Thesis (Ph. D.)--Lehigh University, 2001. / Includes bibliographical references (p. 174-175).

Thesis (MBA)--University of Pretoria, 2001.

Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xi, 130 p.: ill. Includes abstract and vita. Includes bibliographical references (p. 85-88).

As a crucial factor for forming a competitive supply chain, the supplier selection problem has gained a lot of attentions from both researchers and practitioners. To fulfil the increasingly stringent environmental regulations and meet the NGOs’ and customers’ call for environmental conscious and socially responsible business, companies find it necessary to incorporate environmental and social dimensions in the supplier selection process. This thesis presents a supplier selection-model incorporating the sustainability issues. Firstly, a comprehensive set of sustainable supplier selection criteria are identified. Then, a hybrid multi-criteria decision making (MCDM) model comprising the fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and the two-level DEA (Data Envelopment Analysis) algorithms is developed to screen out less qualified suppliers. To begin with, twenty one supplier selection criteria are defined in the categories of economic performance, environmental impacts, and corporate social responsibility (CSR) impacts. The criteria were identified from literature review and a small-scale consultation with industrial practitioners. Regarding the economic criteria, quality, delivery, production capacity, cost, technical capability, financial position, management & organization and reputation are the more important aspects. For environmental impacts, most frequently adopted criteria are targeted at measuring supplier’s current environmental performances, i.e. negative impacts of products and production process on the environment. For CSR impacts, labour, health and safety criteria are received the most attention. A hybrid supplier pre-selection model consists of the fuzzy TOPSIS and the two-level DEA is implemented to shortlist suppliers. The fuzzy TOPSIS is used to measure the supplier’s outcome based performance, while the two-level DEA is used to measure the supplier’s efficiency, that is, how efficiently the supplier uses various resources to achieve that level of performance. With the combination of the performance score and the efficiency score, suppliers are grouped in to four clusters, that is, high performance and efficient (HE), high performance and inefficient (HI), low performance and efficient (LE) and low performance and inefficient (LI). Improvement targets for suppliers in HI, LE and LI clusters are identified. In general, conventional DEA is deficient in the discriminating power when there are a large number of criteria. The two-level DEA is established in this thesis to enhance the discriminating power of the standard DEA. In the two-level DEA, the supplier selection criteria for the same aspect are grouped together and form the hierarchical structure composes of two levels. Thereby, the two-level DEA model is able to handle quite a large number of input and output criteria. A case study has been conducted in the context of automobile industry to validate the feasibility and usefulness of the proposed methodology. The results indicate that the proposed methodology is able to screen out less qualified suppliers considering a comprehensive set of sustainability criteria. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

A headquarters-centered group company considered in this thesis consists of one headquarters and several operationally semi-autonomous production subsidiaries. This research investigates the situation where the headquarters provides supply chain management services shared among subsidiaries to take advantage of risk pooling effect, economies of scale, and information and resource sharing. This thesis considers three different but related scenarios. The first research scenario formulates two customer order management models. One is Headquarters-centered Common Order Management (HQ-COM) where customer orders are processed by the headquarters and then allocated to the subsidiaries. The other is Subsidiary-Autonomous Order Management (SD-AOM) where subsidiaries process customer orders relatively independent of each other. Two scenarios with demand uncertainty are simulated. One is that the order quantity exceeds the production capacity of each individual subsidiary so that the order has to be split before allocating to the subsidiaries. The other scenario is that the total quantity of selected customer orders is within the production capacity of a single subsidiary so that the orders should be merged into one batch before allocating to one subsidiary. The results show that HQ-COM outperforms SD-AOM in terms of both its performance and its robustness against demand variability. This achievement is largely due to the effects of pooling of different customer orders and sharing of production capacity among the subsidiaries. The second research scenario develops two sourcing management models: Headquarters-centered Common Sourcing Management (HQ-CSM) and Subsidiary-Autonomous Sourcing Management (SD-ASM). In HQ-CSM, two management policies are examined. One is Order Coordination policy in which common replenishment epochs are proposed by the headquarters and the subsidiaries are encouraged to coordinate the timing of their orders based on the common replenishment epochs. The other is Order Consolidation policy in which the headquarters places a combined order with the supplier. The results show that HQ-CSM outperforms SD-ASM in terms of cost and robustness against demand uncertainties. This achievement is largely due to the synergistic ordering process, the economies of scale and risk pooling effect by the implementation of transshipments. The results also reveal that Order Consolidation policy always performs better than Order Coordination policy especially in face of high demand uncertainties and high service level requirement. The third scenario considers a headquarters-managed centralized distribution center (HQ-CDC) serving multiple subsidiaries with stochastic demands. There are two kinds of inventory spaces: dedicated space and leased space. Two pricing policie--the constant pricing and the dynamic pricing--are compared. Two decision models are formulated. One is Integrated Model where the group company makes decisions on the replenishment and the space allocation simultaneously. The other is Bilevel Programming Model where the HQ-CDC and the subsidiaries make decisions sequentially. The results show that the HQ-CDC’s profit is noticeably improved in Bilevel Programming Model by the implementation of the constant pricing policy. The results also reveal that the leased space as a supplement of the reserved space leads to a more flexible space utilization and a reduced group company’s total cost especially in face of large demand and high demand fluctuation. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

