فصلنامه چشم انداز مدیریت صنعتی
سال دوازدهم شماره 1 (پیاپی 45، بهار 1401)

This research aims to design a bi-level optimization model for a supply chain that integrates decentralized quantitative and qualitative decisions at strategic and tactical levels. The manufacturer, as upper-level decision-maker, offers quantity discounts to encourage customers to order more quantity. At the lower level, customers tend to obtain economies of scale by aggregating their orders through cooperative purchasing.  This is one of the first studies that investigate the model of customer expectations with the optimization model of manufacturers at the same time with real data from the supply chain in order to find the optimal solutions to the problem of the medical equipment supply chain in Iran. In addition, there have been no studies to date that consider quantitative discount strategies for the seller and customer behavior in a bi-level planning model simultaneously. The results and analyses reveal that the designed bi-level model compared to the one-level model for the customer and the manufacturer is more suited to the real world and will lead to a long-term relationship between the parties through customer participation. Research suggestions and directions for future research are also provided.

Considering the cognitive, random, uncertain, and flexible constraints, a robust, stochastic, possibilistic, and flexible chance-constrained model was developed based on credibility measurement. The ultimate aim was closed-loop supply chain network design. Different attitudes of decision-makers were answered by more flexible measurements of optimistic and pessimistic parameters in the form of credibility measurement. The model has been able to reduce the possible deviation, stochastic deviations, non-fulfillment of demand and capacity constraints, and violation of flexible constraints, which simultaneously include cognitive and random uncertainties and flexibility of constraints in the model. To apply the model, a case study was conducted to design the closed-loop supply chain network of multi-product and multi-period stone paper. The results of implementing the model showed that in different situations and according to the importance of decision makers' opinions, using the range of optimism and pessimism, the number, location of facilities, optimal flow of products and materials between centers in the stone paper supply chain network can be determined. The proposed model was evaluated using robustness and sensitivity analysis, and its performance was evaluated using nominal data in the realization model, which results showed the appropriate performance of the model.

Data in various organizations has become a valuable asset and data governance has become one of the priorities of the organization. A review of previous studies shows that the assessment of the implementation of the data governance system in organizations is done qualitatively and organizations cannot determine a plan to improve their situation based on this type of assessment. The purpose of this paper is to provide a quantitative way to measure the level of implementation of data governance in an organization and subsequently plan to improve the status quo. Due to the qualitative nature of the influential factors in measuring the success rate, fuzzy concepts have been used for modeling and analysis. Also, in order to consider how the factors affect each other, the fuzzy cognitive mapping technique has been used to model the causal relationships between the factors. Then, the identified influential factors have been weighted and prioritized using the best-worst fuzzy method and the DEMATEL technique, and subsequently, scenarios of improving the situation of the organization have been designed to plan for the effective allocation of limited resources of the organization. This research has significant practical implications for the organizations that intend to establish such a system or are in the middle stages of implementation and are looking for a solution to find a plan to improve the situation.

Improving defense deterrence requires the extensive and diverse production of military products and equipment, which requires the use of vast and diverse industrial capacities and capabilities. Since the acquisition of all these capabilities is not economical, part of the production activities in the defense industry is outsourced, which has led to the creation of a large network of suppliers. The expansion of this network has made cooperative interactions and improving the supply chain performance of the defense industry a serious challenge for managers. The breadth of the supply chain of today''s organizations encourages managers to turn to new methods and technologies to improve supply chain performance. The new blockchain technology is one of the most recently considered technologies due to its key features such as forgery, decentralization, and transparency; Therefore, this study has been conducted with the aim of investigating the role of using this technology in improving collaborative interactions and supply chain performance of the defense industry. Structural equation modeling was used to test the research hypotheses. The results showed that the use of blockchain technology with a coefficient of 0.851 and 0.439, respectively, has a positive and significant effect on collaborative interactions and chain performance, and the creation of cooperative interactions with a coefficient of 0.469 leads to improving the supply chain performance of the defense industry.

Today, with the rapid growth of Information Technology, manufacturers provide their products through online direct channels to the consumers as well as the traditional physical channels. In this study, a dual channel supply chain including one manufacturer and one retailer is considered. The manufacturer can sell his products in two ways: the traditional retail channel and online direct channel. It is assumed that the manufacturer offers his product on an online channel via two different transportation modes and correspondingly two different lead times. Initially, a decentralized supply chain is considered, and then it is assumed that the manufacturer is vertically integrated with the retailer in centralized supply chain. The first model is analyzed using the Stackelberg game approach with the manufacturer as the leader, and the second model is optimized using an integrated approach. Optimal decisions of prices and delivery lead times are determined. The major contribution of this paper is existence of two online channels with two different service levels. Our numerical studies reveals that the integration and coordination of the manufacturer and the retailer is beneficial for all members of the supply chain and significantly increase the profit of the whole supply chain.

