Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services

Simulation is a powerful tool for modeling complex systems with intricate relationships between various entities and resources. Simulation optimization refers to methods that search the design space (i.e., the set of all feasible system configurations) to find a system configuration (also called a design point) that gives the best performance. Since simulation is often time consuming, sampling as few design points from the design space as possible is desired. However, in the case of multiple objectives, traditional simulation optimization methods are ineffective to uncover the efficient frontier. We propose a framework for multi-objective simulation optimization that combines the power of genetic algorithm (GA), which can effectively search very large design spaces, with data envelopment analysis (DEA) used to evaluate the simulation results and guide the search process. In our framework, we use a design point's relative efficiency score from DEA as its fitness value in the selection operation of GA. We apply our algorithm to determine optimal resource levels in surgical services. Our numerical experiments show that our algorithm effectively furthers the frontier and identifies efficient design points.     

Penalty guided genetic search for redundancy optimization in multi-state series-parallel power system

This paper presents a genetic algorithm (GA) for parallel redundancy optimization in series-parallel power systems exhibiting multi-state behavior, optimizing the reliability subject to constraints. The components are binary and chosen from a list of products available in the market, and are being characterized by their feeding capacity, reliability, cost and weight. System reliability is defined as the ability to satisfy consumer demand and is presented as a piecewise cumulative load curve. In GA, to handle infeasible solutions penalty strategies are used. Penalty technique keep a certain amount of infeasible solutions in each generation so as to enforce genetic search towards an optimal solution from sides of, both, feasible and infeasible regions. We here present a dynamic adaptive penalty function which helps the algorithm to search efficiently for optimal/near optimal solution. To evaluate system reliability, a fast procedure, based on universal generating function, is used. An example considering a multi-state series-parallel power system is solved considering both homogeneous and heterogeneous types of redundancy. Also an example considering price discounts is solved. The effectiveness of the penalty function and the proposed algorithm is studied and shown graphically.     

蚂蚁算法在组合优化中的应用

蚂蚁算法是近年来新出现的一种随机型搜索寻优算法 ,自从在 TSP等著名问题中得到富有成效的应用之后 ,已引起越来越多的关注和重视 .本文进一步将这种新型的生物优化思想扩展到其他一些组合优化难题 ,包括目前尚缺乏有效求解手段的多目标组合优化问题 ,从实验上探索了蚂蚁算法的优化能力 ,获得了满意的效果     

Reverse exchange of healthcare devices: the case of hearing aid equipment in the UK

Abstract

Reverse exchange (RE) in dealing with the return, recycle and reuse of products is receiving a growing focus. When properly handled, RE in healthcare can deliver an economic benefit of cost minimisation and has extensive positive impacts on both human health and the environment (Li and Olorunniwo 2008 Li, X., and F. Olorunniwo. 2008. “An Exploration of Reverse Logistics Practices in Three Companies.” Supply Chain Management: An International Journal 13 (5): 381–386. doi:10.1108/13598540810894979[Crossref], [Web of Science ®] , [Google Scholar]) but to date, RE research is mostly limited to pharmaceutical return. This paper investigates the potential for RE benefits in the UK National Health Service (NHS) supply chain for medical devices. Hearing aids supplied to adults with hearing loss are used as an illustrative example. This research applied a consensus approach through the use of dispersed nominal groups in order to obtain qualitative data on information, barriers, solutions and priorities to support findings. Findings illustrate that the end user behaviour of returning the device, and the requirement by NHS Procurement for manufacturers to meet RE targets are secondary to the importance of audiology departments who have the autonomy to design RE processes and successfully implement initiatives. A schematic highlighting the information and materials flow of the supply chain and the barriers and facilitators to RE is presented for hearing aid devices with potential for transferability to other small medical device supply chains.     

