Evaluating trade-offs for profitable design of network infrastructure using multi-criteria optimization

Infrastructure design is a critical first step towards the long term success of any business. A well-planned supply chain design for example, is important for efficient flow of goods and information between the producers and the consumers. Similarly a well laid out electricity transmission grid is necessary for distribution of electric power and a well-designed road network is important for distribution of goods on a transportation network. Infrastructure investments are capital intensive and usually made for the long-term. Thorough analysis is very important for making large infrastructure investment decisions, to ensure long term profitability. Operations research is an interdisciplinary branch of applied mathematics that uses mathematical modeling and optimization methods to arrive at optimal solutions to complex decision making problems. Application in chemical industries include, use of real time process optimization, product scheduling and resource planning to name a few. Although there is an increased focus on the use of mathematical programming techniques in the chemical industry, most of the commercially available software packages have focused towards the design and operation of chemical plants. Other topics such as supply chain, logistics and distribution haven't received as much attention by the chemical engineering community. This work focuses on these topics, which are very important for maintaining long-term profitability, while tackling issues from a systems engineering perspective. This work focuses on the use of principles of operations research, to aid in the decision making process. The predominant focus of this dissertation is towards making use of facility location models for finding out the best locations for establishing facilities such as warehouses in a supply chain network and refueling stations on an urban road transportation network. We have also made use of state of the art agent-based modeling and simulation techniques for accurate estimation of inputs required for optimization.