Intelligent Approach to Inventory Control in Logistics under Uncertainty Conditions

[EN] The article presents a proposal for a combined application of fuzzy logic and genetic algorithms to control the procurement process in the enterprise. The approach presented in this paper draws particular attention to the impact of external random factors in the form of demand and lead time uncertainty. The model uses time-variable membership function parameters in a dynamic fashion to describe the modelled output fuzzy (sets) values. An additional element is the use of genetic algorithms for optimisation of fuzzy rule base in the proposed method. The approach presented in this paper was veryfied according to four criteria based on a computer simulation performed on the basis of the actual data from an enterprise.Więcek, P. (2016). Intelligent Approach to Inventory Control in Logistics under Uncertainty Conditions. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 724-734. https://doi.org/10.4995/CIT2016.2015.3508OCS72473

Use of Artificial Intelligence (AI) in Managing Inventory of Medicine in Pharmaceutical Industry

 Inventory is one of the vital components of current assets. Excess holdings of inventory may increase cost as well as wastage. As such, effective and efficient management of inventory is an integral part of supply chain. Especially, in the field of management of pharmaceutical products and medicine it bears more importance. Improper use of pharmaceutical products or shortage of medicine would not only cause financial loss but also may affect the patients adversely. Rather than using the traditional techniques of managing inventory use of Artificial Intelligence (AI) can make the process more effective and efficient. AI is the application of computer program that demonstrates action like a human being, learns from experience, gets new input and processes big data by reasoning. It can acquire large amount of data and create rules for turning the data into actionable information. This study has been conducted based mainly on secondary sources of data. It is a qualitative study that gives a conceptual idea regarding how the functions of AI can support managing inventory of medicine in pharmaceutical industry

Multi-objective economic production quantity model for fully backlogged problem where demand depend on some conditions and permissible delay in payment

For any business, inventory system takes a monumental part. Keep this aspect in mind, we formulate multi-objective displayed EPQ model consider with non-instantaneous deteriorating things where production depends on demand and variable demand pattern depends on display self-space, selling price and frequency of advertisement of the item. The customers are more attracted to buy an item by observing self-space, selling price and advertisement. Imperfect materials are now and again come back to providers for a discount or credit. Here price discount is available for deteriorated and defective items. Holding cost varies with time where shortages are allowed and fully backlogged. Fuzzy environment touches the reality instead of the crisp environment. So, we assumed the cost components as Triangular Fuzzy Numbers and Nearest Interval Approximation Method is used to defuzzify the model. Finally, numerical examples as well as  sketches are given to illustrate the model

A Multiproduct Single-Period Inventory Management Problem under Variable Possibility Distributions

In multiproduct single-period inventory management problem (MSIMP), the optimal order quantity often depends on the distributions of uncertain parameters. However, the distribution information about uncertain parameters is usually partially available. To model this situation, a MSIMP is studied by credibilistic optimization method, where the uncertain demand and carbon emission are characterized by variable possibility distributions. First, the uncertain demand and carbon emission are characterized by generalized parametric interval-valued (PIV) fuzzy variables, and the analytical expressions about the mean values and second-order moments of selection variables are established. Taking second-order moment as a risk measure, a new credibilistic multiproduct single-period inventory management model is developed under mean-moment optimization criterion. Furthermore, the proposed model is converted to its equivalent deterministic model. Taking advantage of the structural characteristics of the deterministic model, a domain decomposition method is designed to find the optimal order quantities. Finally, a numerical example is provided to illustrate the efficiency of the proposed mean-moment credibilistic optimization method. The computational results demonstrate that a small perturbation of the possibility distribution can make the nominal optimal solution infeasible. In this case, the decision makers should employ the proposed credibilistic optimization method to find the optimal order quantities