52 research outputs found

    Inventory management systems:Control and information issues

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    Abstract: This dissertation addresses the management of inventory systems. The thesis starts with an exposition on mathematical models that can be used in inventory theory. Then we deal with some information issues related to the demand process. Namely, how to control products that have intermittent demand. Moreover, we investigated the impact of data collection on the customer performance. Next, we investigated to what extend multiple-sourcing can lead to improvements of the inventory system. Finally two demand management strategies are investigated for smoothing demand. The first re-routes large customer orders to alternative stockpoint, whereas the second strategy splits a customer order in a time-phased delivery scheme.

    Joint location and inventory models and algorithms for deployment of hybrid electric vehicle charging stations.

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    This thesis describes a study of a novel concept of hybrid electric vehicle charging stations in which two types of services are offered: battery swapping and fast level-3 DC charging. The battery swapping and fast-charging service are modeled by using the M/G/s/s model and the M/G/s/∞\infty model, respectively. In particular, we focus on the operations of joint battery swapping and fast charging services, develop four joint locations and inventory models: two for the deployment of battery swapping service, two for the deployment of hybrid electric vehicle charging service. The first model for each deployment system considers a service-level constraint for battery swapping and hybrid charging service, whereas the second for each deployment system considers total sojourn time in stations. The objective of all four models is to minimize total facility setup cost plus battery and supercharger purchasing cost. The service level, which is calculated by the Erlang loss function, depends on the stockout probability for batteries with enough state of charge (SOC) for the battery swapping service and the risk of running out of superchargers for the quick charging service. The total sojourn time is defined as the sum of the service time and the waiting time in the station. Metaheuristic algorithms using a Tabu search are developed to tackle the proposed nonlinear mixed-integer optimization model. Computational results on randomly generated instances and on a real-world case comprised of 714,000 households show the efficacy of proposed models and algorithms

    Optimal Global Supply Chain and Warehouse Planning under Uncertainty

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    A manufacturing company\u27s inbound supply chain consists of various processes such as procurement, consolidation, and warehousing. Each of these processes is the focus of a different chapter in this dissertation. The manufacturer depends on its suppliers to provide the raw materials and parts required to manufacture a finished product. These suppliers can be located locally or overseas with respect to the manufacturer\u27s geographic location. The ordering and transportation lead times are shorter if the supplier is located locally. Just In Time (JIT) or Just In Sequence (JIS) inventory management methods could be practiced by the manufacturer to procure the raw materials and parts from the local supplier and control the inventory levels in the warehouse. In contrast, the lead time for the orders placed with an overseas supplier is usually long because sea-freight is often used as a primary mode of transportation. Therefore, the orders for the raw materials and parts (henceforth, we collectively refer to raw material and part by part) procured from overseas suppliers are usually placed using forecasted order quantities. In Chapter 2, we study the procurement process to reduce the overall expected cost and determine the optimal order quantities as well as the mode of transportation for procurement under forecast and inventory uncertainty. We formulate a two-stage stochastic integer programming model and solve it using the progressive hedging algorithm, a scenario-based decomposition method. Generally, the orders are placed with overseas suppliers using weekly or monthly forecasted demands, and the ordered part is delivered using sea-containers since sea-freight is the primary mode of transportation. However, the end manufacturing warehouse is usually designed to hold around one to two days of parts. To replenish the inventory levels, the manufacturer considered in this research unloads the sea-container that contains the part that needs to be restocked entirely. This may cause over-utilization of the manufacturer\u27s warehouse if an entire week\u27s supply of part is consolidated into a single sea-container. This problem is further aggravated if the manufacturer procures hundreds of different parts from overseas suppliers and stores them in its warehouse. In Chapter 3, we study the time-series forecasting models that help predict the manufacturing company\u27s daily demand quantities for parts with different characteristics. The manufacturer can use these forecasted daily demand quantities to consolidate the sea-containers instead of the weekly forecasted demand. In most cases, there is some discrepancy between the predicted and actual demands for parts, due to which the manufacturer can either have excess inventory or shortages. While excess inventory leads to higher inventory holding costs and warehouse utilization, shortages can result in substantially undesirable consequences, such as the total shutdown of production lines. Therefore, to avoid shortages, the manufacturer maintains predetermined safety stock levels of parts with the suppliers to fulfill the demands arising from shortages. We formulate a chance-constraint optimization model and solve it using the sample approximation approach to determine the daily safety stock levels at the supplier warehouse under forecast error uncertainty. Once the orders are placed with the local and overseas suppliers, they are consolidated into trailers (for local suppliers) and sea-containers (for overseas suppliers). The consolidated trailers and sea-containers are then delivered to the manufacturing plant, where they are stored in the yard until they are called upon for unloading. A detention penalty is incurred on a daily basis for holding a trailer or sea-container. Consolidating orders from different suppliers helps maximize trailer and sea-container space utilization and reduce transportation costs. Therefore, every sea-container and trailer potentially holds a mixture of parts. When a manufacturer needs to replenish the stocks of a given part, the entire sea-container or trailer that contains the required part is unloaded. Thus, some parts that are not imminently needed for production are also unloaded and stored inside the manufacturing warehouse along with the required parts. In Chapter 4, we study a multi-objective optimization model to determine the sea-containers and trailers to be unloaded on a given day to replenish stock levels such that the detention penalties and the manufacturing warehouse utilization are minimized. Once a sea-container or trailer is selected to replenish the warehouse inventory levels, its contents (i.e., pallets of parts) must be unloaded by the forklift operator and then processed by workers to update the stock levels and break down the pallets if needed. Finally, the unloaded and processed part is stored in the warehouse bins or shelves. In Chapter 5, we study the problem of determining the optimal team formation such that the total expected time required to unload, process, and store all the parts contained in the sea-containers and trailers selected for unloading on a given day is minimized

