Decision support systems: learning from visual interactive modelling
Proceedings of the conference on First specialized conference on decision support systems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Common Mistakes in Making Value Trade-Offs
Operations Research
An Evolutionary Metaheuristic for Approximating Preference-Nondominated Solutions
INFORMS Journal on Computing
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Locating logistic facilities, such as plants and distribution centres, in an optimal way, is a crucial decision for manufacturers, particularly those that are operating in large developing countries which are experiencing a process of fast economic change. Traditionally, such decisions have been supported by optimising network models, which search for the configuration with the minimum total cost. In practice, other intangible factors, which add or reduce value to a potential configuration, are also important in the location choice. We suggest in this paper an alternative way to analyse such problems, which combines the value from the topology of a network (such as total cost or resilience) with the value of its discrete nodes (such as specific benefits of a particular location). In this framework, the focus is on optimising the overall logistic value of the network. We conclude the paper by discussing how evolutionary multi-objective methods could be used for such analyses.