A derivative-free approach for a simulation-based optimization problem in healthcare

Hospitals have been challenged in recent years to deliver high quality care with limited resources. Given the pressure to contain costs, developing procedures for optimal resource allocation becomes more and more critical in this context. Indeed, under/overutilization of emergency room and ward resources can either compromise a hospital’s ability to provide the best possible care, or result in precious funding going toward underutilized resources. Simulation-based optimization tools then help facilitating the planning and management of hospital services, by maximizing/minimizing some specific indices (e.g. net profit) subject to given clinical and economical constraints. In this work, we develop a simulation-based optimization approach for the resource planning of a specific hospital ward. At each step, we first consider a suitably chosen resource setting and evaluate both efficiency and satisfaction of the restrictions by means of a discrete-event simulation model. Then, taking into account the information obtained by the simulation process, we use a derivative-free optimization algorithm to modify the given setting. We report results for a real-world problem coming from the obstetrics ward of an Italian hospital showing both the effectiveness and the efficiency of the proposed approach.     

Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling

Simulated computer experiments have become a viable cost-effective alternative for controlled real-life experiments. However, the simulation of complex systems with multiple input and output parameters can be a very time-consuming process. Many of these high-fidelity simulators need minutes, hours or even days to perform one simulation. The goal of global surrogate modeling is to create an approximation model that mimics the original simulator, based on a limited number of expensive simulations, but can be evaluated much faster. The set of simulations performed to create this model is called the experimental design. Traditionally, one-shot designs such as the Latin hypercube and factorial design are used, and all simulations are performed before the first model is built. In order to reduce the number of simulations needed to achieve the desired accuracy, sequential design methods can be employed. These methods generate the samples for the experimental design one by one, without knowing the total number of samples in advance. In this paper, the authors perform an extensive study of new and state-of-the-art space-filling sequential design methods. It is shown that the new sequential methods proposed in this paper produce results comparable to the best one-shot experimental designs available right now.     

ERP system selection using a simulation-based AHP approach: a case of Korean homeshopping company

An enterprise resource planning (ERP) system is a critical investment that can significantly affect future competitiveness and performance of a company. The analytic hierarchy process (AHP) method is often applied to select an ERP system since it is well suited to multiple criteria decision-making problems. This study presents a simulation-based AHP (SiAHP) method for group decision making and is applied to the real-world problem of selecting a suitable ERP system for a Korean homeshopping company. To enhance the fitness of a group AHP method and to facilitate the ERP system selection process, this paper proposes a simulation-based approach for building a group consensus instead of forming point estimates that are aggregated from individual preference judgments. To be specific, the proposed method is based on observations from empirically observed frequency distributions and does not use aggregation procedures, compared to typical group AHP for obtaining a group solution. This approach, reflecting the diversification of group members' opinions as they are, is conceived to be useful as a tool for obtaining insights into agreements and disagreements with respect to the alternatives among the individuals of a group. The real-world example demonstrates the feasibility of our proposed approach.     

Optimal intervention strategies for tuberculosis

This paper deals with the problem of optimal control of a deterministic model of tuberculosis (abbreviated as TB for tubercle bacillus). We first present and analyze an uncontrolled tuberculosis model which incorporates the essential biological and epidemiological features of the disease. The model is shown to exhibit the phenomenon of backward bifurcation, where a stable disease-free equilibrium co-exists with one or more stable endemic equilibria when the associated basic reproduction number is less than the unity. Based on this continuous model, the tuberculosis control is formulated and solved as an optimal control problem, indicating how control terms on the chemoprophylaxis and detection should be introduced in the population to reduce the number of individuals with active TB. Results provide a framework for designing the cost-effective strategies for TB with two intervention methods.     

A simulation-based approach for fleet design in a technician dispatch problem with stochastic demand

We address the problem of developing policies for selecting the proper number of technicians to hire (fleet size) for an on-call repair service operating over a yearly planning horizon in an environment where the number of requests to be serviced each day can vary significantly from day to day and on a seasonal basis. We propose a new approach based on the simulation of a large number of sampled weekly instances and the application of a previously developed optimization procedure for the daily dispatch of technicians. The sampled instances are derived from an extensive statistical analysis of demand data with respect to several key parameters. The results of the simulations are utilized to adjust performance curves in function of fleet size that are then used in an economic analysis of the trade-offs between service quality and cost. Efficient policies for fleet design are then deducted from this analysis.     

Optimizing VVT strategies: a decomposition approach

Verification, validation and testing (VVT) of large systems is an important but complex process. The decisions involved have to consider on one hand the controllable variables associated with investments in appraisal and prevention activities and on the other hand the outcomes of these decisions that are associated with risk impacts and systems' failures. Typically, quantitative models of such large systems use simulation to generate distributions of possible costs and risk outcomes. Here, by assuming independence of risk impacts, we decompose the decision process into separate decisions for each VVT activity and supercede the simulation technique by simple analytical models. We explore various optimization objectives of VVT strategies such as minimum total expected cost, minimum uncertainty as well as a generalized optimization objective expressing Taguchi's expected loss function and provide explicit solutions. A numerical example based on simplified data of a case study is used to demonstrate the proposed VVT optimization procedure.     

