The technical equipment developed and used in both installation and operation processes in refineries, oil and gas pipelines, and gas booster stations has always been expensive. Hence, managers at different organizational levels are keen to find methods to control and reduce these costs. Generally speaking, the operators in a gas booster station choose the operating devices without considering the related costs. This research presents a mixed integer nonlinear programming model designed to minimize the operational costs of gas booster stations in a main pipeline distribution network. The goal is to optimize the choice of operating devices in these stations to minimize costs while still meeting customer demands. Turbo compressors are chosen as the operating devices and the operational costs are fuel, maintenance, start-up, and penalty costs. However, the significance indexes of these costs are valued differently by the three expert managers: the executive officer, operating head, and the overhaul repairing director. Consequently, the analytical hierarchy process (AHP) method is used to calculate the overall weights of costs, and a gas transmission company in the north of Iran is considered as a case study. The model can minimize the total cost, when compared to the selections of ten experienced operators; however, the absolute weights of choosing measures and the essence of the objective function under study mean that an operator choice exists that would represent the optimum selection of turbo compressors.

A two-dimensional strip packing problem is the process of packing a set of rectangular items of given dimensions into a strip of bounded width and infinite height so that the used height of the strip is minimized. In the case that only guillotine packing is permitted, the problem is called the guillotine strip packing problem (GSPP). Guillotine packing commonly arises in different industries such as glass, steel, paper and wood. Nevertheless, there is a lack of explicit mathematical models for GSPP that can globally solve the problem. In this paper, a new mixed-integer programming model inspired by a so-called sequence sub-tour elimination technique for the traveling salesman problem (TSP) is presented as a relaxation of (non-staged) GSPP with orthogonal rotation. The proposed model is able to find good solutions (good upper bounds) for the optimal objective value and more importantly, it is a polynomial model of order $O(n^2)$, i.e. the number of decision variables (and constraints, as well) is a polynomial of order $O(n^2)$ in the number of the rectangular items ($n$). Numerical results show that the solutions obtained from the proposed model are superior to several existing heuristic algorithms in the literature.

Different types of pipes are used in various industries, and linear programming of pipe production is an effective subject in this industry. Especially, efficient planning in operational level leads to reduce total cost and improves competitiveness. In this paper, the mixed integer linear programming (MILP) approach is applied for operational planning of pipe production in different periods. To provide, moreover, a mathematical model for the manufacturing line, the model which is described in this study can choose the best supplier among a variety of suppliers. This model also optimizes the amount of raw materials acquired from suppliers as well as the amount of final product manufacturing, reducing final product and raw material inventory level in the factory. The final product inventory level, raw material inventory level, manufacturing capacity, supplier and warehouse capacity for keeping the final product and raw materials are also taken into account. The model is applied in a case study in GRP pipe production plants in Turkey.

Integrated water resources management is a systematic process for sustainable development, allocation and monitoring of water resources that is used for social, economic and environmental purposes. In this study, a multi-period mixed-integer linear programming (MILP) model for urban water supply network management is proposed. The proposed model considers all echelons of water supply chain from supply centers to wastewater treatment centers. Also, the model optimizes the decisions such as selecting the suitable water supply centers and capacity level optimization. To verify and validate the proposed model a real case study is conducted in Urmia. The model is solved by the General Algebraic Modeling System (GAMS) software and its results have been analyzed. According to the results, the optimal water supply centers, optimal water flow, optimal water inventory, and optimal capacity levels of wastewater treatment centers in different periods are determined. Also, in case of transferring the remaining additional treated water to Urmia lake, its level is increased by about 0. 007 cm.