Implementing Ant Colony Optimization (ACO) In Traveling Salesman Problem (Case Study At Distribution Store PB)  

Oleh:

Tanjung, Widya Nurcahayanty ; Sopiah

Jenis: Article from Proceeding
Dalam koleksi: Proceeding The 7th International Seminar on Industrial Engineering and Management (7th ISIEM) di Bali, March 11th – 13th, 2014, page OR 18-30.
Topik: Traveling Salesman Problem; Ant Colony Optimization; Taguchi
Fulltext: Paper 44-Widya Tanjung UAI.pdf (752.47KB)

Isi artikel

Traveling Salesman Problem is an important issue in the distribution system. it problem is generally described as a case in which a salesman must visit a number of cities from a central facility and back to the original point of departure. The purpose of this problem is to minimize the total distance salesman. Ant Colony Optimization algorithm (ACO) is one method of implementing metaheuristic ant as an agent to pheromone update, to perform the solution search process effective and efficient. There are 5 types of ACO algorithm ASRank, MMAS, EAS and ACS. In this case, the ACO algorithm used is Ant Colony System (ACS). it implementation used is symmetric it the distance from point a to point b is equal to the distance from point B to point A, d_ab = d_ba. find a solution on the distribution schedule X Logistic company PB Store Monday through Saturday. Results obtained from the ACO algorithm for Monday through Saturday Km respectively 4.9374, 5.8512 km, 5.4172 Km, Km 5.1832, 5.4678 and 18.7146 Km Km. With a minimum charge per day in a row that is Rp. 5,431, Rp. 6436, Rp. 5,959, Rp. 5,702, Rp. 6,015 and Rp. 20 586. To produce the optimum, the researchers conducted a 4 parameter tuning ACO with 3 levels using the Taguchi method, the results obtained tuning ants m = 34, a = 4.5, ß = 5 and ? = 0.5

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