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The aim of the traveling salesman problem (TSP) is to find the cheapest way of visiting all elements in a given set of cities and returning to the starting point. In solutions presented in the literature costs of travel between nodes (cities) are based on Euclidean distances, the problem is symmetric and the costs are constant and crisp values. Practical application in road transportation and supply chains are often fuzzy. The risk attitude depends on the features of the given operation. The model presented in this paper handles the fuzzy, time dependent nature of the TSP and also gives solution for the asymmetric loss aversion by embedding the risk attitude into the fitness function of the bacterial memetic algorithm. Computational results are presented as well.

Order picking is the most labor-intensive and costly activities in many warehouses by consuming ca. 55 % of the total operating expenses. Order picking development strategies mostly concentrate on warehouse layout, storage assignment policy, routing, zoning and on batching methods. The main challenges of order picking process improvements are the synchronization of these fields and fit the system with the further influencing factors, like unique demands and product parameters. Researchers of the pallet-loading problem could provide a wider horizon on considerable parameters, but their results are rarely implemented into order picking processes. The physical parameters of the products also have a significant impact on the processes which could strongly influence the picking sequence. The aim of this paper is to describe our own developed methodology for modeling the pallet setup rules. We introduce our product classifying and PSF based decision matrix modeling solutions. These will be the basis of our further research on PSF based harmonized routing and SLA optimization. We examine the complexity of a simple pallet setup features based order picking routing case, where the products of the defined pallet setup classes are stored in separated zones.

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