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Power semiconductor devices are the most important components in power electronics applications. They are also the most fragile components of electronic circuits. A power semiconductor device's switching performance and protection depend on the gate drive circuit specifications. Therefore, choosing an appropriate gate-driver and designing its corresponding circuits is necessary. This paper is a technical review of the proper design of gate drivers for silicon power switches (like Si IGBT and Si MOSFET) in industrial power electronics applications. In this paper first conducts an overview of the main specifications of gate drivers for industrial power electronics applications. Then, concerning the protective role that a gate-driver can provide, crucial points of an effective design are discussed. Finally, a circuit is proposed to test the gate driver's short-circuit protection. The circuit is experimentally evaluated for three gate drives, and the results are discussed. A practical comparison of the protection performance of commercial gate drives ACPL-330J, ACPL331, and PC929 is also conducted.

AbstractOne of the significant factors that can make the behaviors, attitudes and interactions between employees better to offer high quality services is to acquire an organizational citizenship behavior. Focusing on the organizational citizenship behavior is crucial to the organizations operating in non-profit service sectors because of having positive and direct impact and thus making them profitable, efficient and effective. This paper aims to propose a model for organizational citizenship behavior by placing the components of trust in organizational spirituality and participation in non-profit service organizations. In this study, correlation analysis and factor analysis as well as the structural equation modeling (SEM) are applied. The population consists of employees working in the largest non-profit service organization in Iran. The findings show that the proposed model is valid and reliable, and there is a significant relationship between variables in the conceptual research model.

Supply chain management and distribution network design has attracted the attention of many researchers in recent years. The timely satisfaction of customer demands leads to reducing costs, improving service levels, and increasing customer satisfaction. For this purpose, in this research, the mathematical programming models for a two-level distribution network including central warehouses, regional warehouses, and customers are designed so that several products with definite demands in multiple periods are distributed from central warehouses to customers. In this problem, two objective functions are considered. The first objective function seeks to minimize the costs of establishment, transportation, inventory, and shortage, and the second objective function attempts to maximize the satisfaction level corresponding with the supply rate of different goods for numerous customers. The presented models include the basic model, inventory-based model, multi-period inventory-based model, and multi-period inventory-based reverse logistics model. The validation and applicability of the proposed models were demonstrated by implementation in a real case study of the automobile industry. The LINGO software 20.0 was used to solve the models. The results show that incorporating the inventory management policies into the basic model and converting from a single-period to a multi-period reverse logistics model will significantly increase company profitability and customer satisfaction.

Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied in recent years. This paper deals with an extended version of EVRP, in which electric vehicles (EVs) deliver goods to customers. The limited battery capacity of EVs causes their operational domains to be less than those of gasoline vehicles. For this purpose, several charging stations are considered in this study for EVs. In addition, depending on the operational domain, a full charge may not be needed, which reduces the operation time. Therefore, partial recharging is also taken into account in the present research. This problem is formulated as a multi-objective integer linear programming model, whose objective functions include economic, environmental, and social aspects. Then, the preemptive fuzzy goal programming method (PFGP) is exploited as an exact method to solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired by nature, including MOSA, MOGWO, MOPSO, and NSGAII_TLBO, are utilized to solve large-sized problems. The results obtained from solving the numerous test problems demonstrate that the hybrid meta-heuristic algorithm can provide efficient solutions in terms of quality and non-dominated solutions in all test problems. In addition, the performance of the algorithms was compared in terms of four indexes: time, MID, MOCV, and HV. Moreover, statistical analysis is performed to investigate whether there is a significant difference between the performance of the algorithms. The results indicate that the MOSA algorithm performs better in terms of the time index. On the other hand, the NSGA-II-TLBO algorithm outperforms in terms of the MID, MOCV, and HV indexes.

The root of organizational creativity depends on employee’s creativity and any factor increasing the staff ’s creativity. In recent years, specific features of the mind and the role of emotions in people are considered a competitive advantage that has been at the center of attention by organizations. Therefore, the present study focused on the relationship between spiritual intelligence and philosophical mentality on employee creativity and the mediating role of organizational commitment. To analyze the conceptual model of research, philosophical mentality the Smith’s Wellbeing Questionnaire for Workers, spiritual intelligence King’s Questionnaire, organizational commitment Allen and Meyer’s Organizational Commitment Scale, and organizational creativity Randsip’s Creativity Questionnaire were used for collecting data. Data analysis was performed using Pearson correlation coefficient and analysis of variance. The statistical sample included 278 employees of factories located in industrial towns of Isfahan, Iran. The results indicated the positive relationships between philosophical mentality and creativity, philosophical mentality and organizational commitment, spiritual intelligence and employee creativity, spiritual intelligence and organizational commitment, organizational commitment, and employee creativity. In addition, philosophical mentality and spiritual intelligence can affect the employee’s creativity through organizational commitment as a mediator variable.

