نشریه تصمیم گیری و تحقیق در عملیات
سال هشتم شماره 4 (زمستان 1402)

In general, efficiency is a criterion for evaluating the performance of a decision unit from different dimensions. Data envelopment analysis is a mathematical programming method for evaluating the performance of decision-making units. The purpose of this study is to measure the financial efficiency of firms by considering both incoming assets and financing.

In this research, a new method called the three-dimensional model of data envelopment analysis was introduced, and performance analysis was done on 10 active firms in Iran's steel industry for 5 years, from 2016 to 2021.

The results showed that several firms have good performance in managing incoming assets but are inefficient in terms of financing. At the same time, some firms have poor management performance compared to inputs, but they are efficient in terms of financing. Therefore, when analyzing a firm's performance, an indicator that considers both inputs and financing at the same time is needed. According to this, we proposed a new measurement method and analyzed the current financial situation of each decision-making unit through the method of return to scale, and a path has been determined for financial improvement.

Originality/Value: 

Attention to the effect of negative and destructive factors such as borrowings and debts of the decision-making unit in data envelopment analysis has been the key and different aspect of this study, compared to other previous studies. According to the literature review, using the redesigned DEA model has not been considered by Iranian researchers, and due to a new approach to data envelopment analysis, our approach has distinguished itself from the previous works.

In this research, a recyclable waste is used to manufacture a specific product. For this reason, a supply chain is considered containing manufacturer, recycler, and waste warehouse. First, the customers’ demand for the considered product is determined based on its price. Then, the manufacturer produces it from a waste with specific recyclability rate. The waste warehouse collects the waste and the recycler recycles it. The manufacturer meets his requirements through two different channels. He can procure the non-recycled waste from the waste warehouse and then recycles it himself, or can buy the recycled waste from the recycler. The manufacturer selects these channels based on the established situations.

The game theory is applied to make the decisions under two considered channels. In this setting, a Stackelberg game is developed based on the competitive situation established among the members, where the manufacturer has higher decision power than the waste warehouse and recycler.

Eventually, the given strategies are discussed and the obtained results are presented. Results indicate that the manufacturer selects each channel as a threshold is met. Moreover, more recyclability rate of the considered waste leads to higher profits for the members.

Originality/Value:

 In this research, to provide the waste materials required for producing a product, the game-theoretic approach as well as the concept of the channel-selection are used. It can be stated that this issue has been proposed for the first time in the literature.

This study aims to tackle the challenging facility location selection problem in Multiple Criteria Decision Making (MCDM) scenarios, explicitly focusing on type-1 fuzzy MCDM issues. The research introduces Interval Valued Fuzzy Numbers (IVFNs) to express ratings, addressing the difficulty in determining precise membership degrees for fuzzy sets.

The proposed IVF-COPRAS method, centered on uncertainty risk reduction, is employed to enhance decision-making reliability in IVF decision problems. This methodology is applied to a real-world case involving the selection of a location for municipal wet waste landfill pits in a major Iranian city. Comparative analyses with other methods are conducted to assess the proposed approach.

The study demonstrates the effectiveness of the IVF-COPRAS method in addressing facility location selection problems within MCDM. By utilizing IVFNs, the method successfully manages uncertainty, leading to more reliable decisions. Application to a practical scenario highlights the method's efficacy, and the comparative analysis provides insights into its performance relative to other methods.

Originality/Value: 

This research contributes a novel approach with the IVF-COPRAS method for handling facility location selection challenges in MCDM. The reliance on IVFNs offers a unique perspective on uncertainty in decision-making, enhancing decision reliability. The real-world application emphasizes the method's practical significance, providing a valuable contribution to MCDM research and offering a methodological tool for similar decision-making problems across diverse domains.

The ranking of supply chain risks using a combined approach is to optimize the method of analyzing failure factors and their effects and gray theory in Mashhad food industry units.

Due to its nature, the present research belongs to the category of descriptive-analytical researches, with qualitative variables, and from the point of view of the objective, it belongs to the category of applied researches. In this research, the risks in the supply chain of the food industry were first identified. In such a way that; first, the risks in the supply chain were identified through library studies and research literature, and then they were given to the experts using a fuzzy Delphi questionnaire to be rated by the Likert scale. Due to the time limitation and defects in the analysis as well as vague, incomplete and uncertain information data, these points became a gray area. Then, using the method of analysis of failure factors and its effects, the risk priority score number was calculated in order to investigate potential failure situations. In this way, the indicators and dimensions of supply chain risks were ranked with the number of the priority score of risk taking. Risks with a higher risk priority score have a higher risk tolerance and require more attention.

