Improvement of the Optimization of an Order Picking Model Associated With the Components of a Classic Volkswagen Beetle Using an Ant Colony Approach

2019 ◽  
pp. 189-210
Author(s):  
Jared Olmos ◽  
Rogelio Florencia ◽  
Francisco López-Ramos ◽  
Karla Olmos-Sánchez
Keyword(s):  
Mathematical Modeling ◽  
Ant Colony Optimization ◽  
Subject Matter ◽  
Operational Research ◽  
Order Picking ◽  
Ant Colony ◽  
Operational Costs ◽  
Auto Parts ◽  
Warehouse Operations

Warehouse operations, specifically order picking process, are receiving close attention of researches due to the need of companies in minimizing operational costs. This chapter explains an ant colony optimization (ACO) approach to improve the order picking process in an auto parts store associated with the components of a classic Volkswagen Beetle car. Order picking represents the most time-consuming task in the warehouse operational expenses and, according to the scientific literature, is becoming a subject matter in operational research. It implements a low-level, picker-to-part order picking using persons as pickers with multiple picks per route. The context of the case study is a discrete picking where users' orders are independent. The authors use mathematical modeling to improve de ACO metaheuristic approach to minimize the order-picking cost.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

scholarly journals A hybrid technique for single-source shortest path-based on A* algorithm and ant colony optimization

2020 ◽  
Vol 9 (2) ◽  
pp. 356
Author(s):  
Sameer Alani ◽  
Atheer Baseel ◽  
Mustafa Maad Hamdi ◽  
Sami Abduljabbar Rashid
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Shortest Paths ◽  
Ant Colony ◽  
Fast Time ◽  
Hybrid Technique ◽  
Single Source ◽  
A Algorithm ◽  
A New Technique

<span lang="EN-US">In the single-source shortest path (SSSP) problem, the shortest paths from a source vertex v to all other vertices in a graph should be executed in the best way. A common algorithm to solve the (SSSP) is the A* and Ant colony optimization (ACO). However, the traditional A* is fast but not accurate because it doesn’t calculate all node's distance of the graph. Moreover, it is slow in path computation. In this paper, we propose a new technique that consists of a hybridizing of A* algorithm and ant colony optimization (ACO). This solution depends on applying the optimization on the best path. For justification, the proposed algorithm has been applied to the parking system as a case study to validate the proposed algorithm performance. First, A*algorithm generates the shortest path in fast time processing. ACO will optimize this path and output the best path. The result showed that the proposed solution provides an average decreasing time performance is 13.5%.</span>

scholarly journals A modified Ant Colony Optimization algorithm to solve a dynamic traveling salesman problem: A case study with drones for wildlife surveillance

2018 ◽  
Vol 6 (3) ◽  
pp. 368-386 ◽  
Author(s):  
Sudipta Chowdhury ◽  
Mohammad Marufuzzaman ◽  
Huseyin Tunc ◽  
Linkan Bian ◽  
William Bullington
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Real Life ◽  
Dynamic Environments ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Ant Colony Optimization Algorithm ◽  
Pheromone Trails

Abstract This study presents a novel Ant Colony Optimization (ACO) framework to solve a dynamic traveling salesman problem. To maintain diversity via transferring knowledge to the pheromone trails from previous environments, Adaptive Large Neighborhood Search (ALNS) based immigrant schemes have been developed and compared with existing ACO-based immigrant schemes available in the literature. Numerical results indicate that the proposed immigrant schemes can handle dynamic environments efficiently compared to other immigrant-based ACOs. Finally, a real life case study for wildlife surveillance (specifically, deer) by drones has been developed and solved using the proposed algorithm. Results indicate that the drone service capabilities can be significantly impacted when the dynamicity of deer are taken into consideration. Highlights Proposed a novel ACO-ALNS based metaheuristic. Four variants of the proposed metaheuristic is developed to investigate the efficiency of each of them. A real life case study mirroring the behavior of DTSP is developed.

A multi-objective disassembly planning approach with ant colony optimization algorithm

2008 ◽  
Vol 222 (11) ◽  
pp. 1465-1474 ◽  
Author(s):  
C Lu ◽  
H Z Huang ◽  
J Y H Fuh ◽  
Y S Wong
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Uniform Design ◽  
Pareto Frontier ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Multi Objective ◽  
Planning Approach ◽  
Disassembly Planning

This paper proposes a multi-objective disassembly planning approach with an ant colony optimization algorithm. The mechanism of ant colony optimization in disassembly planning is discussed, and the objectives to be optimized in disassembly planning are analysed. In order to allow a more effective search for feasible non-dominated solutions, a multi-objective searching algorithm with uniform design is investigated to guide the ants searching the routes along the uniformly scattered directions towards the Pareto frontier; based on the above searching algorithm, an ant colony optimization algorithm for disassembly planning is developed. The results of a case study are given to verify the proposed approach.

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