The aim of this study was to develop a multi-objective mathematical programming model for the trade-off of time, cost, and quality in the project-scheduling problem (PSP) by taking priorities and resource constraints as well as activity preemption into account. First, a small-sized problem instance that was a sub-project of an oil and gas construction project was used for te validation of the proposed model and algorithm. Subsequently, considering the sensitivity, complexity, and importance of oil and gas projects, the proposed model was implemented in a large-sized oil and gas construction project. Considering the NP-hardness of this problem, the NSGA-II metaheuristic algorithm was used to deal with the time, cost, and quality trade-off problem. Finally, a sensitivity analysis was implemented on the three main parameters of time, cost, and quality to investigate the effects of changes on the results. The findings show that the proposed model is more sensitive to cost changes, so an increase in project costs leads to a drastic change in the values of other objective functions.

To prevent the great dangers caused by emergency situations, providing rapid and high-quality emergency aid highly depends on the location of emergency medical centers. The purpose of this research is to present a multi-objective mathematical programing model based on the minimum P-envy algorithm to locate and construct emergency medical services (EMS). Maximizing the coverage in order to increase the probability of survival of different categories of patients, minimizing the costs of constructing EMS and optimizing the ratio of regions having the right to emergency medical services is one of the fundamental challenges in the health care system of countries. In this paper, a model for maximum utilization of EMS considering budget limitations is presented. In this study, since the problem is NP-Hard, the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm were used to solve this problem. The parameters of the metaheuristic algorithms were tuned using the Taguchi method. Several instance problems were solved to compare the performance of 2 algorithms. The results demonstrate that the validity of the proposed model. Also, the mean of the solutions obtained by GA for small, medium, and large-size problems are better than the SA algorithm. Also, the GA algorithm obtained more efficient solutions than the SA algorithm.

Due to the growing competition of firms in manufacturing industries, the enhancement of the design of new products for quick and collaborative interaction of stakeholders is inevitable. A customer-oriented approach during the enhancing of product design requires the support of various stakeholders’ collaboration from their functionality perspectives. Accordingly, manufacturing firms try to increase the product quality through the continuous collaboration and speeding up of the design process. Moreover, new manufacturing paradigms like cloud manufacturing require global and distributed collaboration of stakeholders. One competitive key necessity for firms is the integration of stakeholders in the design and production stages. This article has proposed an interactive and integrated framework for a cloud collaborative manufacturing system. The proposed framework uses the international STEP standard which ensures data integrity in the whole product development processes. The framework enables stakeholders to work collaboratively and interactively in their disciplines, while it also ensures coherency of modifications to avoid conflicts. The proposed model is introduced using an improvisational design method based on the STEP standard to create an interactive platform with a service-oriented approach and supports stakeholders for their own semantics. In addition to examining the needs of an interactive system, the article provides an architecture for implementation of this perspective. A comprehensive case study is designed to show the capabilities of the proposed framework.

This paper studies the monetary policies of the central bank to determine the inter-est rate on deposits in the interaction with the Iranian banking system in the form of Stackelberg and Nash equilibrium games. The leader of the game is the central bank of the Islamic Republic of Iran, while the followers of the game include three banks called A, B, and C. The leader of the game regulates its monetary policies based on the relationship between inflation rate and interest rate on depos-its in the form of three scenarios of "legal deposit ratio", "legal deposit award rate", and "the rate of commissions received" from the followers. The follower players also determine "the interest rate on deposits," based on the scenarios of the leader player. The results of this research (2010-2019) by MINITAB Soft-ware indicated that in the studied year (2019), the strategy of the players of this game has been mostly Nash (more competitive) rather than cooperative. If the players of this game had chosen cooperative strategy (Stackelberg game), they would have achieved greater profit. Also, the optimal tool for the monetary policy of the leader and follower players has been the “increasing the legal reserve re-ward rate".

