In the field of evolutionary multi-criterion optimization, the hypervolume indicator is the only single set quality measure that is known to be strictly monotonic with regard to Pareto dominance: whenever a Pareto set approximation entirely dominates another one, then the indicator value of the dominant set will also be better. This property is of high interest and relevance for problems involving a large number of objective functions. However, the high computational effort required for hypervolume calculation has so far prevented the full exploitation of this indicator's potential; current hypervolume-based search algorithms are limited to problems with only a few objectives.

This paper addresses this issue and proposes a fast search algorithm that uses Monte Carlo simulation to approximate the exact hypervolume values. The main idea is not that the actual indicator values are important, but rather that the rankings of solutions induced by the hypervolume indicator. In detail, we present HypE, a hypervolume estimation algorithm for multi-objective optimization, by which the accuracy of the estimates and the available computing resources can be traded off; thereby, not only do many-objective problems become feasible with hypervolume-based search, but also the runtime can be flexibly adapted. Moreover, we show how the same principle can be used to statistically compare the outcomes of different multi-objective optimizers with respect to the hypervolume----so far, statistical testing has been restricted to scenarios with few objectives. The experimental results indicate that HypE is highly effective for many-objective problems in comparison to existing multi-objective evolutionary algorithms.

HypE is available for download at http://www.tik.ee.ethz.ch/sop/download/supplementary/hype/ .

Hype: An algorithm for fast hypervolume-based many-objective optimization

This paper provides a comprehensive survey of research on appointment scheduling in outpatient services. Effective scheduling systems have the goal of matching demand with capacity so that resources are better utilized and patient waiting times are minimized. Our goal is to present general problem formulation and modeling considerations, and to provide taxonomy of methodologies used in previous literature. Current literature fails to develop generally applicable guidelines to design appointment systems, as most studies have suggested highly situation-specific solutions. We identify future research directions that provide opportunities to expand existing knowledge and close the gap between theory and practice.

Outpatient scheduling in health care: a review of literature

In recent decades, health care costs have dramatically increased, while health care organisations have been under severe pressure to provide improved quality health care for their patients. Several health care administrators have used discrete-event simulation as an effective tool for allocating scarce resources to improve patient flow, while minimising health care delivery costs and increasing patient satisfaction. The rapid growth in simulation software technology has created numerous new application opportunities, including more sophisticated implementations, as well as combining optimisation and simulation for complex integrated facilities. This paper surveys the application of discrete-event simulation modeling to health care clinics and systems of clinics (for example, hospitals, outpatient clinics, emergency departments, and pharmacies). Future directions of research and applications are also discussed.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2600669.pdf

Application of discrete-event simulation in health care clinics: A survey

Lecture Notes in Economics and Mathematical Systems

Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this field.

/pdf/a-survey-on-metaheuristics-for-stochastic-combinatorial-58c1pv8m6a.pdf

A survey on metaheuristics for stochastic combinatorial optimization

Discrete event simulation optimization is a problem of significant interest to practitioners interested in extracting useful information about an actual (or yet to be designed) system that can be modeled using discrete event simulation. This paper presents a brief survey of the literature on discrete event simulation optimization over the past decade (1988 to the present). Swisher et al. (2000) provides a more comprehensive review of this topic while Jacobson and Schruben (1989) covers the literature preceding 1988. Optimization of both discrete and continuous input parameters are examined. The continuous input parameter case is separated into gradient and non-gradient based optimization procedures. The discrete input parameter case differentiates techniques appropriate for small and for large numbers of feasible input parameter values.

/pdf/a-survey-of-simulation-optimization-techniques-and-1fbd4b2eyy.pdf

A survey of simulation optimization techniques and procedures

Over the past forty years, health care organizations have faced ever-increasing pressures to deliver quality care while facing rising costs, lower reimbursements, and new regulatory demands. Discrete-event simulation has become a popular and effective decision-making tool for the optimal allocation of scarce health care resources to improve patient flow, while minimizing health care delivery costs and increasing patient satisfaction. The proliferation of increasingly sophisticated discrete-event simulation software packages has resulted in a large number of new application opportunities, including more complex implementations. In addition, combined optimization and simulation tools allow decision-makers to quickly determine optimal system configurations, even for complex integrated facilities. This chapter provides an overview of discrete-event simulation modeling applications to health care clinics and integrated health care systems (e.g. hospitals, outpatient clinics, emergency departments, and pharmacies) over the past forty years.

Discrete-event simulation of health care systems

Modeling and analyzing a physician clinic environment using discrete-event (visual) simulation

An important use for discrete-event simulation models lies in comparing and contrasting competing design alternatives without incurring any physical costs. This article presents a survey of the literature for two widely used classes of statistical methods for selecting the best design from among a finite set of k alternatives: ranking and selection (RS guidelines for procedure application are offered.

James R. Swisher

Papers

Application of discrete-event simulation in health care clinics: A survey

A survey of simulation optimization techniques and procedures

Discrete-event simulation of health care systems

Modeling and analyzing a physician clinic environment using discrete-event (visual) simulation

Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey