فصلنامه مطالعات مدیریت صنعتی
پیاپی 68 (بهار 1402)

This paper focuses on supply chain coordination under a revenue sharing contract. Demand plays a crucial role in modeling inventory systems, particularly in the retail industry where products need to be brought in and taken out of retail stores within specified timeframes. The question addressed is how to allocate shelf space effectively. Optimal shelf space allocation in retail significantly impacts product sales and profitability. The research demonstrates that by considering factors such as price, allocated shelf space, product brand image, and advertising, a new demand function can be developed. The study explores decentralized, centralized, and coordinated structures using a Stackelberg game model. The findings show that a revenue sharing contract leads to a win-win outcome in the supply chain. Additionally, a numerical example is provided to illustrate the model, and sensitivity analysis is performed on key parameters. The results highlight the significant impact of price elasticity on demand, emphasizing the need to pay close attention to this parameter in real-world applications.

One of the challenges faced by the steel industry is supply chain management. Based on this, inthe present research, based on the three scenarios of non-cooperation and simultaneousmovement (Cournot), non-cooperation and sequential movement (Stackelberg) and cooperativebehavior (collusion) will be discussed in the steel supply chain.The research method is applied in terms of purpose. The research period is seasonal data from2011 to 2020 and the software used MATLAB softwareIn this paper, a hybrid model based on artificial neural networks and game theory was presentedto help steel industry activists in determining the price level and optimal production. To predictsteel prices, three Bayesian neural networks, support vectors and cross-emission anti-emissionwere used. The results indicate that the cross-emission model of Grossberg is more accurate inpredicting steel prices. Also, the results show that by moving from the Cournot game to theStackelberg game and from the Stackelberg game to the Collusion game In the supply chain, itwill increase the price in the steel industry by 6 dollars per ton and the amount of product supplywill be in the range of 1500 to 4000 tons In other words, with the increase in the level ofcollusion in the steel market, more products have been offered in the market and at the sametime, the price level of the product will also increase, which will reduce the welfare of steelconsumers in the market.

This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate of the system and the minimization of the total cost of reducing mean processing times and the total buffer capacities with respect to the optimal values of the mean processing time of each product in each workstation and the buffer capacity between workstations. For this purpose, in order to configure the structure of the mathematical model, Simulation, Design of Experiments and Response Surface Methodology are used and to solve it, the meta-heuristic algorithms including Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA) are implemented. The validity of the multi-objective mathematical model and the application of the proposed methodology for solving the model is examined on a case study. Finally, the performance of the algorithms used in this study is evaluated. The results show that the proposed multi-objective mathematical model is valid for optimizing unreliable production lines and has the ability to achieve optimal (near optimal) solutions in other similar problems with larger scale and more complexity.

Today, the geographical distribution of crime, particularly in Tehran, is so unpredictable that accurately predicting its location and timing has become impossible. Consequently, the utilization of proactive and retrospective models has become essential for the prevention police, ensuring their ability to maintain maximum proactive and reactive operational power. In this context, logistics serves as a pivotal element supporting police missions and operations, thereby playing a significant role in enhancing the overall performance of the preventive police and establishing sustainable security. Given the nature and sensitivity of the prevention police mission, it is inevitable to encounter insecure factors that frequently manifest as sudden and unexpected disruptions. These disruptions can range from natural disasters, fires, and cyberattacks to economic shocks, illegal gatherings, violent crimes, and drug-related incidents. Consequently, there is a pressing need for the preventive police logistics system to embrace novel approaches and strategies in order to effectively adapt to the dynamic and varied nature of the police mission environment. Due to the nature and sensitivity of police organizations' missions, the utilization of logistics-based approaches that facilitate fast and agile movement, as well as logistics recovery during crises and disruptions, is crucial for effective logistics management in these organizations. Therefore, the objective of this research is to propose a hybrid model for agile and resilient logistics in police organizations.

Nowdays, supply chain specialists are looking for the integrated development of the supply chain model in order to increase the competitiveness, effectiveness and reduce the problems in the supply chain. They always seek to identify and develop this process so that they can cover more aspects of the chain. Adding effectiveness indicators along with LARG indicators and using the basics of the dynamic system to improve the efficiency of the supply chain is one of the innovations of this study. At first, by using research literature and studies, 12 headings of indicators were selected as LARG-E indicators. Then, with the Fuzzy Delphi method, the relationships and importance of each of these components were determined, and more important variables were modeled for further investigation. With using the concepts of dynamic systems, causal loops were drawn. Then, to check the function of the model, dynamic hypotheses were developed with the opinion of experts. In the next step, the flow diagram of the model and also the validation tests of the proposed model were done. Finally, by examining the outputs obtained from the proposed scenarios, it was found that most of variables have better behavior in LARG-E approach.

The closed-loop supply chain is used in practice for several reasons. Firstly, returning products to the production cycle is important for its operation. Secondly, sustainability in the supply chain is a topic of interest for researchers, as the environmental impacts of industries are significant. In this paper, a multi-objective integer fuzzy mathematical programming model is presented to design a sustainable closed-loop supply chain under uncertain conditions. The proposed model aims to maximize profit and social impacts, while minimizing gas emissions into the environment. Since decision makers face uncertainty and doubt, trapezoidal intuitionistic fuzzy numbers are employed to determine the parameter values in the model. To convert the objective functions and model constraints into crisp forms, the expected value and the intuitionistic credibility measure are developed for the objectives and constraints, respectively. Finally, an interactive fuzzy programming approach is utilized to solve the crisp multi-objective problem. Three numerical examples are designed and solved to validate the model and assess the efficiency of the proposed solution method.

This study aims to design a model for measuring the level of maturity of business intelligence in electronic businesses, specifically for Internet Service Provider (ISP) companies. The study adopts a qualitative approach based on the phenomenological approach. A total of 10 specialists, experts, and managers from electronic businesses involved in providing Internet services are selected as participants using the maximal differentiation method. Data are collected through in-depth and semi-structured interviews and analyzed using Colaizzi's method. The findings are classified into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique. Subsequently, a model consisting of 33 dimensions and 232 indicators is designed based on the relevant literature and the researcher's viewpoint, with confirmation from experts. Finally, the model is validated using confirmatory factor analysis in Smart PLS software.