Introduction:The destructive effects of corporate earnings manipulation and the contradictory empirical evidence surrounding the effect of CEO's psychological characteristics on the likelihood of earnings manipulation have doubled need to explain the reasons for this relationship.Given that the decision to manipulate profits is with the management of the company, so his psychological characteristics can be effective in whether or not to manipulate profits. The purpose of this study is to investigate the effect of narcissistic and overconfidence characteristics of the CEO on the possibility of profit manipulation using the Benish model and to analyze the auditor's reaction to the possibility of profit manipulation.MethodsThe present research is applied in terms of purpose and the research method is post-event causal. To test the research hypotheses, 156 companies have been selected from the companies listed on the Tehran Stock Exchange in the period 2012-2018.ResultsThe results of the research using binary logistic regression indicate the effect of CEO narcissism on the possibility of profit manipulation using the modified Banish model. Also, other research hypotheses about the relationship between CEO overconfidence and the possibility of profit manipulation as well as the auditor's reaction to the possibility of profit manipulation were not confirmed.

Location-routing is an extremely important problem in supply chain management. In the location-routing problem, decisions are made about the location of facilities such as distribution centers as well as the set of vehicle routes. Today, organizations seek to reduce the transportation cost by outsourcing leading to a particular kind of transportation problems known as open routing. However, the increasing attention to environment have led to paying more attention to environmental issues and reducing the environmental impacts of logistics activities. To this end, in this paper, both open and closed routes were simultaneously addressed by developing a multi-objective mixed integer linear programming model that included three economic, environmental, and social responsibility aspects. The three objective functions of the proposed model encompass the minimization of total costs and greenhouse gas emissions, and the maximization of employment rate and economic development. Also, in this study, a different type of routing was considered in each echelon. A small-sized problem instance was solved using the Augmented Epsilon Constraint (AEC) method with the CPLEX Optimizer Solver for the validation of the proposed model. Moreover, the sensitivity analysis was performed to investigate the effect of changing main parameters on the values of the objective function. Due to the NP-Hardness of the problem, two efficient metaheuristic algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Stochastic Fractal Search (MOSFS) were exploited to solve the medium and large size problems. The performance of the algorithms was compared on the basis of six different well-known indexes of Time, MID, RAS, Diversity, Spacing, and SNS. According to the obtained results, the performance of the MOSFS algorithm was %20, %9, %11.22, %10.03, and %19.06 higher than the performance of the NSGA-II on the basis of SNS, RAS, MID, Diversity, and Time indexes, respectively. On the other hand, the NSGA-II performance was %6.3 higher than the MOSFS performance in terms of Spacing index.

This study seeks to introduce the influential factors in controlling and dealing with uncertainty in intermittent demand. Hybrid forecasting and Grey Theory, due to their potential in facing complex nature, insufficient data, have been used simultaneously. Different modeling, unbiased weighting results have been used in estimating the safety stock(SS) by both theoretical and experimental methods. In other words, this work deals with the less studied feature of various modeling errors and their effect on SS determination and recommends its use to address the uncertainty of intermittent demand as a criterion for introducing a superior model in the field of inventory.

Predictive control has emerged as a promising control method in a variety of technological fields. Model predictive control, as one of the subdivisions of this control method, has found a growing number of applications in power electronics and motor drives. In practical implementations, model predictive control faces performance degradation of the controlled plant due to its dependency on a model. There are considerable numbers of review papers that are devoted to the different points of view of predictive control. However, the existing literature lacks a review study that addresses the solutions for parameter dependency of the model predictive control method. Recently, model-free predictive control has been used in drives and power electronics as a solution for dealing with the model-dependency of the model predictive control method. There are many papers that have used such methods. In this paper, a classification is proposed for the different implementation types of model-free predictive control or similar methods that address model parameter uncertainties. Additionally, a comparison between the methods is also presented.

Project scheduling models with resource constraints and multi-mode activities aims to create a schedule for carrying out activities considering precedence constraints and available resources in order to minimize the project duration. In the real world, we face uncertainty related to projects, where there are no historical data, hence, we should rely on the experts' judgements to estimate activity durations. For this purpose, in this paper, the 99-simulation method is used to deal with uncertainty. The exact mathematical programming model is presented in this paper and the hybrid algorithm based on Genetic Algorithm is used to solve this type of project scheduling problem which finds the near-optimal solution in a short computational time. Finally, the effectiveness of the proposed model is examined with a numerical example.

