scholarly journals Komparasi Algoritma Cheapest Insertion Heuristic (CIH) Dan Greedy Dalam Optimasi Rute Pendistribusian Barang

2020 ◽  
Vol 2 (2) ◽  
pp. 31-39
Author(s):  
L.Virginayoga Hignasari
Keyword(s):  
Qualitative Research ◽  
Greedy Algorithm ◽  
Greedy Algorithms ◽  
Advantages And Disadvantages ◽  
Shortest Route ◽  
The Difference ◽  
The Greedy Algorithm ◽  
Insertion Heuristic ◽  
Selection Of

This study was aimed to compare algorithms that can effectively provide better solutions related to the problem of determining the shortest route in the distribution of goods. This research was a qualitative research. The object of research was the route of shipping goods of a business that is engaged in printing and convection. The algorithms compared in this study were Cheapest Insertion Heuristic (CIH) and Greedy algorithms. Both algorithms have advantages and disadvantages in finding the shortest route. From the results of the analysis using these two algorithms, the Cheapest Insertion Heuristic (CIH) and Greedy algorithm can provide almost the same optimization results. The difference was only the selection of the journey. The strength of the Greedy algorithm was that the calculation steps are simpler than the Cheapest Insertion Heuristic (CIH) algorithm. While the disadvantage of the Greedy algorithm was that it is inappropriate to find the shortest route with a relatively large number of places visited. The advantage of the Cheapest Insertion Heuristic (CIH) algorithm was that this algorithm is still stable, used for the relatively large number of places visited. While the lack of Cheapest Insertion Heuristic (CIH) algorithm was a complicated principle of calculation and was relatively longer than the Greedy algorithm.

scholarly journals Matroids And Greedy Algorithms. A Deeper Justification of Using Greedy Approach To Find A Maximal set of a Matroid

2017 ◽  
Vol 16 (2) ◽  
pp. 48
Author(s):  
Syed Aqib Haider
Keyword(s):  
Greedy Algorithm ◽  
Local Level ◽  
Greedy Algorithms ◽  
Optimal Solution ◽  
Independent Set ◽  
Computation Time ◽  
Optimal Choice ◽  
Greedy Approach ◽  
The Greedy Algorithm ◽  
Selection Of

<p>Greedy algorithms are used in solving a diverse set of problems in small computation time. However, for solving problems using greedy approach, it must be proved that the greedy strategy applies. The greedy approach relies on selection of optimal choice at a local level reducing the problem to a single sub problem, which actually leads to a globally optimal solution. Finding a maximal set from the independent set of a matroid M(S, I) also uses greedy approach and justification is also provided in standard literature (e.g. Introduction to Algorithms by Cormen et .al.). However, the justification does not clearly explain the equivalence of using greedy algorithm and contraction of M by the selected element. This paper thus attempts to give a lucid explanation of the fact that the greedy algorithm is equivalent to reducing the Matroid into its contraction by selected element. This approach also provides motivation for research on the selection of the test used in algorithm which might lead to smaller computation time of the algorithm.</p>

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 Research on Algorithms of Spatial-Temporal Multi-Channel Allocation Based on the Greedy Algorithm for Wireless Mesh Network

2014 ◽  
Vol 8 (1) ◽  
pp. 690-694
Author(s):  
Yiyong Wang ◽  
David Baker
Keyword(s):  
Wireless Network ◽  
Greedy Algorithm ◽  
Wireless Mesh Network ◽  
Channel Allocation ◽  
Mobile Internet ◽  
Utilization Efficiency ◽  
Mesh Network ◽  
Advantages And Disadvantages ◽  
Wireless Mesh ◽  
The Greedy Algorithm

With the development of mobile Internet and wireless network as well as the rapid increasing number of users, people’s demand for broadband is increasing sharply, making further capacity expansion of the network the major problem for researchers. Wireless Mesh network has the advantages of high network coverage and flexible networking, especially multi-hop wireless network, its important networking format takes full use of the feature of orthogonal channel to raise the throughput of wireless network and utilization efficiency of broadband dramatically and make the channel resources allocation more flexible. This paper points out the advantages and disadvantages of Mesh network by analyzing some relatively classic algorithms based on the research of Mesh network technology in order to increase utilization efficiency of orthogonal channel further. At the same time, it proposes Spatial-Temporal Multi-Channel Allocation Based on the Greedy Algorithm for Wireless Mesh Network which takes the channel allocated in previous slot time into consideration and allocates channels to different ports to reduce the switching time and other costs. Giving the direction of nodes is up stream, it requires well-functioning nodes near the gateway. Any network fault will influence other nodes near these gateways. This allocation realizes the communication between nodes on original channel nodes, increasing utilization efficiency significantly and reducing the burden of parent node. In the end, this paper makes a conclusion of the main work.

scholarly journals Hybrid Invasive Weed Optimization with Greedy Algorithm for an Energy and Deadline Aware Scheduling in Cloud Computing

