scholarly journals The Study of Depot Position Effect on Travel Distance in Order Picking Problem

2021 ◽  
Vol 12 (2) ◽  
pp. 198-189
Author(s):  
Agung Chandra ◽  
Christine Natalia
Keyword(s):  
Traveling Salesman Problem ◽  
Branch And Bound ◽  
Position Effect ◽  
Order Picking ◽  
Travel Distance ◽  
Traveling Salesman ◽  
Approximate Methods ◽  
Exact Approach ◽  
Total Distance ◽  
The Difference

Research of travel distance on single - depot position in warehouse is tremendous. This study focuses more on the effect of two-depot position on travel distance in order picking problem (OPP) by using the concept of traveling salesman problem (TSP) and exact method – Branch and Bound (B\&B) algorithm. The total distance of one-depot position is shorter than two-depot position for single and double block warehouses and the difference is less than 5%. The total distance is also compared with approximate methods – SA and TS which show that the differences are less than 5%. The sequence of location visit for one depot and two depot is similar about two third from the total location visits. For order picking problem that has more than 25 location visits, one need to consider to apply approximate approach to get the solution faster even the difference will be higher from exact approach when the number of location visit or aisle increases.

Implementing Parallel Branch-and-Bound with Extended Sisal 2.0

1998 ◽  
Vol 08 (01) ◽  
pp. 41-50
Author(s):  
Yung-Syau Chen ◽  
Jean-Luc Gaudiot
Keyword(s):  
Traveling Salesman Problem ◽  
Branch And Bound ◽  
Optimization Technique ◽  
Traveling Salesman ◽  
Functional Language ◽  
Global Variables ◽  
Parallel Version ◽  
Parallel Branch And Bound ◽  
Hard Problems ◽  
The Traveling Salesman Problem

Parallel branch-and-bound is an optimization technique which renders more efficient the solution of some hard problems such as the puzzle of colored blocks and the traveling-salesman problem. In a functional language such as Sisal 2.0, it is difficult for the programmer to describe a parallel version of this technique due to the lack of imperative features in the language. In this paper, we propose a version of Sisal 2.0 extended with user-declared mutable global variables in order to enable Sisal programmers to apply the parallel branch-and-bound technique. In a simple example (the puzzle of colored blocks), we show that this approach yields better performance than either conventional functional programs or imperative programs. It is easy to see that the same strategy can be used to solve a number of hard problems such as the traveling-salesman problem.

scholarly journals OPTIMIZATION OF GOODS DISTRIBUTION ROUTE ASSISTED BY GOOGLE MAP WITH CHEAPEST INSERTION HEURISTIC ALGORITHM (CIH)

SINERGI ◽  
2018 ◽  
Vol 22 (2) ◽  
pp. 132
Author(s):  
L. Virginayoga Hignasari ◽  
Eka Diana Mahira
Keyword(s):  
Minimum Distance ◽  
Traveling Salesman ◽  
Optimal Distribution ◽  
Total Distance ◽  
Completion Problem ◽  
Shortest Route ◽  
The Difference ◽  
The Traveling Salesman Problem ◽  
The Relationship ◽  
Insertion Heuristic

In the distribution of goods, the efficiency of goods delivery one of which was determined by the path that passed to deliver the goods. The problem of choosing the shortest route was known as the Traveling Salesman Problem (TSP). To solve the problem of choosing the shortest route in the distribution of goods, the algorithm to be used was Cheapest Insertion Heuristic (CIH). This study aims to determine the minimum distance traveled by using the CIH algorithm.  Researchers determine the route and distance of each place visited by using google map. The concept in the CIH algorithm was to insert an unexpired city with an additional minimum distance until all cities are passed to get the solution of the problem. The step completion problem with CIH algorithm was: 1) search, 2) making sub tour; 3) change the direction of the relationship, 4) repeat the steps so that all places are included in the sub tour. Theoretically, the total distance calculated using the CIH algorithm is 20.2 km, while the total distance calculated previously traveled with the ordered route is 25.2 km. There was a difference of 5 km with the application of CIH algorithm. The difference between the distance certainly has an impact on the optimal distribution of goods to the destination. Therefore, CIH algorithm application can provide a solution for determining the shortest route from the distribution of goods delivery.

scholarly journals PERFORMANCE ANALYSIS OF OPTIMIZATION METHODS FOR SOLVING TRAVELING SALESMAN PROBLEM

2021 ◽  
pp. 69-75
Author(s):  
Chandra Agung ◽  
Natalia Christine
Keyword(s):  
Approximate Method ◽  
Traveling Salesman Problem ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Traveling Salesman ◽  
Exact Method ◽  
Problem Size ◽  
Exact Methods ◽  
Approximate Methods ◽  
Bee Colony

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.

scholarly journals Optimalisasi Rute Distribusi Produk Menggunakan Metode Traveling Salesman Problem

2018 ◽  
Vol 16 (1) ◽  
pp. 15
Author(s):  
Karina Auliasari ◽  
Mariza Kertaningtyas ◽  
Diah Wilis Lestarining Basuki
Keyword(s):  
Traveling Salesman Problem ◽  
Branch And Bound ◽  
Traveling Salesman

