A Heuristic Based Approach to Solve a Capacitated Location-routing Problem

Abstract

In this paper we define a new approach to solve the location routing problem (LRP). LRP is a relatively new research direction within location analysis that takes into account vehicle routing aspects. In this research it is proposed a new heuristic method base on simulated annealing to solve a capacitated location routing problem. At first an initial solution is produced by a greedy approach and then encoding this initial solution to use in optimization language and finally apply neighborhood search method to improve the initial solution. Finally, to verify the quality of our new approach in term of solution quality, one well-known data problem set is selected. The numerical examples show the efficiency of our approach.

Presenting a Multi-Start Hybrid Heuristic for Solving the Problem of Two-Echelon Location-Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD)

“…Previous studies proposed several exact algorithms for small-and medium-sized LRPs and their variants [14][15][16][17][18]. As the LRP and its variants are NP-hard problems, the heuristics for solving them have been established, and they include TS [19,20], evolutionary local search (LS) [21], variable neighbourhood descent (VND) [22], simulated annealing heuristic (SA) [23][24][25], particle swarm optimisation (PSO) [26], variable neighbourhood search algorithm (VNS) [27], very largescale neighbourhood search [28], Lagrangian relaxation granular tabu search (TS) heuristic [29], memetic algorithm (MA) with population management [30,31], greedy randomized adaptive search procedure [32], and multiple ant colony optimisation algorithm [33].…”

“…If a customer is found, then it is swapped with another point (lines 14-15). Otherwise, a misplaced satellite is identi ed, and points are exchanged (lines [16][17][18][19][20][21][22][23][24][25][26][27][28][29]. e obtained big tour may not satisfy the vehicle or satellite capacity constraints.…”

Problema de Localización y Ruteo con Restricciones de Capacidad: Revisión de la Literatura

“…En este set de instancias, el costo de viaje es calculado como la distancia euclidiana de una ruta multiplicada por 100 y redondeado al entero más cercano. La capacidad del vehículo es 70 o 150 y las demandas de los clientes son uniformemente distribuidas entre el intervalo (11,20). Finalmente, el último set de datos (Barreto Instances) propuesto en [5] considera 19 instancias obtenidas de problemas clásicos de ruteo de vehículos con restricciones de capacidad adicionando nuevos depósitos con sus correspondientes capacidades y costos fijos.…”

“…Varios trabajos han sido propuestos utilizando esta metodología [3] y [15][16][17][18][19][20][21]. En [3], se propone un algoritmo basado en una búsqueda tabú de dos fases: localización y ruteo.…”

“…A pesar de ello, para encontrar buenas soluciones se hace necesario la ejecución de muchas iteraciones del algoritmo lo cual incrementa el tiempo computacional. En [20], se propone un algoritmo basado en recocido simulado (SA) con 3 vecindades para el CLRP. Al igual que en [17], solamente se reportan resultados para un pequeño número de instancias.…”