2019 ◽  
Vol 8 (12) ◽  
pp. 1354-1359
Keyword(s):  
Cloud Computing ◽  
Greedy Algorithm ◽  
Greedy Algorithms ◽  
Population Based ◽  
Task Completion ◽  
Network Access ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Resource Pooling ◽  
The Greedy Algorithm

Cloud computing is being envisioned to be the computing paradigm of the next generation primarily for its advantages of on-demand services, risk transference, resource pooling that is independent of location and ubiquitous network access. Service quality is allocated using various resources in the scheduling process. The deadline refers to the time period from task submission until task completion. An algorithm that has good scheduling attempts at keeping the task executed inside the constraint of the deadline. The Genetic Algorithm (GA) is a common metaheuristic that is used often in literature for procuring solutions that are either optimal or near-optimal. The Invasive Weed Optimization (IWO) is an evolutionary algorithm that is population-based with certain interesting specifications like creations of offspring that are based on the levels of fitness of the parents which increases the size of the population and generates new population by making use of the best among parents and the best among off-springs. The Greedy Algorithms will construct an object that is globally best by means of continuously choosing the option that is locally the best. In this work, a hybrid GA with the Greedy Algorithm and a Hybrid IWO with the Greedy Algorithm that has been proposed for the energy and the deadline-aware scheduling in cloud computing.

Tourism Recommendation System in Bali Using Topsis and Greedy Algorithm Methods

2020 ◽  
Vol 8 (3) ◽  
pp. 277
Author(s):  
Muhammad Afif Ubaidillah ◽  
Ida Bagus Gede Dwidasmara
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Greedy Algorithm ◽  
Support System ◽  
Recommendation System ◽  
Ideal Solution ◽  
Tourist Attractions ◽  
Order Preference ◽  
The Greedy Algorithm ◽  
Selection Of

Tourism is the mainstay of the economy of the region of Bali and is an important sector in supporting the level of community welfare. The world of tourism is essentially an important symbol for Bali. Of the many tourists who come to Bali, of course not all tourists know all information about Bali, such as in terms of tourist locations, and tourist attractions closest to other tourist attractions. Therefore, the authors aim to create a recommendation system that can provide planning for the selection of tourist attractions according to the closest distance and the user's budget. This system is designed using the TOPSIS (Technique for Order Preference by Similarity To Ideal Solution) method and the Greedy Algorithm. The TOPSIS method is a SPK (Decision Making System) that will be used for the selection of tourist attractions that will help tourists to arrange vacation planning before going on a tour. While the Greedy Algorithm is used to find the closest or shortest distance between a tourist site and other tourist attractions, this algorithm will be able to determine which path will be taken first or called the local optimal path, so that all the paths are taken at the end of the trip and create a travel route shortest or called the global optimum so that it can also be the expected solution. From this it can be determined the value of the shortest travel route that starts from the location of the user's residence to the tourist attractions and to other nearby tourist attractions. The data used is data obtained from DISPARDA, namely tourist data. Research related to the selection of tourism object decisions based on the type of tourism, prices and facilities, and the results are able to provide recommendations for tourist attractions that meet these criteria. Tourism Selection Using Techniques For Order Preference By Similarity To Ideal Solution (Topsis) With Object Localization Visualization [4], Development of Decision Support System for Hotel Determination in Buleleng District Using Analytic Hierarchy Process (AHP) Method and Technique for Others Reference By Similarity To Ideal Solution (TOPSIS) [3], a Decision Support System for Determining Tourist Locations using the Top-sis Method [6]

scholarly journals Sufficient conditions for the optimality of the greedy algorithm in greedoids

2021 ◽  
Author(s):  
Dávid Szeszlér
Keyword(s):  
Greedy Algorithm ◽  
Greedy Algorithms ◽  
General Theorem ◽  
Sufficient Conditions ◽  
Theoretical Background ◽  
Theoretical Computer Science ◽  
Special Cases ◽  
Original Works ◽  
Optimum Solution ◽  
The Greedy Algorithm

AbstractGreedy algorithms are among the most elementary ones in theoretical computer science and understanding the conditions under which they yield an optimum solution is a widely studied problem. Greedoids were introduced by Korte and Lovász at the beginning of the 1980s as a generalization of matroids. One of the basic motivations of the notion was to extend the theoretical background behind greedy algorithms beyond the well-known results on matroids. Indeed, many well-known algorithms of a greedy nature that cannot be interpreted in a matroid-theoretical context are special cases of the greedy algorithm on greedoids. Although this algorithm turns out to be optimal in surprisingly many cases, no general theorem is known that explains this phenomenon in all these cases. Furthermore, certain claims regarding this question that were made in the original works of Korte and Lovász turned out to be false only most recently. The aim of this paper is to revisit and straighten out this question: we summarize recent progress and we also prove new results in this field. In particular, we generalize a result of Korte and Lovász and thus we obtain a sufficient condition for the optimality of the greedy algorithm that covers a much wider range of known applications than the original one.

scholarly journals PENJADWALAN MATAKULIAH MENGGUNAKAN ALGORITMA GREEDY (STUDI KASUS PENJADWALAN SEMESTER GANJIL 2017-2018 INFORMATIKA ITENAS)