IT服务供需双边匹配的模糊多目标决策方法

描述了IT服务外包中的供给方与需求方的双边匹配决策问题。为了解决该问题,针对IT服务的供给方和需求方分别给出的语言满意度评价信息,将语言评价信息转化为三角模糊数并进行多指标信息的集结运算。在此基础上,考虑以供给方和需求方双方满意度最大为目标,构建了IT服务供需双边匹配的模糊多目标优化模型,并采取将模糊多目标优化模型转化为单目标线性规划模型的方式进行求解得到匹配结果。实例计算表明,提出的方法具有可行性和实用性。     

Reducing the number of setups for CNC punch presses

Large computer numerical control (CNC) punch presses are widely used for punching holes in heavy gage sheet metals. The turret of such presses has limited tool capacity, and any tool changes require dismantling the turret. Dismantling and reloading the turret generally takes several hours and is the dominant element of the total flow time for a batch of sheet metals. Reducing the turret setup time requires judicious grouping of the required tool set. We formulated an integer program to obtain the minimum number of setups. However, since the problem is nondeterministic polynomial-time hard (NP-hard), we offer three heuristics to solve the problem. We compare solutions obtained from the three heuristics with their respective optimal solutions using three simulated data sets and an industrial data set.     

The shadows know me: Exploring the dark side of social media in the healthcare field

Nowadays, the increasing attention paid to the dark side of social media (SM) in the field of healthcare management has moved both researchers and practitioners to investigate the impact of Web 2.0 technologies with reference to SM, because of continuous distortion created by these SM platforms for patients and the real dark side they constitute, which affects both the patient sphere and its relative empowerment.Based on previous theoretical and managerial contributions, the paper aims to investigate both the bright and dark side of the effects of SM in the healthcare field, which affect improvements in patient empowerment. Notwithstanding, the exploratory study was developed through a common theoretical and conceptual framework to improve the understanding of emerging social and economic dynamics towards the paradigm provided by many studies on the dark side of Web 2.0.So far, established on previous contributions to the healthcare management domain, the exploratory study identifies a set of variables related to the conceptualisations of patient empowerment and the dark side of SM, with reference to cases of digital users both with and without chronic illnesses.Moving deeper, matching qualitative and quantitative approaches, the impact of the dark side of SM on patient empowerment was investigated using structural equation modelling through SPSS and R softwares, sampling about 650 individuals on the Italian population using computer-assisted web interviewing.Finally, results highlight considerable correlations between many dimensions explaining patient empowerment and the dark side of SM, showing a positive effect on the improvement in patient engagement, but potential critical risks and concerns due to a misinterpretation of online information highlighting the dark side of SM.     

面向延时敏感患者的转诊系统定价与能力规划

针对我国当前社区医院首诊而后向上转诊系统中医疗资源利用严重失衡的现状,研究了我国医疗转诊系统的服务能力设计与定价问题.通过建立一个排队和博弈的集成模型,分析了延时敏感病人选择行为下,三甲医院以利润最大化、社区医院以服务人数最大化的不同目标的竞争均衡.发现社区医院的最优服务能力随政府的补贴递增、三甲医院的诊疗费在政府补贴超过某个阀值时开始下降.结果表明政府的补贴政策是医疗转诊体系优化设计中有效的协调机制.     

The maximum loading and the optimum number of vehicles in a double-loop of an interbay material handling system

In this paper, the loading analysis of an automated double-loop interbay material handling system (AMHS) in a wafer fabrication was analysed, considering the effects of the vehicle numbers in the inner and outer loops. Discrete-event simulation models were developed in e-M Plant to study this AMHS system with a zone control scheme to avoid any vehicle collision. The layout of an interbay system is a combination configuration in which the hallway contains double loops and the vehicles have double capacity. The combination of the shortest distance with nearest vehicle (SD–NV) and the first-encounter-first-served (FEFS) dispatching rule was used in this study. The maximum loading of this interbay system can be determined by the simulation approach. We also found that the number of vehicles in the inner and outer loops can significantly affect the interbay performance. Furthermore, the optimum combination of the vehicle numbers in the inner and outer loops can be obtained by response surface methodology.     

Sustainable supply chain network design: A case of the wine industry in Australia

This paper comes at a time when many companies and people are increasingly facing various global problems linked to sustainability challenges, and when the literature is still scarce on research incorporating all three dimensions of sustainability in supply chain analytical models. The paper conducts a case-based modeling study to address this gap in the area of supply chain network design. We investigate the wine supply chain and propose a generic model for sustainable wine supply chain network design encompassing economic, environmental and social objectives. The case company is a real large-sized wine company located in Australia, for which a customized model is provided. Both models are formulated as a multi-objective mixed-integer program and solved using the augmented ϵ-constraint method by CPLEX. Social implications of the feasible scenarios are examined through introducing social impact coefficients. Non-dominated solutions are obtained and some balanced scenarios are proposed. The results show trade-offs between the objectives, yet more interestingly demonstrate how large is the gap between the existing supply chain configuration and the proposed scenarios in terms of supply chain cost and emissions.     

