Abstract

Abstract
Number partitioning is a classical NP-hard combinatorial optimization problem, whose solution is challenging for both exact and approximative methods. This work presents a new algorithm for number partitioning, based on ideas drawn from tree search, breadth first search, and beam search. A new set of benchmark instances for this problem is also proposed. The behavior of the new method on this and other testbeds is analyzed and compared to other well known heuristics and exact algorithms.

Abstract
We address a problem that often arises in industry, the multi-item capacitated-lot-sizing and scheduling problem with sequence-dependent setup times and costs. Powerful commercial solvers fail to solve even medium-sized instances of this NP-hard problem, therefore we employ a tabu search and a variable neighbourhood search meta-heuristic to solve it and compare the performance of these metaheuristics over time. In contrast to the majority of the literature on this topic, the solution representation explicitly considers production quantities and setup variables, which enables us to develop fast search heuristics. A comprehensive set of computational experiments shows the effectiveness and efficiency of the proposed approaches in solving medium-to large-sized problems.

Abstract
In this paper we present a genetic algorithm with new components to tackle capacitated lot sizing and scheduling problems with sequence dependent setups that appear in a wide range of industries, from soft drink bottling to food manufacturing. Finding a feasible solution to highly constrained problems is often a very difficult task. Various strategies have been applied to deal with infeasible solutions throughout the search. We propose a new scheme of classifying individuals based on nested domains to determine the solutions according to the level of infeasibility, which in our case represents bands of additional production hours (overtime). Within each band, individuals are just differentiated by their fitness function. As iterations are conducted, the widths of the bands are dynamically adjusted to improve the convergence of the individuals into the feasible domain. The numerical experiments on highly capacitated instances show the effectiveness of this computational tractable approach to guide the search toward the feasible domain. Our approach outperforms other state-of-the-art approaches and commercial solvers.

Abstract
Numerical study is provided of the methods for solving the facility location problem when the clients choose some suppliers by their own preferences. Various formulations of this problem as an integer linear programming problem are considered. The authors implement a cutting plane method based on the earlier proposed family of valid inequalities which arises from connection with the problem for a pair of matrices. The results of numerical experiment are presented for testing this method. An optimal solution is obtained by the two versions of the branch and cut method with the suggested cutting plane method. The simulated annealing method is proposed for obtaining the upper bounds of the optimal solution used in exact methods. Numerical experiment approves the efficiency of the implemented approach in comparison with the previously available methods. © 2010 Pleiades Publishing, Ltd.

Abstract
Customer satisfaction (CS) and retention are key issues for organisations in today's competitive market place, making its proper evaluation a main concern for companies. Recently, the European Customer Satisfaction Index (ECSI) has been assumed as a reliable and independent frame-of-reference way of assessing CS. This article describes one framework based on ECSI, which attempts to evaluate the factors that contribute to CS for the Portuguese moulds industry. In order to pursue this goal, an ECSI model, specific for the injection mould industry, was designed and tested. Owing to the characteristics of the gathered data, partial least squares was used to estimate model parameters. The estimated model, which shows validity and reliability, demonstrates an excellent capacity for explaining CS (80.4%), as well as loyalty (58.2%). We also propose an approach to link the ECSI model parameters to the generation and evaluation of design solutions for moulds. This linkage allows us to identify the critical factors for achieving high levels of molds' design quality, through analytical hierarchical process (AHP) ranking, and to determine the impact of mould's design solutions over CS and retention.