فصلنامه مطالعات مدیریت صنعتی
پیاپی 60 (بهار 1400)

Industries in Iran are not aware of the characteristics of after-sales service and its impact on customer satisfaction. Despite the research, a coherent model has not been developed on the vital issue of after-sales services in the liquid gas industry in Iran. Considering the complexity of designing the after-sales service model in the world-class liquid gas industry, a methodology approach to soft systems has been exploited. In this research, using the above approach, the problem of non-structured design, model design at the world-class level is explained, and then by specifying its boundaries, the image of the various actors of the system and their benefits are depicted. In the third step, the CATWOE approach is used to explain the basic definition of the after-sales service model in this industry, and in the fourth stage, a conceptual model of activities is presented using the root definition. The researcher will exploit this phase of the integration of ISM and Fuzzy Delphi in the process of solution. In the fifth step, the developed model is compared with the real world. In Phase VI, desirable and feasible changes were identified in terms of identifying system whit IAP Technique constraints. Finally, suggestions are given to authorities and stakeholders to develop the model to reach the world-class level.

With the tremendous progress in communications in the world, the transformation and behavior of mobile operators and their digitalization, which in the past were only service providers, as well as the creation of different experiences for customers, is inevitable.

The purpose of this study is to create a hybrid simulation of system dynamics and agent based modeling in order to analyze the revenue of the first operator in the country to enter the field of digital platform and development of native applications. Using the model proposed in this article, first the need for operators to enter the digital arena and produce native applications was expressed. Then, the factors that affect the mobile ecosystem that affect the production of applications and the development of required platforms were described. Benefiting from hybrid simulation of system dynamics and agent based modeling, the income of mobile operator in entering and not entering the digital arena and producing native applications were examined.

The findings showed that with the entry of the operator into the field of production of native applications and the adoption of digital approach, consumers tended to use more data services, but due to different tariffs for data and voice, the operator's income up to 2 Next years will not change much.

Health care solid waste includes all types of waste that are produced as a result of medical and therapeutic activities in hospitals and health centers. About 15% to 20% of these waste materials are infectious waste, which falls within the category of hazardous materials. Infectious waste is the one that must be treated before disposal or recycling. Hence, this paper seeks to develop a bi-objective mixed integer programming model for the management of infectious waste. In the proposed model, in addition to minimizing the chain costs, the reduction of risks for the population exposed to the spread of contamination resulting from infectious waste is also desired. For this purpose, a multi-echelon chain is proposed by taking into account the green location-routing problem, which involves the location of recycling, disposal, and treatment centers through various treatment technologies and routing of vehicles between treatment levels and the hospital. The routing problem has been considered to be multi-depot wherein the criterion of reducing the cost of fuel consumption of heterogeneous cars is used for green routing. Finally, a hybrid meta-heuristic algorithm based on ICA and GA is developed and, following its validation, its function in solving large-scale problems has been investigated. The results indicate that the proposed algorithm is effective and efficient.

Studies show that there are shortcomings in the deployment of the Internet of Things (IoT) in the supply chain of agricultural products, especially in the field of quality control in the logistics sector, and researchers can model the existing theoretical gaps through modeling and optimization. Therefore, the purpose of this paper is to identify the most important categories affecting the deployment of the Internet of Things in the wheat supply chain storage sector and explain and mapping the relationship between these categories. For this purpose, the present article uses systematic review methodology and meta-synthesis method by searching Web of Science and Scopus citation databases. Then, the grounded theory coding procedures were used to determine categories and themes. Finally, the results of meta-synthesis lead to the identification and extraction of 3 macro categories; Internet of Things technology, the main category (IoT-based storage), and the results and consequences of Internet of Things deployment.

One of the most important approaches that can lead to the creation of various advantages for enterprises is the districting regions into the service offering locations and the demand units, which causes the increase in level of customers’ access to get the service. On the other hand, if vehicle routing is carried out in districting regions in order to deliver products to customers, the planning of customer service can be improved. However, in none of the research conducted in the area of design supply chain, vehicle routing in districting regions has been not investigated. Therefore, in the current study, a bi-objective mathematical model is presented to simultaneously focus on districting regions, facility location–allocation, service sharing, intra-district service transfer and vehicle routing. The first objective function is to minimize the total cost of designing the CLSC network, which includes costs of opening facility and vehicle routing. The second objective function is to minimize the maximum volume of surplus demand from service providers in order to achieve an appropriate balance in demand volume across all regions. Moreover, a robust optimization approach is used to take into account uncertainty in some parameters of the proposed model. In addition, the validity of the proposed mathematical model and the proposed solution on a real case study in the oil and gas industry has been investigated.

Knowledge Management (KM) can improve Supply Chain (SC) performance through some frameworks such as EFQM Excellence Model in order to achieve competitive advantage. In this paper first, the KM enablers in SC are classified based on EFQM enabler criteria. Then, the importance of KM enabler are evaluated by fuzzy Dematel-ANP. In addition, fuzzy AHP is applied to evaluate the importance of each KM enabler in knowledge sharing. In the research, the Multi-objective Mix-integer non-linear programming (MINLP) is used to optimize the performance of knowledge management and select KM strategy in each part of SC. Likewise, it is approved to select suitable members of SC for Research and Development unit of SC. The results represent that each part of SC should be developed by concentration on some KM enablers, and selection of a suitable strategy. In addition, they emphasis on the effect of each KM enabler on selection of suitable members.

In the real world, businesses with hybrid flow-shop manufacturing environment have faced the constraints in machinery, human resource constraints and increasing salary and have tried to use their human resource better. Given the limitations of these resources, customer delivery requirements have made the job rejection essential in order to meet distinct customer requirements. Therefore, this paper considers the dual resource constrained hybrid flow-shop scheduling problem with job rejection in order to minimize the total net cost (the sum of the sum of costs obtained from rejected jobs and total penalty cost) which has high practical and operational relevance in many industries. The present study is developed a new mixed integer linear programming model for the problem. In addition, a new improved sooty tern optimization algorithm (STOA) is proposed to solve large-size problems as well as a new decoding method due to the NP-hardness of the problem. In order to evaluate the proposed optimization algorithm, 5 well-known algorithms in the literature including (Immunoglobulin-based artificial immune system (IAIS), Genetic algorithm (GA), Novel discrete artificial bee colony algorithm (DABC), Fruit fly optimization (IFFO), Effective modified migrating birds optimization (EMBO)) are adapted with the proposed problem and finally the performance of the proposed optimization algorithm is investigated against the adapted algorithms. All results and evaluations show the good performance of the new improved sooty tern optimization algorithm.