The result of scoring and calculations determined that the economic dimension has the highest risk in the supply chain. After the economic dimension, the legal, strategic, individual, political and natural dimensions are the second to the sixth, and the cultural and social dimensions are the seventh and the information dimension is the eighth.

Originality/Value:

 The findings of this research will help managers, considering the limited resources, for control and management, especially in conditions of uncertainty, by prioritizing the risks of their supply chain. According to the level of risk-taking of each, as well as considering preventive measures regarding these risks, to prevent possible irreparable and critical injuries.

One of the most vital subcategories of the health care system is organ transplantation, and since organ transplant centers deal directly with surgical operations and, as a result, human lives, the importance of this issue has received more attention. One of the major differences between the organ transplant supply chain and other supply chains is the possibility of corruption of related products. Therefore, the time and also the location of organ transplant centers are of special importance. On the other hand, due to the rapid growth of the demand for organ transplantation and the lack of resources, the patient's waiting time to complete the transplantation process plays a vital role in the organ transplantation system.

This study presents a robust bi-objective mathematical model for the location problem of allocating organ transplant centers under uncertainty, which includes the total costs of the organ transplant system as well as the average patient waiting time for organ transplantation, which follows a G/G/m queuing system.

To solve this model, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been used. Finally, the applicability of this model and the efficiency of the mentioned algorithm compared to the defined indicators have been shown through numerical experiments.

Originality/Value:

 Since each organ can spend a certain amount of time outside the body and there is a possibility of corruption or a decrease in the quality of the transplant, the time between the organ leaving the body and the completion of the transplant operation plays an essential role in the transplant system.

In this paper, the amount required to increase the reliability of the components of a coherent system is determined so that the cost of this increase is minimized and the reliability of the whole system is not less than the predetermined value.

In this research, after introducing the objective and constraint functions in the optimization problem, the Lagrange method is used and then the problem is solved by presenting an algorithm. In this article, several cost functions are considered, and then the results are presented in the general case for a coherent system and then for two special cases, series-parallel and parallel-series systems.

In this article, two numerical examples are presented and solved. In the first example, a bridge structure is evaluated and in the second example, a series-parallel system is studied. In both examples, the required values are determined to increase the reliability of the system components.

Originality/Value: 

This research, using a mathematical model and numerical calculations with the help of R software, examines the optimization problem for a coherent system.

The production routing problem is created by combining the two problems of lot sizing and vehicle routing. Previous research has examined the effectiveness of this combination in reducing costs. In this paper, the production routing problem is considered with the aim of minimizing the risk of production and distribution of hazardous materials. Attention to social and environmental criteria related to sustainability is being increased by researchers today. Hazardous materials are harmful to human health and the environment. Accidents related to these materials often have far-reaching adverse consequences. Risk is a danger measurement criterion for operations related to these materials.

The problem is modeled as a mixed integer program. The risk function in the proposed model is nonlinear and depends on the load of the machine, the population risk, and the type of hazardous material. Given that solving the mathematical model with the nonlinear objective function is more difficult, this function is approximated by a piecewise linear function.

In this research, 8 standard instances have been used to evaluate and solve the model and compare the two nonlinear and linear models. The results show that by using the approximate model, a better answer can be achieved at the same time. Through sensitivity analysis, the effect of changes to production capacity and warehouses on risk has also been looked into.

Originality/Value:

 This research proposed the production routing problem for hazardous materials according to sustainability criteria using a nonlinear mathematical model and uses a piecewise linear approximation to solve the model.

Establishing the structure and expansion of sustainable closed-loop supply chains is critical to meeting environmental, economic, and social standards to strengthen their position in competitive markets. This study aims to decide on operational and tactical levels to configure the Stable Closed Chain Supply Chain Network (SCLSC) to maximize Net Present Value (NPV) and seek to minimize carbon emissions while maintaining environmentally friendly policies and considering inflation.

This paper considers a solid Fuzzy Robust Optimization (FRO) approach to deal with stable, closed-loop supply chain uncertainties. Also, due to the complexity of the model and its multi-objective, a new combined method of Heuristic algorithm (HA) and Multi-Choice Goal Programming with Utility Function (MCGP-UF) is used. The proposed Mixed Integer Linear Programming (MILP) model is applied in the electronics industry.

