. The second is the analysis of multi-echelon inventory systems, in which we derive the optimal inventory control policies for several different systems that have not yet been studied in the current literature. Moreover, simple bounds and heuristics for the optimal policies are developed for the serial systems with and without expedited shipping so that the implementability of the optimal policies is improved.
This dissertation is focused on investigating the integration of inventory and facility location decisions in different supply chain settings. Facility location and inventory decisions are interdependent due to the economies of scale that are inherent in transportation and replenishment costs. The facility location decisions have an impact on the transportation and replenishment costs which, in turn, affect the optimal inventory policy. On the other hand, the inventory policy dictates the frequency of shipments to replenish inventory which, in turn, affects the number of deliveries, and, hence, the transportation costs, between the facilities. Therefore, our main research objectives are to: 0́Ø compare the optimal facility location, determined by minimizing total transportation costs, to the one determined by the models that also consider the timing and quantity of inventory replenishments and corresponding costs, 0́Ø investigate the effect of facility location decisions on optimal inventory decisions, and 0́Ø measure the impact of integrated decision-making on overall supply chain cost performance. Placing a special emphasis on the explicit modeling of transportation costs, we develop several novel models in mixed integer linear and nonlinear optimization programming. Based on how the underlying facility location problem is modeled, these models fall into two main groups: 1) continuous facility location problems, and 2) discrete facility location problems. For the stylistic models, the focus is on the development of analytical solutions. For the more general models, the focus is on the development of efficient algorithms. Our results demonstrate 0́Ø the impact of explicit transportation costs on integrated decisions, 0́Ø the impact of different transportation cost functions on integrated decisions in the context of continuous facility location problems of interest, 0́Ø the value of integrated decision-making in different supply chain settings, and 0́Ø the performance of solution methods that jointly optimize facility location and inventory decisions.
Since the outbreak of COVID-19 in 2020, the global industrial chain has had a massive impact on the multi-directional disruption caused by the bilateral imbalance between supply and demand, exposing the vulnerability of the global industrial chain. As the world's largest manufacturing country, once the impact disrupts domestic production, it will interrupt the supply chain of many enterprises worldwide. Therefore, it is urgent to study the pressure situation of the industrial chain. Based on the traditional dual-channel model of the downstream of the industrial chain, this study introduces the pressure factor of the industrial chain and builds a price optimization model to study the downstream ports of China's industrial chain and verify the chip sanctions incident implemented by the United States as a case. The study shows that for the downstream port of the industrial chain, the product supplier and the retailer will obtain the corresponding risk reward by increasing the direct sale and retail price, respectively; for the asymmetric information condition, the wholesale price of the product supplier will decrease with the pressure of the industrial chain, but the direct sale price and the retail price will become higher accordingly. This study provides theoretical support for relevant enterprises to formulate industrial development strategies and for the government to formulate applicable industrial policies to cope with the risk of industrial chain interruption.
[Truncated abstract] The objective of the thesis is to integrate three core production considerations in an optimization model namely: cycle service level, trim loss when cutting stock, and grade changeover costs associated with lot-sizing. Various industries encounter the cutting stock and lot-sizing problems in successive manufacturing processes. The lot-sizing problem (LSP) finds a trade-off between setup and inventory holding costs, whereas the cutting stock problem (CSP) involves cutting large objects into smaller ones while minimizing the trim loss. These two processes are strongly interlinked because the latter is governed by the customer demand and the demand for production lots is derived from it. Particularly for the paper industry, the end demand for smaller rolls of different grades drives the production schedules for the paper machine producing jumbo reels. However, the literature has mostly dealt with these two processes separately, which has important repercussions especially for cycle service levels. A separate optimization approach restricts cycle service levels by putting an upper bound on the total number of different grades of jumbo reels to be produced on the paper machine. This study jointly optimizes the two successive manufacturing processes of lotsizing at the paper machine and determining the cutting pattern during paper conversion with cycle service level considerations. Initially, an integrated formulation is developed as a conventional single objective function embracing the costs of trim loss, grade changeover and inventory holding as well as the tardiness penalty incurred whenever an order fails to meet its due date. Standard genetic algorithm is used as the solution method for the joint problem of simultaneously solving the two NP-hard combinatorial problems. The results reveal that the service levels are maximized by simultaneously solving the trim loss and lot-sizing problem...
This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.
Supply Chain Optimization captures the latest results in a segment of current research activity in supply chain management. This research area focuses on applying optimization techniques to supply chain management problems. The research papers that make up the volume provide a snapshot of state-of-the-art optimization methods within the field. This book presents rigorous modelling approaches for supply chain operations problems with a goal of improving supply chain performance (or the performance of some segment thereof). It contains high-quality works from leading researchers in the field whose expertise fits within this scope. The book provides a diverse blend of research topics and novel modelling and solution approaches for difficult classes of supply chain operations, planning, and design problems.
Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution and approximate (fuzzy) reasoning to create programs that can be considered intelligent. Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research.
This text illustrates how companies that create, distribute and sell products can join forces to establish a supply network with a competitive advantage. It includes action studies which demonstrate how the concepts described in the book have been implemen
In today's business landscape, companies compete more and more as integrated supply chains rather than as individual firms. The success of the entire supply chain determines the economic well-being of each company involved. With management attention shifting to supply chains, the role of management accounting naturally must extend to the cross-company layer as well. This book demonstrates how management accounting can make a significant contribution to supply chain success. It targets students who are already familiar with the fundamentals of accounting and want to extend their expertise in the field of cross-company (or network) management accounting. Practitioners will draw valuable insights from the text as well. This second edition includes a new Chapter on Digitalization and Supply Chain Accounting, as well as new opener cases to each chapter that provide real-world examples. Additional questions via app: Download the Springer Nature Flashcards app for free and use exclusive additional material to test your knowledge.