Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil

Motivated by a proposal of the local authority for improving the existing healthcare system in the Parana State in Brazil, this article presents an optimization-based model for developing a better system for patients by aggregating various health services offered in the municipalities of Parana into some microregions. The problem is formulated as a multi-objective partitioning of the nodes of an undirected graph (or network) with the municipalities as the nodes and the roads connecting them as the edges of the graph. Maximizing the population homogeneity in the microregions, maximizing the variety of medical procedures offered in the microregions, and minimizing the inter-microregion distances to be traveled by patients are considered as three objective functions of the problem. An integer-coded multi-objective genetic algorithm is adopted as the optimization tool, which yields a significant improvement to the existing healthcare system map of the Parana State. The results obtained may have a strong impact on the healthcare system management in Parana. The model proposed here could be a useful tool to aid the decision-making in health management, as well as for better organization of any healthcare system, including those of other Brazilian States.     

Flexible work planning of service agents with load balancing

This work emerged from the need to better plan the daily work of 29 travelling service agents, who provide 1090 services to 412 customers in 283 sites, on daily average. A handy and flexible tool was developed and is presented herein. A major contribution of this study is an explicit consideration of the multi-dimensional nature of the problem by the inclusion of workload balancing, which may stand in conflict to cost minimisation. Further, the geographical distribution of the demand is highly irregular. Therefore, two load measures are required and balanced. This required to fitting a proper planning scheme. The planning tool has been applied successfully by the commercial service provider. Improvements in the order of 20% and more were obtained in key performance measures. Moreover, cost reductions, service improvement and load balance were obtained simultaneously: the standard deviations of the service times and working day’s duration were reduced by 18 and 58%, respectively. This enables to reduce the number of agents with no significant harm in performance. Additional practical advantages of the proposed tool are also discussed and demonstrated, for example, the ability to cope with geographical distributions and the flexibility to respond to daily variations in demand.     

Evaluation of multi-objective optimization approaches for solving green supply chain design problems

This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO₂ and fine dust (also known as PM – Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies, and their interactive counterparts that incorporate Decision Maker׳s (DM׳s) indirect preferences into the search process. Within this case study, the CO₂ emissions could be lowered significantly by accepting a marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of the Pareto front, but produced significantly worse solutions than the exact optimization methods. The interactive methods outperformed their a posteriori counterparts, and could discover solutions corresponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and starting generation of the elicitation, the number of pairwise comparisons needed by the interactive evolutionary methods to construct a satisfactory solution could be decreased.     

遗传算法在WMS货位优化分配中的应用

从一汽轿车股份有限公司仓储中心WMS的实际业务出发,考虑物料的进出库频率和货架的受力情况,为货位分配问题建立多目标优化模型.利用遗传算法中的并列选择法,设计遗传算子并进行求解.优化后结果表明:可以减少堆垛机的运行距离,合理安排货物的分配,减少人工作业,提高仓储的利用效率,降低企业成本.     

A multi-objective geographic information system for route selection of nuclear waste transport

Nuclear power is widely used throughout the world today. Functioning nuclear power plants produce large quantities of radioactive wastes needing to be transported to safe sites for proper management. With public emphasis on environmental protection and concern for safe transport of nuclear wastes, the problem of selecting an appropriate route for transporting nuclear wastes is a vitally important issue. The aforementioned route selection problem involves conflicting objectives among interested parties; therefore, we develop a multi-objective geographic information system (GIS) with ESRI ArcView GIS 3.x interface to practically support the involved parties for such a multi-objective route selection problem in engineering practices.     

A simulation-based multi-objective optimization framework: A case study on inventory management

We propose a simulation-based solution framework for tackling the multi-objective inventory optimization problem. The goal is to find appropriate settings of reorder point and order quantity to minimize three objective functions simultaneously, which are the expected values of the total inventory cost, the average inventory level, and the frequency of inventory shortage. We develop new algorithms that can exploit statistically valid ranking and selection (R&S) procedures and the desirable mechanics of conventional multi-objective optimization techniques. Two simulation algorithms are proposed to be applied in different scenarios depending on the preference information that is revealed either during or after the actual optimization process. Experimental results are provided to evaluate the efficiency of the developed algorithms and other existing solution frameworks.     

基于多目标优化和效用理论的高阶矩动态组合投资

 存在高阶矩风险偏好条件下,组合投资选择必须考虑最大化收益、偏度和最小化方差、峰度四个相互冲突的目标。同时,考虑到高阶矩风险的时变特征,应建立高阶矩动态组合投资模型。基于多目标优化技术和效用理论,讨论了高阶矩动态组合投资模型的构建,并利用MATLAB软件中的非线性优化函数fmincon对模型进行了求解,从理论和实证两个层面对两类模型进行对比。     

基于多目标的非常规突发事件资源布局模型

近年来,随着社会经济的发展,国内综合性非常规突发事件频繁发生,造成了巨大的经济损失和社会危害。作为应急管理系统的资源保障,合理的资源布局就显得尤为重要。对此,文章在充分考虑了资源储备库的公平性和效率性的基础上,通过追求时间和成本的最优性,考虑更多的约束因素,建立多目标模型,并利用改进蚁群算法来求得合理的资源布局。最后,通过一个算例来验证模型和算法的可行性和有效性。     

Interactive fuzzy goal programming for multi-objective transportation problems

This paper presents an interactive fuzzy goal programming approach to determine the preferred compromise solution for the multi-objective transportation problem. The proposed approach considers the imprecise nature of the input data by implementing the minimum operator and also assumes that each objective function has a fuzzy goal. The approach focuses on minimizing the worst upper bound to obtain an efficient solution which is close to the best lower bound of each objective function. The solution procedure controls the search direction via updating both the membership values and the aspiration levels. An important characteristic of the approach is that the decision maker's role is concentrated only in evaluating the efficient solution to limit the influences of his/her incomplete knowledge about the problem domain. In addition, the proposed approach can be applied to solve other multi-objective decision making problems. The performance of this solution approach is evaluated by comparing its results with that of the two existing methods in the literature.     

Multi-objective permutation flow shop scheduling problem: Literature review,classification and current trends

The flow shop scheduling problem is finding a sequence given n jobs with same order at m machines according to certain performance measure(s). The job can be processed on at most one machine; meanwhile one machine can process at most one job. The most common objective for this problem is makespan. However, many real-world scheduling problems are multi-objective by nature. Over the years there have been several approaches used to deal with the multi-objective flow shop scheduling problems (MOFSP). Hence, in this study, we provide a brief literature review of the contributions to MOFSP and identify areas of opportunity for future research.