A hybrid generational genetic algorithm for the periodic vehicle routing problem with time windows

2014 ◽  
Vol 20 (4) ◽  
pp. 383-416 ◽  
Author(s):  
Phuong Khanh Nguyen ◽  
Teodor Gabriel Crainic ◽  
Michel Toulouse
Author(s):  
Mohammad Mirabi

AbstractA genetic algorithm is a metaheuristic proposed to derive approximate solutions for computationally hard problems. In the literature, several successful applications have been reported for graph-based optimization problems, such as scheduling problems. This paper provides one definition of periodic vehicle routing problem for single and multidepots conforming to a wide range of real-world problems and also develops a novel hybrid genetic algorithm to solve it. The proposed hybrid genetic algorithm applies a modified approach to generate a population of initial chromosomes and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. Moreover, during the implementation a hybrid algorithm, cyclic transfers, an effective class of neighborhood search is applied. The author uses three genetic operators to produce good new offspring. The objective function consists of two terms: total traveled distance at each depot and total waiting time of all customers to take service. Distances are assumed Euclidean or straight line. These conditions are exactly consistent with the real-world situations and have received little attention in the literature. Finally, the experimental results have revealed that the proposed hybrid method can be competitive with the best existing methods as asynchronous parallel heuristic and variable neighborhood search in terms of solution quality to solve the vehicle routing problem.


Author(s):  
Hande Öztop ◽  
Damla Kizilay ◽  
Zeynel Abidin Çil

The periodic vehicle routing problem (PVRP) is an extension of the well-known vehicle routing problem. In this paper, the PVRP with time windows and time spread constraints (PVRP-TWTS) is addressed, which arises in the high-value shipment transportation area. In the PVRP-TWTS, period-specific demands of the customers must be delivered by a fleet of heterogeneous capacitated vehicles over the several planning periods. Additionally, the arrival times to a customer should be irregular within its time window over the planning periods, and the waiting time is not allowed for the vehicles due to the security concerns. This study, proposes novel mixed-integer linear programming (MILP) and constraint programming (CP) models for the PVRP-TWTS. Furthermore, we develop several valid inequalities to strengthen the proposed MILP and CP models as well as a lower bound. Even though CP has successful applications for various optimization problems, it is still not as well-known as MILP in the operations research field. This study aims to utilize the effectiveness of CP in solving the PVRP-TWTS. This study presents a CP model for PVRP-TWTS for the first time in the literature to the best of our knowledge. Having a comparison of the CP and MILP models can help in providing a baseline for the problem. We evaluate the performance of the proposed MILP and CP models by modifying the well-known benchmark set from the literature. The extensive computational results show that the CP model performs much better than the MILP model in terms of the solution quality.


2019 ◽  
Vol 20 (2) ◽  
pp. 68
Author(s):  
Annisa Kesy Garside ◽  
Nabila Rohmatul Laili

This paper discusses periodic vehicle routing problems that allow vehicles to travel on multiple trips in a single day. It is known as the Multi-Trip Periodic Vehicles (MTPVRP) Problem Route. Cluster-first route-second (CFRS) heuristics to solve MTPVRP was proposed in this study. In phase 1, customers were divided into clusters using the formulation of integer programming. Phase 2 determined the route of the cluster and verifies that the total journey time to visit the trips does not exceed the working hours of the vehicle. The implementation of the heuristic CFRS to solve the real problem faced by the LPG distributor shows that the procedure could provide a better routing solution.


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