Intense competitive pressures have led to compressed product life cycles and frequent introduction of new products. This creates demand volatility and a consequent pressure on manufacturing to meet this variable demand. In this paper we model the manpower planning issues for a computer manufacturer during the product introduction phase when a quick ramp-up of production to meet rapidly increasing demand is a key requirement. A mix of permanent and contingent workers with different skill sets is considered. Some important issues addressed in this research are (a) how to assign workers with different skills to maximize production (b) what is the induction rate of contingent workers to achieve the desired ramp-up and (c) what are the key decision factors that impact manufacturing performance An LP model is proposed to minimize overall costs subject to complex scheduling, skills, and learning rate requirements. Our analysis indicates that cost of induction of contingent workers, overtime cost premium, and the amount of overtime have significant impact on performance. The findings of the study will be useful to managers in planning and allocation of workers of different skills to various manufacturing processes and to determine the optimal number of contingent workers to induct. / Singapore-MIT Alliance (SMA)

This paper presents an optimal method and a heuristic approach which aims at maximizing the profit when responding to a set of customer enquiries under limited capacity. The model takes into consideration the quantity of available-to-promise (ATP) which measures the capability to fill customer orders, along with enquiry quantity and product price. The optimal method and the heuristic approach are tested using ATP, product price and enquiry quantity each at their different levels. From the example conducted, it is found that (1) the optimal model can help to make appropriate decision for selecting a subset of enquiries, and (2) the heuristic approach can produce a result within 5% from the optimum achieved by optimal method for most parameter settings. / Singapore-MIT Alliance (SMA)

In an e-retailing setting, the efficient utilization of inventory, storage space, and labor is paramount to achieving high levels of customer service and company profits. To optimize the storage space and labor, a retailer will split the warehouse into two storage regions with different densities. One region is for picking customer orders and the other to hold reserve stock. As a consequence, the inventory system for the warehouse is a multi-item two-stage, serial system. We investigate the problem when demand is stochastic and the objective is to minimize the total expected average cost under some space constraints. We generate an approximate formulation and solution procedure for a periodic review, nested ordering policy, and provide managerial insights on the trade-offs. In addition, we extend the formulation to account for shipping delays and advanced order information. / Singapore-MIT Alliance (SMA)

Most logistics network design models assume exogenous customer demand that is independent of the service time or level. This paper examines the benefits of segmenting demand according to lead-time sensitivity of customers. To capture lead-time sensitivity in the network design model, we use a facility grouping method to ensure that the different demand classes are satisfied on time. In addition, we perform a series of computational experiments to develop a set of managerial insights for the network design decision making process. / Singapore-MIT Alliance (SMA)

Thesis (M.A.)--Ohio University, June, 2006. / Title from PDF t.p. Includes bibliographical references (p. 30-32)

<p>In recent years, with the fast development of resource capability of both the Internet and personal computers, multimedia applications like video-on-demand (VOD) streaming have gained dramatic growth and been shown to be potential killer applications in the current and next-generation Internet. Scalable deployment of these applications has become a hot problem area due to the potentially high server and network bandwidth required in these systems.</p><p>The conventional approach in a VOD streaming system dedicates a media stream for each client request, which is not scalable in a wide-area delivery system serving potentially very large numbers of clients. Recently, various efficient delivery techniques have been proposed to improve the scalability of VOD delivery systems. One approach is to use a scalable delivery protocol based on multicast, such as periodic broadcast or stream merging. These protocols have been mostly developed for single-server based systems and attempt to have each media stream serve as many clients as possible, so as to minimize the required server and network bandwidth. However, the performance improvements possible with techniques that deliver all streams from a single server are limited, especially regarding the required network bandwidth. Another approach is based on proxy caching and content replication, such as in content delivery networks (CDN). Although this approach is able to effectively distribute load across multiple CDN servers, the cost of this approach may be high.</p><p>With the focus on further improving the system efficiency regarding the server and network bandwidth requirement, a new scalable streaming protocol is developed in this work. It adapts a previously proposed technique called hierarchical multicast stream merging (HMSM) to use a peer-to-peer delivery approach. To be more efficient in media delivery, the conventional early merging policy associated with HMSM is extended to be compatible with the peer-to-peer environment, and various peer selection policies are designed for initiation of media streams. The impact of limited peer resource capability is also studied in this work. In the performance study, a number of simulation experiments are conducted to evaluate the performance of the new protocol and various design policies, and promising results are reported.