基于顾客心理期望等待时间的服务策略研究

顾客在排队系统获取服务时,会存在心理上的期望等待时间,该期望会影响顾客在排队系统中的行为变化和流动,从而影响企业收益。本文以传统的M/M/1排队系统为背景,基于顾客存在期望等待时间的前提下,以企业收益最大化为优化目标进行研究。首先,对相应基础理论和模型假设进行介绍;其次,对顾客存在心理期望等待时间情形提出三种新的策略：重新定价、通过折扣对顾客期望值进行调整、提高服务率;然后,分别对上述三种策略进行优化分析,并同现有结果进行比较;研究表明：三种策略都比维持原有定价带来更大收益;当折扣力度较小时或顾客对费用感知强于时间感知时,折扣策略优于重新定价策略;当折扣力度较大或顾客对时间感知强于费用感知时,重新定价策略优于折扣策略;最后,通过对最优结果分析提出相应管理启示。本文的研究对于顾客存在心理期望等待时间的服务定价具有重要的指导意义和实际应用价值。     

基于广义均衡模型的我国三大产业从业人员配置分析

基于优化理论和广义均衡模型,以2003-2012年中国三大产业就业人员数量和收入,以及对GDP的贡献为基本数据,对我国三大产业的从业人员配置和区域流向进行分析,研究使生产总值最大化的配置方案。首先,采用回归分析给出了三大产业的就业趋势,接下来,建立生产总值的优化模型,并结合matlab软件计算出人员在三大产业中的最优配置,最后,采用广义均衡模型模拟就业岗位在区域间的流动,通过数值分析讨论低端制造业、高级制造业、以及服务业在发达地区和欠发达地区之间的就业流向。研究表明：我国需要遏制务农人员锐减的趋势,在此基础上,通过政策引导和专业培训将失业人口流向至第三产业,同时,由于存在就业流动的均衡状态,需要控制好产业转型的时间范围,避免经济发展超前或滞后的现象,在这段时间内引导低端制造业就业岗位从发达地区流向欠发达地区,高级制造业没有区域流动变化,仍然主要保留在发达地区,服务业所产生的就业岗位则集中于发达地区。     

改进粒子群优化算法在电源规划中的应用

电源规划是一类复杂、非线性组合优化问题.传统的方法随着规划期的延长,考虑因素的增多,难以有效的进行优化,在实际应用中作用有限.首先,对电源规划优化问题进行了建模.然后,对于粒子群(PSO)的迭代策略进行改进,在此基础上,运用遗传粒子群(GPHA)混合优化算法进行了优化尝试.考虑到电源规划中相关参数众多,在优化过程中引入了虚拟变量对电源规划中的问题进行了简化描述;GHPA算法的适应度评价函数设计中,运用了罚函数的思想,以提高算法优化的效果.最后本文使用某省实际负荷预测和系统负荷实际数据,进行了电源规划方案优化,得到了优化后的电源规划方案,并与普通的遗传算法、粒子群算法以及传统的动态规划算法得到的结果进行了比较.比较的结果显示出了本文提出的算法在优化结果和速度方面具有明显效果.     

Optimal Workforce Mix in Service Systems with Two Types of Customers

We consider a service system with two types of customers. In such an environment, the servers can either be specialists (or dedicated) who serve a specific customer type, or generalists (or flexible) who serve either type of customers. Cross‐trained workers are more flexible and help reduce system delay, but also contribute to increased service costs and reduced service efficiency. Our objective is to provide insights into the choice of an optimal workforce mix of flexible and dedicated servers. We assume Poisson arrivals and exponential service times, and use matrix‐analytic methods to investigate the impact of various system parameters such as the number of servers, server utilization, and server efficiency on the choice of server mix. We develop guidelines for managers that would help them to decide whether they should be either at one of the extremes, i.e., total flexibility or total specialization, or some combination. If it is the latter, we offer an analytical tool to optimize the server mix.     