Developing efficient and effective health care systems has become a major concern for governments and health care decision makers. In-time supply of medicine and efficiency of the inventory and distribution system play a vital role in the hospital supply chain and require a proper decision-making process in inventory management. This study suggests a two-objective model of mixed-integer, linear programming to manage medicine and information flow. The main goals of this model are to minimize the costs of purchasing, maintaining, manpower, and medicine expiration, and to minimize medicine shortages. Uncertainty parameters are related to demand, storage, and supply capacity, as well as the aspiration level of each goal. The initial model is first transformed into a fuzzy mixed-integer, goal programming model. Then, the resulting model is transformed into a one-objective model using methods suggested by Torabi and Hassini and solved in GAMS. The supply chain of Shahid Beheshti Hospital in Kashan is used to explain the proposed approach. The results indicate that decisions related to the purchase and logistics of medicine have a great impact on medicine shortages and control various costs relating to medicine inventory in this hospital.

We present a mathematical model for the problem of collaborative production system based on order with fairness to allocate production loads. The main objectives of the model are to minimize total production costs and maximize the use of resources in order to distribute production loads fairly in conditions of uncertainty. Fuzzy programming was used to control uncertain parameters. The results show that, with increasing the uncertainty rates, production system costs have increased. Since the capacity of factories is constant, with the increase in demand, the amount of production has increased and the maximum use of resources of each factory has also increased. Also, contrary to the trend of system cost changes, with the increase in the number of factories, the maximum use of available resources has decreased. To solve large sample problems, the NSGA II algorithm with a suitable chromosome is used to search the problem space. Numerical results of solving 15 sample problems show the high efficiency of NSGA II algorithm in solving the problem of cooperative production system in a very short time.

Home Healthcare provides a wide range of home care services from prevention to rehabilitation and primary care to professional nursing services. This research presents a two-objective mathematical model based on the mixed integer linear programming approach for the home healthcare routing and scheduling problem (HHCRSP) with the objectives of minimization the nurses’ travel costs and the maximal working time difference among nurses. Considering the multimodal transportation, several initial health centers and one final health center and patient and nurse time windows are important features of the studied mathematical model. Small-sized problems have solved by using the Ɛ-constraint method on GAMS software. Also, due to the NP-hardness of the problem, MOPSO and NSGA-II algorithms have used to solve the medium and large-sized problems. The statistical results showed that the NSGA-II performed better than the MOPSO for medium and large sizes of problems in both MID and NOS performance metrics. Overall, Results of the performance metrics on different sizes of problems indicate the efficient and effective performance of NSGA-II in solving the understudied problem.

Organ transplantation is one of the most important pillars of health systems and has helped to treat many incurable diseases. Every day, 7 to 10 patients in Iran die because they do not have a transplant on time. Due to the criticality of the organ transplant chain for human health, the management and planning of this chain is of great importance. Timely transfer of organ and patient from one hospital to transplant hospital is very important considering the effect of seconds on the quality of the transferred organ and the success of the transplant. In this paper, a mathematical model for scheduling the pickup and delivery of transplanted organs and routing the ambulances carrying organs transplant and patients is presented. The problem is formulated in the form of a mixed integer nonlinear programming and then transformed into an equivalent linear mathematical model using exact operations research methods. The proposed model seeks to find the optimal schedule and sequence of pickup and delivery of organs and patients; under the operational constraints such as cold ischemia, urban traffic and limited fleet. The proposed model is optimally solved in CPLEX 12.8 software, and the computational results confirm the applicability of the proposed model.

Long-term delays to complete residential units, due to lack of adequate and punctual financing, is considered as a developer's problem, in the process of housing production. In this regard, the present study, with the consideration of the challenges that obstacle the efficient pre-sale of residential units, aims to determine the pre-sale policies of housing by providing a model based on the system dynamics approach in order to provide sufficient and punctual financial resources, reduce delays in completion, reduce opportunity cost and to achieve a balance between developers’ and buyers’ profit. After making a dynamic model and validating it, 48 scenarios consisting of the states of five lever variables were simulated and after analyzing the results, improvement policies which include four alternatives were extracted. The results indicate that to produce the residential units with average prices, whether the percentage offered of area for initial pre-sale is 0/3, the initial pre-sale discount coefficient relative to the price of the completed unit is 0/06 and the down payment coefficient is 0/3, from which the mentioned variables will lead to a significant reduction in completion delays and opportunity cost, and also will balance the profit of both developers and buyers.