Thesis (Ph. D.)--Ohio State University, 1986. / Includes bibliographical references (leaves 261-272). Available online via OhioLINK's ETD Center.

Thesis (MScEng (Industrial Engineering))--University of Stellenbosch, 2005. / This thesis introduces an adaptation of the Population-Based Incremental Learning (PBIL) meta-heuristic implemented on a variant of the General Pickup and Delivery Problem. The mapping of the customers in the problem and the vehicle routes on a time grid enables the utilization of the powerful genetic search that the PBIL algorithm provides in liaison with competitive learning. The problem consists of a number of customers who may at any time of the day place an order on another customer for some package. The fleet of vehicles travelling between the customers must then combine powers to pickup and deliver the package as fast as possible without ever leaving their assigned routes. The solution to this problem then, is a set of routes for the fleet that will minimize some percentile of the delivery times between customers. The PBIL meta-heuristic provides the blueprint of the final algorithm, where the final algorithm is actually just a normal PBIL algorithm with some external solution generation and evaluation techniques employed. The final algorithm can easily solve an instance of the problem in polynomial time, given that the resolution of the time grid used is not too small.

M.Com. (Transport Economics) / Please refer to full text to view abstract

M.Com. / Please refer to full text to view abstract

There is a great need for a pharmaceutical manufacturer to provide its customers with the right product at the right time, place and price and also to deliver the product in the right quantity and quality. Should pharmaceutical customers not receive their orders on time from the pharmaceutical manufacturer, the customers might face out-of-stock situations. As a result, the patients or customers might turn to an alternative product from a different pharmaceutical manufacturer, which has the required stock readily available. This could lead to loss in sales and even death amongst its customers. The main objective of this study was to determine the key factors that form part of the demand and operations planning strategies to ensure that these factors are in place to effectively manage and execute the supply chain and logistics processes to reduce possible out-of-stock situations amongst its customers. As a starting point to the study, an analysis was performed based on the review of relevant research and literature from various text books, journals, publications, as well as internet sources. The literature study covered various aspects such as demand planning, production planning, the use of different demand and operations planning strategies, as well as the importance of continuous internal and external customer service levels to ensure that customer orders are delivered on time. The analysis of the literature review was used to establish a theoretical basis for the design and structure of the questionnaire that was used as a research instrument in this study. A survey was conducted amongst the employees that are responsible for executing the supply chain and logistics processes within Aspen Pharmacare. These employees resided within the three main departments of the pharmaceutical manufacturer, namely, the Demand and Operations Planning, Production and Packing, as well as the Warehouse and Distribution departments. Each question posed in the questionnaire related to the role of each employee within these departments that are responsible for the logistics processes within Aspen Pharmacare. The empirical results from the study indicated that the respondents were in support that specific key factors are important for a pharmaceutical manufacturer to manage incoming customer orders efficiently and effectively to reduce possible out-of-stock situations amongst its end customers. However, the respondents also highlighted that there were a number of obstacles preventing them from implementing the key factors that are required to manage incoming customer orders more efficiently within the pharmaceutical manufacturer. Some of these obstacles include: the lack of pro- activeness; lack of accountability; and the lack of constant communication between the employees from the three main departments. Furthermore, incomplete hand-over information during shift changes amongst the employees of the Production and Packing department created unnecessary down-time. Also, the employees from the Warehouse and Distribution department were not informed timeously when changes to the production schedule were made. These obstacles will have a negative impact on the ultimate customer delivery dates and need to be addressed. The empirical results highlighted some recommendations to assist pharmaceutical manufacturing companies to more effectively and efficiently manage their incoming customer orders to reduce or eliminate any possible out-of-stock situations amongst its end customers. Some recommendations include: improved communication between the three main departments; to react immediately to out-of-stock products; and to ensure that internal and external customer service levels are adhered to at all times. The implementation of the recommendations based on the empirical findings of the study will assist the pharmaceutical manufacturing company to achieve excellence and to become a world-class supplier of generic pharmaceutical products. In this way, all incoming customer orders can be fulfilled on time.

