In the last few years, a significant number of multi-objective metaheuristics have been proposed in the literature in order to address real-world problems. Local search methods play a major role in many of these metaheuristic procedures. In this paper, we adapt a recent and popular indicator-based selection method proposed by Zitzler and Künzli in 2004, in order to define a population-based multi-objective local search. The proposed algorithm is designed in order to be easily adaptable, parameter independent and to have a high convergence rate. In order to evaluate the capacity of our algorithm to reach these goals, a large part of the paper is dedicated to experiments. Three combinatorial optimisation problems are tested: a flow shop problem, a ring star problem and a nurse scheduling problem. The experiments show that our algorithm can be applied with success to different types of multi-objective optimisation problems and that it outperforms some classical metaheuristics. Furthermore, the parameter sensitivity analysis enables us to provide some useful guidelines about how to set the parameters

Shelf space allocation is the problem of efficiently arranging retail products on shelves in order to maximise profit, improve stock control, improve customer satisfaction, etc. Most work reported in the literature on this problem has focused on the case of large retailers such as big supermarkets. The interest here is to tackle this problem in the context of small retail shops where different issues arise when compared to large retailers. This paper proposes a heuristic approach to automate shelf space allocation in small retail shops. Several initialisation heuristics and local search moves are incorporated into the proposed method which generates high quality practical arrangements represented graphically as simple planograms.

This paper proposes an improved version of volume dominance to assign fitness to solutions in Pareto-based multi-objective optimisation. The impact of this revised volume dominance on the performance of multi-objective evolutionary algorithms is investigated by incorporating it into three approaches, namely SEAMO2, SPEA2 and NSGA2 to solve instances of the 2-, 3-and 4-objective knapsack problem. The improved volume dominance is compared to its previous version and also to the conventional Pareto dominance. It is shown that the proposed improved volume dominance helps the three algorithms to obtain better non-dominated fronts than those obtained when the two other forms of dominance are used.

Summary. We present a multi-objective approach to tackle a real-world nurse scheduling problem using an evolutionary algorithm. The aim is to generate a few good quality non-dominated schedules so that the decision-maker can select the most appropriate one. Our approach is designed around the premise of 'satisfying individual nurse preferences' which is of practical significance in our problem. We use four objectives to measure the quality of schedules in a way that is meaningful to the decision-maker. One objective represents staff satisfaction and is set as a target. The other three objectives, which are subject to optimisation, represent work regulations and workforce demand. Our algorithm incorporates a self-adaptive decoder to handle hard constraints and a re-generation strategy to encourage production of new genetic material. Our results show that our multi-objective approach produces good quality schedules that satisfy most of the nurses' preferences and comply with work regulations and workforce demand. The contribution of this paper is in presenting a multi-objective evolutionary algorithm to nurse scheduling in which increasing overall nurses' satisfaction is built into the self-adaptive solution method.

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