This thesis is an extension of a recent study into the relationship between merger size and profitability. It studies a class of Cournot oligopoly with linear cost and quadratic demand. Its focus is to analyze how a mergers profitability is affected by its size and by the demand elasticity. Such results have not yet been reported in previous studies, perhaps due to the complexity of the equilibrium equation involved. It shows an increase in the demand elasticity also raises a mergers profitability. Consequently, an increase in the demand elasticity reduces merged members critical combined per-merger market share for the merger to be profit enhancing. Comparing with 80% minimum market share requirement for a profitable merger in Salant, Switzer, and Reynolds (1983), a greater market share is needed when the demand function is concave (demand is relatively inelastic), while a smaller market share may still be profitable when the demand function is convex (demand is relatively elastic). In our model, mergers are generally detrimental to public interests by increasing market price and reducing output. However, the merger will be less harmful when the goods are very inelastic.

Peer-assisted Video-on-Demand (VoD) systems have not only received substantial recent research attention, but also been implemented and deployed with success in large-scale real-world streaming systems. Despite the remarkable popularity in real-world systems, the design of such systems are not well understood. In this thesis, we seek to address two design problems in peer-assisted VoD systems. First, we focus on the design of cache replacement algorithms. We construct an analytical framework based on dynamic programming, to help us form an in-depth understanding of optimal strategies to design cache replacement algorithms. Second, we shift our attention to the surplus upload bandwidth allocation problem in multi-channel systems. Through theoretical analysis and realistic simulations, we conclude that surplus upload bandwidth from peers can be utilized more efficiently than conventional prefetching strategies when it is devoted to redistributing content to those channels in deficit state.

Peer-assisted Video-on-Demand (VoD) systems have not only received substantial recent research attention, but also been implemented and deployed with success in large-scale real-world streaming systems. Despite the remarkable popularity in real-world systems, the design of such systems are not well understood. In this thesis, we seek to address two design problems in peer-assisted VoD systems. First, we focus on the design of cache replacement algorithms. We construct an analytical framework based on dynamic programming, to help us form an in-depth understanding of optimal strategies to design cache replacement algorithms. Second, we shift our attention to the surplus upload bandwidth allocation problem in multi-channel systems. Through theoretical analysis and realistic simulations, we conclude that surplus upload bandwidth from peers can be utilized more efficiently than conventional prefetching strategies when it is devoted to redistributing content to those channels in deficit state.

In this thesis, a demand elasticity model is developed and tested for the dispatch of high voltage power systems and microgrids. The price obtained from dispatching a network in a base-case scenario is used as input to a price-elastic demand model. This demand model is then used to determine the price-responsive demand for the next iteration, assuming that the load schedule is defined a day-ahead. Using this scheme, trends for demand, hourly prices, and total operation costs for a system can be obtained to study the impact of demand response on unit commitment and dispatch of distributed energy resources. This way, the effect on the scheduling of dispatchable generators and energy storage systems can be analyzed with respect to price-elastic loads. The results for a test power system and a benchmark microgrid show that as the demand is more elastic, the longer it takes for the dispatch to converge to a final condition. The 24-hour model eventually converges to a steady state, with prices and costs at their lowest values for different scenarios, which is good for most system participants and desirable in a market environment, thus highlighting the importance of price-responsive loads in electricity markets.