A Novel Discrete Differential Evolution Algorithm for the Vehicle Routing Problem in B2C E-Commerce

2015 ◽  
Vol 25 (14) ◽  
pp. 1540033
Author(s):  
Chao Xia ◽  
Ying Sheng ◽  
Zhong-Zhong Jiang ◽  
Chunqiao Tan ◽  
Min Huang ◽  
...  

In this paper, a novel discrete differential evolution (DDE) algorithm is proposed to solve the vehicle routing problems (VRP) in B2C e-commerce, in which VRP is modeled by the incomplete graph based on the actual urban road system. First, a variant of classical VRP is described and a mathematical programming model for the variant is given. Second, the DDE is presented, where individuals are represented as the sequential encoding scheme, and a novel reparation operator is employed to repair the infeasible solutions. Furthermore, a FLOYD operator for dealing with the shortest route is embedded in the proposed DDE. Finally, an extensive computational study is carried out in comparison with the predatory search algorithm and genetic algorithm, and the results show that the proposed DDE is an effective algorithm for VRP in B2C e-commerce.

2013 ◽  
Vol 303-306 ◽  
pp. 2227-2230 ◽  
Author(s):  
Ling Juan Hou ◽  
Zhi Jiang Hou

Aiming at the stochastic vehicle routing problems with simultaneous pickups and deliveries, a novel discrete differential evolution algorithm is proposed for routes optimization. The algorithm can directly be used for the discrete domain by special design. Computational simulations and comparisons based on a medium-sized problem of SVRPSPD is provided. Results demonstrate that the proposed algorithm obtains better results than the basic differential evolution algorithm and the existing genetic algorithm.


2018 ◽  
Vol 7 (1) ◽  
pp. 45-56 ◽  
Author(s):  
Anita Nurul Firdaus ◽  
Pipit Pratiwi Rahayu

Pendistribusian produk berperan penting dalam dunia industri.  Salah satu usaha yang dapat dilakukan perusahaan untuk mengoptimalkan pendistribusian produk adalah meminimalkan biaya tranportasi melalui penentuan rute optimal kendaraan yang disebut dengan VRP (Vehicle Routing Problem). Tujuan dari VRP adalah menentukan rute optimal yaitu rute dengan jarak minimum untuk mendistribusikan produk kepada konsumen. Salah satu variasi VRP adalah Capacitated Vehicle Routing Problem (CVRP), yaitu VRP dengan kendala kapasitas kendaraan. Kasus CVRP tersebut dapat diselesaikan dengan menggunakan Algoritma Tabu Search. Cara kerja Algoritma Tabu Search dimulai dengan penentuan initial solution menggunakan Nearest Neighbor, evaluasi move menggunakan  Exchange, 2-Opt, Relocated, dan Cross Exchange, update Tabu List, kemudian apabila kriteria pemberhentian terpenuhi  maka proses Algoritma Tabu Search berhenti jika tidak, maka kembali pada evaluasi move. Proses perhitungan Algoritma Tabu Search dilakukan secara manual pada PT IAP. Setiap perusahaan distributor atau pun jasa selalu mengadakan persediaan, salah satunya adalah Safety Stock. Perhitungan sederhana Safety Stock dapat membantu menyelesaikan persediaan pengaman yang harus dipersiapkan perusahaan untuk mengurangi tingkat kerugian. Berdasarkan proses perhitungan manual diperoleh solusi pendekatan optimal yaitu rute dengan total jarak terpendek sebesar 138,834 km dan nilai untuk Safety Stock adalah ± 9 karton. [Distribution of the product play an important role in the industry field. The effort done by the companies to optimize the distribution is minimize transportation fee by deciding the shortest route of the vehicle, known as Vehicle Routing Problem (VRP). The purpose of VRP is to determine the optimal route of the route with a minimum distance to distribute product to the consumer. One of the varieties of VRP is Capacitated Vehicle Routing Problem (CVRP), which is VRP with vehicle capacity problems. CVRP case can be solved by using Tabu Search Algorithm. How it works Tabu Search Algorithm starts with the determination of the initial solution using the Nearest Neighbor, evaluating the move using Exchange, 2-Opt, Relocated, and Cross Exchange, updates Tabu List, then when the criteria for termination are met then the Tabu Search algorithm stop if not, then go back to the evaluation of the move. Tabu Search Algorithm calculation process is done manually PT IAP.  Every distributor or service company always hold inventory, one of them is Safety Stock. The simple calculation of Safety Stock can help solve the safety availability that should be prepared by the companies and reduce the level of losses. Based on the manual calculation process obtained optimal solution approach that is route with the shortest route to the optimal total distance of 138,834 km and the value of safety stock is ± 9 cartons.]


2019 ◽  
Vol 1 (1) ◽  
pp. 75-93 ◽  
Author(s):  
Peerawat Chokanat ◽  
Rapeepan Pitakaso ◽  
Kanchana Sethanan

This research aims to solve the problem of the raw milk collection and transportation system which can be interpreted as a special case of the vehicle routing problem. In the proposed problem, the factory will send the trucks, multiple fleets composed of several compartments, to collect the raw milk from the raw milk farms. The objective of this research is to minimize the total transportation cost and the trucks’ and tanks’ cleaning costs. The transportation cost directly depends on the fuel usage. The fuel usage occurs during the transportation of the milk and during the waiting times when it arrives at the factory and cannot transfer the raw milk into the production tank. We develop the modified differential evolution algorithm (MDE) to solve the proposed problem. The original process of the Differential Evolution algorithm (DE) has been modified in two folds which are as follows: (1) In the recombination process, the 2nd order of trial vectors has been generated using 3 different strategies and compared with the 1st order trial vector; the better from the 1st and the 2nd order of trial vectors will move to the selection process. (2) The probability function has been used to select the new target vector from one of two sources which are the trial vector and the current target vector so that the worse solution can be accepted in order to increase the diversity of the original DE. The computational result shows that the modified DE (MDE) outperforms the original DE in finding a better solution.


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