فصلنامه چشم انداز مدیریت صنعتی
سال سیزدهم شماره 3 (پیاپی 51، پاییز 1402)

In recent years, many studies have been conducted about how to select a sustainable supplier so different methods have been proposed for this rigard in fuzzy and deterministic environments. In this article a new model of BWM improved using Rough Sets Theory for Sustainable Supplier Selection (SSS) in the supply chain. One of the advantages of the proposed method is modeling the uncertainty related to personal evaluations and zeroing the subjective judgment error of the decision maker, using the decision table approach. Also, using the concept of conditional entropy in rough sets, the significance of criteria is calculated and the selection of the best and the worst criterion, as well as making pairwise comparisons in the best-worst method, is done respectively. Using this approach, the inconsistency ratio of the best-worst method becomes zero in all stages. Moreover TOPSIS and SAW methods have been used to determine the weight of suppliers and the results have been compared. To illustrate the model and demonstrate its capability, a real SSS case in Iran Khodro Company (IKCO), was examined by three experts in the automotive industry. The results show that the first supplier (ISACO Parts Supply Company) is the best sustainable supplier.

Selection and allocation in the resilient supply chain, when disruption threatens the supply chain, has become a strategic decision and the focus of many researches; increase in the applications of machine learning in supply chain studies has led to the emergence of faster and reliable decision-making methods, however, in few studies, machine learning has been used to deal with the problem of selecting and assigning suppliers to customers in resilient mode. The purpose of this research is to take a step towards solving this gap by using machine learning algorithms on real world data from the automotive supply chain in Iran. the performance data of 441 suppliers and 7 customers in 1401 was used. In this research, two clustering algorithms have been used to generate labels based on the concept of resilience capacity; Then, since the interpretability of the results was a priority, based on the labeling of the clusters by the experts, the decision tree was used to classify the suppliers based on their performance. The results showed the K-means tree performs better than the DBSCAN tree and criteria such as on-time delivery, capacity, production line stoppage, quality alert, logistics performance and quality performance are effective on suppliers' resilience.

One of the challenges in non-spot contracts is evaluating the price the trading party (supplier) offers. His past behavior in the way of exchanging the traded goods as well as in the way of the financial settlement of the concluded contract plays a key role in the evaluation of his proposed price. This research has been done with the aim of solving this challenge. First, using historical data, the behavior of strategic suppliers of a steel trading company has been investigated and their behavior has been categorized into specific patterns. Then, a formula is presented that relates the proposed non-spot price and the cash market price in each pattern. Besides evaluating the proposed price using the presented method, how to use it as a decision support tool for pricing in non-spot contracts is explained. Moreover, by example, it is stated how to use this method in order to predict the future price more precisely. Sensitivity analysis is performed on the main parameters and the results are illustrated. This work provides a tool to evaluate and determine the price in non-spot contracts based on the historical behavior of the trading party. In addition, it can be used to predict future prices more accurately.

In this paper, the economic production is considered by considering defective products during production, a percentage of which can be recycled at a fixed cost, also machine failure, and variable demand. It is assumed that the products are produced at a fixed rate, but the machine may break down during production. Machine failure during production is a random variable that follows an exponential distribution with a specified parameter. If the machine breaks down during production, production stops immediately and the machine is repaired, and this study assumes that the machine repair time is a fixed value. Also, unlike the classical models, the demand for the manufactured product is expressed as a non-incremental function of time. The main goal is to determine the optimal policies for reproducing the problem in such a way that the total annual cost is minimized. For this purpose, first, a mathematical model is presented, then, the average cost per unit of time is determined, and based on the concepts of global optimization, the optimal values are determined. Finally, by solving a numerical example, the problem is analyzed. The results show that, the repair time has a great impact on the cost of the entire system, and as it increases, the system cost increases exponentially.

By increasing competition in global markets, paying attention to environmental requirements in agricultural supply chain has been more important. The greenness agricultural process mainly affects several issues, such as increasing customer demand, increasing costs of supply chain members over traditional agriculture, and specific conditions for holding crops. also increasing customer satisfaction on the proposed products and services is the necessary condition for a successful supply chain. In this study, a two-level agricultural supply chain is investigated; farmer determines the greenness degree of crops, and retailer increases the customer satisfaction level by making the necessary investments. The contribution of this research is specifically the optimization of the final customer satisfaction level as a decision variable in agricultural supply chain and the simultaneous examination of three variables of price, the greenness degree of crops and the customer satisfaction level in the final customer demand function. In the modeling process, first, decentralized and centralized approaches are developed. Subsequently, by using revenue sharing contract, the chain members have attained into a win-win relationship. In the numerical results, the tomato industry is examined and the results show that the supply chain will reach the optimal profit obtained through the centralized approach after the contract.

