Cristina Azcárate

Optimization with simulation and multiobjective analysis in industrial decision-making: A case study

Abstract In order to ensure that end of life vehicles (ELVs) are discarded without endangering the environment, appropriate collection systems should be set up. In accordance with the European Community Directive, the construction of an industrial plant for the decontamination and recycling processes of ELVs is currently being planned in Navarra (Spain). The aim of this study is to optimize the design and management of this industrial plant. We will provide a modelling framework that integrates different OR methodologies: queueing networks, optimization with simulation, evolutionary computation and multiobjective methods.

Including risk in management models for the simulation of energy production systems

The increased interest in renewable energies has motivated the economic assessment study of different energy systems. Energy systems based on some natural renewable sources, as wind power, have the drawback of a random input, and then, peak user demand does not always coincide with peak power production. Regulation of output energy requires the introduction of new equipment with the capacity to store it. Due to its versatility, the hydrogen is being used as an energy storage system.

Operational management of renewable energy systems with storage using an optimisation-based simulation methodology

It has been widely recognised that renewable energy, such as wind, provides valuable benefits for the environment, human health, and the economy. Nevertheless, renewable energy has several drawbacks: high variability in its availability, uncertainty in its forecast, and difficulty in matching production to demand. The storage of energy would enable solving of most of these problems. In this paper, we obtain operative management policies for energy storage under two criteria: maximising the profit of selling the energy and maximising the reliability of the system as a provider of committed energy. Decisions take into account data concerning the structure of selling prices and penalties, as well as updated probabilistic wind speed forecasts. We use a sequence of rolling horizon stochastic optimisation problems to determine the parameters of the proposed management strategies. To solve these problems we propose a simulation-based optimisation methodology.

Optimal control of ICU patient discharge: from theory to implementation

This paper deals with the management of scarce health care resources. We consider a control problem in which the objective is to minimize the rate of patient rejection due to service saturation. The scope of decisions is limited, in terms both of the amount of resources to be used, which are supposed to be fixed, and of the patient arrival pattern, which is assumed to be uncontrollable. This means that the only potential areas of control are speed or completeness of service. By means of queuing theory and optimization techniques, we provide a theoretical solution expressed in terms of service rates. In order to make this theoretical analysis useful for the effective control of the healthcare system, however, further steps in the analysis of the solution are required: physicians need flexible and medically-meaningful operative rules for shortening patient length of service to the degree needed to give the service rates dictated by the theoretical analysis. The main contribution of this paper is to discuss how the theoretical solutions can be transformed into effective management rules to guide doctors’ decisions. The study examines three types of rules based on intuitive interpretations of the theoretical solution. Rules are evaluated through implementation in a simulation model. We compare the service rates provided by the different policies with those dictated by the theoretical solution. Probabilistic analysis is also included to support rule validity. An Intensive Care Unit is used to illustrate this control problem. The study focuses on the Markovian case before moving on to consider more realistic LoS distributions (Weibull, Lognormal and Phase-type distribution).

Optimal bed-control in an ICU when elective surgery patient's arrivals are known. A simulation-based optimization approach

Early discharge of patients is frequently used as means to control the ratio of rejected incoming patients due to a full Intensive Care Unit. These patient discharge decisions are discussed in the medical literature but few mathematical works have included them in their models. Recently, pioneering works modeled these decisions and obtained different bed-control policies. The so-called cautious policy was accepted by the physicians as the closest one to their own practice. However, this policy does not emerge when the expected Length-of-Stay shortening is minimized. The purpose of this research is to extend the mathematical model including a representation of the real discharge procedures as well as the knowledge of the near-future elective patients arrivals from surgery. Discharge probabilities are modeled by using a logistic function of the number of expected arrivals and the number of occupied beds. To prevent the early discharge of patients not sufficiently recovered the LoS is modeled by using a phase-type distribution with the states representing the health status of the patient. Optimal policies are obtained by a simulation-based optimization methodology. The coefficients of the logistic function are interpreted in terms of odd ratios. Cautious policy is now obtained even when minimizing the expected LoS shortening.

Combining linear programming and multiobjective evolutionary computation for solving a type of stochastic knapsack problem

In this paper, the design of systems using mechanical or electrical energy-transformation devices is treated as a knapsack problem. Due to the well-known NP-hard complexity of the knapsack problem, a combination of integer linear programming and evolutionary multicriteria optimization is presented to solve this real problem with promising experimental results.

Dealing with Stress and Workload in Emergency Departments

Emergency Departments (EDs) are widely known for their stochastic nature, unpredictable arrivals and—in recent years—overcrowding problems. These could cause stress to physicians and, as a consequence, bad quality of patient’s medical care. This problem is investigated in a Spanish ED by developing a dynamic measure of stress, analyzing the patient flow control considering not only waiting time but also physicians’ stress, and finally by suggesting a computer-based tool to help triage nurses to manage the workload distribution among physicians.

Economic assessment of energy systems with simulation and linear programming

Summary form only given. Energy systems based on some natural renewal sources have the drawback of a random input, making them a non reliable supplier of energy. The regulation of the produced energy requires the introduction of new equipment able to storage this energy. The advantage of these transformation-storage systems is that the energy can be sold when the demand is higher and then also the prices are higher. The disadvantages are two, the costs of the new equipments and the lost of energy because of inefficiencies in the transformation processes. Our purpose is to develop a simulation model useful to the economic assessment of this type of energy systems. We also consider the analysis of optimal management policies, which are obtained by solving linear programming problems.

A nonparametric estimator from autopsy data

In this paper, we propose a nonparametric estimator of the component lifetime distribution in a binary reliability system, from a type of censored data that arise from the autopsy of black box systems. The observed data consist of the failure time of the system, the set of components that are dead at the instant of the system failure, and the knowledge of the structure function, We study the conditions for the application of this estimator, and we observe its good behaviour in the parallel-series, series-parallel and (tu, a) additive binary reliability systems.