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At the beginning of the period of transition from petroleum-based fuels to alternative green fuels, determining the optimal location of alternative fuel stations (AFSs) would be an important task. This paper addresses this issue under two main assumptions. First, the capacity of AFSs is limited and each AFS can only serve a number of vehicles up to its capacity. Second, drivers may have to deviate from their pre-determined shortest path to get refueling services. This problem is formulated as a mixed integer linear programming (MILP) model and a heuristic algorithm is developed to solve it. The heuristic method involves solving small and easy to solve linear programing (LP) models, embedded within a greedy approach, and hence, it requires an LP software. Although the proposed MILP model requires that the set of deviation paths be pregenerated with respect to the maximum tolerated deviation distance, the heuristic uses only a restricted set of such paths. The performance of the proposed model and algorithm is evaluated on some randomly generated instances. [ABSTRACT FROM AUTHOR]

The optimal design of a wind farm with the aim of increasing the exploitation rate and power production is one of the challenging problems in the field of renewable energies. In this paper, the effect of using different hub height wind turbines in a farm on total power is studied. An exact mathematical formulation is presented in terms of the interaction matrix for multi-turbine wake effects considering different hub height wind turbines, and a new mixed-integer quadratic optimization model is developed. Then, the model is generalized to include the changes in wind characteristics and uncertain parameters. The model is solved using the linearization technique and an iterative method. The performance of the model and solution algorithm is evaluated by solving different problems borrowed from the literature. The computation results show the possibility of having a high-quality solution in a reasonable time in almost all cases. [ABSTRACT FROM AUTHOR]

Multistage stochastic programming with endogenous uncertainty is a new topic in which the timing of uncertainty realization is decision-dependent. In this case, the number of nonanticipativity constraints (NACs) increases very quickly with the number of scenarios, making the problem computationally intractable. Fortunately, a large number of NACs are typically redundant and their elimination leads to a considerable reduction in the problem size. Identifying redundant NACs has been addressed in the literature only in the special case where the scenario set is equal to the Cartesian product of all possible outcomes for endogenous parameters; however, this is a scarce condition in practice. In this paper, we consider the general case where the scenario set is an arbitrary set; and two approaches, able to identify all redundant NACs, are proposed. The first approach is by mixed integer programming formulation and the second one is an exact polynomial time algorithm. Proving the fact that the proposed algorithm is able to make the uppermost reduction in the number of NACs is another novelty of this paper. Computational results evaluate the efficiency of the proposed approaches. [ABSTRACT FROM AUTHOR]

This study addresses the scheduling of an oil derivatives transportation system by a multiproduct pipeline that connects a single refinery to a distribution center (DC) and that meets the customer demands on a daily basis by considering integrated inventory management at the DC and refinery. A batch sequencing method is presented, which leads to a mixed integer linear programming formulation for determining the optimal pipeline batch sequence and the timing of product deliveries to the DC. Operational restrictions are also considered such as the continuous or discrete flow rate and high peak electricity periods. The experimental results obtained for several examples, including a real-world multiproduct pipeline in different scenarios, demonstrate that the proposed model yields an optimal pipeline schedule with low computational costs. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a two-stage stochastic refueling station location model, where the first stage locates permanent stations and the second locates portable stations. The portable alternative fuel stations are an innovative feature in transportation network. The models are applied to an intercity network for Arizona. Computational results show that the permanent stations locate in and around heavily populated nodes. In addition, the results obtained for the portable stations can be utilized to set up permanent stations when the investor intends to increase the number of such stations. The computational results of the exact and greedy approach are reported. [ABSTRACT FROM AUTHOR]

Under study is a special class of mixed-integer bi-level programming problem (BLPP) which arises in several areas such as engineering, transportation and control systems. The main characteristic of BLPP is that the outer optimization (upper level) problem is constrained by an inner optimization (lower level) problem. In this paper, a Lagrangean relaxation method is proposed to obtain an acceptable feasible solution by solving a sequence of one-level mixed-integer problems. The computational results on some examples were taken from literatures and randomly generated problems indicate that the method is able to solve the BLPP efficiently. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a heuristic method for scheduling a real life pipeline system. [•] The output is an operational program for running pipeline efficiently in given time horizon. [•] The method is able to achieve a near-optimal solution in reasonable time. [•] The approach is successfully applied to a number of pipelines. [Copyright &y& Elsevier]