Construction projects play a vital role in fostering development by creating essential infrastructures, attracting private sector investment, and promoting sustainable growth. However, issues such as non-compliance with development strategies, cost and time overruns, and poor quality implementation can hinder progress. To address these challenges, it is crucial to analyze the current state of construction plans and devise appropriate strategies to achieve desired outcomes. This study employs a mixed-method research approach, combining thematic analysis in the qualitative section and a survey of relevant stakeholders in the quantitative section. The study analyzes 33 indicators of strength, 11 indicators of opportunity, 8 indicators of weakness, and 12 indicators of threats across seven dimensions, including financing, structure, system, process, stakeholders, leadership, management, human capital, and culture. The findings suggest that the current state of construction plans is in the defensive area based on the situation assessment matrix and strategic measures. This study provides valuable insights for policymakers and practitioners to adopt appropriate strategies to move towards the desired outcomes in construction projects.

Early models of data envelopment analysis are not suitable for evaluating two-stage structures due to the black box view and lack of attention to internal processes. In these structures, Deriving the efficiency frontier and fairly determining the optimal value of variables is the most important challenge. In many existing two-stage models, the efficiency frontier is not plotted or the optimal value of intermediate variables is determined by one of two steps. This leads to incorrect calculation of the efficiency of the next stage and the total efficiency. In fact, in these models, poor performance of one stage leads to reduced efficiency of the other stage. In this study, by keeping the intermediate variables constant at the current level and with an input-output oriented approach, radial and non-radial models were developed on a constant and variable returns to scale in terms of efficiency. Using mathematical relations, the validity of the models was proved and shown that in the proposed models, the performance of the units in steps is compared with a unit on the efficiency frontier, and the models make the whole structure efficient by bringing the steps to the efficiency frontier. The proposed models were used in an applied study to evaluate the sustainability of nine supply chains of tomato producers. Their performance results were expressed by four models as well as the optimal value of inefficient unit variables in each of these models.

Today, due to some challenges and competition, such as external pressures, factories are forced to reduce production time, traditional centralized production scheduling is not flexible enough to respond to rapid market changes. In such an environment, factories decide to merge and form a multi-factory production network to work more closely together.  In this research, the multi-factory scheduling problem is considered, which factories belong to a company. The problem is assigning the jobs to appropriate factory and scheduling jobs on machines in each factory. In this paper, it is assumed machines in each factory are unrelated parallel machines. For scheduling jobs on machines sequence-dependent setup times are considered. After proposing a novel mixed integer linear programming model for the problem which is a combination of two types of modeling based on sequence and assignment, we developed an evolutionary metaheuristic namely imperialist competitive algorithm (ICA) to minimize the maximum completion time or makespan among the factories. We compare the obtained solutions using the proposed ICA with those using an adopted genetic algorithm to show the efficiency of the proposed algorithm. Finally, the results are reported. Numerical results show that the proposed algorithm has good performance.

Increasing operating profit is a challenge faced by pharmaceutical distribution companies. Most of the researches conducted in this field have a cost reduction approach. The online monitoring system is one of the effective methods that can be used for managers' decision-making and improving the performance of the pharmaceutical distribution chain with the approach of reducing costs and increasing revenues. To create this system, conceptual, mathematical and computer modeling is needed. The aim of this research is to develop a mathematical model of the online monitoring system to improve the pharmaceutical distribution system based on a conceptual model using a reinforcement learning approach. The mathematical model was derived based on the factor-based conceptual model and using the multi-factorial reinforcement learning approach. After extracting the mathematical model, the effectiveness of the model was validated by comparing the results obtained from the output of the mathematical model and the actual results in a pharmaceutical distribution company. The findings of the research showed that the developed mathematical model has the ability to continuously improve the goals, decisions and performance of the pharmaceutical distribution chain processes, according to the interactions and changes in the behavior of agents and the state of the environment.

The coronavirus outbreak has highlighted the importance of supply chain integration in reducing disruption impacts and maintaining organizational sustainability. Previous studies showed that supply chain integration can directly improve corporate performance. Meanwhile, supply chain integration could improve performance by increasing the company's resilience in catastrophes like the Corona outbreak. In this way, supply chain resilience mediates the relationship between integration and performance. The purpose of this research was to examine how different aspects of supply chain integration affect an organization's performance and how supply chain resilience is a mediator during an outbreak. Based on a review of the literature, the research conceptual model was designed and tested using survey data collected from food sector enterprises in Tehran province during the coronavirus epidemic. To evaluate the data and test the hypotheses, structural equation modeling was employed. The study's findings indicate that process and product integration are critical in building supply chain resilience. The supply chain's resilience also has significant effects on the relationship between supply chain integrity and firm performance. The study's findings provide useful insights for Food industry supply chain managers on the relevance of integration and resilience in ensuring sustainability and company viability during times of disruption.