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Description
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.
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.
ContributorsMason De Rada, Andrew Nicholas (Author) / Villalobos, Jesus R (Thesis advisor) / Griffin, Paul (Committee member) / Kempf, Karl (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation explores the use of deterministic scheduling theory for the design and development of practical manufacturing scheduling strategies as alternatives to current scheduling methods, particularly those used to minimize completion times and increase system capacity utilization. The efficient scheduling of production systems can make the difference between a thriving and a failing enterprise, especially when expanding capacity is limited by the lead time or the high cost of acquiring additional manufacturing resources. A multi-objective optimization (MOO) resource constrained parallel machine scheduling model with setups, machine eligibility restrictions, release and due dates with user interaction is developed for the scheduling of complex manufacturing systems encountered in the semiconductor and plastic injection molding industries, among others. Two mathematical formulations using the time-indexed Integer Programming (IP) model and the Diversity Maximization Approach (DMA) were developed to solve resource constrained problems found in the semiconductor industry. A heuristic was developed to find fast feasible solutions to prime the IP models. The resulting models are applied in two different ways: constructing schedules for tactical decision making and constructing Pareto efficient schedules with user interaction for strategic decision making aiming to provide insight to decision makers on multiple competing objectives.
Optimal solutions were found by the time-indexed IP model for 45 out of 45 scenarios in less than one hour for all the problem instance combinations where setups were not considered. Optimal solutions were found for 18 out of 45 scenarios in less than one hour for several combinations of problem instances with 10 and 25 jobs for the hybrid (IP and heuristic) model considering setups. Regarding the DMA MOO scheduling model, the complete efficient frontier (9 points) was found for a small size problem instance in 8 minutes, and a partial efficient frontier (29 points) was found for a medium sized problem instance in 183 hrs.
Optimal solutions were found by the time-indexed IP model for 45 out of 45 scenarios in less than one hour for all the problem instance combinations where setups were not considered. Optimal solutions were found for 18 out of 45 scenarios in less than one hour for several combinations of problem instances with 10 and 25 jobs for the hybrid (IP and heuristic) model considering setups. Regarding the DMA MOO scheduling model, the complete efficient frontier (9 points) was found for a small size problem instance in 8 minutes, and a partial efficient frontier (29 points) was found for a medium sized problem instance in 183 hrs.
ContributorsMunoz-Estrada, Luis Fernando (Author) / Villalobos, Jesus R (Thesis advisor) / Fowler, John (Thesis advisor) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2020