Abstract: Pipelines are one of the most economic and safe ways to transport oil derivatives to depots near the local markets. This is so, especially when there is a need for carrying a huge amount of petroleum products to long distance points. A pipeline may connect several refineries and depots where the connected points are able to inject products or receive them, or both (dual purpose depots). The main point of pipeline scheduling is to identify an optimal sequence of batches that satisfy demand with minimum costs. This paper presents an MILP continuous formulation for pipeline scheduling. The proposed model results in the reduction of problem size with respect to available models, and increases the accuracy. Computational results and data are reported. [Copyright &y& Elsevier]

Abstract: This paper presents a real-value version of particle swarm optimization (PSO) for solving the open vehicle routing problem (OVRP) that is a well-known combinatorial optimization problem. In OVRP a vehicle does not return to the depot after servicing the last customer on a route. A particular decoding method is proposed for implementing PSO for OVRP. In the decoding method, a vector of the customer’s position is constructed in descending order. Then each customer is assigned to a route with taking into account feasibility conditions. Finally one-point move has been applied on constructed routes that seem promising to result in a better solution. Experimental evaluations on benchmark data sets demonstrate the competitiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: A multi-period stochastic planning model with finite number of realizations has been developed and implemented for a supply and distribution network of a petroleum organization operating under uncertain market conditions. In this paper, we present the analysis of capacity expansion under uncertainty from the viewpoints of the decision making process on the optimal allocation of investment. The optimization model is developed using real data from oil industry and described an actual petroleum products distribution system as a case study. The developed model could be regarded as an applicable tool for decision support to provide a better understanding in planning and policy analysis. [Copyright &y& Elsevier]

Abstract: The problem discussed in this paper is short-term scheduling of distribution of petroleum derivatives from a single oil refinery to a number of depots through a tree-structure pipeline. Scheduling product batches in pipelines is a very complex task with many constraints to be considered. Batches of refined products and grades are pumped back-to-back in the pipeline, often with no separation device between batches. In this work a continuous-time, MILP problem representation for tree-structure pipelines is proposed. The approach is successfully applied to a number of pipeline scheduling problems, including a real-world problem. The data and experimental results are reported. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an integer programming approach to oil derivative transportation scheduling. The system reported is composed of an oil refinery, one multi-branch multi-product pipeline connected to several depots and also local consumer markets which receive large amounts of refinery products. Batches of refined products and grades are pumped back-to-back in the pipeline, without any separation device between them. The sequence and lengths of such pumping runs should be carefully selected in order to meet market demands while satisfying many pipeline operational constraints such as minimum interfaces. [Copyright &y& Elsevier]

Abstract: This paper shows how capacitated network may be used to analyze possible long-term transportation of oil derivatives by pipeline, truck, railway, and ship and reduce the distribution cost. It deals with the scheduling of a multi-product, multi-depot system receiving a number of petroleum products from different refineries and distributes them among several depots and market areas while the demand is an uncertain parameter. Depots typically operate independently and solely within their own territories. However, it may be beneficial to allow those depots to operate interdependently, particularly when the product supplies or network capacity is limited. It also discusses the issue of constraints in shipping oil to/from ports for export/import purposes. [Copyright &y& Elsevier]

Abstract: One of the desirable attributes of real-life examination timetabling solutions is the maximization of paper spread, which is a measure of the amount of study time that each student has between examinations. In this study, we face with a predefined examination schedule that must be modified in order to maximize paper spread in course of examination. We will show how the integer programming can be employed to achieve this aim. The model presents constraints for the quality of feasible examination timetables and all the requirements found in most academic institutions. The approach is tested on real-world exam timetabling problems. The computational experiments and results will be reported. [Copyright &y& Elsevier]