The construction supply chain network has been facing challenges in relation to reducing cost and delivery time, increasing the quality of the built assets, and reducing environmental pollution. These issues have caused contractors and project managers in this industry to note the concept of green construction supply chain management (GCSCM). This study examined the most important challenges and barriers to the implementation of GSCM in the construction industry. In this paper, the components and sub-components of GCSCM were identified using the literature review and opinions of the experts according to the supply chain management. The opinions of construction experts and project managers were collected through focus group meetings. The components were categorized into five main and supporting groups, with “Green Design”, “Green Procurement”, and “Green Production” as the main components and “Green Management” and “Green Information” as the supporting components. Subsequently, the sub-components, in regard to each component, were distinguished. Finally, the fuzzy best–worst method (BWM) was utilized to determine the importance weights of the identified components and sub-components through the opinions of five experts with practical relevant experience. The findings of the fuzzy BWM method show that “Green Design” is the most important component, followed by “Green Management” and “Green Implementation”. Additionally, “Lack of designers, contractors and planners” was ranked the first among the identified sub-components. This paper can assist construction managers, contractors, and policymakers with finding and overcoming the barriers and obstacles of implementing GCSCM.

International laws and increasing consumer awareness have led to drastic changes in traditional supply chain network designs. Moreover, because of environmental and social requirements, traditional supply chain networks have changed to sustainable supply chain networks. On the other hand, reverse logistics can be effective in terms of environmental and economic aspects, so the design of the supply chain network as a closed loop is necessary. In addition, customers have a demand for different products with different quality levels. Considering different types of customers with a variety of consumption trends can be a challenging issue, and is addressed in this study. The main contributions of this research are considering different quality levels for products as well as different tendencies of customers towards environmental issues. In this study, a sustainable closed-loop supply chain model is designed that seeks to balance economic, environmental, and social responsibilities. In this paper, costs and customer demands for different types of products at different quality levels are considered under uncertain conditions using a robust possibilistic programming method. The proposed multi-objective model is solved using the Augmented Epsilon Constraint (AEC) method that provides an efficient set of solutions for all decision-making levels. The results show that the robust possibilistic programming method is more effective in dealing with uncertainties than the possibilistic programming method.

Recently, manufacturing firms and logistics service providers have been encouraged to deploy the most recent features of Information Technology (IT) to prevail in the competitive circumstances of manufacturing industries. Industry 4.0 and Cloud manufacturing (CMfg), accompanied by a service-oriented architecture model, have been regarded as renowned approaches to enable and facilitate the transition of conventional manufacturing business models into more efficient and productive ones. Furthermore, there is an aptness among the manufacturing and logistics businesses as service providers to synergize and cut down the investment and operational costs via sharing logistics fleet and production facilities in the form of outsourcing and consequently increase their profitability. Therefore, due to the Everything as a Service (XaaS) paradigm, efficient service composition is known to be a remarkable issue in the cloud manufacturing paradigm. This issue is challenging due to the service composition problem’s large size and complicated computational characteristics. This paper has focused on the considerable number of continually received service requests, which must be prioritized and handled in the minimum possible time while fulfilling the Quality of Service (QoS) parameters. Considering the NP-hard nature and dynamicity of the allocation problem in the Cloud composition problem, heuristic and metaheuristic solving approaches are strongly preferred to obtain optimal or nearly optimal solutions. This study has presented an innovative, time-efficient approach for mutual manufacturing and logistical service composition with the QoS considerations. The method presented in this paper is highly competent in solving large-scale service composition problems time-efficiently while satisfying the optimality gap. A sample dataset has been synthesized to evaluate the outcomes of the developed model compared to earlier research studies. The results show the proposed algorithm can be applied to fulfill the dynamic behavior of manufacturing and logistics service composition due to its efficiency in solving time. The paper has embedded the relation of task and logistic services for cloud service composition in solving algorithm and enhanced the efficiency of resulted matched services. Moreover, considering the possibility of arrival of new services and demands into cloud, the proposed algorithm adapts the service composition algorithm.

