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This research has concentrated on the project portfolio selection (PPS) in the petroleum industry. In this study, the PPS has been treated as a multi-attribute decision-making (MADM) problem; therefore, a hybrid framework comprising five MADM techniques has been proposed to tackle this problem. Several MADM techniques have been integrated to acquire more reliable decisions and consequently to decrease the risk of failure in the decision-making process. Since the proposed methodology is an MADM-based framework, there was a need to discover the influential attributes on selection of petroleum projects. In this respect, the literature of the PPS has been comprehensively reviewed and the most influential attributes have been detected. Sustainable development has been a concern for the researchers; hence, the sustainability-related attributes have been embraced in the decision-making process as well. To strengthen the practicality of the developed framework, the Delphi method has been employed to gather and converge the viewpoints of experts on the identified attributes. The Kruskal–Wallis statistical test has been implemented to compute the weights of the attributes. Having determined the influential attributes and their weights, the embedded MADM techniques in the proposed framework have been implemented to prioritize the potential petroleum projects of a real case study. To obtain the ultimate ranking of alternatives, the proposed framework consolidates the outputs of the aforementioned techniques through using the Copeland method. This paper has also proposed a mixed-integer mathematical formulation for the PPS problem to assess the precision and validity of the results delivered by the decision-making framework. Comparing the outputs of the proposed framework and the model revealed that the developed framework is capable of providing credible outcomes. Furthermore, several sensitivity analyses have been performed to demonstrate the robustness of the framework.

This paper presents a new integrated model for the multi-skilled project scheduling and multi-project scheduling problems, where the activities are technically linked through the generalized precedence relations (GPR). The proposed model is energy-efficient since the concerns regarding the energy consumptions of projects have been embraced as well. Reviewing the pertinent literature has revealed that the energy-efficient formulations for the multi-skill and multi-project scheduling problems with the GPR connections are very rare. For this formulation, two minimization objectives have been defined: (1) the overall duration to finish all projects and (2) the total energy consumption of all projects. This research offers a new version of the multi-objective vibration damping optimization (MOVDO) method to solve the proposed model. For the MOVDO, a new crossover-like operator has been designed that generates offspring solutions through the swapping procedure. Furthermore, a new population-updating strategy inspired by the Toom’s rule cellular automaton has been devised for the MOVDO. In this strategy, each solution of the population can be replaced with its neighbor solutions based on the dominance criterion and the rules of the Toom’s cellular automaton. Three other solution methodologies have been hired to solve the model in order to have a fair judgment on the efficacy of the MOVDO. The comparisons between the MOVDO and other optimizers have been conducted based on five performance assessment metrics. The results demonstrate the remarkable dominance of the MOVDO over other algorithms.

PurposeThe target of this research is to develop a mathematical model which combines the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and the Multi-Skilled Resource-Constrained Project Scheduling Problem (MSRCPSP). Due to the importance of resource management, the proposed formulation comprises resource leveling considerations as well. The model aims to simultaneously optimize: (1) the total time to accomplish all projects and (2) the total deviation of resource consumptions from the uniform utilization levels.Design/methodology/approachThe K-Means (KM) and Fuzzy C-Means (FCM) clustering methods have been separately applied to discover the clusters of activities which have the most similar resource demands. The discovered clusters are given to the scheduling process as priori knowledge. Consequently, the execution times of the activities with the most common resource requests will not overlap. The intricacy of the problem led us to incorporate the KM and FCM techniques into a meta-heuristic called the Bi-objective Symbiosis Organisms Search (BSOS) algorithm so that the real-life samples of this problem could be solved. Therefore, two clustering-based algorithms, namely, the BSOS-KM and BSOS-FCM have been developed.FindingsComparisons between the BSOS-KM, BSOS-FCM and the BSOS method without any clustering approach show that the clustering techniques could enhance the optimization process. Another hybrid clustering-based methodology called the NSGA-II-SPE has been added to the comparisons to evaluate the developed resource leveling framework.Practical implicationsThe practical importance of the model and the clustering-based algorithms have been demonstrated in planning several construction projects, where multiple water supply systems are concurrently constructed.Originality/valueReviewing the literature revealed that there was a need for a hybrid formulation that embraces the characteristics of the RCMPSP and MSRCPSP with resource leveling considerations. Moreover, the application of clustering algorithms as resource leveling techniques was not studied sufficiently in the literature.

Outsourcing is recognized as a tool to gain strategic advantages. Maintenance outsourcing is a common practice in many industries, including chemical, petroleum, petrochemical, and medical equipment manufacturing. Nevertheless, outsourcing is associated with many risks. This study designed a model based on the system dynamics to identify variables that affect the effectiveness of equipment, efficacy, and profitability. Moreover, the extent of the effects of these variables was examined and their relationships to decide on maintenance outsourcing in gas refineries were assessed. First, the influential variables were identified by reviewing the literature and considering experts' opinions. Next, a system dynamics model was designed, and the optimal values of the variables were investigated by creating five different scenarios. The results showed how the investigated variables affected our objectives and how we could achieve them by keeping the values of these variables close to those determined in the selected scenarios. If the variable of equipment effectiveness was preferred by the managers, Scenario 3 would be selected, as the equipment effectiveness reached its maximum level in this scenario. On the other hand, if the efficacy and profitability variables were preferred, Scenario 4 would be selected in which efficacy and profitability were at maximum levels.

A 40-year-old man presented with atypical chest pain and fatigue from 15 days ago a suspicious mass in the right ventricle based on a bed side transthoracic echocardiography. Preoperative diagnosis of a cardiac hemangioma comes to mind in a minority of cases. In our case, a cardiac tumor was diagnosed and the vascular nature of the tumor was suggested by vascular blush on the coronary angiography. In addition, right ventriculotomy was the approach of choice in our case because of its inaccessibility and its particular location.