فصلنامه چشم انداز مدیریت صنعتی
سال یازدهم شماره 3 (پیاپی 43، پاییز 1400)

The supply of rotating equipment for the oil industry poses a serious risk to the sustainable production of the products due to equipment wear, high costs, and high downtime. In this research, a systematic intervention is done in the system of sustainable supply of rotating equipment spare-parts in the oil industry as to the Abadan Oil Refinery National Company’s data, applying a combined approach of System Dynamics (SD) and Strategic Assumptions Surfacing and Testing (SAST), and the agreed solutions are extracted as: 1) economic sustainability, namely oil industry investment in spare-parts’ manufacturing industry, reforming the repair-maintenance contracts, and monitoring the process of designing and manufacturing the spare-parts, 2) social sustainability, say, amending the law on supply tenders, transferring the experience among the spare parts’ manufacturers and the oil industry specialists, and increasing the user training and safety training, and 3) environmental sustainability, including decommissioning worn-out equipment and replacing with green equipment, observing the safety and environmental standards, and reviewing the preventive predictive maintenance program. The results show that the sustainable supply of spare-parts will improve by combining the three strategies of economic, social and environmental sustainability, accompanied by government support of the spare-parts’ manufacturing industry and local specialists’ training plans.

Credit risk is one of the most important risks which banks and financial organizations face. It is related to unpaid and delayed installments. Banks evaluate their customers' credit in order to prevent this hazard. Development banks, which are the focus of this research, fund facilities based on working capital, so customers sometimes do fraud in declaring working capital. Considering fraud consequences and making a credit scoring model with sensitivity to fraud are the main aims of this research.  The statistical population of this research includes companies who have referred to branches of an Iranian Bank. This research includes 55 financial and non-financial variables based on the credit scoring model. In the first step, fraudulent companies have been realized. Finally, in order to offer an optimized and sustainable model through merging machine learning methods and reporting performance evaluation indicators, the impacts of fraud have been considered.

Hub location-routing problem is a practical subject in the last decades. This study considers a many-to-many hub location-routing problem where the best locations of hubs and tours for each hub are determined with simultaneous pickup and delivery. First, an optimization model is proposed to minimize the total sum of fixed costs of locating hubs, the costs of handling, traveling, assigning, and transportation costs. To find practical solutions, the hubs have constrained capacity, in which single allocations can service every node to the hubs. What is more, the balancing requisites are imposed on the network by allocating the appropriate number of demand nodes to the hubs. Then the problem is solved using GAMS software for small-size instances of the problem. Due to the NP-hard nature of the problem, the proposed optimization model is solved by the Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA). For the problem instances, the comparative results indicate that GA has a better performance compared to ICA, and incorporating capacity and balancing considerations can influence the reduction of costs of the investigated network.

According to previous research, most studies on the evaluation of sustainable supply chain capabilities are limited to statistical variables and mathematical modeling. The purpose of this study is to propose multiple capacity evaluation models in the sustainable supply chain. The spatial scope of this research includes a survey of 16 companies active in the ceramic & tile industry in Iran. According to fuzzy expert system modeling, four capabilities, including competitiveness, operational, technology, and resilience as input variables and three levels for sustainable supply chain capabilities as output variables were determined. Simulink was used to simulate and integrate the designed fuzzy systems. The evaluation results show that most of the companies studied fall into the level 2 capability. It is recommended that the ceramic tile manufacturing companies evaluate the level of capability and determine the sustainable capabilities of their supply chain capabilities to exploit these variables. Resiliency capabilities have an impact on upgrading the evaluation of multiple capabilities in the sustainable supply chain. Therefore, companies are advised to incorporate flexibility and adaptability into developing domestic and foreign markets and market orientation and not to define future projects as environmental changes.

Inventory control of blood and its products is the most important challenge in efficiently managing a blood supply chain as blood products are perishable. In addition, the replenishment of blood, which donors supply, and the demand of patients for blood products are uncertain. In particular, platelets with the shortest lifespan, up to five days, are the most expensive blood products. Therefore, this study investigates the problem of platelet inventory control in a hospital network using integer programming. Due to the uncertainty of the demand of patients in hospitals, a scenario-based robust optimization approach is applied to minimize the worst-case performance of the system. The proposed model is coded and solved using GAMS software. The results demonstrate that using a robust optimization approach reduces hospital costs by 7.6% on average compared to a solution that ignores uncertainty. Moreover, the efficiency of hospitals' inventory control depends on the capacity of the Blood Transfusion Organization to deliver fresh platelets.

