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Operating room (OR) planning has gradually become an important element of Hospital administration in recent years. The OR performance plays a significant role in enhancing the quality of the care provided to the patients, and reducing patients' length of stay (LOS), which is the function of the OR performance, decreases the costs. Following a survey of the existing research and conducted interviews with clinical and non-clinical OR staff, there are several factors influencing patients’ length of stay (LOS) in OR, and consequently the costs of OR. In this work, first, we implemented the DEMATEL to define the relationship between OR factors, next, employed ANP to identify the most influential factors on the LOS in the OR. The results show the most influential factors are operative time, operative setup, patient waiting time, anesthesia, and patient preparation. Running two rooms concurrently and minimizing turnover time (TOT) reduce patients' LOS by 10% and 5.6%, respectively, and improve the patients' and clinical personnel's satisfaction. Reducing the LOS not only leads to reduced OR costs but also helps increase the number of operations and thereby hospitals' revenue. [ABSTRACT FROM AUTHOR]

In vehicle routing problems (VRP), the optimal allocation of transportation by considering factors such as route hardness, driver experience and vehicle worn-out has a significant effect on costs reduction and approaching realworld conditions. In this paper, a novel fuzzy mixed integer non-linear mathematical model to address the two-echelon allocation-routing problem under uncertainty is proposed by applying route and fleet conditions. The cost of allocating drivers to diverse vehicles is computed at the first echelon of the problem, considering factors such as vehicle type, vehicle wear-out, and driver experience. Additionally, different routes are defused with varying levels of hardness. The goal of the second echelon of the model is to improve reliability by defining the reliability of routes within each section. To solve the model, the Torabi and Hessini (TH), the Selimi and Ozkarahan (SO) methods, and a newly proposed approach (PIA) were utilized to transform the multiobjective model into a single-objective one. Numerical tests and performance indicators were used to validate the effectiveness of both the multi-objective mathematical model and the proposed solution method. The validation computation results indicate that the proposed solution approach outperforms both the TH and SO approaches. [ABSTRACT FROM AUTHOR]

Taking an integrated approach towards production, maintenance, and control in manufacturing systems is crucial due to the profound impact of their interconnections. Investigating these aspects in isolation may lead to infeasible solutions. This research focuses on the real-time and autonomous decision-making process concerning joint production planning, maintenance, and quality problem in a stochastic deteriorating production system with limited maintenance activities. Formulating the problem as a continuous semi-Markov decision process accounts for the complexities of the real production system and the occurrence of events over an uneven and continuous period. While dynamic programming is a common tool for addressing joint optimization problems, it has limitations, such as the curse of dimensionality. In this study, the optimal policy of the decision-maker agent is obtained by the goal-directed machine learning method called (R-SMART) and agent-based modeling. To the author's knowledge, the proposed approach is novel, and there is little research on such an implementation of the joint optimization problem. The quality of the optimal policy is evaluated through heuristic and simulation-optimization methods in various scenarios. The results demonstrate that the proposed RL-based method outperforms others in most scenarios, achieving a stable, integrated optimal policy. [ABSTRACT FROM AUTHOR]

Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of production, maintenance, and quality in a two-machine-single-product production system with an intermediate buffer and final product storage. The production machines have degradation levels from as-good-as-new to the breakdown state. The failures increase the production machine's degradation level, and maintenance activities change the status to the initial state. Also, the quality of the final product depends on the level of degradation of the machines and the correlation between the degradation level of the production machines and the product's quality in the case that high degradation of the previous production machines leads to a high probability to produce wastage by the following machines is considered. The production system studied in this research has been modeled using the agent-based simulation, and the Reinforcement Learning (RL) algorithm has obtained the optimal integrated policy. The goal is to find an integrated optimal policy that minimizes production costs, maintenance costs, inventory costs, lost orders, breakdown of production machines, and low-quality production. The meta-heuristic technique evaluates the joint policy obtained by the decision-maker agent. The results show that the acquired joint policy by the RL algorithm offers acceptable performance and can be applied to the autonomous real-time decision-making process in manufacturing systems. [ABSTRACT FROM AUTHOR]

Purpose: High efficiency in the operating room may significantly improve the overall performance of the hospital and the service quality provided to patients. The operating room is the de facto financial hub of the hospital and maximizing its efficiency may lead to considerable improvements. To this end, we seek ways of accelerating the patient flow in order to save time and cost in healthcare facilities. Methodology: In this study, we use agent-based simulation to simulate patient care in the operating room. After performing the required validations, a number of improvement scenarios were developed and evaluated. Findings: A hybrid scenario including modifications to the referral time of the patient by the surgeon, transfer time of the surgical set and supplies to the operating room, and the timing of anesthesia proved to have the most positive impact on the criteria i.e. activities, reducing the average Length of Stay (LOS) by 9.69 minutes. The second-most effective scenario involved modifying the referral time of the patient by the surgeon, reduced the LOS by 7.31 minutes. Originality/Value: Through this research, it became apparent that minimizing the patients' LOS improves the efficiency of the operating room as it helps reduce the overall idle time and increases the number of operations carried out in each shift. Making time even for one additional operation per day significantly increases the operating room income. Moreover, a shorter LOS means less fatigue for the medical staff and reduces the total cost of running the operating room by reducing the staffs overtime hours. [ABSTRACT FROM AUTHOR]

Contextualized in the field of customer satisfaction in after-sales service in the automotive industry, the authors contribute to increasing customer satisfaction in three fundamental ways. First, by offering a new conceptual framework that integrates prior studies and Delphi methodology to survey the automotive industry's experts to identify essential and influential variables on customer satisfaction. Second, by developing a system dynamics and modeling method to evaluate the effect of these variables on each other, and unlimitedly investigate these variables' impact on customer satisfaction. Third, by offering an improvement scenario, we indicated what combination of observed and latent variables leads to a numerical increase in the customer satisfaction index. The survey's analytical results suggest that the cost of services and repair time is reduced by 30%, and the time of car acceptance in the repair shop is reduced by 20%. At the same time, the satisfaction with repair order description is increased by 30%, resulting in a 5% increase in numerical satisfaction with the overall service. Heretofore did not use a dynamic system model to determine the numerical amount of increasing customer satisfaction with aftersales service in the automotive industry. Therefore, the results of this study can be used as a basis for developing new policies in customer relationship management in automotive companies to increase customer satisfaction. This fact can lead to an increase in sales and ultimately the company's profitability. The article contributes to future research by quantitatively determining the number of changes in the significant factors to increase customer satisfaction. [ABSTRACT FROM AUTHOR]

Network DEA models deal with measurements of relative efficiency of Decision-Making Units (DMUs) when the insight of their internal structures is available. In network models, sub-processes are connected by links or intermediate products. Links have the dual role of output from one division or sub-process and input to another one. Therefore, improving the efficiency score of one division by increasing its output may reduce the score of another division because of increasing its input. To address this conflict, in the present paper we proposed a new approach in Slack-Based Measure (SBM) framework which provides deeper insights regarding the sources of inefficiency. The proposed approach is a two-phase procedure in which Phase-I determine the role of intermediate measures by solving a linear program and partitions the intermediate measures into three groups of "input type", "output type" and "fixed-flows" and Phase-II measures the scores of the DMUs under evaluation. Providing a classification for intermediate products and account their excesses or shortfalls in efficiency calculation while the continuity of link flows between subunits are kept, are the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]