فصلنامه چشم انداز مدیریت صنعتی
سال یازدهم شماره 1 (پیاپی 41، بهار 1400)

Coalition formation is an important step towards developing the social welfare by improving the performance. This is pursued in two main research streams: (i) algorithmic approaches to achieve the optimal coalition structure and (ii) cooperative game theory to distribute the coalition payoff based on fairness and stability criteria. The aim of this paper is to integrate the strengths of the two approaches in order to achieve an optimal and stable coalition structure. The main innovation of the paper is using mathematical modeling to incorporates stability condition in a set partitioning problem through core solution concept to overcome decentralized procedures of coalition formation and payoff distribution. A numerical example is used to investigate the performance of overlapping and non-overlapping optimal coalition structure models. The results show that cooperation leads to improve social welfare. This improvement has an ascending trend with a decreasing slope and does not change after increasing the upper limit of players allowed to join the coalition to the certain extent. This is due to several reasons which prevent players to form grand coalition and suggests that, to form large coalitions, one should compare achieved gains with the managerial complexities and the increased costs of coordination and communication between players.

The purpose of this study was to develop an agent based model that could simulate the steel supply chain and estimate its production and consumption, taking into account the key factors of the steel industry. The approach of the present study is mixed (quantitative and qualitative). In the first part of the research (qualitative), the agents of the steel chain consumption model were obtained through interviews with experts using thematic analysis method. In the second part of the research (quantitative), a questionnaire was used to survey the causal relationships of the factors extracted from the interviews and the thematic analysis method, and then the relationship model was tested by the DEMATEL method. Finally, by using AnyLogic software and coding in Java language, a model of steel supply chain and its consumption was designed throughan agent-based  approach, and according to the opinion of steel industry experts, the model explanation process was also approved. The combination of agents identified in this study is consistent with the influence of factors on production, consumption, import and export of the steel chain in the proposed structural model.

The efficiency of universities and higher education institutions is considered by many researchers because of their strategic role in the development and economy of each country, because the evaluation of the efficiency of universities helps to implement effective programs for the development of higher education. The literature on evaluating the efficiency of universities and higher education institutions has evolved over the past decades. However, the divergence of approaches, process areas, differences in output and input variables of previous studies unveils the importance of conducting a systematic review on the use of data envelopment analysis technique in evaluating the performance of universities and higher education institutions. The purpose of this study is conducting such a review and identifying future trends in this field of research using a combination of systematic literature review and citation network analysis. After determining the search protocol and article selection criteria, 165 articles were finally selected and analyzed. The results show that, in recent years, in addition to educational and research activities, the performance of universities has been evaluated in terms of the performance of entrepreneurship and university-industry relations, which can be considered in development and improvement programs.

Neglecting the supply chain management of perishable goods could create a lot of costs for organizations and companies. Blood is a perishable product in the healthcare supply chain, the  shortage of which could prove quite problematic and disastrous. Any improvements in the blood supply chain management operations may increase service efficiency and decrease the cost of the healthcare system, saving the lives of lots of people. In this paper, a mixed-integer nonlinear programming model is proposed for comprehensive blood supply chain management, which includes gathering, processing and distributing blood and blood products by taking into account the demand lifetime and age. This model aims at decreasing supply chain costs and blood product deficiency. Robust optimization is utilized to take into account the inherent uncertainty and volatility of the demand and supply. The proposed model is first tested on a small-scale numerical example in GAMS software. Then a large-scale problem is solved using Whale and Imperialist Competition algorithms and the results are compared. In addition, a case study is presented to show the applicability of the proposed model.

Despite the increasing importance of cash flow management in the financial supply chain, limited works have been conducted in this field. This research optimizes the flow of money in the medical supply chain from the viewpoint of a distribution company. In this context, the focal company receives the medical supplies from the upstream suppliers and sells them to the downstream retailers and makes payments to suppliers with earned money from retailers. The imbalance between the cash inflow and outflow causes the imposition of a penalty for late-payments and supply risk as a result of the poor reputation in the market. In this context, the question is which payment sequence will minimize the total monetary outflows and the risk of supply. To answer this question, a bi-objective 0-1 linear programming model was developed. Solving the model by genetic algorithm determined the best sequence of payments and minimized the cash outflow as well as the risk of violation of the due date for the invoices.

