An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses

This paper proposes a new metaheuristic routing algorithm for the minimization of the travel distance of pickers in manual warehouses. The algorithm is based on the ant colony optimization (ACO) metaheuristic, which is combined and integrated with the Floyd–Warshall (FW) algorithm, and is therefore referred to as FW–ACO. To assess the performance of the FW–ACO algorithm, two sets of analyses are carried out. Firstly, the capability of the algorithm to provide effective solutions for the picking problem is analyzed as a function of the settings of the main ACO parameters. Secondly, the performance of the FW–ACO algorithm is compared with that of six algorithms typically used to optimize the travel distance of pickers, including exact algorithms for the solution of the travelling salesman problem (where available), two heuristic routing strategies (i.e. S-shape and largest gap) and two metaheuristic algorithms (i.e. the MIN–MAX ant system and Combined+). The comparison is made considering different warehouse layouts and problem complexities. The outcomes obtained suggest that the FW–ACO is a promising algorithm generally able to provide better results than the heuristic and metaheuristic algorithms, and often able to find an exact solution. The FW–ACO algorithm also shows a very efficient computational time, which makes it suitable for defining the route of pickers in real time. The FW–ACO algorithm is finally implemented in a real case study, where constraints exist on the order in which items should be picked, to show its practical usefulness and quantify the resulting savings.