2018 ◽  
Vol 4 (3) ◽  
Author(s):  
Yasmin Math Khader ◽  
Youllia Indrawaty Nurhasanah ◽  
Afriyanti Dwi Kartika
Keyword(s):  
College Student ◽  
Greedy Algorithm ◽  
Computational Algorithm ◽  
Greedy Algorithms ◽  
Computational Algorithms ◽  
Shortest Distance ◽  
College Subject ◽  
The Greedy Algorithm

Arranging college subject becomes one of the problem for the institute. Limited of class rooms, lecturer time that should be adjusted, many courses should be attended by college student cause arranging college subject should carefully arranged. Making manual schedule need more time and obstructing lectures. The purpose of this study is to implement greedy algorithms so that the results of scheduling lectures that have no clash, class capacity and number of students can adjust. Greedy algorithm is a computational algorithm to find the shortest distance, from the theory then diadposi into computational algorithms to find the lowest value in the combination of scheduling a class. By applying the greedy algorithm to the system, the system can generate class schedules without clashes, class capacity and number of students can adjust. The output of this system is the course schedule stored in the calendar.

scholarly journals Randomized Greedy Algorithms for Independent Sets and Matchings in Regular Graphs: Exact Results and Finite Girth Corrections

2009 ◽  
Vol 19 (1) ◽  
pp. 61-85 ◽  
Author(s):  
DAVID GAMARNIK ◽  
DAVID A. GOLDBERG
Keyword(s):  
Greedy Algorithm ◽  
Greedy Algorithms ◽  
Independent Set ◽  
Independent Sets ◽  
Simple Expression ◽  
Regular Graphs ◽  
Bounded Degree ◽  
Constant Degree ◽  
Image Position ◽  
The Greedy Algorithm

We derive new results for the performance of a simple greedy algorithm for finding large independent sets and matchings in constant-degree regular graphs. We show that forr-regular graphs withnnodes and girth at leastg, the algorithm finds an independent set of expected cardinalitywheref(r) is a function which we explicitly compute. A similar result is established for matchings. Our results imply improved bounds for the size of the largest independent set in these graphs, and provide the first results of this type for matchings. As an implication we show that the greedy algorithm returns a nearly perfect matching when both the degreerand girthgare large. Furthermore, we show that the cardinality of independent sets and matchings produced by the greedy algorithm inarbitrarybounded-degree graphs is concentrated around the mean. Finally, we analyse the performance of the greedy algorithm for the case of random i.i.d. weighted independent sets and matchings, and obtain a remarkably simple expression for the limiting expected values produced by the algorithm. In fact, all the other results are obtained as straightforward corollaries from the results for the weighted case.

scholarly journals Shortest These search Heading Attractions Lubukpakam Using Greedy Algorithm

2020 ◽  
Vol 2 (2) ◽  
pp. 245-249
Author(s):  
IRMA YOLANDA SITORUS ◽  
PENDA SUDARTO HASUGIAN
Keyword(s):  
Greedy Algorithm ◽  
Web Based ◽  
Tourist Attractions ◽  
Maximum Value ◽  
Shortest Route ◽  
Optimum Path ◽  
Different Types ◽  
The Many ◽  
Route Search ◽  
The Greedy Algorithm

In the case of this shortest route search actually has many different types of solutions for path searching, for example the greedy algorithm, dijkstra, floyd-warshall and bellman-ford but what is commonly used to solve this problem is the Greedy algorithm because this algorithm is an algorithm that uses a problem solving approach with look for a temporary maximum value at each step. Of all the tourist attractions such as the Fruit Garden, Deli Serdang Swimming Pool, Deli Serdang Museum, Deli Serdang Regency Government Square, Tengku Raja Muda Field, the writer will look for the shortest path to take the closest route because of the many paths that can be taken to get the optimum path as well as using google maps. This application is made web-based with PHP and MySQL scripts as a database manager so that it is enough to connect to the internet network that everyone can easily access.

Implementasi Algoritma Dijkstra Pada Game Pacman

CCIT Journal ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 170-176
Author(s):  
Anggit Dwi Hartanto ◽  
Aji Surya Mandala ◽  
Dimas Rio P.L. ◽  
Sidiq Aminudin ◽  
Andika Yudirianto
Keyword(s):  
Artificial Intelligence ◽  
Greedy Algorithm ◽  
Dijkstra Algorithm ◽  
Testing Phase ◽  
A Value ◽  
Route Problem ◽  
Starting Point ◽  
The Greedy Algorithm

Pacman is one of the labyrinth-shaped games where this game has used artificial intelligence, artificial intelligence is composed of several algorithms that are inserted in the program and Implementation of the dijkstra algorithm as a method of solving problems that is a minimum route problem on ghost pacman, where ghost plays a role chase player. The dijkstra algorithm uses a principle similar to the greedy algorithm where it starts from the first point and the next point is connected to get to the destination, how to compare numbers starting from the starting point and then see the next node if connected then matches one path with the path). From the results of the testing phase, it was found that the dijkstra algorithm is quite good at solving the minimum route solution to pursue the player, namely by getting a value of 13 according to manual calculations

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