Book ChapterDOI
Lorenz versus Pareto dominance in a single machine scheduling problem with rejection
Atefeh Moghaddam,Farouk Yalaoui,Lionel Amodeo +2 more
- pp 520-534
TLDR
This paper addresses the problem of single machine scheduling in which due to some constraints like capacity, rejection of a set of jobs is accepted, and suggests the application of Lorenz-dominance to an adapted bi-objective simulated annealing algorithm.Abstract:
Scheduling problems have been studied from many years ago. Most of the papers which were published in this domain are different in one or many of issues as following: objective functions, machine environment, constraints and methodology for solving the problems. In this paper we address the problem of single machine scheduling in which due to some constraints like capacity, rejection of a set of jobs is accepted. The problem is considered as bi-objective one: minimization of the sum of weighted completion times for the accepted jobs and minimization of the sum of penalties for the rejected jobs. We find that in this problem, the solutions are not handled in a satisfactory way by general Paretodominance rule, so we suggest the application of Lorenz-dominance to an adapted bi-objective simulated annealing algorithm. Finally we justify the use of Lorenz-dominance as a useful refinement of Pareto-dominance by comparing the results.read more
Citations
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Journal ArticleDOI
A survey of resource constrained shortest path problems: Exact solution approaches
TL;DR: This article surveys the main contributions that have appeared in the scientific literature addressing resource constrained shortest path problems to provide a starting point for researchers who want to address the problems at hand.
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Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey
TL;DR: In this article, a taxonomy of multi-objective multi-agent decision-making settings is presented, based on the reward structures and utility functions of a system. But the taxonomy does not consider the trade-offs between conflicting objective functions.
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Game Theory Based Evolutionary Algorithms: A Review with Nash Applications in Structural Engineering Optimization Problems
TL;DR: In this article, a general review of game-theory based evolutionary algorithms (EAs) is presented, with special emphasis in structural optimization and particularly in skeletal structures, and a set of three problems are solved: reconstruction inverse problem, fully stressed design problem and minimum constrained weight for discrete sizing of frame skeletal structures.
Journal ArticleDOI
A practical guide to multi-objective reinforcement learning and planning
Conor Hayes,Roxana Radulescu,Eugenio Bargiacchi,John Källström,Matthew Macfarlane,Mathieu Reymond,Timothy Verstraeten,Luisa M. Zintgraf,Richard Dazeley,Fredrik Heintz,Enda Howley,Athirai A. Irissappane,Patrick Mannion,Ann Nowé,Gabriel Ramos,Marcello Restelli,Peter Vamplew,Diederik M. Roijers +17 more
TL;DR: In this article , a guide to the application of multi-objective decision-making methods to difficult problems is presented, aimed at researchers who are already familiar with singleobjective reinforcement learning and planning methods and who wish to adopt a multiobjective perspective on their research.
Journal ArticleDOI
Efficient meta-heuristics based on various dominance criteria for a single-machine bi-criteria scheduling problem with rejection
TL;DR: In this article, a bi-objective single machine scheduling problem with rejection is studied and different algorithms are developed to find the best estimation of Pareto-optimal front for this problem.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
Performance assessment of multiobjective optimizers: an analysis and review
TL;DR: This study provides a rigorous analysis of the limitations underlying this type of quality assessment in multiobjective evolutionary algorithms and develops a mathematical framework which allows one to classify and discuss existing techniques.
Journal ArticleDOI
Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems
TL;DR: For nonlinear programming problems which are factorable, a computable procedure for obtaining tight underestimating convex programs is presented to exclude from consideration regions where the global minimizer cannot exist.
Journal ArticleDOI
MOSA method: a tool for solving multiobjective combinatorial optimization problems
TL;DR: In this paper, the authors developed the so-called MOSA (Multiobjective Simulated Annealing) method to approximate the set of efficient solutions of a MOCO problem.
Journal Article
Multiprocessor scheduling with rejection
TL;DR: In this article, the authors considered a version of multiprocessor scheduling with the special feature that jobs may be rejected at a certain penalty, and they gave a 1 + ρ ≈ 2.618 competitive algorithm for the on-line version of the problem, where ρ is the golden ratio.
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