The Modeling and Algorithm for a Multi-Depot Vehicle Routing Problem Based on the Difference of Customer Demands

2013 ◽  
Vol 5 (4) ◽  
pp. 778-785
Author(s):  
LIAO Wei ◽  
HE Zhenggang ◽  
SONG Jinyu
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
The Difference ◽  
Customer Demands

scholarly journals A complexity task of optimization in logistic distribution: A new approach to the green multi-objective vehicle routing problem

2022 ◽  
Vol 38 (1) ◽  
Author(s):  
Ferreira J. ◽  
Steiner M.
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Logistic Distribution ◽  
Nsga Ii ◽  
Routing Problem ◽  
Multi Objective ◽  
The Difference ◽  
Stage 1

Logistic distribution involves many costs for organizations. Therefore, opportunities for optimization in this respect are always welcome. The purpose of this work is to present a methodology to provide a solution to a complexity task of optimization in Multi-objective Optimization for Green Vehicle Routing Problem (MOOGVRP). The methodology, illustrated using a case study (employee transport problem) and instances from the literature, was divided into three stages: Stage 1, “data treatment”, where the asymmetry of the routes to be formed and other particular features were addressed; Stage 2, “metaheuristic approaches” (hybrid or non-hybrid), used comparatively, more specifically: NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOPSO (Multi-Objective Particle Swarm Optimization), which were compared with the new approaches proposed by the authors, CWNSGA-II (Clarke and Wright’s Savings with the Non-dominated Sorting Genetic Algorithm II) and CWTSNSGA-II (Clarke and Wright’s Savings, Tabu Search and Non-dominated Sorting Genetic Algorithm II); and, finally, Stage 3, “analysis of the results”, with a comparison of the algorithms. Using the same parameters as the current solution, an optimization of 5.2% was achieved for Objective Function 1 (OF{\displaystyle _{1}}; minimization of CO{\displaystyle _{2}} emissions) and 11.4% with regard to Objective Function 2 (OF{\displaystyle _{2}}; minimization of the difference in demand), with the proposed CWNSGA-II algorithm showing superiority over the others for the approached problem. Furthermore, a complementary scenario was tested, meeting the constraints required by the company concerning time limitation. For the instances from the literature, the CWNSGA-II and CWTSNSGA-II algorithms achieved superior results.

A Hybrid Particle Swarm Optimization for Solving Vehicle Routing Problem with Stochastic Demands

2014 ◽  
Vol 971-973 ◽  
pp. 1467-1472 ◽  
Author(s):  
Ning Qiang ◽  
Feng Ju Kang
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Stochastic Demands ◽  
Hybrid Particle Swarm Optimization ◽  
Customer Demands

As one of the most popular supply chain management problems, the Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades, most of these studies focus on deterministic problem where the customer demands are known in advance. But the Vehicle Routing Problem with Stochastic Demands (VRPSD) has not received enough consideration. In the VRPSD, the vehicle does not know the customer demands until the vehicle arrive to them. This paper use a hybrid algorithm for solving VRPSD, the hybrid algorithm based on Particle Swarm Optimization (PSO) Algorithm, combines a Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, and Variable Neighborhood Search (VNS) algorithm. A real number encoding method is designed to build a suitable mapping between solutions of problem and particles in PSO. A number of computational studies, along with comparisons with other existing algorithms, showed that the proposed hybrid algorithm is a feasible and effective approach for Vehicle Routing Problem with Stochastic Demands.

scholarly journals A Genetic Algorithm on Inventory Routing Problem

2014 ◽  
Vol 3 (3) ◽  
pp. 59-66 ◽  
Author(s):  
Nevin Aydın
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Inventory Routing ◽  
Routing Problems ◽  
Routing Problem ◽  
Inventory Routing Problem ◽  
Combinatorial Optimization Problems ◽  
The Difference ◽  
The Relationship

Inventory routing problem can be defined as forming the routes to serve to the retailers from the manufacturer, deciding on the quantity of the shipment to the retailers and deciding on the timing of the replenishments. The difference of inventory routing problems from vehicle routing problems is the consideration of the inventory positions of retailers and supplier, and making the decision accordingly. Inventory routing problems are complex in nature and they can be solved either theoretically or using a heuristics method. Metaheuristics is an emerging class of heuristics that can be applied to combinatorial optimization problems. In this paper, we provide the relationship between vendor-managed inventory and inventory routing problem. The proposed genetic for solving vehicle routing problem is described in detail.

scholarly journals Dynamic Vehicle Routing Problem—Predictive and Unexpected Customer Availability

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 546 ◽  
Author(s):  
Kucharska
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
General Rule ◽  
Dynamic Vehicle Routing ◽  
Dynamic Vehicle Routing Problem ◽  
Routing Problem ◽  
Collective Decisions ◽  
The Difference ◽  
Enterprise Logistics

The Dynamic Vehicle Routing Problem (DVRP) is one of the most important problems in the area of enterprise logistics. DVRP problems involve these dynamics: the appearance of customers, travel times, service times, or vehicle availability. One of the most often considered aspects of the DVRP is the availability of customers, in which a part or all of the customers are revealed dynamically during the design or execution of the routes. A classification of the DVRP problem due to various elements causing dynamism is proposed. The aim of the paper is to distinguish dynamic VRP, which takes into account the dynamic appearance of customers to serve during the design or execution of the routes. In particular, the difference between the predictive and unexpected aspects of the customer’s availability is considered. Above all, the variant of customer’s availability which is predicted according to an appropriate general rule is modeled using the algebraic-logical meta-model (ALMM). It is a methodology which enables making collective decisions in successive process stages, not separately for individual vehicles. The algebraic-logical model of the dynamic vehicle routing problem with predicted consumer availability is proposed. The paper shows the possibilities of applying the ALMM approach to dynamic problems both with predicted and unexpected customer availability.

scholarly journals A New Approach to the Bi-objective Green Vehicle Routing Problem: Optimization in Newspaper Distribution

Exacta ◽  
2021 ◽  
Author(s):  
Júlio César Ferreira ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
New Approach ◽  
Three Stages ◽  
The Difference ◽  
Newspaper Distribution ◽  
Stage 1

The purpose of this work is to present a methodology to provide a solution to a Bi-objective Green Vehicle Routing Problem (BGVRP). The methodology, illustrated using a case study (newspaper distribution problem) and instances from the literature, was divided into three stages: Stage 1, data treatment; Stage 2, metaheuristic approaches (hybrid or non-hybrid), used comparatively, and, Stage 3, analysis of the results, with a comparison of the algorithms. An optimization of 19.9% was achieved for Objective Function 1 (OF1; minimization of CO2 emissions) and consequently the same percentage for the minimization of total distance, and 87.5% for Objective Function 2 (OF2; minimization of the difference in demand). Metaheuristic approaches hybrid achieved superior results for case study and instances.

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