An Ant Colony Optimization Algorithm For Job Shop Scheduling Problem

Abstract

The nature has inspired several metaheuristics, outstanding among these is Ant Colony Optimization(ACO), which have proved to be very effective and efficient in problems of high complexity (NP-hard) incombinatorial optimization. This paper describes the implementation of an ACO model algorithm known asElitist Ant System (EAS), applied to a combinatorial optimization problem called Job Shop SchedulingProblem (JSSP). We propose a method that seeks to reduce delays designating the operation immediatelyavailable, but considering the operations that lacklittle to be available and have a greater amount ofpheromone. The performance of the algorithm was evaluated for problems of JSSP reference, comparingthe quality of the solutions obtained regarding thebest known solution of the most effective methods.Thesolutions were of good quality and obtained with aremarkable efficiency by having to make a very lownumber of objective function evaluations

An Improved Ant Colony Optimized Tabu Search Algorithm for Makespan Improvement in Job Shop

“…Florez et al [4] described the elitist ant system (EAS) commonly known as an implementation of an act model algorithm which is applied to job shop scheduling problem. It reduces the delays so that the operations are immediately available.…”

Analysis of six degrees of separation in Facebook using Ant colony optimization

“…The ants taking the longer route naturally accumulate less amount of pheromone while the ants taking shortest route accumulates high while moving to and from food and nest. After a course of time every ant starts following the path which has higher pheromone concentration obviously which will be the shortest path [8].…”

“…The pheromone trails on each a ij edge must be updated and the amount of pheromone that deposits each ant at the edges depends on the total length of the path. [8] The movement of the ant can be guided by edges of the graph that has two types of associated information, η ij represents the heuristic information that measures the heuristics preference of moving from node i to node j, while touring the edge a ij . This information is not changed by the ants during the execution of the algorithm.…”

The Characteristics of Metaheuristic Method in Selection of Path Pairs on Multicriteria Ad Hoc Networks

“…e application of ACO is done in the traveling salesman problem by renewing and with the strategy of adaptive pheromone adjustment that is modified [1]. e problemsolving is done in job-shop scheduling with a method that tries to reduce the delay of appointment of process operation through the elitist ant system (EAS) [2] and in vehicle traffic system to guide vehicles so that it can reduce congestion [3]. Furthermore, it is applied to the conical tank system to optimize the parameter in controlling design [4], in a sensor network for node selection that requires smaller energy, reducing packet loss rate [5].…”