Search

There are many problems with hospital departments that decrease patients' satisfaction and hospitals' performance. Although there are many qualitative and quantitative methods to improve related performances, some of them consider a single department and the rest don't consider related priorities. Therefore, this paper proposes an integrated model based on Six Sigma to consider the performances of all departments with priorities based on experts' knowledge. This model utilizes qualitative methods including fishbone diagrams and Healthcare Failure Mode and Effect Analysis to find problems and their priorities based on experts' knowledge and data. Then, quantitative methods including a mathematical model, Discrete Event Simulation, and Non-dominated Sorting Genetic Algorithm II are utilized to find solutions for preferred problems as a simulation-based multi-objective optimization model. Based on a case study in a well-known general hospital, the proposed model finds the hospital's most important problems and represents the impacts of different improvement plans before implementation. [ABSTRACT FROM AUTHOR]

Increasing people participation in preventive healthcare programs can improve the quality of their life and create a lot of saving. Governments can more effectively utilize their healthcare expenditures, as well. Usually, governments do not make it mandatory for people to participate in these programs but encourage them. It is necessary to design the network of preventive healthcare facilities optimally based on client choice and requirements for increasing the participation of people in such programs. Therefore, this research focuses on two models of probabilistic selection and optimal selection as different client choice for predicting and managing the preventive services networks. The limits of waiting time and the workload of each facility are also considered for ensuring the quality of care. The main characteristics of the network configuration in this paper are the number of facilities, their location, and capacity. Both models are formulated using mixed integer linear programming and a genetic algorithm is utilized in the high dimensions problems for solving them effectively and quickly. These models are implemented for actual data of Isfahan city and then their results are analyzed. The results demonstrate a significant impact of the client choice on network efficiency. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to design an applied mathematical model to maximize the profits of maintenance activities in manufacturing organizations, also providing an efficient solution method for that. Design/methodology/approach: Reviewing published articles in the field of maintenance planning and then trying to model the problem to optimal decision making in this field. Findings: Maintenance optimization can be done more appropriately by the accurate use of mathematical programming. Research limitations/implications: The existence of probabilistic parameters in this problem leads to hard stochastic programming. Practical implications: Designing and organizing maintenance activities will increase productivity. This paper attempts to use mathematical models to optimize this matter. Originality/value: This paper highlights the importance of using optimization methods for maintenance planning. [ABSTRACT FROM AUTHOR]