Journal of Industrial and Systems Engineering
Volume:15 Issue: 2, Spring 2023

Hospitals are critical facilities which have a great role to affects the number of mass casualties after disasters. Hence, it is necessary to adopt strategies to increase hospitals preparedness and to improve their resilience. The present paper tries to propose a strategy to cope with surge of demands under disruptions in a hospital. An optimization model for bed management considering collaboration between hospital wards in order to minimize the waiting times of the patients provided in this research and the objective function under the proposed strategy and without the proposed strategy were compared. The results show that the proposed strategy can reduce the patients waiting time under disruptions. Due to the complexity of the proposed model, a Lagrangian relaxation-based heuristic is developed to solve the model. Computational results show that the proposed algorithm is able to reach desirable gap in a reasonable time.

Due to narrow streets, dense traffic, and transportation restrictions, city logistics operations are under increasing pressure and need innovative approaches. Recently coordination of trucks and drones has been used as a new solution, which can improve the efficiency of city logistics operations. This paper also focuses on a truck and drone delivery system. As the major contributions, this paper develops a new mix integer programming model to formulate the hybrid truck and drone routing problem with soft time windows and proposes an effective two-phased metaheuristic algorithm. To evaluate the performance of the proposed metaheuristic, we carried out numerous computational experiments, where the results show the efficiency of the proposed metaheuristic. Finally; a detailed sensitivity analysis is performed.

This research proposes and solves a mathematical problem of parallel machine scheduling to minimize the total completion time and energy cost. This research aims to design and optimize a multi-objective mathematical model by minimizing energy consumption and total completion time for the parallel machine scheduling problem in Semnan Polyethylene Factory. First, the mathematical model of the problem is provided, and then the solution method is investigated using the epsilon constraint method in the GAMS optimization software and the meta-heuristic imperialist competitive algorithm (ICA). The mathematical model is validated using GAMS software and the constraint epsilon method and a real problem is implemented in large dimensions regarding the case study of the polyethylene factory in Semnan province using the meta-heuristic ICA. Finally, the performance of the ICA is measured in terms of the RPI index for small dimensions and the MID index for examples with large dimensions. Numerical results show that the value of the index for distance from the ideal point in the ICA is lower than that of the index obtained from solving the problem in GAMS. With these interpretations, it can be concluded that the ICA has a better performance than GAMS for optimizing the parallel machine scheduling problem in this research. According to the obtained answers, it can be concluded that with the increase in the time to do a task, the time to complete all tasks also increases and the cost of energy remains constant. While the cost of doing the task and the price of the electricity signal increase, energy costs increase and the time to complete tasks remains constant.

Empirical studies have indicated that linking advertising and pricing will bring significant advantages to the supply chain components. With the exponential extension of online social networks and society’s greater interest in receiving information from this space, many firms have been encouraged to use online social networks and maximize the effects of advertising campaigns; however, literature on designing this type of advertising and linking it with pricing in the supply chain is still rare. To fill this gap, this paper uses a data-driven support vector optimization framework to link influencer-based advertising and pricing in a two-echelon SC. Also, the impact of the passage of time and uncertainty on advertising message diffusion has been examined. The results show that advertising in social media is a complex task and is affected by various factors, such as the time of serving the primary and supporting ads. Based on our results, only after six weeks of releasing the primary ads did the effect of the advertisement decrease significantly. It seems that disseminating supporting advertising messages in advertising campaigns is vital. Also, results obtained from the data-driven robust optimization models show that the slightest change in the degree of conservatism significantly changes the profitability of the company (an increase of only 5% of the degree of conservatism increases profitability by about 1.4 on average), therefore, determining this coefficient has a significant effect on the performance advertising campaigns.

Spare parts with intermittent demand cause complexity in forecasting and planning decisions since the demand is zero in some periods. Therefore, the difficulties in planning regarding the demand quantity and arrival time motivate this study to investigate a well-structured strategy that aggregates the demand in low-frequency time intervals to reduce the zero-demand occurrence. In this way, the planning accuracy increases since the forecasting error is reduced by the use of demand aggregation. We fill the research gap by integrating the aggregation and planning model for repairable spare parts to answer the question of what the optimal aggregation level is. In this paper, we develope a planning model for a repairable spare part supply chain (SPSC) to examine the effect of aggregation on cost, stock level, and shortage. An empirical investigation from an Iranian oil company is used to validate the benefit of integrated demand aggregation. The results show that choosing an optimal aggregation level optimizes the cost, stock level, and shortage. It also improves the demand estimation gap. The provided model, measures, and managerial insights help the practitioners make robust decisions since the coefficient of variation decreases due to optimal aggregation level, which leads to improvement in the planning performance.