Permasalahan yang sering terjadi dalam proses pendistribusian produk pada perusahaan ini adalah belum optimalnya rute pendistribusian produk sehingga seringkali terjadi perubahan rute dan penjadwalan ulang pengiriman produk. Hal ini terjadi karena pihak manajemen pemasaran belum mengaplikasikan suatu metode optimalisasi dan belum adanya dukunga terkomputerisasi untuk menghasilkan informasi yang mendukung dalam pengambilan keputusan rute distribusi. Tujuan diterapkannya metode traveling salesman problem (TSP) adalah memberikan pilihan solusi rute distribusi yang dapat meminimalisir keterlambatan pengiriman barang dan mengoptimalkan sarana transportasi, sumber daya manusia, waktu dan biaya yang dimiliki untuk mengirimkan produk. Berdasarkan hasil uji performa penerapan metode TSP menunjukkan bahwa menggunakan parameter jarak dan waktu tempuh maka dapat dihasilkan pilihan dua rute pengiriman yang berbeda. Pilihan dua rute yang dihasilkan dari komputerisasi menggunakan metode TSP dengan teknik branch and bound dilengkapi dengan penyajian pohon keputusan dari titik awal hingga titik tujuan yang pada akhirnya membentuk rute yang optimal.

Algorithms based on Branch and Bound for the Flying Sidekick Traveling Salesman Problem

Omega ◽  
2021 ◽  
pp. 102493
Author(s):  
Mauro Dell’Amico ◽  
Roberto Montemanni ◽  
Stefano Novellani
Keyword(s):  
Traveling Salesman Problem ◽  
Branch And Bound ◽  
Traveling Salesman

scholarly journals OPTIMASI RUTE DISTRIBUSI PRODUK NESTLE MENGGUNAKAN METODE BRANCH AND BOUND DAN TWO-WAY EXCHANGE IMPROVEMENT HEURISTIC (STUDI KASUS : PT. PARIS JAYA MANDIRI – AMBON)

2021 ◽  
Vol 1 ◽  
pp. 156-162
Author(s):  
Daniel B Paillin ◽  
Johan M Tupan
Keyword(s):  
Traveling Salesman Problem ◽  
Branch And Bound ◽  
Combinatorial Problem ◽  
Traveling Salesman

Abstrak Traveling Salesman Problem (TSP) merupakan permasalahan optimasi pencarian rute terpendek dari satu kota ke n-kota lain tepat satu kali dan akan kembali ke titik awal keberangkatan. TSP dikategorikan sebagai hard combinatorial problem sehingga banyak teknik maupun aproksimasi yang dikembangkan untuk pemecahannya. Penelitian ini bertujuan untuk membandingkan teknik branch and bound dengan two-way exchange improvement dalam pemecahan Traveling Salesmen Problem (TSP) didasarkan pada jarak tempuh terkecil dan total waktu tempuh terkecil kendaraan. Penelitian ini diaplikasikan pada kasus nyata permasalahan penentuan rute kendaraan untuk pengiriman produk nestle dari PT. Paris Jaya Mandiri di kota Ambon. Hasil penelitian menunjukan bahwa teknik Two-Way Exchange Improvement memberikan hasil terbaik dibandingkan dengan Branch and Bound, dengan persentase penghematan jarak sebesar 18.09% dan penghematan total waktu sebesar 7.99% dari rute regular perusahaan.

scholarly journals On the Classical Version of the Branch and Bound Method

2021 ◽  
pp. 21-44
Author(s):  
Boris Melnikov ◽  
◽  
Elena Melnikova ◽  
Keyword(s):  
Traveling Salesman Problem ◽  
Branch And Bound ◽  
Discrete Optimization ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Branch And Bound Method ◽  
Main Subject ◽  
Classical Version ◽  
The Traveling Salesman Problem ◽  
Discrete Optimization Problems

In the computer literature, a lot of problems are described that can be called discrete optimization problems: from encrypting information on the Internet (including creating programs for digital cryptocurrencies) before searching for “interests” groups in social networks. Often, these problems are very difficult to solve on a computer, hence they are called “intractable”. More precisely, the possible approaches to quickly solving these problems are difficult to solve (to describe algorithms, to program); the brute force solution, as a rule, is programmed simply, but the corresponding program works much slower. Almost every one of these intractable problems can be called a mathematical model. At the same time, both the model itself and the algorithms designed to solve it are often created for one subject area, but they can also be used in many other areas. An example of such a model is the traveling salesman problem. The peculiarity of the problem is that, given the relative simplicity of its formulation, finding the optimal solution (the optimal route). This problem is very difficult and belongs to the so-called class of NP-complete problems. Moreover, according to the existing classification, the traveling salesman problem is an example of an optimization problem that is an example of the most complex subclass of this class. However, the main subject of the paper is not the problem, but the method of its soluti- on, i.e. the branch and bound method. It consists of several related heuristics, and in the monographs, such a multi-heuristic branch and bound method was apparently not previously noted: the developers of algorithms and programs should have understood this themselves. At the same time, the method itself can be applied (with minor changes) to many other discrete optimization problems. So, the classical version of branch and bound method is the main subject of this paper, but also important is the second subject, i.e. the traveling salesman problem, also in the classical formulation. The paper deals with the application of the branch and bound method in solving the traveling salesman problem, and about this application, we can also use the word “classical”. However, in addition to the classic version of this implementation, we consider some new heuristics, related to the need to develop real-time algorithms.

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