Routing Optimization with Generalized Consistency Requirements

Author(s):  
Kai Wang ◽  
Lu Zhen ◽  
Jun Xia ◽  
Roberto Baldacci ◽  
Shuaian Wang

The consistent vehicle routing problem (ConVRP) aims to design synchronized routes on multiple days to serve a group of customers while minimizing the total travel cost. It stipulates that customers should be visited at roughly the same time (time consistency) by several familiar drivers (driver consistency). This paper generalizes the ConVRP for any level of driver consistency and additionally addresses route consistency, which means that each driver can traverse at most a certain proportion of different arcs of routes on planning days, which guarantees route familiarity. To solve this problem, we develop two set partitioning-based formulations, one based on routes and the other based on schedules. We investigate valid lower bounds on the linear relaxations of both of the formulations that are used to derive a subset of columns (routes and schedules); within the subset are columns of an optimal solution for each formulation. We then solve the reduced problem of either one of the formulations to achieve an optimal solution. Numerical results show that our exact method can effectively solve most of the medium-sized ConVRP instances in the literature and can also solve some newly generated instances involving up to 50 customers. Our exact solutions explore some managerial findings with respect to the adoption of consistency measures in practice. First, maintaining reasonably high levels of consistency requirements does not necessarily always lead to a substantial increase in cost. Second, a high level of time consistency can potentially be guaranteed by adopting a high level of driver consistency. Third, maintaining high levels of time consistency and driver consistency may lead to lower levels of route consistency.

2017 ◽  
Vol 21 ◽  
pp. 255-262 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
Raed Ibraheem Hamed ◽  
Salama A. Mostafa ◽  
Mohd Sharifuddin Ahmad ◽  
...  

Author(s):  
S. P. Anbuudayasankar ◽  
K. Ganesh ◽  
Tzong-Ru Lee

This chapter presents the development of simulated annealing (SA) for a health care application which is modeled as Single Depot Vehicle routing problem called Mixed Vehicle Routing Problem with Backhauls (MVRPB), an extension of Vehicle Routing Problem with Backhauls (VRPB). This variant involves both delivery and pick-up customers and sequence of visiting the customers is mixed. The entire pick-up load should be taken back to depot. The latest rapid advancement of meta-heuristics has shown that it can be applied in practice if they are personified in packaged information technology (IT) solutions along with the combination of a Supply Chain Management (SCM) application integrated with an enterprise resource planning (ERP) resulted to this decision support tool. This chapter provides empirical proof in sustain of the hypothesis, that a population extension of SA with supportive transitions leads to a major increase of efficiency and solution quality for MVRPB if and only if the globally optimal solution is located close to the center of all local optimal solutions.


2020 ◽  
Vol 10 (7) ◽  
pp. 2403
Author(s):  
Yanjun Shi ◽  
Lingling Lv ◽  
Fanyi Hu ◽  
Qiaomei Han

This paper addresses waste collection problems in which urban household and solid waste are brought from waste collection points to waste disposal plants. The collection of waste from the collection points herein is modeled as a multi-depot vehicle routing problem (MDVRP), aiming at minimizing the total transportation distance. In this study, we propose a heuristic solution method to address this problem. In this method, we firstly assign waste collection points to waste disposal plants according to the nearest distance, then each plant solves the single-vehicle routing problem (VRP) respectively, assigning customers to vehicles and planning the order in which customers are visited by vehicles. In the latter step, we propose the sector combination optimization (SCO) algorithm to generate multiple initial solutions, and then these initial solutions are improved using the merge-head and drop-tail (MHDT) strategy. After a certain number of iterations, the optimal solution in the last generation is reported. Computational experiments on benchmark instances showed that the initial solutions obtained by the sector combination optimization algorithm were more abundant and better than other iterative algorithms using only one solution for initialization, and the solutions with distance gap were obtained using the merge-head and drop-tail strategy in a lower CPU time compared to the Tabu search algorithm.


2019 ◽  
Vol 53 (4) ◽  
pp. 1043-1066 ◽  
Author(s):  
Pedro Munari ◽  
Alfredo Moreno ◽  
Jonathan De La Vega ◽  
Douglas Alem ◽  
Jacek Gondzio ◽  
...  

We address the robust vehicle routing problem with time windows (RVRPTW) under customer demand and travel time uncertainties. As presented thus far in the literature, robust counterparts of standard formulations have challenged general-purpose optimization solvers and specialized branch-and-cut methods. Hence, optimal solutions have been reported for small-scale instances only. Additionally, although the most successful methods for solving many variants of vehicle routing problems are based on the column generation technique, the RVRPTW has never been addressed by this type of method. In this paper, we introduce a novel robust counterpart model based on the well-known budgeted uncertainty set, which has advantageous features in comparison with other formulations and presents better overall performance when solved by commercial solvers. This model results from incorporating dynamic programming recursive equations into a standard deterministic formulation and does not require the classical dualization scheme typically used in robust optimization. In addition, we propose a branch-price-and-cut method based on a set partitioning formulation of the problem, which relies on a robust resource-constrained elementary shortest path problem to generate routes that are robust regarding both vehicle capacity and customer time windows. Computational experiments using Solomon’s instances show that the proposed approach is effective and able to obtain robust solutions within a reasonable running time. The results of an extensive Monte Carlo simulation indicate the relevance of obtaining robust routes for a more reliable decision-making process in real-life settings.


2014 ◽  
Vol 556-562 ◽  
pp. 4693-4696
Author(s):  
Yue Li Li ◽  
Ai Hua Ren

With the development of the market economy, the logistics industry has been developed rapidly.It is easy to understand that good vehicle travel path planning has very important significance in the logistics company,especially in the general production enterprises. This paper mainly studies the microcosmic traffic system in the type of vehicle routing problems: capacity-constrained vehicle routing problem. We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem, treated as nodes in a spatial network. For the networks where the nodes are concentrated, the use of hybrid heuristic optimization can greatly improve the efficiency of the solution. The algorithm produces high-quality solutions for the capacity-constrained vehicle routing problem.


2012 ◽  
Vol 468-471 ◽  
pp. 2047-2051 ◽  
Author(s):  
Ai Ling Chen

Vehicle routing optimization problem is one of the major research topics in logistics distribution field. Suitable vehicle routing selection is vital to reduce the logistics cost. The paper presents a hybrid optimization method to solve the vehicle routing problem with time windows. In the hybrid optimization method, discrete particle swarm optimization algorithm is used to assign the customers on routes and simulated annealing (SA) algorithm to avoid becoming trapped in local optimum. The simulation results have shown that the proposed method is feasible and effective for the vehicle routing problem with time windows.


Author(s):  
Krittika Kantawong ◽  
Sakkayaphop Pravesjit

This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.


Author(s):  
Joydeep Dutta ◽  
Partha Sarathi Barma ◽  
Samarjit Kar ◽  
Tanmay De

This article has proposed a modified Kruskal's method to increase the efficiency of a genetic algorithm to determine the path of least distance starting from a central point to solve the open vehicle routing problem. In a vehicle routing problem, vehicles start from a central point and several customers placed in different locations to serve their demands and return to the central point. In the case of the open vehicle routing problem, the vehicles do not go back to the central point after serving the customers. The challenge is to reduce the number of vehicles used and the distance travelled simultaneously. The proposed method applies genetic algorithms to find the set of customers those are covered by a particular vehicle and the authors have applied the proposed modified Kruskal's method for local routing optimization. The results of the new method are analyzed in comparison with some of the evolutionary methods.


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