A General Iterative Procedure of the Non-Numerical Ranking Preferences Method for Multiple Objective Decision Making

AbstractMultiple objective evolutionary algorithms (MOEAs), which are biologically-inspired optimization methods, have become popular approaches to solve problems with multiple objective functions. With the use of MOEAs, multiple objective optimization becomes a two-part problem. First, the multiple objective optimization problem needs to be formulated and successfully solved using an MOEA. Then, a non- dominated set -also known as efficient or Pareto frontier- needs to be analyzed to select a solution to the problem. This can represent a challenging task to the decision-maker because this set can contain a large number of solutions. This decision- making stage is usually known as the post-Pareto analysis stage. This paper presents the generalization of a post-Pareto optimality method known as the non-numerical ranking preferences (NNRP) method originally proposed by Taboada et al. (2007). This method can help decision makers reduce the number of design possibilities to small subsets that clearly reflect their objective function preferences. Previous research has only presented the application of the NNRP method using three and four objective functions but had not been generalized to the case of n objective functions. The present paper expands the NNRP method to be able to consider multiple objective optimization problems with n number of objective functions

Pilot3 D2.1 - Trade-off report on multi criteria decision making techniques

This deliverable describes the decision making approach that will be followed in Pilot3. It presents a domain-driven analysis of the characteristics of Pilot3 objective function and optimisation framework. This has been done considering inputs from deliverable D1.1 - Technical Resources and Problem definition, from interaction with the Topic Manager, but most importantly from a dedicated Advisory Board workshop and follow-up consultation. The Advisory Board is formed by relevant stakeholders including airlines, flight operation experts, pilots, and other relevant ATM experts. A review of the different multi-criteria decision making techniques available in the literature is presented. Considering the domain-driven characteristics of Pilot3 and inputs on how the tool could be used by airlines and crew. Then, the most suitable methods for multi-criteria optimisation are selected for each of the phases of the optimisation framework

Search Techniques for Multi-Objective Optimization of Mixed-Variable Systems Having Stochastic Responses

A method is proposed for solving stochastic multi-objective optimization problems. Such problems are typically encountered when one desires to optimize systems with multiple, often competing, objectives that do not have a closed form representation and must be estimated via simulation. A two-stage method is proposed that combines generalized pattern search/ranking and selection (GPS/R&S) and and Mesh Adaptive Direct Search (MADS) developed for single-objective stochastic problems with three multi-objective methods: interactive techniques for the specification of aspiration/reservation levels, scalarization functions, and multi-objective ranking and selection. This combination is devised specifically so as to keep the desirable convergence properties of GPS/R&S and MADS while extending application to the multi-objective case

Domain-driven multiple-criteria decision-making for flight crew decision support tool

During the flight, the crew might consider modifying their planned trajectory, taking into account currently available information, such as an updated weather forecast report or the already accrued amount of delay. This modified planned trajectory translates into changes on expected fuel and flying time, which will impact the airline’s relevant performance indicators leading to a complex multiple-criteria decision-making problem. Pilot3, a project from the Clean Sky Joint Undertaking 2 under European Union’s Horizon 2020 research and innovation programme, aims to develop an objective optimisation engine to assist the crew on this process. This article presents a domain-driven approach for the selection of the most suitable multiple-criteria decision-making methods to be used for this optimisation framework. The most relevant performance indicators, based on airline’s objectives and policies, are identified as: meeting on-time performance, leading to a binary value in a deterministic scenario; and total cost, which can be disaggregated into sub-cost components. The optimisation process consists of two phases: first, Pareto optimal solutions are generated with a multi-objective optimisation method (lexicographic ordering); second, alternative trajectories are filtered and ranked using a combination of multi-criteria decision analysis methods (analytic hierarchy process and VIKOR). A realistic example of use shows the applicability of the process and studies the sensibility of the optimisation framework

Multicriteria Methodology for the NEEDS Project

This report begins with an overview of multicriteria analysis methods, and the basic principles of developing mathematical models for such analysis. An overview of various representation of user prefereces is then presented, including methods based on pairwise comparisons of criteria and those based on scalarizing functions. This is followed by a summary of structures of criteria and alternatives. Next, basic properties of multi-criteria analysis are discussed, followed by a more detailed presentation of the similarities of and differences between the main methods based on scalarizing function. This report concludes that existing methods do not best meet the needs of the NEEDS project, presents the reasons, and proposes a new methodology for development. Depending upon the development and testing of this new methodology, an existing method will also be chosen as a backup for comparative or alternate use

The Pareto Frontier for Random Mechanisms

We study the trade-offs between strategyproofness and other desiderata, such as efficiency or fairness, that often arise in the design of random ordinal mechanisms. We use approximate strategyproofness to define manipulability, a measure to quantify the incentive properties of non-strategyproof mechanisms, and we introduce the deficit, a measure to quantify the performance of mechanisms with respect to another desideratum. When this desideratum is incompatible with strategyproofness, mechanisms that trade off manipulability and deficit optimally form the Pareto frontier. Our main contribution is a structural characterization of this Pareto frontier, and we present algorithms that exploit this structure to compute it. To illustrate its shape, we apply our results for two different desiderata, namely Plurality and Veto scoring, in settings with 3 alternatives and up to 18 agents.Comment: Working Pape

Multicriteria Methodology for the NEEDS Project

This report begins with an overview of multicriteria analysis methods, and the basic principles of developing mathematical models for such analysis. An overview of various representation of user preferences is then presented, including methods based on pairwise comparisons of criteria and those based on scalarizing functions. This is followed by a summary of structures of criteria and alternatives. Next, basic properties of multi-criteria analysis are discussed, followed by a more detailed presentation of the similarities of and differences between the main methods based on scalarizing functions. This report concludes that existing methods do not best meet the needs of the NEEDS project, presents the reasons, and proposes a new methodology for development. Depending upon the development and testing of this new methodology, an existing method will also be chosen as a backup for comparative or alternate use

Π18.4 – Έκθεση 4ου Επιστημονικού Workshop

Το συγκεκριμένο παραδοτέο αφορά το 4ο Επιστημονικό Workshop του έργου που πραγματοποιήθηκε στον Πειραιά, το χρονικό διάστημα 2-3 Απριλίου 201

Evolutionary multi-objective optimization for gating and riser system design of metal castings

The gating and riser system design plays an important role in the quality and cost of a metal casting. Due to the lack of existing theoretical procedures to follow, the design process is carried out on a trial-and-error basis. The casting design optimization problem is characterized by multiple design variables, conflicting objectives, and a complex search space, making it unsuitable for sensitivity-based optimization. In this study, a formal optimization method using evolutionary techniques was developed to overcome such complexities. A framework for integrating the optimization procedure with numerical simulation for the design evaluation is presented. The comparison between a scalar and vector optimization approach was explored using the weighted-sum and multi-objective Genetic Algorithm methods. The proposed optimization framework was applied to the gating and riser system of a sand casting and the results were compared to a popular Design-of-Experiment (DOE) method. It showed that the multi-objective method gave better results and provided more flexibility in decision making