Optimal Assignment of Customer-Desired Items to the Fetching Robots in Superstores

This paper discusses a task assignment problem. The scenario under consideration is a superstore with a team of fetching robots. There is a set of customers each requiring a unique set of items. The goal is to assign the task of fetching the items to the available robots in such a way that the time and effort required for fetching the item is minimized. For this purpose, a Markov Decision Process based model has been proposed. The proposed-model is solvable using stochastic dynamic programming algorithms such as value iteration for the calculation of optimal task assignment policy. The analysis of the characteristics of the resulting optimal policy has been presented with the help of a numerical case study.