Sustainable development in supply chain management is of great importance, as it emphasizes that the procurement process in sustainable supply chains addresses not only current needs but also future requirements. This subject, by improving processes, reducing waste and energy consumption, increasing efficiency, and establishing sustainable relationships with suppliers and customers, contributes to the sustainability and success of companies in international competition. Moreover, hub location is crucial for optimizing distribution processes and reducing costs in the supply chain, especially for perishable products with a defined and limited shelf life, which imposes various constraints on the supply chain. Additionally, to bring mathematical models closer to reality, uncertainty approaches can be employed, and strategies to deal with uncertainty can be considered. This research delves into these issues in the realm of supply chain management, focusing on sustainability and uncertainty, and provides a review of research conducted in the literature of this field.

Nowadays the use of fossil fuels as a non-renewable energy source has become a major challenge because of the pollution and the environmental impact. Substitution of biomass as an energy source and its supply chain design is the main question. Because of difficulties such as supply chain complexity, uncertainty in variables and selecting the site of bio-refineries many studies have been conducted in this regard. Studies in the field of biomass and biofuel production are described and classified. Also the strategic decisions such as choosing the sites, selecting energy conversation technology, ensuring economic, environmental, technical, and social sustainability and tactical decisions including allocating resources to productive plants, selecting transport modes, and types of warehouses are addressed in the reviewed papers. We have reviewed 140 papers in the interval between 1997 and 2016 and classified them based on the objective functions. The articles are classified based on their being single or multi objectivity, linearity or nonlinearity. Finally, a classification based on the regions in which the studies have been done.

The project scheduling problem is both practically and theoretically of paramount importance. From the practical perspective, improvement of project scheduling as a critical part of project management process can lead to successful project completion and significantly decrease of the relevant costs. From the theoretical perspective, project scheduling is regarded as one of the in-teresting optimization issues, which has attracted the attention of many researchers in the area of operations research. Therefore, the project scheduling issue has been significantly evaluated over time and has been developed from various aspects. In this research, the topics related to Re-source-Constrained Project Scheduling Problem (RCPSP) are reviewed, recent developments in this field are evaluated, and the results are presented for future studies. In this regard, first, the standard problem of RCPSP is expressed and related developments are presented from four as-pects of resources, characteristics of activities, type of objective functions, and availability level of information. Following that, details about 216 articles conducted on RCPSP during 1980-2017 are expressed. At the end, in line with the statistics obtained from the evaluation of previ-ous articles, suggestions are made for the future studies in order to help the development of new issues in this area.

One of the primary concerns for investment in oil and gas projects is to have a comprehensive understanding on different issues associated with this industry. The industry is mainly influ-enced by the price of oil and gas and in some events, many production units have been forced to shut down solely because of low price of oil and gas. Environmental issues are other important factors, which may put pressure on Canada’s political affairs since the country has strong com-mitment to reduce green gas effect. In this paper, we introduce a multi-criteria decision making method, which helps us rank different projects in terms of investment. The proposed study con-siders different investment factors including net present value, rate of return, benefit-cost analy-sis and payback period along with the intensity of green gas effects for ranking the present oil and gas projects in Canada.

Many supplier selection problems are involved with various criteria such as quality of supplier, price, delivery time, etc. This paper presents a survey on the implementation of using different multi criteria decision making (MCDM) methods for supplier selection problems. The reviews covers recent advances of MCDM techniques such as data envelopment analysis (DEA), analytical hierarchy process, etc. over the period 2000-2012. The review also reveals that nearly 60% of the applications are associated with business unit, 15% is related to economy, 9% is devoted to service and development and 8% is dedicated to research and development. In our survey, DEA has become the most popular technique for supplier selection problem followed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchy Process (AHP).

Safety is one of the main reasons for choosing railway to other transportation modes and improvement of transportation safety has attracted many researchers in recent years. In this paper, we aim to investigate the influence of safety measures on railways performance evaluation, empirically. The proposed model of this paper uses data envelopment analysis (DEA) to estimate the railways efficiency scores in the presence of safety measure. According to three proposed factors, the most appropriate model is selected to compare its result with output-oriented DEA model. The results of the survey are surprising since inefficient railroads become efficient through adding undesirable outputs in evaluation model.

In this paper, we examine advanced optimization approach for portfolio problem introduced by Black and Litterman to consider the shortcomings of Markowitz standard Mean-Variance optimization. Black and Litterman propose a new approach to estimate asset return. They present a way to incorporate the investor’s views into asset pricing process. Since the investor’s view about future asset return is always subjective and imprecise, we can represent it by using fuzzy numbers and the resulting model is multi-objective linear programming. Therefore, the proposed model is analyzed through fuzzy compromise programming approach using appropriate membership function. For this purpose, we introduce the fuzzy ideal solution concept based on investor preference and indifference relationships using canonical representation of proposed fuzzy numbers by means of their corresponding?-cuts. A real world numerical example is presented in which MSCI (Morgan Stanley Capital International Index) is chosen as the target index. The results are reported for a portfolio consisting of the six national indices. The performance of the proposed models is compared using several financial criteria.

Land subsidence in Iran is one of the environmental issues of recent years in Iran, which is mostly due to shortage of the groundwater resources. Iran is considered as the country with the most land subsidence. The amount of land subsidence in Iran is believed to be far more than the average of developed countries. Presently, over 300 plains of Iran are suffering from land subsidence and in some plains of Iran, the conditions have passed the subsidence and the ground has entered the critical stage of creating sinkholes. This paper presents an overview of the current status and challenging issues of land subsidence in Iran.

Data Envelopment Analysis (DEA) is one of the most popular techniques for measuring the relative efficiencies of a set of decision making units (DMUs), which use different inputs producing various outputs. Ranking of efficient DMUs is one of the most interesting DEA perspectives. However, there are cases where we see some limitations on available resources and the proposed model of this paper is associated with Indicator with Limited Sources (ILS), which affects ranking methods. The ILS exists as fixed amount in a community and the DMUs can own it with their abilities. When a DMU loses the same amount of the indicator, the rest of the DMUs are able to own some without even changing their capacities of other indicators and or vice versa. If a DMU looks for more of the same amount of the indicator, the rest of the DMUs have to supply it without even changing their capacity of other indicators. This paper develops a ranking method based on the ILS for the efficient DMUs, when there is changes either in inputs/ outputs ILS. The implementation of the proposed model is applied for a case study of banking system.

Many supply chain problems are involved with different parameters, which are under uncertainties. One of the primary concerns on supplier selection is to handle the uncertainty under different circumstances. The primary objective of this paper is to design a model to select suppliers and to determine the amount of purchase from any supplier in the supply chain system. For this purpose, we select the most important criteria using fuzzy questionnaires where the questionnaire uses experts’ opinions in terms of linguistic values. Then, a hierarchy multiple criteria decision-making (MCDM) model based on fuzzy-sets theory is proposed to rank different suppliers and using a goal programming approach, we determine the amount of order product from each supplier. The implementation of the proposed model is demonstrated using a real-world case study.