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The paper describes an eventual combination of discrete-event simulation and genetic algorithm to define the optimal inventory policy in stochastic multi-product inventory systems. The discrete-event model under consideration corresponds to the just-in-time inventory control system with a flexible reorder point. The system operates under stochastic demand and replenishment lead time. The utilized genetic algorithm is distinguished for a non-binary chromosome encoding, uniform crossover and two mutation operators. The paper contains a detailed description of the optimization technique and the numerical example of six- product inventory model. The proposed approach contributes to the field of industrial engineering by providing a simple, but still efficient way to compute nearly-optimal inventory parameters with regard to risk and reliability policy. Besides, the method may be applied in automated ordering systems.

AbstractInventory control has been a major point of discussion in industrial engineering and operations research for over 100 years. Various advanced numerical methods can be used for solving inventory control problems, which makes it a highly multidisciplinary filed attracting researchers from different academic disciplines. This fact makes it a daunting task to subsume the gargantuan spectrum of literature related to inventory control theory in one treatise. In light of this fact, this paper focuses on the timeline of inventory control models with respect to methodologies behind deriving optimal control parameters. Such methodologies include analytical approaches, optimal control theory, dynamic programming, simulation-based optimization and metamodel-based optimization.

The work is devoted to the study of the possibilities to apply the System Dynamics method for the analysis of processes at the road transport enterprises. The study contains a concise review on examples of three simulation paradigms application for creating models, associated with the road motor transport. The following paradigms were under consideration: Discrete Event, Agent Based, and System Dynamics. Furthermore, the paper describes the causal loop diagram designed using the principles of system dynamics. This diagram is a qualitative model that represents the relationships between the main factors affecting the performance indicators of a road transport enterprise. The quantitative model constructed using the principles of system dynamics is also represented. The mentioned model reflects the process of functioning of a road transport enterprise during the year. The model is developed using Vensim simulation software.

To manage perfectly an efficient and effective supply chain of continuous and undisturbed flow of goods is needed. To achieve this identification, location and sensor technologies must be implemented to generate state data of the logistics objects. However, the amount of information overstrains the operational logistics planner and the information systems have to face enormous data streams. Data mining methods are useful to cope with such big data streams, and they are well developed in the literature. But these methods are not often applied to logistical state data. Without knowledge of the processes, the results of the algorithms cannot be understood. Therefore, the objective of this work is to introduce a general concept to model and to analyse logistical state data, in order to find irregularities and their causes and dependences. This work shows that it is possible to use data mining methods on logistical state data to filter irregularities and their causes.