مصطفی حاجی آقایی کشتلی

Most research in the field of designing and planning bioethanol supply chains has been based on deterministic models, which do not consider dynamic environmental conditions and thus do not provide reliable outputs. Classic robust models did not have this weakness, but due to their excessive conservatism, they increased supply chain costs, making them unattractive to investors. Therefore, the aim of this study is to design and optimize the biomass-to-bioethanol supply chain network using data-driven robust optimization methods and disjunctive uncertainty sets.

The methodology of this study is a multi-methodology approach based on mathematical modeling and machine learning algorithms. Initially, uncertainty sets for the non-deterministic model parameter were created using K-means and SVC methods. Then, a data-driven optimization model was designed to optimize the biomass-to-bioethanol supply chain network, addressing the issues of previous classic approaches.

The findings of this study are presented in two categories: strategic and operational decisions. The strategic section focuses on determining the optimal locations for biomass cultivation, preprocessing centers, and refineries. In the operational section, the optimal amounts of biomass sent to preprocessing centers and refineries were determined.

This study, by producing robust solutions without the conservatism of traditional robust optimization approaches, can significantly attract public and private sector investors. Additionally, using a three-objective model based on a sustainable development approach that simultaneously considers economic, social, and environmental components, enhances the comprehensiveness of this research, providing more realistic and detailed results.

This research aims to develop a three-dimensional approach for evaluating and segmenting suppliers in the online retail market, using a combined qualitative and quantitative approach.

In this research, a mixed methodology based on four different methods including Meta-synthesis, Thematic analysis, Fuzzy Delphi, and Confirmatory Factor Analysis has been employed.

In this paper, a systematic review of research in the field of supplier segmentation was conducted using the meta-synthesis method. Ninety-six criteria were extracted and categorized into economic, social, and environmental components. Subsequently, content analysis was employed to gather opinions from other supply chain members, such as suppliers and customers. To adapt and customize the criteria to the nature of online retail activities, a Fuzzy Delphi method was used. The hierarchical model for evaluating and segmenting suppliers of dairy products in the Digikala online store was then presented. Furthermore, the proposed research model was evaluated and validated using the factor analysis method.

The results of data analysis in two qualitative and quantitative phases of the research indicated that, according to experts’ opinions, the hierarchical model of sustainable development is the most appropriate model for the evaluation and segmentation of dairy food suppliers in the online retailing market. In conclusion, 17 criteria framed within the economic, social, and environmental dimensions of sustainable development were introduced as components and criteria for evaluating and segmenting Digikala’s dairy food suppliers.

The supply chain management is an important factor in current competitive market. In recent years, the shortage of resources for answering an increasing food demand has increased researchers’ attention to the food supply chain. Given the importance of fish in the Household Food Basket, the development of aquaculture and recycling of returned goods in reverse logistics would significantly help with preserving water resources, as well as sustainable development. Therefore, government agencies and aquaculture industry beneficiaries are interested in reverse logistics. This study is focused on the optimization of a closed-loop supply chain of fish. To this end, a new bi-objective mathematical model is proposed that both minimizes total costs and maximizes fulfilling customers demand in uncertainty situation. Several well-known multi-objective meta-heuristic algorithms and a proposed hybrid meta-heuristic algorithm are applied to identify Pareto solutions. The solutions are then compared in terms of performance metrics. Also, the epsilon-constraint method and sensitivity analysis are used to validate the algorithms and evaluate the performance of the model. Lastly, the VIKOR method is used to select the superior method. To demonstrate the capability of the proposed model, a closed-loop supply chain of trout in northern Iran is investigated as a case study. The results show that the developed model could be effective in reducing the costs and increasing customer satisfaction.

Today, natural disaster is threatening the lives of people and their assets and properties.Relief supply chain is the most agile and dynamic supply chain. Indeed, the pivotal role of relief supply chainhas been identified in responding to crises and reducing the burden on humans. In this regard, the goal of allocatingand locating issues is to allow each injured person to receive the services and goods from the distributioncenters according to demand, and suppliers will also provide goods and services for the distribution centers. Thepresent study tried to provide a mathematical model for the problem of allocating earthquake victims to existingand potential hospitals using ultra-innovative algorithms. For this purpose, first, the design of its mathematicalmodel and its innovation aimed at minimizing the costs of establishing potential hospitals, sending injured peopleand sending services and goods from suppliers to hospitals. Then, the validation and accuracy of the mathematicalmodel were evaluated using Gamz software. In the next step, acceptable results were obtained with codingin MATLAB, using fire suppression algorithms and optimizing particle swarm. After the efficiency was established,the problem test was used for the data in order to compare the algorithms. Finally, it was identified thatthe particle swarm optimization algorithm was superior to the firewall algorithm in both runtime and cost values.

Green building has attracted widespread attention in recent years. Increasing building projects in Iran has had significant environmental impacts and green building implementation is an appropriate approach to reduce the environmental risks. Green building projects are unwittingly exposed to risks that reduce their efficiency, Therefore, identifying and ranking the risk can play a significant role in the success of green building projects. Due to the few studies in this area in Iran, This study aims to provide a new comprehensive framework of all the criteria and risks of green building projects. For this purpose, the first step aims to identify and to screen the risks from the viewpoint of experts in green building projects and introduction of risk assessment criteria. After that, the next step is to apply the SWARA method to obtain the weight of the criteria. Finally, Ranking of the risks of green building projects which is taking into account the results of the initial steps, has been done using the COPRAS method. This is the first attempt to solve a green building project by using a hybrid of SWARA and COPRAS through a case study (Amol). The results show that risks of: the low quality of materials and equipment, the stakeholder resistance to approve the green ideas and lack of realistic goals are very important. The proposed framework can help stakeholders of green building projects in developing countries to manage project risks more efficiently.