Use of discrete-event simulation to evaluate strategies for the prevention of mother-to-child transmission of HIV in developing countries

HIV/AIDS affects over 40 million people worldwide, and more than 70% of these people live in Africa. Mother-to-child transmission of HIV accounts for over 90% of all HIV infections in children under the age of 15 years. However, implementing HIV prevention policies in Africa is extremely difficult because of the poor medical and socio-economic infrastructure. In this paper, we present a discrete-event simulation model that evaluates the relative benefits of two potentially affordable interventions aimed at preventing mother-to-child transmission of HIV, namely anti-retroviral treatment at childbirth and/or bottlefeeding strategies. The model uses rural Tanzanian data and compares different treatment policies. Our results demonstrate that strategic guidelines about breastfeeding are highly dependent on the assumed increase in infant mortality due to bottlefeeding, the efficacy of anti-retroviral treatment at childbirth, and the maternal health stage. The cost of averted infections, though low by Western standards, may represent significant obstacles to policy implementation in developing countries.     

MKPLS approach: switching strategies for the non-linear multi-kernel PLSR

We present two strategies to determine the kernel switching order for the non-linear multi-kernel PLSR algorithm. The multi-kernel PLS (MKPLS) algorithm builds upon a one kernel PLSR which uses a kernel matrix to hold the inner products of the projection of the independent data set onto a feature space. After building a PLSR model, MKPLS deflates the kernel matrix so that only that part which cannot be predicted by the model remains. This remainder is projected onto a different feature space and a new PLSR model is built. The switching algorithms presented for this approach address two questions: which kernel should be used at each iteration and; how many factors should be extracted before switching to another kernel.     

Fuzzy cross-efficiency evaluation: a possibility approach

Cross-efficiency evaluation is an extension of data envelopment analysis (DEA) aimed at ranking decision making units (DMUs) involved in a production process regarding their efficiency. As has been done with other enhancements and extensions of DEA, in this paper we propose a fuzzy approach to the cross-efficiency evaluation. Specifically, we develop a fuzzy cross-efficiency evaluation based on the possibility approach by Lertworasirikul et al. (Fuzzy Sets Syst 139:379–394, 2003a) to fuzzy DEA. Thus, a methodology for ranking DMUs is presented that may be used when data are imprecise, in particular for fuzzy inputs and outputs being normal and convex. We prove some results that allow us to define “consistent” cross-efficiencies. The ranking of DMUs for a given possibility level results from an ordering of cross-efficiency scores, which are real numbers. As in the crisp case, we also develop benevolent and aggressive fuzzy formulations in order to deal with the alternate optima for the weights.     

On interval estimation of the coefficient of variation for the three-parameter Weibull,lognormal and gamma distribution: A simulation-based approach

The coefficient of variation (CV) of a population is defined as the ratio of the population standard deviation to the population mean. It is regarded as a measure of stability or uncertainty, and can indicate the relative dispersion of data in the population to the population mean. CV is a dimensionless measure of scatter or dispersion and is readily interpretable, as opposed to other commonly used measures such as standard deviation, mean absolute deviation or error factor, which are only interpretable for the lognormal distribution. CV is often estimated by the ratio of the sample standard deviation to the sample mean, called the sample CV. Even for the normal distribution, the exact distribution of the sample CV is difficult to obtain, and hence it is difficult to draw inferences regarding the population CV in the frequentist frame. Different methods of estimating the sample standard deviation as well as the sample mean result in different shapes of the sampling distribution of the sample CV, from which inferences about the population CV can be made. In this paper we propose a simulation-based Bayesian approach to tackle this problem. A set of real data is used to generate the sampling distribution of the CV under the assumption that the data follow the three-parameter Gamma distribution. A probability interval is then constructed. The method also applies easily to lognormal and Weibull distributions.     

Towards an auction-driven gas supply: a simulation-based optimization framework for utilities

A platform for the study of the whole transmission problem (arrival of ships, regasification, transportation and distribution) faced by gas utilities companies is proposed. The main objective of this research is to develop a platform that includes the analysis of the new capacity auctions (and not the traditional commodity auctions) that will govern the supply chain in the near future. A simulation-optimization approach has been used to favour the more realistic abstraction of the system. The discrete-event model includes a genetic algorithm to reach the solution in a satisfactory short time, a requisite in auction markets. Design and optimization studies for the utilities are addressed using the platform, which has been validated with real data for one of the main zones in the Spanish market.     

On the evaluation of strategies for branching bandit processes

Glazebrook [1] has given an account of improved procedures for strategy evaluation for resource allocation in a stochastic environment. These methods are extended in the paper in such a way that they can be applied to problems which, for example, have precedence constraints and/or an arrivals process of new jobs. Theoretical results, backed up by numerical studies, show that quasi-myopic heuristics often perform well.     