In this research, it has been tried to optimize the efficiency of employees by considering the concept of human factor engineering in scheduling. Due to the importance of human parameters such as learning and forgetting in employees' skills, especially during job rotation, these factors have been studied and modeled in the issue of staff job rotation scheduling. For this purpose, a nonlinear integer programming model is proposed for scheduling problem of employees with two types of skills. The objective function of the model is to maximize the employee performance. Different examples are solved by considering different parameters to analyze the effects of staff costs, learning and forgetting on staff scheduling efficiency. To solve this problem, GAMZ software is used. The results showed that the proposed model has the ability to provide employee scheduling plans with the aim of maximizing employees. The computational results also indicated that learning and forgetting rate play an important role in determining the optimal scheduling plan and the use or non-use of semi-skilled workers and the movement of employees between machines. The proposed model and the results of this research help employers in using a variety of scheduling schemes and system optimization with dual constraints.

In this research, an integrated scheduling problem of job shop systems with an assembly stage and transportation to minimize the total tardiness time is studied. In this problem, the parts are processed in a job shop system and then assembled in the assembly stage. Ultimately, the products are shipped in packages to customers. Setup time is assumed to depend on sequence. At first, a mixed-integer linear model is developed. Since the problem is NP-hard, a hybrid imperialist competitive and simulated annealing (ICA-SA) algorithm is proposed to solve the problems with the medium and large sizes. To validate the performance of the proposed algorithm, results are compared to an imperialist competitive algorithm and a hybrid imperialist competitive and tabu search (ICA-TS) algorithm. Analysis of variance random block design is used to compare the results of the algorithms. P-values of algorithms and blocks in this test are smaller than the significance level of 0.05. The computational results show that the proposed hybrid algorithm achieves better performance than the imperialist competitive algorithm and hybrid imperialist competitive and tabu search.

Attention to environmental issues has led to the application of reverse logistics in the supply chain. But, the implementation of reverse logistics is weak because of the type of inventory management models. Therefore, in this research, to improve supply chain performance was used lean, agile, resilient, and green (LARG) paradigms. The purpose of this research is to identify and prioritize the solutions of reverse logistics implementation in the LARG supply chain to improve supply chain performance. In this research, the Interval-valued Intuitionistic Fuzzy expert-driven approach was used. Interval-valued Intuitionistic Fuzzy sets were used for weighting the evaluation criteria, and the Interval-valued Intuitionistic Fuzzy WASPAS method was used to prioritize solutions. The findings indicated that the first solution (creation, development, and investment in reverse logistics technology), the Tenth solution (development of the closed-loop supply chain through integration with reverse logistics), and the ninth solution (building electronic collaboration for rapid and effective coordination in among the members of the supply chain), respectively, were introduced as the best solutions in this study. The development and investment in reverse logistics technologies, electronic integration, and collaboration, and improved coordination are essential to improve the performance of reverse logistics implementation in the supply chain.

Natural crises Threaten Human life and property every year. So planning for disaster Preparation is essential. Flood Threatens Thousands of People around the World. Flood damage is different in different areas. Therefore, identifying and classifying floodplains in each region is one of the measures that can be taken to manage and reduce flood damage. By identifying and prioritizing flood vulnerable areas can Reduce flood damage. In this study pre-identified flood-vulnerable areas in Amol city are prioritized according to criteria such as population density, distressed areas, distance from rivers and access to cities and roads. Using analytic hierarchy process (AHP) five flood-vulnerable areas are prioritized. Then, a bi-objective mathematical model is provided to determine the best locations to set up relief sites and the amount of relief goods and machines required as preparation for quick disaster response. Finally, solutions are provided to increase the allocation to areas with higher accountability priorities and lower costs.

In this research, the issue of product allocation in a situation that there are a large number customers and goods are various, is investigated. Expanding the level of Internet access and increasing the desire of online shopping, raise the number of customers. In a situation where there is a great variety of goods and a large number of customers, it is difficult to solve issues such as on-time delivery of goods or services, selection and ordering in decentralized warehouses, and the issue of warehouse allocation to customers. To solve these challenges, the use of mathematical modeling with meta-heuristic solution methods has been proposed so far, but due to the large number of allocation modes, solving mathematical models is very complex and it takes time. With the improvement of computing power and storage space, data-driven methods have been studied by researchers to solve these challenges. In this study, a hybrid data-driven solution that uses data mining and mathematical modeling to manage the variety of goods and the number of customers has been proposed, that manages the variety of goods and the number of customers, and can solve mathematical models in less time. This method has been implemented on the data of "DigiKala".