The purpose of the study was to apply the method of Vehicle Routing Problem (VRP) Method to accelerate product distribution and minimize the use of fuel. The method of VRP is one of the solutions to find the shortest route from 57 locations in Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi), four locations in Bandung, and three locations in Surabaya. The result shows that the most efficient method of VRP is by combining the heuristics and meta-heuristics – simulated annealing methods which reduce the distance about 11.79 % in Jabodetabek, 0 % in Bandung, and 8.98 % in Surabaya.

Reducing distance in delivery service from distribution center to subsidiaries can be reached through well-managed routing which known as VRP - Vehicle Routing Problem. This study was conducted in a food industry PT. XYZ. Two methods, Cluster First Route Second algorithm and linier programming were used to obtain the minimum distance between distribution center to outlets in Jabodetabek area. The cluster first route second method was improved using linier programming – solver. The improved method shows 774.18 kilometer is better than Cluster Firs Route Second, 832.19 kolometer which is 6.97% shortened.

The subject of this research is distance and time of several city tour problems which known as traveling salesman problem (tsp). The goal is to find out the gaps of distance and time between two types of optimization methods in traveling salesman problem: exact and approximate. Exact method yields optimal solution but spends more time when the number of cities is increasing and approximate method yields near optimal solution even optimal but spends less time than exact methods. The task in this study is to identify and formulate each algorithm for each method, then to run each algorithm with the same input and to get the research output: total distance, and the last to compare both methods: advantage and limitation.  Methods used are Brute Force (BF) and Branch and Bound (B&B) algorithms which are categorized as exact methods are compared with Artificial Bee Colony (ABC), Tabu Search (TS) and Simulated Annealing (SA) algorithms which are categorized as approximate methods or known as a heuristics method. These three approximate methods are chosen because they are effective algorithms, easy to implement and provide good solutions for combinatorial optimization problems. Exact and approximate algorithms are tested in several sizes of city tour problems: 6, 9, 10, 16, 17, 25, 42, and 58 cities. 17, 42 and 58 cities are derived from tsplib: a library of sample instances for tsp; and others are taken from big cities in Java (West, Central, East) island. All of the algorithms are run by MATLAB program. The results show that exact method is better in time performance for problem size less than 25 cities and both exact and approximate methods yield optimal solution. For problem sizes that have more than 25 cities, approximate method – Artificial Bee Colony (ABC) yields better time which is approximately 37% less than exact and deviates 0.0197% for distance from exact method. The conclusion is to apply exact method for problem size that is less than 25 cities and approximate method for problem size that is more than 25 cities. The gap of time will be increasing between two methods when sample size becomes larger.

AbSTrAKJarak tempuh memegang peranan penting dalam produktivitas pergudangan. Salah satu cara untuk mengetahui jarak tempuh adalah dengan menggunakan metode analitik, estimasi jarak tempuh rata -rata. Pada penelitian di PT GMS ini, strategi Pada pergudangan, permasalahan utama pada tata letak gudang adalah mendapatkan tata letak

Metaheuristic algorithm is a state of the art optimization method which suitable for solving large and complex problem. Single solution technique – Smetaheuristic is one of metaheuristic algorithm that search near optimal solution and known as exploitation based. The research conducted to seek a better solution for deliverying goods to 29 destinations by comparing two well known optimization methods that can produce the shortest distance: Simulated Annealing (SA) and Tabu Search (TS). The result shows that TS – 107 KM has a shorter distance than SA – 119 KM. Exploration based method should be conducted for next research to produce information in which one is a better method