Pareto Set as a Model for Dispatching Resources in Emergency Centres

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarc�sio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Pareto Set

PROCEDURES OF AGGREGATION OF THE INDICATОRS OF SUSTAINABLE DEVELOPMENT OF THE HIGHER SCHOOL INSTITUTIONS

2020 ◽  
pp. 28-31
Author(s):  
Д.В. Довгая ◽  
М.Х. Мальсагов
Keyword(s):  
Sustainable Development ◽  
Pareto Set

В статье описан подход к оценке функционирования высших учебных заведений на уровне кафедры, факультета и университета в целом. Он включает в себя - агрегирование показателей с помощью специальных функций; построение множества Парето в пространстве индексов; выбор наилучшей точки множества Парето с помощью скаляризации. In this paper we describe an approach to the evaluation of the higher school institutions on the levels of a chair, a faculty, and the whole university. The approach includes - an aggregation of the indicators by means of specific functions; building of Pareto set in the space of the research, teaching, and innovative criteria; a choice of the best point in the Pareto set by scalarization.

Pareto Set as a Model for Dispatching Resources in Emergency Centres (Preprint)

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarcísio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Operational Research ◽  
Real Data ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Four Dimensions ◽  
Multi Objective ◽  
The Best Approximation ◽  
One Year ◽  
The Cost

UNSTRUCTURED The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

scholarly journals Multiobjective Monotonicity Analysis: Pareto Set Dependency and Tradeoffs Causality in Configuration Design

2021 ◽  
pp. 1-18
Author(s):  
Nökkvi S. Sigurdarson ◽  
Tobias Eifler ◽  
Martin Ebro ◽  
Panos Y. Papalambros
Keyword(s):  
Oral Drug Delivery ◽  
Pareto Set ◽  
Oral Drug ◽  
Configuration Design ◽  
Analysis Process ◽  
Trade Offs ◽  
Design Changes ◽  
Pareto Sets ◽  
Multiobjective Design ◽  
Monotonicity Analysis

Abstract Multiobjective design optimization studies typically derive Pareto sets or use a scalar substitute function to capture design trade-offs, leaving it up to the designer's intuition to use this information for design refinements and decision making. Understanding the causality of trade-offs more deeply, beyond simple post-optimality parametric studies, would be particularly valuable in configuration design problems to guide configuration redesign. This paper presents the method of Multiobjective Monotonicity Analysis to identify root causes for the existence of trade-offs and the particular shape of Pareto sets. This analysis process involves reducing optimization models through constraint activity identification to a point where dependencies specific to the Pareto set and the constraints that cause them are revealed. The insights gained can then be used to target configuration design changes. We demonstrate the proposed approach in the preliminary design of a medical device for oral drug delivery

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