Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile

Abstract

The research evaluates the vehicular routing problem for distributing refrigerated products. The mathematical model corresponds to the vehicle routing problem with hard time windows and a stochastic service time (VRPTW-ST) model applied in Santiago de Chile. For model optimization, we used tabu search, chaotic search and general algebraic modeling. The model’s objective function is to minimize the total distance traveled and the number of vehicles using stochastic waiting restrictions at the customers’ facilities. The experiments were implemented in ten scenarios by modifying the number of customers. Experiments were established with several customers that can be solved using the general algebraic modeling technique in order to validate the tabu search and the chaotic search methods. The study considered two algorithms modified with Monte Carlo (tabu search and chaotic search). Additionally, two modified algorithms, TSv2 and CSv2, were proposed to reduce execution time. These algorithms were modified by delaying the Monte Carlo procedure until the first set of sub-optimal routes were found. The results validate the metaheuristic chaotic search to solve the VRPTW-ST. The chaotic search method obtained a superior performance than the tabu search method when solving a real problem in a large city. Finally, the experiments demonstrated a direct relationship between the percentage of customers with stochastic waiting time and the model resolution time.

Stochastic scheduling of autonomous mobile robots at hospitals

“…Pan et al [ 14 ] focused on a multi-trip VRP with known service times, hard time windows, and variable running speeds. Similarly, Jie et al [ 15 ] considered a time-dependent VRP that is affected by the traffic environment, while Dávila et al [ 16 ] studied the VRP with hard time windows and stochastic service time.…”

A Routing Model for the Distribution of Perishable Food in a Green Cold Chain

“…The novelty of our approach consists of integrating into a single real application model the characteristics of limited transport capacity, random time windows, random demands and routing times and the level of customer satisfaction included. In this regard, there are several authors who have addressed the problem with approaches similar to ours, among the most important of which are [1][2][3][4].…”

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

“…Efficient hybrid methods have also been proposed by combining these algorithms. Based on a single solution (direct search algorithms), the following algorithms can be found: simulate annealing (SA) [6], taboo search (TS) [7,8], random walk (RW) [9], and hill climbing (HC) [10], among others, and population-based algorithms such as spider monkey optimization (SMO) [11]; particle swarm optimization (PSO) [12][13][14]; ant colony optimization (ACO) [15]; artificial immune system (AIS) [16]; whale optimization [17]; genetic algorithm (GA) [18,19]; firefly algorithm [20]; grey wolf optimizer (GWO) [21]; bee algorithm (BA) [22]; artificial bee colony (ABC) [23]; queen bee evolution (QBE) [24]; bee system (BS) [25,26]; bee colonies optimization (BCO) [27]; BeeAdHoc [28,29]; and marriage in honey bees optimization (MBO) [30,31] and its different versions such as honey bees mating optimization (HBMO) [32,33] fast marriage in honey bees optimization (FMHBO) [34], and honey bees optimization (HBO) [35].…”