Automated Storage and Retrieval Systems (AS/RS) are being widely used in the logistics industry. This typical System, Systems are computer controlled storage systems that can automatically store and retrieve loads. Major advantages of AS/RS include high throughput, efficient use of space, high reliability and improvement of safety. There are some different operational modes in typical system. The choice of operational mode depends on the storage location assignment policy. Shared storage approach is one of these policies. In this approach, the empty space created by an act of accumulation throughout recovery can be quickly filled which conduces to save time and money. This paper examines the joint optimization of (AS/RSs) scheduling in multi-shuttle AS/RSs under shared storage in fuzzy and dynamic environment. From the view of analytical model, the advantage of operational mode under shared storage is verified. For optimization multi-shuttle automated storage and retrieval system to minimize travel time, present a zero-one fuzzy mathematical programming model. Use Genetic algorithm (GA) with Matlab software to solve large-sized problems. Various numerical experiments are conducted to evaluate the performance of the proposed algorithm and investigate the impact of different parameters on computational efficiency. The result indicates that in matters where the primary objective is to reduce travel time cycle, shared storage policy is appropriate. Furthermore, the fuzzy sets theory can be an effective tool to model the uncertainties which are available around the real applications. 1. INTRODUCTION Due to the merits of higher operational efficiency, multi-shuttle automated storage/retrieval systems (AS/RSs) become increasingly popular for storing products in factories and distribution centers. A typical multi-shuttle AS/RS has a series of storage aisles each of which is served by a multi-shuttle storage and retrieval (S/R) machine. The multi-shuttle S/R machine can carry multiple stock keeping units (SKUs) at a time, which implies that performing multiple operations (storage or retrieval) on a trip is feasible. The increase of storage and retrieval operations performed on a trip naturally leads to reducing the empty travel time of the S/R machine. Then the higher operational efficiency can be obtained compared with the single-shuttle AS/RS [1]. Nowadays companies need powerful and up-to-date tools to be able to survive competitions. Stock keeping systems are considered key components in the processes of production and storage of a product in manufacturing organizations and warehouses of large companies. Automated storage systems can play a decisive role in this regard. Manufacturers use fully automated stock keeping systems to improve accuracy and speed in performing storage and retrieval operations of products in the warehouses. Along with the technological progress in the present area, storage systems have grown
The health care system is characterized by limited resources, including the physical facilities as well as skilled human resources. Due to the extensive fixed cost of medical facilities and the high specialization required by the medical staff, the problem of resource scarcity in a health care supply chain is much more acute than in other industries. In the pandemic of the Coronavirus, where medical services are the most important services in communities, and protective and preventive guidelines impose new restrictions on the system, the issue of resource allocation will be more complicated and significantly affect the efficiency of health care systems. In this paper, the problem of activating the operating rooms in hospitals, assigning active operating rooms to the COVID-19 and non-COVID-19 patients, assigning specialty teams to the operating rooms and assigning the elective and emergency patients to the specialty teams, and scheduling their operations is studied by considering the new constraints of protective and preventive guidelines of the Coronavirus. To address these issues, a mixed-integer mathematical programming model is proposed. Moreover, to consider the uncertainty in the surgery duration of elective and emergency patients, the stochastic robust optimization approach is utilized. The proposed model is applied for the planning of operating rooms in the cardiovascular department of a hospital in Iran, and the results highlight the role of proper management in supplying sufficient medical resources effectively to respond to patients and scheduled surgical team to overcome the pressure on hospital resources and medical staff results from pandemic conditions.
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