Routing Sales Territory by Solving a Multiobjective TSP Variant with Evolutionary Algorithms The Traveling Salesman Problem is a classic NPhard combinatorial problem. However, many realworld scenarios do not match the classic TSP modeling. Thus, this paper proposes generating vendorsu0027 routes in a sales territory by extending and combining different TSP variants. The proposal deals with many constraints such as prioritizing the most valuable customers, resource limits to visit all customers, time and day windows, multiple days horizon, lunch stop, visit frequency and destination importance. We also introduce a permutational representation with a series of directed arcs, and with a slot to indicate customers that could not be visited. We evaluate SPEA2, NSGAII, and IVFNSGAII MultiObjective Evolutionary Algorithms against three realworld scenarios. The results demonstrate that the proposed model can produce good results in a realistic scenario, maintaining a balance between prioritizing important customers and economic routes. Besides, IVFNSGAII outperformed the other algorithms in most cases, and also the manuallycreated routes, e.g., reducing 35% the routeu0027s distance and increasing 60% the routeu0027s importance.

Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking The Generalised Travelling Salesman Problem (GTSP) is a wellknown problem that, among other applications, arises in warehouse order picking, where each stock is distributed between several locations xe2x80x93 a typical approach in large modern warehouses. However, the instances commonly used in the literature have a completely different structure, and the methods are designed with those instances in mind. In this paper, we give a new pseudorandom instance generator that reflects the warehouse order picking and publish new benchmark testbeds. We also use the Conditional Markov Chain Search framework to automatically generate new GTSP metaheuristics trained specifically for warehouse order picking. Finally, we report the computational results of our metaheuristics to enable further competition between solvers.