The proposed model is evaluated in several experiments and discussed in different scenarios to confirm the efficiency and validity of the proposed model and method. The results were compared with the two factors of optimal gap and solution time, which showed the proper performance of the proposed method. Then, the tactical results and model strategy were presented for a case study in which the optimal flow between facilities, selection of suitable suppliers, selection of transportation type, and opening of facilities were presented. The findings showed that in different scenarios, the effective improvement of the obtained solutions by reducing the solution time by twenty percent could address large-scale problems.

Originality/Value:

 By considering a new combined method of heuristic algorithm and multi-choice ideal programming with a utility function, this paper is done to solve the problem of designing a stable closed-loop supply chain network under uncertainty.

This paper presents an intelligent method for applying Data Envelopment Analysis (DEA) to design a sustainable supply chain.

In the proposed method, for defuzzification of the ERM model, we used the -cutting technique. Then, to measure the productivity in the presence of environmental uncertainty via different -levels, a genetic algorithm is implemented to find an optimal -cutting. Finally, an intelligent DEA model for ranking the supplier companies via optimal  value is designed.

This paper presents a new fuzzy DEA model based on a Genetic Algorithm for evaluating the productivity of suppliers in a sustainable supply chain.

Originality/Value: 

In the proposed method, since the -cut obtained from the Genetic Algorithm is optimal, there is no longer a need to calculate the efficiency for different α-cuts through trial and error. Therefore, the proposed method's advantage is that it offers a more sustainable ranking in addition to increasing productivity for each supplier. The example presented in this article demonstrates the method's superiority and advantages.

The current research aims to present a multi-objective mathematical model with an integrated approach to scheduling and financial flow in production projects using Non-dominated Sorting Genetic Algorithm II (NSGA-II).

This research presents a multi-objective mathematical model integrating scheduling and financial flow optimization in civil engineering projects. This research addresses the scheduling and financial flow challenges in construction companies' production projects. The objective is to develop a multi-objective mathematical model that integrates scheduling and financial considerations to optimize resource allocation and minimize costs. The statistical population is in the form of a case study, and the required information and data were collected through interviews with managers of Kisson Construction Company.

NSGA-II was used as an optimization algorithm to find efficient multi-objective solutions, and optimal results were presented to select civil and construction projects.

Originality/Value: 

This research contributes to the field by proposing a novel multi-objective mathematical model that integrates scheduling and financial flow considerations in production projects. The use of the NSGA-II algorithm enhances the efficiency of finding optimal solutions. The findings can be valuable for decision-making when selecting construction and production projects.

The main goal of this study is to present a novel model for managing organizational processes possessing the necessary agility in complex situations. One of the most critical requirements of such a model is using systems thinking and a soft operations research approach because the methods introduced in the background to achieve the agility of the process management generally include fragmentary optimization because of the one-dimensional view and the lack of a system view to identifying agility variables in process management.

In this study, an attempt was made to provide a comprehensive model using the interpretative paradigm's capabilities. To this end, the main components were identified using the meta-synthesis technique and interviews with eighteen experts. Further, components were prioritized by using the interpretative ranking process.

The findings show that the process leadership component is given first priority based on the interpretive ranking. Therefore, organizations that want to be agile in their process management system should consider leadership characteristics in processes to implement transformational systems. After that, the results showed that environmental awareness, knowledgeable and competent human resources, organizational learning dimension, contingent and appropriate structures, governance, improvisation-based strategy-making, technological infrastructure, dynamic adaptation, culture, and creative sustainability are prioritized, respectively.

Originality/Value:

 Using soft operations research techniques, including the interpretive ranking process, in designing an agile model for managing business processes will significantly help process organizations in complex and dynamic environments to simultaneously exploit the benefits of stability in managing processes and dynamism coming from being agile.

Commercial and industrial organizations worldwide are under severe threat of instability. Unsustainability issues arise because of environmental disturbances such as climate change, global warming, resource scarcity, and ecological degradation. Circular Supply Chain (CSC) has gained momentum in recent years and serves as one of the most sustainable and innovative approaches to the manufacturing industry. Adoption of the CSC increases the social, economic, and environmental aspects of the production system and the supply chain. However, researchers have conducted significantly less research on identifying and exploring the multiple challenges to implementing CSC in developing countries. Therefore, this research investigates and evaluates the challenges of CSC implementation in the production sector. We have identified 29 CSC challenges in the literature review.

The research proposes the Pythagorean Fuzzy SWARA (PF-SWARA) technique. This technique prioritizes CSC adoption challenges based on their relative importance. We used the proposed method in the pharmaceutical industry of the Guilan province.