In this research, a model with three objective functions is presented to solve the problem of time, cost and quality trade-off in project planning. What distinguishes this model is that, in addition to considering different executive methods for each activity, rework activity is defined for some activities in order to prevent a decrease in quality. Other features of this model include covering various costs including incentive cost and tardiness cost. Because of the NP-Hardness of such large-scale problems, genetic algorithm is used to solve the proposed model.The results obtained from solving a real problem in screen filter production indicate that considering different executive methods for activities as well as different costs and defining rework activity can lead to better results towards the final goal by presenting a comprehensive model.If more accurate and detailed information is used for time, cost and quality in the model, it can achieve more rational results, similar to those of the real world more confidently. Under such conditions the least time and cost and most quality are achieved for successful implementation of project.

This paper aims at proposing a novel quadratic assignment-based mathematical model for designing an optimal facility layout in each period of the stochastic dynamic facility layout problem (SDFLP). Considering routing flexibility is the main assumption of this problem so that parts can pass through multiple routes. It is also assumed that product demands are independent, normally distributed random variables with known expected value and variance changing from period to period at random. In addition, to solve the proposed model, a new hybrid meta-heuristic algorithm is developed by combining simulated annealing (SA) and the CRAFT approaches. Finally, the proposed model and the hybrid algorithm are verified and validated using design of experiment, real case study and sensitivity analysis methods as well as solving some numerical examples.The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time perspectives. Moreover, the proposed model can be used to design the layout of facilities in both of the stochastic and deterministic environments of traditional and modern manufacturing systems.

Roadmap and especially technology roadmap is a management tool and technique to achieve future goals in order to link business to technology. With the increasing trend and the need to use different types of roadmaps, new tools are needed to analyze the complex relationships between layers and roadmap elements. The purpose of this study is to focus on a new tool for analyzing the relationships between roadmap layers with a combined method with a proposed framework. Previous research has addressed relationships between layers only with tools such as quality function deployment (QFD) and the linking grid. Although DSM and TRM have been extensively studied independently so far, this study, therefore, proposes an integrated six-step framework combining a multi-domain matrix and design structure matrix and fuzzy set theory in designing, creating, and developing technology roadmaps for Suggest systems to support decision making and case studies. In this study, multi-domain MDM matrix and fuzzy inference, and network theory were used in designing the technology roadmap. The advantage of using a multi-domain matrix is ​​the simultaneous analysis of each domain specifically in the DSM format as well as the entire domain in the MDM format. The results of the present study indicate the provision of detailed instructions for managers to prepare a suitable roadmap.

In this research, a multi-objective mathematical model is proposed for the airline multi-skilled crew scheduling problem. The multi-skilled crew can be assigned to flights and airplanes according to their skills. The objective functions of the proposed model are: (1) Maximizing the number of leave days planned according to the days announced by the flight crew, and (2) Minimizing the penalty costs associated with violation of minimum and maximum working hours. Several test problems have been designed based on the data acquired by the airline studied in this research. Due to the NP-hard essence of the model, we have employed two meta-heuristics, namely the multi-objective differential evolution (MODE) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). These algorithms are calibrated using the Taguchi method. The algorithms have been compared based on several multi-objective performance measures. Each algorithm has been more successful in terms of some metrics. The comparisons between algorithms and sensitivity analysis show that the proposed model and algorithms can produce appropriate schedules for the airline crew scheduling problem.

After the success of Digikala as the largest online retailer in Iran, many online retailers emerged to achieve their appropriate market share. It seems that examining customer behavior in online retailers needs to be further explored and enhanced to achieve competitive power. The purpose of this study is to investigate the impact of pre-buying, buying, and post-buying processes on customer experience and the effects of customer experience on customer satisfaction, then customer satisfaction and customer experience on Repurchase behavior in online retailers. The current research is descriptive and applied. The community of this study is all those who have experience in buying from Digikala retailers. To determine the sample size, the Cochran formula was applied, and because of the infinite statistical population, a sample size of 384 people was selected. A researcher-made questionnaire was created, and a combination was used to collect data. In order to analyze the data obtained from the questionnaire, the structural equation modeling method was used. The hypothetical test was performed using descriptive and inferential statistics. The results suggest that pre-buying, buying, and post-buying processes affect the customer experience in Digikala online retail. Customer experience also affects customer satisfaction and re-purchasing from Digikala online retailers.