This study addresses the Hub Location-Routing Problem (HLRP) in transportation networks, considering the inherent uncertainty in travel times between nodes. We employed a method centered on data-driven robust optimization, utilizing Support Vector Clustering (SVC) to form an uncertainty set grounded in empirical data. The proposed methodology is compared against traditional uncertainty sets, showcasing its superior performance in providing robust solutions. A comprehensive case study on a retail store's transportation network in Tehran is presented, demonstrating significant differences in hub locations, allocations, and vehicle routes between deterministic and robust models. The SVC-based model proves to be particularly effective, yielding substantially improved objective function values compared to polyhedral and box uncertainty sets. The study concludes by highlighting the practical significance of this research and suggesting future directions for advancing transportation network optimization under uncertainty.

This paper discusses the modeling of a location-routing-inventory problem for perishable products. The model presented in this paper includes a three-echelon supply chain of suppliers, distribution centers, and retailers. Supplier selection, assigning suppliers to distribution centers and retailers, vehicle routing and economic order quantity, lead time, and confidence inventory are the main decisions of the problem. These decisions are aimed at optimizing the total supply chain network costs. The nonlinear model presented in this article has been solved using two algorithms, WOA and ALO, in 12 sample problems. The results show that the solving speed of these algorithms and the high quality of the obtained answers are very high compared to the exact method. So, the maximum percentage of relative difference between the obtained results is less than 1%. The sensitivity analysis on the perishability rate also shows the increase in total costs in line with the increase in this parameter. By examining the outputs of 12 sample problems in large size, the WOA showed its efficiency compared to the ALO in terms of two indicators of average total costs and CPU time.

Value chain (VC) is one of the fundamental concepts in production and operations management, used for creating and developing products and services, and distributing them to the end customers. Because of its importance to organizations success or failure, especially in the developing countries, this study aims to investigate the factors influencing VC in pharmaceutical industry, as a very important sector in Iran. For this purpose, pharmaceutical value chain experts in a public company (Sobahan Darou Co.) were extracted. Next, systems dynamics as a powerful approach for modeling complex systems, is applied to simulate the dynamics of the pharmaceutical VC. So, the interactions of the main variables of the system were translated to the dynamic hypotheses and constitute the causal loop diagram. Then, stock and flow diagram was formulated in form of the differential equations. To validate the proposed model, some structural and behavioral validation test were implemented which indicated model’s accuracy. Finally, four potential scenarios were defined based on the of the company's possibilities and the amount of change in marketing and sales costs, investment in skilled human resources and investment in research and development, and the effect of implementing these scenarios on four basic variables, namely customer satisfaction, financial strength, polution and VC efficiency were investigated. Based on the results, the fourth scenario has the greatest impact on customer satisfaction and emission reduction. The second scenario has the greatest increase on VC efficiency and financial strength.

The digital transformation of construction enables engineers to harness the power of technology-based data to help make more accurate decisions. IoT sensors collect details about temperature, fluid levels, vibrations, etc. Similarly, using artificial intelligence along with the Internet of Things provides high analytical power to evaluate the big data resulting from the Internet of Things. Therefore, understanding the dimensions of supply chains based on these technologies can help to facilitate and empower supply chain processes in this key industry. This research presents a framework by identifying the dimensions and key components of success in the supply chain based on the artificial intelligence of things (AIoT). This framework identifies key areas of the smart construction supply chain. Properly understanding this framework can be a valuable guide for the optimal implementation of smart supply chain processes.

Examining the countries' financial systems shows that this field has also been affected by technological developments and has benefited from its advantages and benefits to overcome challenges and create opportunities. Therefore, technology development in the banking sector has meaningful implicit concepts in bank marketing, especially in digital banking, because it affects the standard level of customers. This research aims to design a model for evaluating the effectiveness of the digital transformation of the banking system. Based on the purpose, this research is of developmental and evaluation type, and in terms of method, it is considered descriptive research of mathematical modeling type. In this research, in the quantitative part, two categories of statistical techniques are used using SPSS software. Also, in the second part of the research, content analysis was used to identify the model, dimensions, main categories, and subcategories. In the final part, it was used to analyze the banking system to establish digital transformation based on system dynamics and fuzzy network data coverage analysis method. The research findings showed that identifying and analyzing the practical components in the digital transformation of the banking system is essential for designing a model to evaluate the effectiveness of these transformations.