Background: Effective Decision Making on the resources of the ED plays a significant role in the performance of the department. Since wrong decisions can have irreparable consequences on the quality of services, the decision-makers should analyze and allocate the resources effectively. Methods: The present study aimed to investigate the effective resources in the emergency department and provide an optimal combination of these resources based on the meta-modeling optimization approach to reduce the wait time for patients in the ED. Results:The results demonstrated that the number of CHWs and beds played a significant role in the total average wait time for patients. Although the effect of other variables was not statistically significant, they were deliberately used in this study to determine the optimal combination of such variables by solving the problem. Conclusion: The findings of the present simulation-model approach provide hospital managers with valuable data in order to control and re-design the admission to discharge procedure in the emergency in order to enhance efficiency. By considering the budget, the new configuration of 2 Community Health Worker, 1 Receptionist, 1 nurses, 3 Cardiologist and 10 beds, with 142 minutes of a patient's wait time shows 49.6% wait time improvement and a reduction of 51% in the cost of resource usage.

No abstract

Most of manufacturing industries in our country practice the traditional production systems. Effective management of the steady state operation is no longer enough to ensure the survival,let alone the successof an organization. The performance of the operations has to be improved continually in all its aspects, and it is driven by the quest for increased productivity, flexibility and continuously changing competitive environment.The increasing global character of market for goods and services,is stimulated by the factors like improvements in transport, data communication systems, and primarily the automation of manufacturing operations. These features need for continuous performance analysis and improvement of manufacturing systems. Therefore, the key to stay at the apex of global competition is to meet the dynamically changing need of customers. Manufacturing systems performance analysis using Petri Nets(PN) is one of the promising tools employed for assessing. PN models are now common place within the sphere of performance modeling of manufacturing systems due to reasons like graphical and precise representation of system activities and models at various levels of detail and ability to capture the existence of concurrency,parallelism,resource constraints and process dependencies accurately. This paper, focuses on analyzing the performance of the manufacturing process of pars metal, one of the manufacturing industries in the country, using PN so as to evaluate various performance parameters such as utilization rate of machines, bottleneck detection, cycle time,and throughput rate of system under consideration and providing solutions and recommendations for the pitfalls and ramification for attaining the optimum productivity.

In recent decades, the use of supply chain management is essential for developing new technologies and setting the ground in expanding major global markets to integrate suppliers, manufacturers, warehouses, and stores effectively. In addition, the growing competition in the modern business environment has created an increasing trend of new products and improved product quality for attracting more consumers. However, an increase in the related costs and uncertainty in these innovations requires the development of algorithms to solve optimization problems. Therefore, this study is aimed to implement a multi-product supply chain including raw material suppliers, factories, distributors, and customers to maximize the quality and minimize costs. To this aim, supplier quality, distribution centers, and manufacturing products were considered for the quality model, while warehousing costs, product production, transportation, defective raw materials, and the like were regarded for the cost model. Then, the NSGAII algorithm was used for solving the created optimization problem, and accordingly, the optimal Pareto points were calculated. Based on the results, the proposed model can give the manufacturer the ability to decide on a multi-product and multi-time supply chain by involving cost and quality variables. Thus, the owner can manage the supply chain of the factory effectively.

Purpose One of the practical issues in the area of location and allocation is the location of the hub. In recent years, exchange rates have fluctuated sharply for a number of reasons such as sanctions against the country. Natural disasters that have occurred in recent years caused delays in hub servicing. The purpose of this study is to develop a mathematical programming model to minimize costs, maximize social responsibility and minimize fuel consumption so that in the event of a disruption in the main hub, the flow of materials can be directed to its backup hub to prevent delays in flow between nodes and disruptions in hubs. Design/methodology/approach A multi-objective mathematical programming model is developed considering uncertainty in some parameters, especially cost as fuzzy numbers. In addition, backup hubs are selected for each primary hub to deal with disruption and natural disasters and prevent delays. Then, a robust possibilistic method is proposed to deal with uncertainty. As the hub location-allocation problem is considered as NP-Hard problems so that exact methods cannot solve them in large sizes, two metaheuristic algorithms including a non-dominated sorting genetic algorithm non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied to tackle the problem. Findings Numerical results show the proposed model is valid. Also, they demonstrate that the NSGA-II algorithm outperforms the MOPSO algorithm. Practical implications The proposed model was implemented in one of the largest food companies in Iran, which has numerous products manufactured in different cities, to seek the hub locations. Also, due to several reasons such as road traffic and route type the difference in the rate of fuel consumption between nodes, this model helps managers and decision-makers to choose the best locations to have the least fuel consumption. Moreover, as the hub set up increases the employment rate in that city and has social benefits as it requires hiring some staff. Originality/value This paper investigates the hub location problem considering backup hubs with multiple objective functions to deal with disruption and uncertainty. Also, this study examines how non-hub nodes are assigned to hub nodes.