Abstract
This paper addresses the problem of resource distribution control in logistic systems influenced by uncertain demand. The considered class of logistic topologies comprises two types of actors—controlled nodes and external sources—interconnected without any structural restrictions. In this paper, the application of continuous-domain genetic algorithms (GAs) is proposed in order to support the optimization process of resource reflow in the network channels. GAs allow one to perform simulation-based optimization and provide desirable operating conditions in the face of a priori unknown, time-varying demand. The effectiveness of inventory management process governed under an order-up-to policy involves two different objectives—holding costs and service level. Using the network analytical model with the inventory management policy implemented in a centralized way, GAs search a space of candidate solutions to find optimal policy parameters for a given topology. Numerical experiments confirm the analytical assumptions.
Highlights
In recent years, in particular since the turn of the 20th and 21st centuries, a significant development of the global economy has been noticed
target inventory levels (TILs) vector determined for this demand is equal to [1491, 743, 360, 1347, 784, 1337] units and results in costs of 1.6 × 10
The baseline setting for the optimization process leads to the holding costs equal to 5.9 × 104 with
Summary
In particular since the turn of the 20th and 21st centuries, a significant development of the global economy has been noticed. In spite of numerous regional problems that involved diverse international sanctions in different sectors and the global financial crisis, many industrial branches have been created as well as other ones have made a noticeable progress [1,2] One of these branches is logistics, which incorporates complex processes combining various activities related to production, transport, and trade [3]. Available computers enable to analyze various data using soft computing methods based on artificial intelligence ideas [4,5,6] Nowadays, both global enterprises and regional companies are collecting as much data as possible from their clients and users. From the logistics sector point of view, data processing frequently
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