New retail trends show the increasing importance of providing cost efficient deliveries in cities, where congestion and compliance with driving hours regulations should be incorporated into routing software. This paper introduces a large neighbourhood search algorithm that substantially improves the benchmark solutions for the vehicle routing problem variant considering time windows, time-dependent travel times and driving hours regulations (EC) 561/2006 that apply to vehicles over 3.5 tons in European cities in terms of required number of vehicles, travelled distance and duty time. Additionally, instances for The Road Transport (Working Time) Regulation 2005 that applies to drivers in the United Kingdom are introduced. The proposed algorithm is also used in scenarios that represent home delivery conditions to evaluate the impacts of the length of time windows, customer density, congestion and regulations in terms of cost and environmental impact.
This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an 'always feasible' search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable time.
cuad. contab. / bogotá, colombia, 17 (44): 575-594 / julio-diciembre 2016 / 575 Comparación a través del picking en tienda de dos alternativas de entrega en un entorno de servicio a domicilio en supermercados. Área temática: logística en ciudad * doi:10.11144/Javeriana.cc17-44.ctpt Ricardo Otero-CaicedoPontificia Universidad Javeriana, sede Bogotá.Correo electrónico: r.otero@javeriana.edu.co Stevenson BolívarPontificia Universidad Javeriana, sede Bogotá.Correo electrónico: s_bolivar@javeriana.edu.co Nicolás Rincón-GarcíaPontificia Universidad Javeriana, sede Bogotá.Correo electrónico: nicolas.rincon@javeriana.edu.co * Artículo científico, revisión de la literatura.576 / vol. 17 / no. 44 / julio-diciembre 2016 Resumen En Colombia, el comercio electrónico está aumentando considerablemente según cifras de la Cámara Colombiana de Comercio Electrónico, CCCE. En este mercado, las grandes superficies como Jumbo, La 14, Almacenes Éxito y Carulla, entre otras, participan por medio del servicio de entregas a domicilio (Home delivery). Este servicio se compone de 3 etapas principales, que comienzan con la recepción de la orden, continúan con la recolección en el almacén de los productos que componen la orden (order picking) y finalizan con la entrega al cliente (delivery). La eficiencia en los procesos logísticos es esencial para garantizar la rentabilidad de los supermercados en este segmento. En particular, la etapa de order picking es fundamental, ya que representa cerca de la mitad de los costos de bodega.Enmarcado en el proceso picking en tienda, en este documento se presenta y analiza la comparación de dos alternativas de entrega de productos: i) durante el mismo día, ii) en el día siguiente. En el primer caso, los pedidos se despachan a medida que van llegando, siguiendo el criterio FIFO (first in first out) para la asignación de cada orden a cada operario. En el segundo caso, las órdenes se acumulan y se despachan al día siguiente, lo que permite agrupar las órdenes en lotes (batching) y asignar a cada operario uno o varios lotes para realizar el picking. Estas dos alternativas se compararon utilizando simulación por eventos discretos.Los resultados indicaron que sostener al cliente la promesa de entrega durante el mismo día de colocación del pedido, incrementa los costos operacionales de picking en 450% en promedio. Framed in the picking in store process, this document presents and analyzes the comparison between two alternatives of product delivery: i) on the same day, ii) on the following day. In the first case, the orders are dispatched as they arrive, following the FIFO (first in first out) criterion for the assignment of each order to each operator. In the second case, the orders are accumulated and dispatched the next day, which allows batching (grouping orders in lots) and assigning one or several lots to each operator to perform the picking. These two alternatives were compared using discrete event simulation. Results indicated that keeping the promise to the customer of delivery on the same day the ...
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