Warehouse Layout Method Based on Ant Colony and Backtracking Algorithm

Warehouse is one of the important aspects of a company. Therefore, it is necessary to improve Warehouse Management System (WMS) to have a simple function that can determine the layout of the storage goods. In this paper we propose an improved warehouse layout method based on ant colony algorithm and backtracking algorithm. The method works on two steps. First, it generates a solutions parameter tree from backtracking algorithm. Then second, it deducts the solutions parameter by using a combination of ant colony algorithm and backtracking algorithm. This method was tested by measuring the time needed to build the tree and to fill up the space using two scenarios. The method needs 0.294 to 33.15 seconds to construct the tree and 3.23 seconds (best case) to 61.41 minutes (worst case) to fill up the warehouse. This method is proved to be an attractive alternative solution for warehouse layout system.Comment: 5 pages, published in proceeding of the 14th IAPR International Conference on Quality in Research (QIR

Algoritma Emperor Penguin pada Efisiensi Pengiriman Produk UMKM dengan Konsep Pembagian Ongkos Kirim

Di Era Pandemi saat ini, perkembangan ekonomi atau bisnis Usaha Mikro, Kecil, dan Menengah (UMKM) semakin menjamur di masyarakat. Perkembangan UMKM ini tidak hanya berhenti pada jumlah saja akan tetapi juga pada sistem pemesanan UMKM tersebut. Saat ini banyak UMKM yang menerapkan konsep Pre-Order (PO) di dalam pemesanannya dimana mayoritas UMKM tersebut merupakan UMKM yang memasarkan produknya melalui media sosial atau chating. Munculnya sistem baru ini memunculkan juga beberapa permasalahan bagi UMKM, salah satu diantaranya adalah besarnya biaya pengiriman apabila sebuah produk dikirim secara khusus ke seorang pembeli. Hal ini mungkin tidak terlihat memberatkan bagi UMKM akan tetapi permasalahan ini memberatkan pelanggan dan semakin mahal biaya yang harus dikeluarkan pelanggan maka akan semakin kecil kemungkinan pelanggan membeli produk di UMKM tersebut. Penelitian ini berfokus pada dua hal, yang pertama adalah menyediakan marketplace bagi UMKM Pre-Order yang dapat mengatasi permasalahan biaya pengiriman dan kedua adalah dampak Algoritma Emperor Penguin pada pencarian rute pengiriman. Marketplace UMKM Pre-Order yang dikembangkan akan menerapkan teknologi fluter dan berbasiskan mobile apps. Hal ini dikarenakan semakin besarnya pengaruh mobile apps di kalangan masyarakat dibandingkan web apps atau desktop apps. Mobile Apps akan secara otomatis melakukan pencarian rute pengiriman untuk seluruh pre-order yang terjadi pada hari tertentu dan melakukan assign kepada driver yang bertugas dimana rute yang dicari bukan hanya berdasarkan jarak melainkan juga berdasarkan biaya pengiriman yang dikeluarkan oleh keseluruhan pembeli pada 1x pengiriman. Algoritma Emperor Penguin akan berusaha mencari rute pengiriman yang terdekat dan biaya yang dikeluarkan pembeli yang terkecil. Setelah melalui berbagai ujicoba, dapat disimpulkan bahwa 94.7% UMKM yang didukung oleh penelitian ini puas dan merasa terbantu, 97.4% pelanggan merasa terbantu dengan sistem pembagian ongkos kirim, dan Algoritma Emperor Penguin bekerja dengan baik dan dapat menghasilkan rute optimal dengan ongkos terkecil

An open close multiple travelling salesman problem with single depot

This paper introduces a novel practical variant, namely an open close multiple travelling salesmen problem with single depot (OCMTSP) that concerns the generalization of classical travelling salesman problem (TSP). In OCMTSP, the overall salesmen can be categorized into internal/permanent and external/outsourcing ones, where all the salesmen are positioned at the depot city. The primary objective of this problem is to design the optimal route such that all salesmen start from the depot/base city, and then visit a given set of cities. Each city is to be visited precisely once by exactly one salesman, and only the internal salesmen have to return to the depot city whereas the external ones need not return. To find optimal solutions, an exact pattern recognition technique based Lexi-search algorithm (LSA) is developed which has been subjected in Matlab. Comparative computational results of the LSA have been made with the existing methods for general multiple travelling salesman problem (MTSP). Further, to test the performance of LSA, computational experiments have been carried out on some benchmark as well as randomly generated test instances for OCMTSP, and results are reported. The overall computational results demonstrate that the proposed LSA is efficient in providing optimal and sub-optimal solutions within the considerable CPU times

Ant colonies using arc consistency techniques for the set partitioning problem

In this paper, we solve some benchmarks of Set Covering Problem and Equality Constrained Set Covering or Set Partitioning Problem. The resolution techniques used to solve them were Ant Colony Optimization algorithms and Hybridizations of Ant Colony Optimization with Constraint Programming techniques based on Arc Consistency. The concept of Arc Consistency plays an essential role in constraint satisfaction as a problem simplification operation and as a tree pruning technique during search through the detection of local inconsistencies with the uninstantiated variables. In the proposed hybrid algorithms, we explore the addition of this mechanism in the construction phase of the ants so they can generate only feasible partial solutions. Computational results are presented showing the advantages to use this kind of additional mechanisms to Ant Colony Optimization in strongly constrained problems where pure Ant Algorithms are not successful.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods