Agricultural supply chains are complex systems which pose significant challenges beyond those of traditional supply chains. These challenges include: long lead times, stochastic yields, short shelf lives and a highly distributed supply base. This complexity makes coordination critical to prevent food waste and other inefficiencies. Yet, supply chains of fresh produce suffer from high levels of food waste; moreover, their high fragmentation places a great economic burden on small and medium sized farms.

This research develops planning tools tailored to the production/consolidation level in the supply chain, taking the perspective of an agricultural cooperative—a business model which presents unique coordination challenges. These institutions are prone to internal conflict brought about by strategic behavior, internal competition and the distributed nature of production information, which members keep private.

A mechanism is designed to coordinate agricultural production in a distributed manner with asymmetrically distributed information. Coordination is achieved by varying the prices of goods in an auction like format and allowing participants to choose their supply quantities; the auction terminates when production commitments match desired supply.

In order to prevent participants from misrepresenting their information, strategic bidding is formulated from the farmer’s perspective as an optimization problem; thereafter, optimal bidding strategies are formulated to refine the structure of the coordination mechanism in order to minimize the negative impact of strategic bidding. The coordination mechanism is shown to be robust against strategic behavior and to provide solutions with a small optimality gap. Additional information and managerial insights are obtained from bidding data collected throughout the mechanism. It is shown that, through hierarchical clustering, farmers can be effectively classified according to their cost structures.

Finally, considerations of stochastic yields as they pertain to coordination are addressed. Here, the farmer’s decision of how much to plant in order to meet contracted supply is modeled as a newsvendor with stochastic yields; furthermore, options contracts are made available to the farmer as tools for enhancing coordination. It is shown that the use of option contracts reduces the gap between expected harvest quantities and the contracted supply, thus facilitating coordination.

Ramping up a semiconductor wafer fabrication facility is a challenging endeavor. One of the key components of this process is to schedule a large number of activities in installing and qualifying (Install/Qual) the capital intensive and sophisticated manufacturing equipment. Activities in the Install/Qual process share multiple types of expensive and scare resources and each activity might potentially have multiple processing options. In this dissertation, the semiconductor capital equipment Install/Qual scheduling problem is modeled as a multi-mode resource-constrained project scheduling problem (MRCPSP) with multiple special extensions. Three phases of research are carried out: the first phase studies the special problem characteristics of the Install/Qual process, including multiple activity processing options, time-varying resource availability levels, resource vacations, and activity splitting that does not allow preemption. A modified precedence tree-based branch-and-bound algorithm is proposed to solve small size academic problem instances to optimality. Heuristic-based methodologies are the main focus of phase 2. Modified priority rule-based simple heuristics and a modified random key-based genetic algorithm (RKGA) are proposed to search for Install/Qual schedules with short makespans but subject to resource constraints. Methodologies are tested on both small and large random academic problem instances and instances that are similar to the actual Install/Qual process of a major semiconductor manufacturer. In phase 3, a decision making framework is proposed to strategically plan the Install/Qual capacity ramp. Product market demand, product market price, resource consumption cost, as well as the payment of capital equipment, are considered. A modified simulated annealing (SA) algorithm-based optimization module is integrated with a Monte Carlo simulation-based simulation module to search for good capacity ramping strategies under uncertain market information. The decision making framework can be used during the Install/Qual schedule planning phase as well as the Install/Qual schedule execution phase when there is a portion of equipment that has already been installed or qualified. Computational experiments demonstrate the effectiveness of the decision making framework.

This thesis presents a model for the buying behavior of consumers in a technology market. In this model, a potential consumer is not perfectly rational, but exhibits bounded rationality following the axioms of prospect theory: reference dependence, diminishing returns and loss sensitivity. To evaluate the products on different criteria, the analytic hierarchy process is used, which allows for relative comparisons. The analytic hierarchy process proposes that when making a choice between several alternatives, one should measure the products by comparing them relative to each other. This allows the user to put numbers to subjective criteria. Additionally, evidence suggests that a consumer will often consider not only their own evaluation of a product, but also the choices of other consumers. Thus, the model in this paper applies prospect theory to products with multiple attributes using word of mouth as a criteria in the evaluation.