Stochastic optimization in perishable food supply chain: a holistic approach

Doctor of PhilosophyDepartment of Industrial & Manufacturing Systems EngineeringAshesh K. SinhaWith continuous expansion in world population, total food demand across the globe is anticipated to increase by 56% within a span of 30 years. Predictions state that food production needs to increase by 70% and adhere to quality standards. Better informed consumers now want to have precise knowledge about the origin of food till it reaches the final shelves of the supermarket. Perishable food supply chain is a complex network involving multiple stakeholders and several interconnected stages. Presence of uncertainties like demand, outbreak, or contamination and a limited product shelf life adds further complexity and the need to uphold food quality and safety standards throughout the supply chain, from crop production to consumers. Without traceability in food supply chains, severe problems of product recall, consumer dissatisfaction, and contamination insecurities have occurred in the past. The lack of a transparent system resulted in food loss and aggravated the global challenge of feeding a growing population. In contrast to centralized systems which lack trustworthy information and deterministic optimization models, our goal is to incorporate a dynamic system like blockchain to monitor the quality of food, keeping the entire network transparent among the stakeholders, embedded with stochastic models to make it a robustly optimized supply chain. This research develops stochastic optimization models to comprehensively combat uncertainty in item quality, transportation of perishable items (not limited to the ones considered below) and provide network transparency. We consider sausage, wheat, and cheese as perishable products for our research. First, we examine a five-level sausage supply chain where at each level, the output product is manufactured by combining/mixing correct proportions of the raw materials from the previous stage. The demand for the final product is uncertain. We develop a two-stage stochastic model and analyze it using the L-shaped algorithm to improve traceability, optimize dispersion, and fulfil demand among the batches. Next, to maintain safety standards, detailed wheat and cheese supply chains are analyzed separately to filter relevant parameters responsible for food quality at any point in the network. We implement Q-learning algorithm to optimize values of parameters based on which the decision maker can choose the best or worst decision in determining the quality of the perishable product. Later, we analyze a large-scale rich tanker trailer routing problem with stochastic transit times for perishable bulk orders. Unlike classical transportation problems, bulk transportation falls under the umbrella of rich vehicle routing problems that involve several intermingled decisions. Typically, the bulk orders are characterized by a set of attributes consisting of an origin-destination location pair, pickup and delivery time windows, order specification, restrictions based on prior orders of food leading to incompatibility, and possibly special equipment (washed and prepped) or handling instructions. We propose concepts of a novel graph decomposition algorithm, column generation and shortest path to generate feasible optimal routes and account for incompatibility constraints. We overcome the challenge of network transparency by storing supply chain data in a decentralized ledger, blockchain where data is visible to all stakeholders but immune to tampering, thus enabling transparency

Optimizing Block-Stacking Operations with Relocation

The focus of the dissertation is developing the optimization problem of finding the minimum-cost operational plan of block stacking with relocation as well as devising a solution procedure to solve practical-sized instances of the problem. Assuming changeable row depth instead of permanent row depth, this research is distinguished from conventional block stacking studies. The first contribution of the dissertation is the development of the optimization problem under the assumption of deterministic demand. The problem is modeled using integer programming as a variation of the unsplittable multi-commodity flow problem. To find a good feasible solution of practical-sized instances in reasonable time, we decompose the original problem into a series of generalized assignment problems. In addition, to establish a good lower bound on the optimal objective function value, we apply a relaxation based upon Lagrangean decomposition in which the relaxed problem separates into a set of shortest path problems and a set of binary knapsack problems. The second contribution of the dissertation is the development of the optimization problem under the assumption of stochastic demand. The problem is formulated as a discrete time finite horizon Markov decision process model, incorporating the recursive daily situation of determining the assignment of product lots to storage areas for a day based on uncertain daily demand and observed system information. To tackle computational intractability in solving practical-sized instances, we develop a heuristic solution approach taking an on-line manner by instantly determining an action for a single observed state rather than an off-line manner by predetermining an action for every state