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

Sim-heuristics low-carbon technologies’ selection framework for reducing costs and carbon emissions of heavy goods vehicles

UK logistics fleets face increasing competitive pressures due to volatile fuel prices and the small profit margins in the industry. By reducing fuel consumption, operational costs and carbon emissions can be reduced. While there are a number of technologies that can reduce fuel consumption, it is often difficult for logistics companies to identify which would be the most beneficial to adopt over the medium and long terms. With a myriad of possible technology combinations, optimising the vehicle specification for specific duty cycles requires a robust decision-making framework. This paper combines simulated truck and delivery routes with a metaheuristic evolutionary algorithm to select the optimal combination of low-carbon technologies that minimise the greenhouse gas emissions of long-haul heavy goods vehicles during their lifetime cost. The framework presented is applicable to other vehicles, including road haulage, waste collection fleets and buses by using tailored parameters in the heuristics model

An Analysis of black-box optimization problems in reinsurance : evolutionary-based approaches

Black-box optimization problems (BBOP) are de ned as those optimization problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program). This paper is focussed on BBOPs that arise in the eld of insurance, and more speci cally in reinsurance problems. In this area, the complexity of the models and assumptions considered to de ne the reinsurance rules and conditions produces hard black-box optimization problems, that must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in BBOP, so new computational paradigms must be applied to solve these problems. In this paper we show the performance of two evolutionary-based techniques (Evolutionary Programming and Particle Swarm Optimization). We provide an analysis in three BBOP in reinsurance, where the evolutionary-based approaches exhibit an excellent behaviour, nding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods

Modelling of a Solar Thermal Power Plant for Benchmarking Blackbox Optimization Solvers

RÉSUMÉ : On propose une famille de problèmes d’optimisation originaux pouvant servir de banc d’essai pour les algorithmes d’optimisation de boîtes noires. Les problèmes proposés varient en terme de nombre de variables (5 à 29), de leur type (discrètes, continues, de catégories), du nombre et du type de contraintes (de 5 à 17 contraintes binaires ou continues) ainsi qu’au niveau du nombre de fonctions objectif et de leur nature. Le but étant de tester la performance d’algorithmes d’optimisation pour des problèmes réels d’ingénierie, un modèle numérique d’une centrale électrique solaire thermique avec système de stockage de chaleur à sel fondu a été développé et implémenté. Le modèle simule le fonctionnement des principales composantes d’une telle centrale, soit un champs d’héliostats, un récepteur solaire à cavité, un système de stockage thermique, un échangeur de chaleur et une turbine à vapeur reliée à un alternateur. Afin d’éviter un trop grand nombre de variables, le champs d’héliostats est généré selon une stratégie gloutonne qui consiste à choisir les positions au plus haut rendement individuel en tenant compte de l’efficacité de surface, de l’atténuation atmosphérique et des effets de dépassement. La performance de l’ensemble du champs d’héliostats est calculée par une méthode de Monte-Carlo afin de tenir compte des effets d’ombrage. Les résultats de cette évaluation sont utilisés comme valeurs d’entrées afin de calculer l’évolution du niveau et de la température des unités de stockage au cours de la période simulée. La méthode NUT (nombre d’unité de transfert) est utilisée afin de simuler la performance de l’échangeur de chaleur pour transférer l’énergie du stockage vers le cycle thermique servant à alimenter la turbine de façon à répondre à un profil de demande variable. Quelques modèles auxiliaires sont utilisés afin de générer des contraintes d’optimisation sur le budget, les pertes d’opération et les défaillances. Des résultats sommaires d’optimisation réalisés à l’aide des paramètres par défaut du logiciel NOMAD sont fournis afin de démontrer la validité des problèmes proposés.----------ABSTRACT : A new family of problems is provided to serve as a benchmark for blackbox optimization solvers. The problems are single or bi-objective and vary in complexity in terms of the number of variables used (from 5 to 29), the type of variables (integer, real, category), the number of constraints (from 5 to 17) and their types (binary or continuous). In order to provide problems exhibiting dynamics that reflect real engineering challenges, they are extracted from an original numerical model of a concentrated solar power (CSP) power plant with molten salt thermal storage. The model simulates the performance of the power plant by using a high level modeling of each of its main components, namely, an heliostats field, a central cavity receiver, a molten salt heat storage, a steam generator and an idealized powerblock. The heliostats field layout is determined through a simple automatic strategy that finds the best individual positions on the field by considering their respective cosine efficiency, atmospheric scattering and spillage losses as a function of the design parameters. A Monte-Carlo integral method is used to evaluate the heliostats field’s optical performance throughout the day so that shadowing effects between heliostats are considered, and the results of this evaluation provide the inputs to simulate the levels and temperatures of the thermal storage. The molten salt storage inventory is used to transfer thermal energy to the powerblock, which simulates a simple Rankine cycle with a single steam turbine. Auxiliary models are used to provide additional optimization constraints on the investment cost, parasitic losses or components failure. The results of preliminary optimizations performed with the NOMAD software using default settings are provided to show the validity of the problems