We study the integrated logistics network design and inventory stocking problem as characterized by the interdependency of design and stocking decisions in service parts logistics. These two sets of decisions usually have been considered sequentially in practice, and their associated problems have been tackled separately in the research literature. The overall problem is typically further complicated due to time-based service constraints that provide lower limits for the percentages of demand satisfied within specified time windows. We introduce an optimization model that explicitly captures the interdependency between network design (location of facilities, and allocation of demands to facilities) and inventory stocking decisions (stock levels and their corresponding stochastic fill rates), and present computational results from our extensive experiments that investigate the effects of several factors including demand levels, time-based service levels, and costs. Our findings indicate that the integrated approach can provide significant cost savings over the decoupled approach (solving the network design first and inventory stocking next), shifting the whole efficient frontier curve between cost and service level to superior regions. Furthermore, we show that the decoupled and integrated approaches may generate totally different solutions, even in the number of located facilities and in their locations, magnifying the importance of considering inventory as part of the network design models. Our analysis consists of a special case of integrated logistics network design and inventory stocking problem in service parts logistics, where each customer requires a certain time-based service level. Introduced is a non-linear mixed integer optimization model that is beyond our current solution technologies, yet it explicitly captures the interdependency between network design (locating facilities, and allocating customers to facilities) and inventory stocking decisions (stock levels and their corresponding stochastic fill rates). We provide two different linearized mixed integer formulations for this problem that can solve small and medium size instances. We reveal that this problem can be formulated as a capacitated facility location problem with polynomially solvable sub cases. However, it is still a challenging problem for which we have a Lagrangian-relaxation based approach that provides extremely tight lower and upper bounds. By applying the methodology and insights of the customer-centric problem, we succeed in providing upper and lower bounds to the original system-wide service level problem, even with large instances and with medium demand levels. / text

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This thesis presents an integrated methodology developed for the modelling and optimisation of distribution networks based on a Hybrid Genetic Algorithm. This work focuses on solving the capacitated location-allocation problem in the context of distribution network design, which is a large scale, highly constrained, NP-hard, combinational problem. / Thesis (PhD)--University of South Australia, 2006.

This thesis presents an integrated methodology developed for the modelling and optimisation of distribution networks based on a Hybrid Genetic Algorithm. This work focuses on solving the capacitated location-allocation problem in the context of distribution network design, which is a large scale, highly constrained, NP-hard, combinational problem. / Thesis (PhD)--University of South Australia, 2006.

This thesis presents an integrated methodology developed for the modelling and optimisation of distribution networks based on a Hybrid Genetic Algorithm. This work focuses on solving the capacitated location-allocation problem in the context of distribution network design, which is a large scale, highly constrained, NP-hard, combinational problem. / Thesis (PhD)--University of South Australia, 2006.