Facility layout problem is one of the most important problems in a huge range of industries and services organizations. Simultaneous study of some qualitative and quantitative parameters like closeness relationship between facilities, physical constraints such as input/output points and how to arrange facilities can play a key role to determine the facility layout. Considering these parameters can lead to reduce production costs, increase production capacity, and remove additional displacements. A two-stage approach is proposed to achieve these goals. In the first stage, a goal programming model is proposed to determine weights of the attributes based on the experts' opinions with different preference representation structures. Afterwards, the closeness ratings of the facilities are calculated using weights of attributes. In the second stage, an efficient layout is designed by determining facilities placement sequence, location of the next facility adjacent to the previous one, location of input/output points, and rectilinear feasible shortest path between facilities. Two meta-heuristic algorithms including particle swarm optimization and genetic algorithm are designed due to computational complexity. The objective function is to minimize the sum of products distance between facilities and closeness rating and also to minimize the dead space. A case study of an Auto Body Parts company is demonstrated to verify the efficiency of the proposed two-stage approach. Furthermore, in order to assess the performance of the proposed approach, a comparison is drawn between the proposed approach and five existing approaches in the literature to solve different problems by using the two meta-heuristic algorithms.

Considering necessary fundamental and structural changes in the production and manufacturing industries to fulfill the industry 4.0 paradigm, the proposal of new ideas and frameworks for operations management of production and manufacturing system is inevitable. This research focuses on traditional methods proposed for storage assignment problem and struggles for new methods and definitions for industry 4.0 based storage assignment concepts. At the first step, the paper proposes a new definition of storage assignment and layout problem for fulfilling storage mechanism agility in terms of automated store and retrieval process (AS/RS) in modern inventories. Then considering the shortcomings of traditional algorithms for storage assignment problem, the paper contributes a new algorithm called SAO/FEM (storage assignment optimization technique), inspired from mechanical engineering discipline for analysis and optimization of storage assignment problem. The proposed new algorithm about stress distribution analogy, and the help of the Finite Element Method and minimum total potential energy theory, proposes a new model for storage assignment optimization. The efficiency of the proposed algorithm in terms of calculation time and the best answer investigated through numerical examples. The article has developed an application for SAO/FEM algorithm as a value creation module and applied new optimized storage positioning in the warehouses.

This study aims to investigate and identify the factors affecting the empowerment and implementation of knowledge management in organizations as well as the impact of knowledge management on organizational performance. This study also examines the mediating role of human capital in the relationship between knowledge management and performance of Kabul Steel Plant, which is the largest steel plant in Afghanistan. The research model was developed through the literature review. The initial data were collected through a questionnaire containing 48 questions. Participants were 108 managers and administrative staff of the company. The collected data were analyzed by using the SPSS and SmartPLS software. The hypotheses regarding the impact of strategy and technology on knowledge management were rejected by using correlation analysis and t-test statistic. Finally, the findings showed the positive effects of variables of structure, culture, leadership, and trust on knowledge management in an organization. Also, knowledge management influences the organizational performance, both directly and through the mediating variable of human capital. This research encourages the managers and employees of organizations to use the available organizational resources to implement knowledge management in organizations and improve knowledge management practices and human resources that are the most valuable resources of any organization in order to remain competitive in the markets.

One of the main factors in the success of projects is communications management and proper and timely distribution of information among all internal and external project stakeholders. Despite of emphasizing on the importance of project communications in the literature, there are few studies identifying factors influencing project communications. This research aims to address this shortcoming by identifying and determining sequences and relationships factors affecting project communications and their clustering. The informed communication strategy allows managers to structure the information flow in a better and more controlled manner and to avoid the costs caused by lack of effective and timely communication. The present study is conducted to help clarify the views of the organization's managers and project managers on project communications, and to identify factors affecting it and how they effectively communicate to successfully accomplish the projects. First a number of factors influencing project communications are identified on the basis of previous studies and interviews with experts and project managers working in oil, gas and power plant construction megaprojects in Iran. Then, these factors are analyzed by using the combination of fuzzy DEMATEL and Interpretive Structural Modeling (ISM) techniques. The relationships and sequence between the indicators are determined so that it can be effective in project communications planning and project success through providing an insight for senior managers and project managers.

