Abstract
Genetic Algorithm, Tabu Search, Simulated Annealing, and Ant Colony Optimization showed a good performance in solving vehicle routing problem. However, the generated solution of those algorithms was changeable regarding on the input parameter of each algorithm. CODEQ is a new, parameter free meta-heuristic algorithm that had been successfully used to solve constrained optimization problems, integer programming, and feed-forward neural network. The purpose of this research are improving CODEQ algorithm to solve vehicle routing problem and testing the performance of the improved algorithm. CODEQ algorithm is started with population initiation as initial solution, generated of mutant vector for each parent in every iteration, replacement of parent by mutant when fitness function value of mutant is better than parent’s, generated of new vector for each iteration based on opposition value or chaos principle, replacement of worst solution by new vector when fitness function value of new vector is better, iteration ceasing when stooping criterion is achieved, and sub-tour determination based on vehicle capacity constraint. The result showed that the average deviation of the best-known and the best-test value is 6.35%. Therefore, CODEQ algorithm is good in solving vehicle routing problem.
Highlights
Ant Colony Optimization showed a good performance in solving vehicle routing problem
changeable regarding on the input parameter of each algorithm
CODEQ algorithm is started with population initiation as initial solution
Summary
Terdapat dua macam pendekatan heuristik yang digunakan para peneliti untuk menyelesaikan permasalahan VRP, yaitu route first-cluster second dan cluster first-route second. Berdasarkan pendekatan tersebut, pengembangan algoritma dilakukan dengan memodifikasi algoritma CODEQ yang diusulkan Omran dan Salman [3] pada langkah pembangkitan vektor mutan dan vektor baru sehingga diperoleh giant tour (urutan node yang dikunjungi) dalam bilangan integer. Bila vektor merupakan bilangan real, maka harus diubah menjadi bilangan integer dengan metode integer order criterion Penetapan bilangan sebesar 0,5 mengacu pada algoritma yang telah dikembangkan oleh Omran dan Salman [3]. Sama dengan prosedur yang diterapkan pada langkah 2, bila vektor merupakan bilangan real, maka harus diubah menjadi bilangan integer dengan metode integer order criterion (Mingyong dan Erbao [10]). 5. Penggantian solusi rute terburuk oleh vektor bila nilai fitness function vektor baru tersebut lebih baik dari rute terburuk.
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