A Robust Optimization Model for the Strategic and Operational Design of the Oil Supply Chain

The oil industry has a great share in the energy structure and the global economy, and the planning of strategic and operational levels of its supply chain is done with the objective of improving the competitive status of countries on the global level and economic development. In this paper, a mathematical model is presented for designing the crude oil supply chain through considering related facility location, demand allocation, transportation planning, and distribution. In the proposed model, environmental requirements for emitted greenhouse gas are considered such that the greenhouse gas emission from the transportation of oil may not be greater than a given limit. Since the exact values of parameters can rarely be determined in the real world, therefore, the uncertainty associated with parameters such as budget, transportation capacity, production units capacity, export volume, the amount of crude oil extraction and production, demand for refinery products and their production rate are considered in the proposed model. To handle the uncertainty of the model parameters, the robust optimization approach is applied. Numerical results verify the efficiency of the proposed model and show that the profitability of oil industry can be guaranteed by handling the uncertainties of parameters and appropriate production and distribution management

The competitive facility location based on the spatial interaction model

In this paper, a nonlinear model for locating service facilities is introduced in a competitive ‎ region. In the proposed model the factors of travel time, quality of service facilities and price ‎ are considered that are the most important effective factors in attracting and maintaining the ‎ customers in the competitive environments. For identifying the customers’ probabilistic ‎ behavior in selecting the competitor’s and entering firm’s facilities, the spatial interaction ‎ model and Logit function are used. Since the decision making for locating is not only based on ‎ the sale quantity and market demand captured and ignoring other important organizational ‎ goals such as minimizing the cost may provide problems in long term for firms, therefore in ‎ addition to the capturing demand, the fixed cost of locating is considered in the model that ‎ will provide better results in the real world problems. Due to the computational complexity, a ‎ genetic algorithm is developed for solving the model. The numerical results verify the ‎ performance of the proposed model and effectiveness of ‏ ‏ ‎ the genetic algorithm in solving it ‏ ‏ and show that the study of costumers’ ‎ ‏ ‏ probabilistic demand considering the factors of price, ‎ travel time and quality of service facilities has the major role in the increasing of sale and profit ‎ of the entering firm so that, in spite of selecting the most appropriate locations by the ‎ competitor, the ‏ ‏ sale and profit quantities of entering firm are more than the competitor’s, in the ‎ equal situation of price, quality and number of facilities.