Genetic Algorithms may Improve Managerial Decision-Making

In this paper we imagine to display a new approach to a academic mesh optimisation difficulty: the transport problem. Firstly, permitting the vehicles that can set up routes, and, also, allowing to manage with imprecise or vague information. We propose to use the Fuzzy Sets Theory, with the aim of being able to handle the uncertainty, which is a characteristic of decision-making processes in distribution problems. Moreover, to optimise the circulation network we suggest to use a Genetic Algorithm (GA). The main reason for this is that the GAs are heuristic optimisation methods which don't impose restrictions to the posing of the problem. In this study, the algorithm is distinuished by its use of a Fuzzy Fitness Function that permits the evaluation of imprecise information. In the light of the overhead, the next part shows a recount of the difficulty to be solved. Third section presents the fuzzy approach to this problem. In fourth section we introduces the genetic algorithm used to solve the aforementioned approach. Fifth part offers a practical example of the circulation problem and, finally, we include some completing remarks.

Some authors have directed Genetic Algorithms to transport problems. In many cases they solve the problem reducing their complexity or using precise knowledge for the variables. In our approach, we propose a fuzzy representation (TFN) of the data on held and a less restrictive model. Due to this, the GA should determine the quantities supplied from each source to each destination and the routes travelled for the vehicles. For both objectives the criteria is the same: minimising the total distribution costs. GAs are search algorithms which use values inspired by natural genetics to evolve answers to problems. The rudimentary idea is to sustain a community of chromosomes, which comprises nominee answers to the concrete ...