Decision support system for the school districting problem
Operations Research
Multiobjective Metaheuristics for the Bus Driver Scheduling Problem
Transportation Science
A GRASP Algorithm for the Multi-Objective Knapsack Problem
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
SSPMO: A Scatter Tabu Search Procedure for Non-Linear Multiobjective Optimization
INFORMS Journal on Computing
A multi-objective model for environmental investment decision making
Computers and Operations Research
A reactive GRASP for a commercial territory design problem with multiple balancing requirements
Computers and Operations Research
A multi-objective GRASP for partial classification
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithm for redesigning sales territories
ICCL'11 Proceedings of the Second international conference on Computational logistics
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
A dual bounding scheme for a territory design problem
Computers and Operations Research
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A bi-objective commercial territory design problem motivated by a real-world application from the bottled beverage distribution industry is addressed. The problem considers territory compactness and balancing with respect to number of customers as optimization criteria. Previous work has focused on exact methods for small- to medium-scale instances. In this work, a GRASP framework is proposed for tackling considerably large instances. Within this framework two general schemes are developed. For each of these schemes two strategies are studied: (i) keeping connectivity as a hard constraint during construction and post-processing phases and, (ii) ignoring connectivity during the construction phase and adding this as another minimizing objective function during the post-processing phase. These strategies are empirically evaluated and compared to NSGA-II, one of the most successful evolutionary methods known in literature. Computational results show the superiority of the proposed strategies. In addition, one of the proposed GRASP strategies is successfully applied to a case study from industry.