Recently, concerns have been raised about the environmental and social impacts of commercial activities. Also, most papers on the design of the supply chain network, focus on economic performance. Recently, some studies have considered environmental and social dimensions. There are still some gaps in modelling social impacts along with environmental and economic impacts. In this study, a multi-objective probabilistic model for designing a sustainable closed-loop logistics network is presented under uncertainty. The goals of this model consist of cost reduction, increasing social impact and decreasing environmental impacts. Then the model was solved not only using well-known Metaheuristic algorithms including genetic algorithm and Simulated Annealing (SA) algorithm, but also new whale optimization algorithm along with its improved combination method was used. Performance of algorithms was compared through examining different experiments and designed test problems. They compared in different situations and some important criteria. The results show the superiority of the whale optimization algorithm.

Green buildings are consistant objects which consider environment issues and natural resources throughout the life cycle. The risk of a green construction project reduces the accuracy of the estimation of the projects objectives and reduces the efficiency of green building projects. Therefore, identification and prioritization of risks can play a significant role in the success of green construction projects. In this research, we present a comprehensive model of all criteria and indicators of the risks of green construction projects; we have made the final ranking of green building construction risk indicators so that we can reduce the risk of green projects at run-time. For this purpose , at the first step, the identification and screening of indicators from the viewpoint of experts in green construction projects is considered by using the Fuzzy Delphi technique and then, the new method is used to combine the results of the network analysis process and the DEMATEL technique in fuzzy conditions to rank and evaluate the causal relationships between the factors and ultimately final prioritizing green building risk indicators have been done in terms of the results of the initial steps using the fuzzy Analysis Network Process (FANP) and DEMATEL technoique. The results of the research show that the risks associated with poor quality of materials and inappropriate equipment are very important and the lack of scale for the cost of activities has the least importance.

Nowadays, one of the most significant aims of supply chain, and also one of the most substantial evaluation criteria of any organization’s performance is making customers' satisfaction. Therefore, delivering products to the right place, at the right time, and with the lowest cost are considered to be important goals in supply chain management. The transportation cost is one of the most important and effective factor for pricing goods and the final price for customers. Also, fixed-charge transportation problem (FCTP) is a primary and important problem which attracts researchers in the last decade. Therefore, focusing on transportation costs in order to reducing the final price of goods is necessary to increase the place of industry among the competitors and the satisfaction of customers. In the problem, when a route in a solution is used, both fixed and variable costs are calculated for opening the rout and also for the amount of the transferred goods in the route. Contrary to the general TP, the FCTP is more difficult to solve because of the fixed costs that result in discontinuities in the objective function and makes it indissoluble by the straight application of the transportation algorithms. In this research, the fixed cost transportation problem with considering the discount limitation is modeled and solved. Due to NP-hardness of the problem, three metaheuristics Simulated Annealing (SA), Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA) are developed. Prufer number encoding is utilized to represent the solution in all algorithms. Because of importance of the parameters calibration, Taguchi method is used for tuning the parameters in algorithm designing. Besides, 28 test problems with different sizes are solved and compared with the results of GAMS.

In modern societies, production centers should be able to supply the variety of demand and type of productions correctly, exactly, clearly and straightly. So, this study aims to present the dynamic facility layout problem (DFLP) in order to minimize the total cost of the facilities and costumes. Design an optimal plan leads to reduce the production process and cost of inventory. Transportation cost plays a key-role to specify the performance designing of part systems. This content displays the Dynamic Facility Location Problem (DFLP) in this paper. In order to handle the proposed problem, five metaheuristic methods are introduced: Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) are tackled to solve the problem. Taguchi approach is considered to select the proper values for presented algorithms. The results explain that proposed methods are provided the optimal design for the dynamic facility location problem. As a result, ICA and PSO reach the most performance output in the comparison of other algorithms with assumed conditions in the problem.

In recent decades, the selection of suitable suppliers as one of the important issues in the field of supply chain management is considered. The evaluation and selection of the best suppliers need to make decisions based on different criteria. In this study, a hybrid MCDM approach consists of AHP and VIKOR methods has been proposed that at first paid to identify the most important criteria in the supplier selection based on previous studies and interviews with company executives. Then by using questionnaires, the paired comparisons of criteria and paired comparisons matrix of groups according to the views of managers and based on Analytic Hierarchy Process (AHP) is performed. Next, using a multi-criteria optimization technique (VIKOR), firm's suppliers are ranked. Furthermore, a case study in the automotive industry is applied to accumulate data for proposed framework. Finally, by identifying effective criteria, appropriate suppliers, and similarity of obtained results with other studies, the efficiency of the proposed model is proved.

Population growth, changing lifestyles, increased diseases, and natural and unnatural accidents are the cause of growing importance of health care chains. On one hand, the importance of blood as a rare and crucial stuff and on the other hand, the specific conditions of transportation and maintenance make researchers to be challenged in recent years. The importance of this area especially in a crisis and a severe shortage of blood can be increased. In this paper, a mathematical model of a closed-loop blood supply chain under uncertainty is proposed, in which reverse logistics and emergencies situation are also to be considered. Due to the complexity in solving the proposed model, after proposing accurate method to validate the model, a hybrid algorithm based on genetic algorithm (GA) and simulated annealing (SA) is used. Then, the validation of this hybrid algorithm is evaluated in comparison with the results of GAMS IDE/Cplex software in solving small, medium and large-sized problems. Finally, the results of the sensitivity analysis are presented.