Multi-Objective Simulation Optimization: A Case Study In Healthcare Management

This study presents an approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. This method was modified to perform the search considering the mean and the variance of the responses. This way, the selection process of the genetic algorithm is performed stochastically, and not deterministically like most of the approaches reported in the literature. The methodology was tested using a simulation model of a cancer treatment facility created by the authors. The multi-objective optimization heuristic was successfully used to improve the performance of the model relative to four different system objectives. Empirical results show that the methodology is capable of generating an important part of the Pareto optimal frontier, mostly concentrated in the center portion, where practical solutions are generally located. Significance: Even though real life problems present more than one objective, most simulation optimization studies have been faced with a single objective. This article presents an approach for solving this type of situations and also applies the methodology to a case in the healthcare industry, a field that lacks of applications in simulation optimization. ©INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING

Multi-Response Simulation Optimization Using Stochastic Genetic Search Within A Goal Programming Framework

This study presents a new approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. The genetic algorithm technique offers a very flexible and reliable tool able to search for a solution within a global context. This method was modified to perform the search considering the mean and the variance of the responses. In this way, the search is performed stochastically, and not deterministically like most of the approaches reported in the literature. The goal programming model integrated with the genetic algorithm and the stochastic search present a new approach able to lead a search towards a multi-objective solution

Multi-Objective Simulation Optimization For A Cancer Treatment Center

This paper presents a case study application of a cancer treatment center facility. A simulation model was created and integrated to a multi-objective optimization heuristic developed by the authors with the purpose of finding the best combination of control variables that optimize the performance of four different objectives related to the system. The results obtained show that the implementation of the proposed solution could improve the four objectives in comparison to the existing solution

Use Of Simulation For Process Improvement In A Cancer Treatment Center

This work addresses experience with a simulation model of a full service cancer treatment center. The objective was to analyze patient flow throughout the unit, evaluate the impact of alternative floor layouts, using different scheduling options and to analyze resources and patient flow requirements of a new building. The simulation model provided strong justification to relocate the center\u27s laboratory and pharmacy as well as identifying changes in scheduling procedures that would allow a 30% increase in patient throughout with the same resources. The new building analysis identified a waiting room area that was too small for the increased patient flow

Look-Ahead Constructive Heuristic For The Unrelated Parallel Machine Problem With Sequence Dependent Setup Times

This paper presents a look-ahead constructive heuristic for solving the unrelated parallel machine problem with sequence dependent setup times, where the objective is to minimize the completion time of the last job, or makespan (C max)- This is an NP-hard problem. Although a variety of practical situations can be modeled using this problem, still a modest number of articles devoted to it can be found in literature. The constructive heuristic proposed in this paper has a look-ahead mechanism based on a saving criterion, which permits to improve the quality of solutions against another constructive heuristic rule already reported in literature