This paper considers a Capacitated Location-Arc Routing Problem (CLARP) with Deadlines (CLARPD) and a fleet of capacitated heterogeneous vehicles. The proposed mixed integer programming model determines a subset of potential depots to be opened, the served roads within predefined deadlines, and the vehicles assigned to each open depot. In addition, efficient routing plans are determined to minimize total establishment and traveling costs. Since the CLARP is NP-hard, a Genetic Algorithm (GA) is presented to consider proposed operators, and a constructive heuristic to generate initial solutions. In addition, a Simulated Annealing (SA) algorithm is investigated to compare the performance of the GA. Computational experiments are carried out for several test instances. The computational results show that the proposed GA is promising. Finally, sensitivity analysis confirms that the developed model can meet arc routing timing requirements more precisely compared to the classical Capacitated Arc Routing Problem (CARP). First published online 26 September 2019

Time, cost, and quality have been known as the project iron triangles and substantial factors in construction projects. Several studies have been conducted on time-cost-quality trade-off problems so far, however, none of them has considered the time value of money. In this paper, a multi-objective mathematical programming model is developed for time-cost-quality trade-off scheduling problems in construction projects considering the time value of money, since the time value of money, which is decreased during a long period of time, is a very important matter. Three objective functions of time, cost, and quality are taken into consideration. The cost objective function includes holding cost and negative cash flows. In this model, the net present value (NPV) of negative cash flow is calculated considering the costs of non-renewable (consumable) and renewable resources in each time period of executing activities, which can be mentioned as the other contribution of this study. Then, three metaheuristic algorithms including multi-objective grey wolf optimizer (MOGWO), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective particle swarm optimization (MOPSO) are applied, and their performance is evaluated using six metrics introduced in the literature. Finally, a bridge construction project is considered as a real case study. The findings show that considering the time value of money can prevent cost overrun in projects. Additionally, the results indicate that the MOGWO algorithm outperforms the NSGA-II and MOPSO algorithms.

The increasing number of construction projects together with the limited resources of organizations led to tough competition for achieving project goals. Time, cost, and quality have been known as the project iron triangle. Project managers attempt to allocate the appropriate resources and make the best decisions for accomplishing projects with the shortest durations, lowest costs, and the highest quality. No study has examined the time–cost–quality trade-off problem with decision-making approaches. In this study, the fuzzy multi-criteria decision-making (MCDM) methods are exploited to choose the best mode for performing each activity. For this purpose, the SWARA method is applied to determine the importance weights of time, cost, and quality. In addition, the TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) technique is used to rank and select the best activity execution modes. The proposed model is implemented on two medium- and large-size construction projects to evaluate its efficiency. Several execution modes with fuzzy duration, cost, and quality are considered for each project activity. Finally, sensitivity analysis is conducted taking three different conditions into account: the shortest duration of the execution modes, the lowest cost of the execution modes, and the highest quality of execution modes for each activity. The solution of each trade-off is compared with the solution obtained from the fuzzy SWARA–TOPSIS method. The schedule is developed according to the best execution mode for each project activity. The obtained results in two different construction projects show significant improvements in the overall project objectives so that the projects can be completed in fewer durations and costs along with higher quality. Because of the higher importance of cost, the cost of each activity is closer to the lowest cost. The activity duration is also closer to the most likely duration, and quality is closer to the high-quality level. The application of this approach can create new opportunities for research and knowledge development in the field of construction project scheduling.

Purpose: Nowadays, governments and people pay more attention to use green products due to environmental pollution, irreplaceable energy and shortage of resources. Green products are resulted from the application of green supply chain management strategies to the organizations' performance strategies, so that we can reduce environmental pollutants and wastes and take a step towards saving energy with limited resources. Methodology: In this paper, the effect of reducing energy consumption in green supply chain is examined by using queuing theory and transportation models. Data was generated and solved by a commercial optimization epackage. Findings: The findings indicate that suitable assignment of existing transportation fleet with specified capacity, and using queueing theory in a closed-loop network to reduce the queue length and handle congestion, can cause a reduction in energy consumption by optimizing transportation and waiting times in a green supply chain. Originality/value: Adopting investment strategy in improving the environmental performance of the supply chain, will yield in many advantages and benefits. This article investigates the effect of queuing theory on reducing waiting time, optimizing energy consumption in green supply chain, and consequently decreasing pollution.

The present paper aims to propose a fuzzy multi-objective model to allocate order to supplier in uncertainty conditions and for multi-period, multi-source, and multi-product problems at two levels with wastages considerations. The cost including the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects, partial coverage of suppliers in respect of distance and finally, suppliers weights which make the products orders more realistic are considered as the measures to evaluate the suppliers in the proposed model. Supplier's weights in the fifth objective function are obtained using fuzzy TOPSIS technique. Coverage and wastes parameters in this model are considered as random triangular fuzzy number. Multi-objective imperial competitive optimization (MOICA) algorithm has been used to solve the model,. To demonstrate applicability of MOICA, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms and results are analyzed using quantitative criteria and performing parametric.