    Multiple sourcing in single- and multi-echelon inventory systems

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    This thesis deals with stochastic inventory models that focus on the following two aspects in particular: (i) the coordination of multiple supply sources and (ii) the optimization of the inventory allocation and sizing in multi-echelon systems. Initially, single-echelon inventory models with multiple sourcing and multi-echelon inventory models with single sourcing are analyzed separately. In the former case, the goal is the identification of effective inventory control policies. In the latter case, the focus lies on the development of a new multi-echelon approach, which combines the two major frameworks currently available in the literature. Subsequently, both aspects are integrated into a multi-echelon inventory model with multiple sourcing

    Stochastic Models of Critical Operations

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

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    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

    Structuring postponement strategies in the supply chain by analytical modeling

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    The impact of product complexity on ramp-up performance

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    Fast product ramp-ups are crucial in consumer electronics because short product lifecycles prevail and profit margins diminish rapidly over time. Yet many companies fail to meet their volume, cost and quality targets and the ramp-up phase remains largely unexplored in new product and supply chain management research. This study identifies the key product characteristics that affect ramp-up performance using operational data from the cell phone industry. We investigate three research questions: (1) How to measure software and hardware complexity characteristics of consumer electronics products – and specifically cell phones? (2) To what extent drive product complexity characteristics manufacturing performance? and (3), in turn, to what extent drive manufacturing performance and complexity characteristics ramp up performance? The findings contribute to operations management literature in three ways: First, our model reflects the growing importance of software characteristics in driving hardware complexity, an aspect that prior empirical ramp-up studies have not yet addressed. Second, specific hardware and software complexity characteristics (i.e., component count, parts coupling and SW code size) primarily drive the performance of the manufacturing system in terms of final yield and effective capacity. And finally, effective capacity together with the novelty aspects of both software and hardware complexity (i.e., SW novelty and product novelty) are the key determinants of ramp-up performance

    Additive Manufacturing in After-Sales Service Supply Chains

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    Additive Manufacturing (AM, also known as 3D printing) is developing into a powerful complement to more conventional manufacturing (CM) methods. In comparison to CM methods such as milling, drilling, casting and forging, AM technologies build complete parts by adding materials layer upon layer without using any dedicated tooling. The resulting ability to produce complex structures without lengthy and expensive setup procedures could turn out particularly valuable for the low-volume spare parts business. Short AM lead times are likely to significantly improve the balance between spare parts inventory investment and system downtime. Generic AM processes could relax the dependence on suppliers and therefore decrease risks and costs associated with supply disruptions. Ultimately, AM could even enable the implementation of a decentralized production concept that holds the promise of increased supply chain responsiveness at low costs. However, it is necessary to deconstruct these concepts and to separate the hype from reality to leverage the potentials of AM technology in after-sales service supply chains. In this dissertation, we aim to contribute to this undertaking by offering a scientific perspective on how and to what extent after-sales service supply chains can benefit from AM technology. To that end, we develop and apply techniques from the field of Operations Research to learn from the various case studies that were conducted at different organizations throughout this research
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