Optimización por colonia de hormigas para el ruteo de la preparación de pedidos en almacenes de múltiples bloques  <span class="so-article-trans-title" dir="auto"> Translated title: Ant colony optimization for picker routing in multiple block warehouses </span>

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Abstract

Resumen: Este artículo tiene como objetivo minimizar la distancia total recorrida para la preparación de pedidos (PRP), considerando almacenes 2D de múltiples bloques. La PRP se encarga de planificar la forma como se visitarán las diferentes posiciones de almacenamiento para satisfacer los requerimientos de cada orden del cliente. Para esto, aquí se formulan las distancias mínimas entre cada par de posiciones de almacenamiento a visitar en un tour. Se propone un algoritmo de optimización por colonia de hormigas (ACO, en inglés), al cual se le ajustan parámetros para garantizar un mejor desempeño. Con base en experimentos computacionales, se establece que el ACO propuesto brinda soluciones de alta calidad y genera ahorros satisfactorios frente a una metaheurística y dos heurísticas, requiriendo tiempos de computación viable para ambientes de simulación y ambiente de operación real de almacenes y centros de distribución. Se concluye que el algoritmo ACO propuesto para solucionar la PRP genera soluciones de alta calidad en tiempos de computación razonables.

Translated abstract

Abstract: This study aims to minimize the total traveled distance for the picker routing problem (PRP) by assessing 2D multi-block warehouses. The PRP is responsible for planning tours to visit different storage positions to satisfy the requirements of each customer order. Therefore, the minimum distances between each pair of storage locations to visit on a tour are formulated. An ant colony optimization (ACO) algorithm is proposed and adjusted to improve performance. Based on computational experiments, it is established that the proposed ACO provides high-quality solutions. It also generates satisfactory savings when compared to a metaheuristic and to two widely used heuristics, requiring viable computing times for simulation environments and real warehouse operation environments. It is concluded that the proposed ACO algorithm for PRPs provides high-quality solutions within reasonable computational time.

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Ant system: optimization by a colony of cooperating agents.

M. Dorigo, V Maniezzo, A Colorni (1996)

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.