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Submitted in fulfillment of the requirements of the degree of Master of Technology: Industrial Engineering, Durban University of Technology, Durban, South Africa, 2014. / The SADC region has seen both man-made and natural disasters killing over 90 thousand people and affecting millions in the past 33 years. Most of these deaths were as a result of lack of infrastructure and preparedness. Looking at the challenges for providing relief to victims/evacuees throughout the entire disaster and post-disaster periods in the region, the emphasis of this thesis is on last mile transportation of resources, victims, emergency supplies, aiming to optimize the effectiveness (quick­I response) and efficiency (low-cost) of logistics activities including humanitarian supply chain. A survey was used for data collection. Statistical analysis helped determine the impact of disaster relief chains and lead to the development of a mathematical model that shall equip the region with mechanisms for response and recovery operations. An EXCEL optimization tool was used to find the optimal way of transporting relief in the region in case of a disaster. / PDF Full-text unavailable. Please refer to hard copy for Full-text / M

Wong Chi Fat. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 107-115). / Abstracts in English and Chinese. / List of Tables --- p.ix / List of Figures --- p.x / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Literature Review --- p.3 / Chapter 1.2.1 --- Shortest Path Problem --- p.4 / Chapter 1.2.2 --- Vehicle Routing Problem with Time Windows --- p.6 / Chapter 1.3 --- Thesis Outline --- p.9 / Chapter 2. --- Time-varying Shortest Path with Constraints in a 2-level Network --- p.11 / Chapter 2.1 --- Introduction --- p.11 / Chapter 2.2 --- Problem Formulation of TCSP --- p.12 / Chapter 2.3 --- Arbitrary Waiting Time --- p.13 / Chapter 2.4 --- TCSP in a 2-level Network --- p.15 / Chapter 2.4.1 --- Problem Formulation of TCSP in a 2-level Network --- p.17 / Chapter 2.5 --- Algorithms Solving TCSP in a 2-level Network --- p.20 / Chapter 2.5.1 --- Exact Algorithm --- p.21 / Chapter 2.5.2 --- Heuristic Algorithm --- p.23 / Chapter 2.6 --- Concluding Remarks --- p.30 / Chapter 3. --- Vehicle Routing Problem with Time Windows and Stochastic Travel Times --- p.32 / Chapter 3.1 --- Introduction --- p.32 / Chapter 3.2 --- Problem Formulation --- p.34 / Chapter 3.3 --- General Branch-and-cut Algorithm --- p.42 / Chapter 3.4 --- Modified Branch-and-cut Algorithm --- p.44 / Chapter 3.4.1 --- Prefixing --- p.45 / Chapter 3.4.2 --- Directed Partial Path Inequalities --- p.47 / Chapter 3.4.3 --- Exponential Smoothing --- p.50 / Chapter 3.4.4 --- Fast Fathoming --- p.54 / Chapter 3.4.5 --- Modified Branch-and-cut algorithm --- p.56 / Chapter 3.5 --- Computational Analysis --- p.57 / Chapter 3.5.1 --- "Performance of Prefixing, Direct Partial Path Inequalities and Exponential Smoothing" --- p.57 / Chapter 3.5.2 --- Performance of Fast Fathoming --- p.63 / Chapter 3.5.3 --- Summary of Computational Analysis --- p.67 / Chapter 3.6 --- Concluding Remarks --- p.67 / Chapter 4. --- System Features and Implementation --- p.69 / Chapter 4.1 --- Introduction --- p.59 / Chapter 4.2 --- System Features --- p.70 / Chapter 4.2.1 --- Map-based Interface and Network Model --- p.70 / Chapter 4.2.2 --- Database Management and Query --- p.73 / Chapter 4.3 --- Decision Support Tools --- p.75 / Chapter 4.3.1 --- Route Finding --- p.75 / Chapter 4.3.2 --- Delivery Planning --- p.77 / Chapter 4.4 --- System Implementation --- p.80 / Chapter 4.5 --- Further Development --- p.82 / Chapter 5. --- Vehicle Routing Software Survey / Chapter 5.1 --- Introduction --- p.83 / Chapter 5.2 --- Essential Features in CVRS Nowadays --- p.84 / Chapter 5.2.1 --- Common Features --- p.34 / Chapter 5.2.2 --- Advanced Features --- p.90 / Chapter 5.3 --- Concluding Remarks --- p.94 / Chapter 6. --- Summary & Future Work --- p.97 / Appendix A --- p.101 / Appendix B --- p.104 / Bibliography --- p.107