Abstract
Globalization opens up new perspectives for handling goods distribution in logistic networks. However, establishing an efficient inventory policy is challenging by virtue of the analytical and computational complexity. In this study, the goods distribution process that was governed by the order-up-to policy, implemented in either a distributed or centralized way, was investigated in the logistic systems with complex interconnection topologies. Uncertain demand may be imposed at any node, not just at conveniently chosen contact points, with a lost-sales assumption that introduces a non-linearity into the node dynamics. In order to adjust the policy parameters, the continuous genetic algorithm (CGA) was applied, with the fitness function incorporating both the operational costs and customer satisfaction level. This study investigated how to select the parameters of the popular inventory management policy when operating in the non-trivial networked structures. Moreover, precise guidelines for the CGA tuning in the considered class of problems were provided and evaluated in extensive numerical experiments.
Highlights
One of the key areas of economic and entrepreneurship activity in modern production-inventory systems, irrespective of the scale, i.e., regional or international, is logistics
This study investigated the application of genetic algorithms (GAs) for optimizing the goods flow process in logistic networks with a complex, fully-connected topology
This work explored how continuous genetic algorithm (CGA) could be tuned with respect to the selection of population size, selection and recombination methods, mutation probability, etc., to obtain high efficiency when solving optimization problems in modern logistic networks with non-trivial topologies subjected to the popular OUT policy control
Summary
One of the key areas of economic and entrepreneurship activity in modern production-inventory systems, irrespective of the scale, i.e., regional or international, is logistics. More sophisticated approaches, such as evolutionary algorithms, have been gradually employed in the planning stage of real-life systems in which the space of potential solutions should be considered in order to find the optimum [13] These methods, depending on their specifics and implementation choices, can solve a given optimization problem by self-adjusting to the changes in the system variables and constraints. The computational intelligence approach is recommended for complex systems in which the optimal solution cannot be formulated in an analytical way, e.g., owing to model non-linearities, and the evaluation of the fitness function requires substantial resources and is time-consuming [17].
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