Asymmetry and multiple endemic equilibria in a model for HIV transmission in a heterosexual population

The difference in transmissibility of HIV between heterosexual males and females in specific social contexts is known to play an important role in determining the form of HIV/AIDS epidemics across the globe. A fundamental constraint here is the conservation of the number of new partnerships formed between the sexes. We examine the impact of general asymmetry in sexual behaviour between the sexes, subject to this group contact constraint, on the transient and long term behaviour of a HIV epidemic. A homogeneously mixing heterosexual population is modelled in which males and females differ only in their infectivity rates (average sexual risk per infected partner) and sexual activity rates (the mean number of sexual partners per unit time for a typical individual). A dominance form of sexual activity rates yields conditions for the existence of multiple endemic equilibria for R₀. the reproductive number, just less than unity. We interpret this as a resilience of the disease persistence for R₀ > 1, which requires significant differences between the sexes' transmissibility. Model simulations in this region of the parameter space show that the time scale and shape of an epidemic curve can be considerably altered. Sexual activity rates modelling the proportions of sexually active groups are also used to address the role of asymmetry. We discuss the consequences of our results for management of the disease.     

Performance evaluation of distribution strategies for the inventory routing problem

Direct shipping strategy is an easy-to-implement distribution strategy frequently used in industrial distribution systems. In this paper, an analytic method is developed for performance evaluation of the strategy for the infinite horizon inventory routing problem with delivery frequency constraint. With the method, the effectiveness of direct shipping strategy can be represented as a function of some system parameters. We demonstrate that the effectiveness of direct shipping is at least the square root of the smallest utilization ratio of vehicle capacity. This implies that the effectiveness of the strategy can reach 100% (respectively, 94.86%) whenever the demand rate of each retailer is 100% (respectively, 90%) of the vehicle capacity multiplied by the upper bound of the delivery frequency. This insight can help a firm answer questions such as: under what conditions direct shipping strategy is effective and why, and how effective the strategy is under a specific condition? In case direct shipping strategy is proven ineffective, a more general Fixed Partition Policy (FPP) that combines direct shipping strategy and multiple-stop shipping strategy must be used. An analytic method is also developed for performance evaluation of general FPPs. We demonstrate that the effectiveness of an FPP depends on the total demand rate of the retailers in each partition (each retailer set) and their closeness level. This insight provides a useful guideline to the design of effective FPPs. The analytic methods make the performance improvement of a distribution system possible through adjusting its system parameters.     

A simulation-based genetic algorithm approach for reducing emissions from import container pick-up operation at container terminal

Emissions from idle truck engines are a main source of pollution at container terminals. In this study, we focus on reducing such emission from waiting trucks as well as the related crane operations with a new truck arrival control method that gives individual truck limited time slots for entry. We develop a method to optimize the time slot assignment for individual trucks, aiming at minimizing total emissions from trucks and cranes at import yards. The method applies discrete event simulation to estimate total truck waiting times and crane moving distance, and then applies a genetic algorithm to minimize the generated emissions from these trucks and cranes. The experiment result shows that the truck arrivals should be controlled based on the stacking of import containers, and that such control is necessary for reducing truck idling emissions at a congested container terminal.     

A network modelling approach for assessing the efficiency of prevention strategies

Survey data and a simulation model based on a stochastic pair formation process are used to construct networks of sexual contacts. We model heterosexual partnerships which can be steady or casual depending on their average duration. Transmission of an infectious disease can take place in pairs of a susceptible and an infected individual. We study networks of sexual contacts accumulated during 1 year for different types of mixing patterns. The networks are constructed on the basis of data from a survey in The Netherlands. We analyze the network structure for different mixing patterns and investigate the relationship between network structure and disease spread; furthermore we study the effect of prevention measures on the structure of the network.     

Application of a simulation-based dynamic traffic assignment model

The evaluation of on-line intelligent transportation system (ITS) measures, such as adaptive route-guidance and traffic management systems, depends heavily on the use of faster than real time traffic simulation models. Off-line applications, such as the testing of ITS strategies and planning studies, are also best served by fast-running traffic models due to the repetitive or iterative nature of such investigations. This paper describes a simulation-based, iterative dynamic equilibrium traffic assignment model. The determination of time-dependent path flows is modeled as a master problem that is solved using the method of successive averages (MSA). The determination of path travel times for a given set of path flows is the network-loading sub-problem, which is solved using the space-time queuing approach of Mahut. This loading method has been shown to provide reasonably accurate results with very little computational effort. The model was applied to the Stockholm road network, which consists of 2100 links, 1191 nodes, 228 zones, representing and 4964 turns. The results show that this model is applicable to medium-size networks with a very reasonable computation time.     

A simulation-based study of a NHS Walk-in Centre

This paper describes a simulation-based project to help North Mersey Community National Health Service Trust to design and plan the operation of a NHS Walk-in Centre. The simulation model developed of this multi-service facility was used to facilitate managers and health professionals to recognize existing problems and potential future problems, and to investigate ideas for their ‘solution’. In the fast-moving NHS where initiatives to improve access, such as walk-in centres, are a recent development and where no two centres are the same, ideas for best practice borrowed from elsewhere can be quickly tested for suitability in the local situation.