scholarly journals MENYELESAIKAN VEHICLE ROUTING PROBLEM MENGGUNAKAN ALGORITMA FUZZY EVOLUSI

2018 ◽  
Vol 7 (3) ◽  
pp. 252
Author(s):  
I PUTU ARYA YOGA SUMADI ◽  
I PUTU EKA NILA KENCANA ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Evolutionary Algorithm ◽  
Vehicle Routing ◽  
Relative Error ◽  
Vehicle Routing Problem ◽  
Average Relative Error ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Optimal Value ◽  
Evolution Algorithm ◽  
Capacitated Vehicle

The purpose of this research is to know the performance of Fuzzy Evolutionary Algorithm in solving one type of Vehicle Routing Problem that is Capacitated Vehicle Routing Problem (CVRP). There are 8 different CVRP data to be solved. The performance of the algorithm can be determined by comparing the value obtained by AFE with the optimal value of the data. The result of this research is fuzzy evolution algorithm yields the best average relative error from all data for distance that is equal to 69,5855% and for minimum vehicle equal to 26%.

scholarly journals A New Improved Quantum Evolution Algorithm with Local Search Procedure for Capacitated Vehicle Routing Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Ligang Cui ◽  
Lin Wang ◽  
Jie Deng ◽  
Jinlong Zhang
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Quantum Evolution ◽  
Search Procedure ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Local Search Procedure ◽  
Evolution Algorithm ◽  
Capacitated Vehicle

The capacitated vehicle routing problem (CVRP) is the most classical vehicle routing problem (VRP); many solution techniques are proposed to find its better answer. In this paper, a new improved quantum evolution algorithm (IQEA) with a mixed local search procedure is proposed for solving CVRPs. First, an IQEA with a double chain quantum chromosome, new quantum rotation schemes, and self-adaptive quantum Not gate is constructed to initialize and generate feasible solutions. Then, to further strengthen IQEA's searching ability, three local search procedures 1-1 exchange, 1-0 exchange, and 2-OPT, are adopted. Experiments on a small case have been conducted to analyze the sensitivity of main parameters and compare the performances of the IQEA with different local search strategies. Together with results from the testing of CVRP benchmarks, the superiorities of the proposed algorithm over the PSO, SR-1, and SR-2 have been demonstrated. At last, a profound analysis of the experimental results is presented and some suggestions on future researches are given.

scholarly journals Modified Migrating Birds Optimization Algorithm: Multi-Depot Capacitated Vehicle Routing Problem

2021 ◽  
Vol 3 (2) ◽  
pp. 75-86
Author(s):  
Dini Nur Wasilah
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Applied Mathematics ◽  
Iterated Local Search ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Migrating Birds Optimization ◽  
Optimal Value ◽  
Migrating Birds ◽  
Capacitated Vehicle

The multi-depot capacitated vehicle routing problem (MDCVRP) is a variation of the vehicle routing problem (VRP) modeled from distribution problems in the industrial world. This problem is a complex optimization problem in the field of operations research in applied mathematics. The MDCVRP is very interesting to discuss and find the best solution method. In this study, the authors apply the modified migrating birds' optimization (MMBO) algorithm, which is a hybrid of the migrating birds' optimization (MMBO) and iterated local search (ILS) algorithms. The purpose of this study is to analyze the results of applying the algorithm in solving MDCVRP. We used 20 MDCVRP data in simulation, grouped into four sizes (25, 50, 75, and 100 points). Based on the results of this research, it is known that the MMBO algorithm can produce the following solutions. First, on the data of 25 points, the experiment reaches the optimal value with small convergent iterations. Second, the best results on the data of 50 points have reached optimal value, but some other results have not been optimal. And, third, for data of 75 and 100 points, there is no optimal solution obtained by the MMBO algorithm. These results conclude that the MMBO algorithm effectively solves the MDCVRP problem with small data, but the bigger data, the more ineffective.Keywords: MDCVRP; VRP; optimization; operation research; applied Mathematics; MMBO. AbstrakMulti-depot capacitated vehicle routing problem (MDCVRP) adalah salah satu variasi dari vehicle routing problem (VRP) yang dimodelkan dari permasalahan distribusi di dunia industri. Permasalahan ini merupakan permasalahan optimasi kompleks dalam bidang riset operasi ilmu matematika terapan. MDCVRP sangat menarik untuk dibahas dan dicari metode penyelesaian terbaik. Dalam penelitian ini, penulis menerapkan algoritma modified migrating birds optimization (MMBO) yang merupakan hybrid algoritma migrating birds optimization (MBO) dan iterated local search (ILS). Tujuan penelitian ini adalah menganalisis hasil penerapan algoritma dalam menyelesaikan MDCVRP. Untuk simulasi, penulis menggunakan 20 data MDCVRP yang dikelompokkan menjadi empat ukuran (25, 50, 75, dan 100 titik). Berdasarkan hasil penelitian yang telah dilakukan, diketahui bahwa algoritma MMBO mampu menghasilkan solusi sebagai berikut. Pertama, Pada data 25 titik, percobaan mencapai nilai optimal dengan iterasi konvergen yang kecil. Kedua, Hasil terbaik pada data 50 titik telah mencapai nilai optimal namun sebagain hasil lainnya belum optimal. Dan ketiga, untuk data 75 dan 100 titik, tidak terdapat solusi optimal yang dihasilkan algoritma MMBO. Dari hasil tersebut dapat disimpulkan bahwa algoritma MMBO efektif untuk menyelesaikan MDCVRP data kecil, namun semakin besar datanya menjadi kurang efektif.Kata kunci: MDCVRP; VRP; optimasi; riset operasi; matematika terapan; MMBO. 

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