In the final results of the research, Strategic Challenges (SCs) and operational and environmental challenges are prioritized.

Originality/Value:

 This research serves as a step for industry practitioners to efficiently and effectively adopt a CSC.

This paper focuses on modeling a sustainable electricity industry supply chain network under uncertainty. The aim of presenting this supply chain network is to meet customer demands for solar panels to generate clean energy.

A mixed-integer linear programming model, including facility location, supplier selection, optimal flow allocation, and determination of the optimal price of solar panels in the network, is considered. The sustainability objectives of the model include maximizing the profit of the supply chain network, minimizing greenhouse gas emissions, and maximizing reliability. A robust optimization method is also considered to control uncertain parameters, and precise and innovative techniques are used to solve the model.

The results of the model show that with an increase in network reliability, the current net value in the network decreases, and greenhouse gas emissions in the network increase. Additionally, the analysis of the results shows that with an increase in the network's uncertainty rate, the network's current net value and reliability decrease, and greenhouse gas emissions increase. Finally, the statistical test results also show that there was no significant difference between the averages of the number of practical solutions, the maximum spread, and the metric distance between the two algorithms, and only a significant difference exists between the solution times of the two algorithms. The results of the presented solution methods demonstrate their high efficiency in solving the sustainable electricity industry supply chain model.

Originality/Value:

 In the proposed model, essential decisions such as supplier selection, establishment of production centers, optimal product flow allocation, and pricing of solar panels are made. On the other hand, further analyses of 15 numerical examples show the high efficiency of the MOALO and MOWOA algorithms compared to the epsilon-constraint method.

The order-picking problem is important as one of the warehouse's logistics activities. This problem is defined as collecting orders from different warehouse locations to respond to customers' orders quickly. This paper aims to provide a multi-objective mathematical programming model for integrating the decisions of batching, routing, and scheduling of selectors with the packaging problem in a multi-warehouse environment. The objective functions include depreciation of the delivery times and total order picking costs.

In this research, first, by reviewing the literature in the field of order picking, the research gaps of the problem have been identified. Then, taking into account the main constraints of the problem, a multi-objective mathematical model has been formulated for the multi-warehouse order-picking problem. The classic Benders decomposition algorithm and the accelerated Benders decomposition algorithm have been used to solve the problem. The data related to the warehouses of a company producing sanitary products in Iran was used as a case study to validate the applicability of the proposed model, and its results were reported in the article.

The proposed model's results indicate that CPLEX can solve these problems up to small sizes in an acceptable time. Also, the numerical results show the performance of the Benders decomposition algorithm and the accelerated Benders algorithm as suitable alternatives for solving the model in large-sized problems. The calculation results obtained from the implementation of the solution methods for the proposed model showed that in terms of the number of iterations and the calculation time, the accelerated Benders algorithm had better results than the classic Benders algorithm.

Originality/Value:

 In this research, the order-picking problem with the integrity of operational decisions has been formulated as a multi-objective mathematical model for a multi-warehouse environment for the first time. Also, in this article regarding the solution method, exact solution approaches have been used for the first time considering the structure of the problem. The computation results show that the proposed algorithms are efficient and suitable methods for problem-solving.

One of the fundamental problems in the field of supply chain management is the problem of supply chain design. In this problem, the goal is to determine the location for establishing a number of facilities in different geographical areas to cover customers' demands.

The static and dynamic networks are considered to solve the problem. If the network is static, with the help of the iterated local search algorithm, the number and location of distribution centers are determined, and customers are assigned to each. If the network is dynamic, after determining the initial number and location of distribution centers and assigning customers to distribution centers, events related to network dynamics, such as customer decrease, customer increase, and variable size of demands, are investigated by customers. In addition, our proposed method considers the failure modes of network components. Also, the ability to survive the network can be seen in both network modes.

The results obtained from the experiments were analyzed regarding evaluation criteria. The results of the experiments show the superiority of the proposed method compared to the CLSC and TSCFL methods.

Originality/Value:

 The background investigation of the subject indicates that the design of multi-level supply chains and the issue of lesser-addressed facility locations have received less attention. In addition, facility dynamics and network survivability are not considered at the time of failure. Therefore, in this research, we have tried to reduce the cost by choosing the appropriate location of facilities and assigning customers to each of these facilities. Also, we will design the network so that it can be in a stable state and continue to operate when a network failure occurs.