Abstract: We show in this paper that the integer programming approach is well suited for solving the timetabling problem: the definition of the integer programming model is simple; its implementation is immediate by using, a mathematical programming language and an integer programming solver. The power of the integer programming approach allows us to extend the model in different interesting directions. A novel 0–1 integer programming formulation of the university timetabling problem is presented. The model provides constraints for a number of operational rules and requirements found in most academic institutions. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach for computing the non-Archimedean ε which leads to an efficient procedure to determine the overall assurance interval based on integrated form of DEA models. The non-Archimedean ε can be found such that, the optimal values of all CCR models, which are corresponding to all DMUs, are bounded and an assurance value is obtained. The procedure is applied to a set of data related to oil and gas industry, power plants industry and economics. [Copyright &y& Elsevier]

• Joint CCS-EOR planning in a multi-reservoir EOR system and a long-term horizon is studied. • The deterministic model proposed in the literature is improved by reducing the binary variables. • The deterministic model is extended to stochastic with endogenous uncertainty. Computational experiments over two case studies are provided. • The results confirm that incorporating uncertainty may cause significant cost-saving. Carbon-capture-and-storage (CCS) is one of the leading technologies to reduce CO 2 emissions. A commercial way to deploy CCS on a large scale is to sequestrate CO 2 in depleted oil reservoirs and to combine it with enhanced oil recovery (EOR) operations. In this manner, not only the CO 2 emission is reduced, but also the oil production increases. The collaborative CCS-EOR planning problem determines the proper allocation of available CO 2 to depleted reservoirs and the scheduling of the EOR operations. This problem is of great importance, especially when there are multiple oil reservoirs. This paper presents a deterministic mixed-integer linear programming model as an improvement of an existing model in the literature. Then, it is extended to a multistage stochastic model with endogenous uncertainty in which the parameters expressing the initial oil yields and the periodic depletion factor of oil yields associated with reservoirs are uncertain, and the time of uncertainty realization is decision-dependent. Our deterministic model is computationally more efficient than the existing model in the literature, due to the reduction of binary variables to about one-third. Also, providing the possibility of selecting pipeline types among different options as well as incorporating uncertainty may lead to a significant cost-saving. The proposed models are examined over two case-studies taken from the literature. The results indicate that in comparison to the deterministic model, the cost-saving achieved by incorporating uncertainty is about 8.8%, on average. [ABSTRACT FROM AUTHOR]

• A novel method is presented to tackle classification problems with imbalanced data. • It is in the categories of under-sampling data-level and ensemble-based approaches. • It partitions majority samples into clusters by using the convex-hull concept. • Computational results confirm the performance of the proposed two-phase method. Classification problems with imbalanced data are challenging because traditional classifiers tend to misclassify minority samples. This paper introduces a novel two-phase method in the categories of under-sampling data-level and ensemble-based approaches to tackle these problems. Compared to classic k-means clustering, the main property of our method is that the majority class is partitioned into clusters so that there are no minority samples in the convex-hull of the majority samples of each cluster, and at the same time, the size of each cluster is controlled. Thus, it is expected that by using our method, the general pattern of data in the feature space is kept, and the possibility of changing the data distribution is reduced. Computational results over a variety of imbalanced classification datasets confirm the superiority of our method over the existing methods from different metrics. [ABSTRACT FROM AUTHOR]

• A novel method is presented to tackle classification problems with imbalanced data. • It is in the categories of under-sampling data-level and ensemble-based approaches. • It partitions majority samples into clusters by using the convex-hull concept. • Computational results confirm the performance of the proposed two-phase method. Classification problems with imbalanced data are challenging because traditional classifiers tend to misclassify minority samples. This paper introduces a novel two-phase method in the categories of under-sampling data-level and ensemble-based approaches to tackle these problems. Compared to classic k-means clustering, the main property of our method is that the majority class is partitioned into clusters so that there are no minority samples in the convex-hull of the majority samples of each cluster, and at the same time, the size of each cluster is controlled. Thus, it is expected that by using our method, the general pattern of data in the feature space is kept, and the possibility of changing the data distribution is reduced. Computational results over a variety of imbalanced classification datasets confirm the superiority of our method over the existing methods from different metrics. [ABSTRACT FROM AUTHOR]