基于遗传算法的分辨系数的优化方法

灰关联分析中分辨系数的选取会影响到分析结果的信息分辨程度,甚至灰关联序。把遗传算法和差异信息熵理论结合给出了优化分辨系数的方法。此方法把分辨系数作为优化变量,关联度序列的熵最小为优化目标,利用遗传算法的优化机制获得优化的分辨系数。最后,给出了一个计算实例。     

偏好市场下制造 /再制造系统最优生产决策

从消费者对再制造产品和新产品有着不同的需求偏好入手, 引入市场需求偏好函数, 构建单一垄断生产商利润最大化的带约束生产决策模型, 探讨偏好市场下的两期和无限期的最优产量 -价格决策策略.此外, 通过对再制造经济优化模型的数据模拟, 分析消费者偏好系数对生产商利润、成本节约、产品价格的影响, 为生产商的生产决策, 以及政府如何影响消费者偏好决策提供指导建议     

偏好市场下制造/再制造系统最优生产决策

从消费者对再制造产品和新产品有着不同的需求偏好入手,引入市场需求偏好函数,构建单一垄断生产商利润最大化的带约束生产决策模型,探讨偏好市场下的两期和无限期的最优产量-价格决策策略.此外,通过对再制造经济优化模型的数据模拟,分析消费者偏好系数对生产商利润、成本节约、产品价格的影响,为生产商的生产决策,以及政府如何影响消费者偏好决策提供指导建议.     

On the fair optimization of cost and customer service level in a supply chain under disruption risks

This paper presents a new decision-making problem of a fair optimization with respect to the two equally important conflicting objective functions: cost and customer service level, in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers, and schedule the orders over the planning horizon, to equitably optimize expected cost and expected customer service level. The supplies of parts are subject to independent random local and regional disruptions. The fair decision-making aims at achieving the normalized expected cost and customer service level values as much close to each other as possible. The obtained combinatorial stochastic optimization problem is formulated as a stochastic mixed integer program with the ordered weighted averaging aggregation of the two conflicting objective functions. Numerical examples and computational results, in particular comparison with the weighted-sum aggregation of the two objective functions are presented and some managerial insights are reported. The findings indicate that for the minimum cost objective the cheapest supplier is usually selected, and for the maximum service level objective a subset of most reliable and most expensive suppliers is usually chosen, whereas the equitably efficient supply portfolio usually combines the most reliable and the cheapest suppliers. While the minimum cost objective function leads to the largest expected unfulfilled demand and the expected production schedule for the maximum service level follows the customer demand with the smallest expected unfulfilled demand, the equitably efficient solution ensures a reasonable value of expected unfulfilled demand.     

Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China

China is a major developing country where farmers account for over 57% of the population. Thus, promoting a rural economy is crucial if the Chinese government is to improve the quality of life of the nation as a whole. To frame scientific and effective rural policy or economic plans, it is useful and necessary for the government to predict the income of rural households. However, making such a prediction is challenging because rural households income is influenced by many factors, such as natural disasters. Based on the Grey Theory and the Differential Evolution (DE) algorithm, this study first developed a high-precision hybrid model, DE-GM(1,1) to forecast the per capita annual net income of rural households in China. By applying the DE algorithm to the optimization of the parameter λ, which was generally set equal to 0.5 in GM(1,1), we obtained more accurate forecasting results. Furthermore, the DE-Rolling-GM(1,1) was constructed by introducing the Rolling Mechanism. By analyzing the historical data of per capita annual net income of rural households in China from 1991 to 2008, we found that DE-Rolling-GM(1,1) can significantly improve the prediction precision when compared to traditional models.     

考虑碳配额差值的选址-路径-库存集成问题优化模型与算法

考虑碳配额差值对产供销一体化系统中的选址-路径-库存集成问题的影响,构建了嵌入碳配额差值和选址-路径-库存运作成本的非线性整数规划模型,并对模型中的碳配额差值和选址-路径-库存运作成本两个目标同时进行优化;通过对目标函数进行等价处理,设计了基于BFA-PSO的组合优化求解算法;数值仿真结果显示企业可以通过运作层的决策优化,以较小的经济成本获得较大的碳减排绩效。