| Summary: | In this thesis, we study three emerging applications of OR/MS, namely, (i) disease spread modeling, intervention strategies, and food supply chain management during an influenza pandemic, (ii) the practical applications of production planning and scheduling in the commercial lithographic printing industry, and (iii) packing/placement problems in chip design in the semiconductor industry.
In the first part of the thesis, we study an emergency response planning problem motivated by discussions with the American Red Cross, which has taken on a responsibility to feed people in case of an influenza pandemic. During an emergency such as an influenza pandemic or a bioterror attack, regular distribution channels of critical products and services including food and water may be disrupted, or some of the infected individuals may not be able to go to grocery stores. We analyze the geographical spread of the disease and develop solution approaches for designing the food distribution supply chain network in case of an influenza pandemic. In addition, we investigate the effect of voluntary quarantine on the disease spread and food distribution supply chain network. Finally, we analyze the effect of influenza pandemic on the workforce level.
In the second part, we study a real life scheduling/packing problem motivated by the practices in the commercial lithographic printing industry which make up the largest segment of the printing industry. We analyze the problem structure and develop efficient algorithms to form cost effective production schedules. In addition, we propose a new integer programming formulation, strengthen it by adding cuts and propose several preprocessing steps to solve the problem optimally.
In the last part of the thesis, motivated by the chip design problem in the semiconductor industry, we study a rectangle packing/placement problem. We discuss the hardness of the problem, explore the structural properties, and discuss a special case which is polynomially solvable. Then, we develop an integer programming formulation and propose efficient algorithms to find a ``good' placement.
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