Industrial applications of the ant colony optimization algorithm

The ant colony optimization (ACO) algorithm is a fast suboptimal meta-heuristic based on the behavior of a set of ants that communicate through the deposit of pheromone. It involves a node choice probability which is a function of pheromone strength and inter-node distance to construct a path through a node-arc graph. The algorithm allows fast near optimal solutions to be found and is useful in industrial environments where computational resources and time are limited. A hybridization using iterated local search (ILS) is made in this work to the existing heuristic to refine the optimality of the solution. Applications of the ACO algorithm also involve numerous traveling salesperson problem (TSP) instances and benchmark job shop scheduling problems (JSSPs), where the latter employs a simplified ant graph-construction model to minimize the number of edges for which pheromone update should occur, so as to reduce the spatial complexity in problem computation.

[1]  Koichi Nara,et al.  Maintenance scheduling by using simulated annealing method (for power plants) , 1991 .

[2]  Manoj Kumar Tiwari,et al.  Scheduling of flexible manufacturing systems: An ant colony optimization approach , 2003 .

[3]  Jacek Blazewicz,et al.  Scheduling in Computer and Manufacturing Systems , 1990 .

[4]  G. Thompson,et al.  Algorithms for Solving Production-Scheduling Problems , 1960 .

[5]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[6]  Michael Sampels,et al.  Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[7]  Takeshi Yamada,et al.  Job-Shop Scheduling by Simulated Annealing Combined with Deterministic Local Search , 1996 .

[8]  Christian Blum,et al.  An Ant Colony Optimization Algorithm for Shop Scheduling Problems , 2004, J. Math. Model. Algorithms.

[9]  Christopher C. Skiscim,et al.  Optimization by simulated annealing: A preliminary computational study for the TSP , 1983, WSC '83.

[10]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[11]  Christian Blum,et al.  Beam-ACO - hybridizing ant colony optimization with beam search: an application to open shop scheduling , 2005, Comput. Oper. Res..

[12]  Marco Dorigo,et al.  The hyper-cube framework for ant colony optimization , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Thomas Stützle,et al.  Improvements on the Ant-System: Introducing the MAX-MIN Ant System , 1997, ICANNGA.

[14]  Marco Dorigo,et al.  Ant system for Job-shop Scheduling , 1994 .

[15]  X. Chao,et al.  Operations scheduling with applications in manufacturing and services , 1999 .

[16]  William J. Cook,et al.  A Computational Study of the Job-Shop Scheduling Problem , 1991, INFORMS Journal on Computing.

[17]  B. Bullnheimer,et al.  A NEW RANK BASED VERSION OF THE ANT SYSTEM: A COMPUTATIONAL STUDY , 1997 .

[18]  R. Haupt,et al.  A survey of priority rule-based scheduling , 1989 .

[19]  J. P. Kelly,et al.  Meta-heuristics : theory & applications , 1996 .

[20]  Erwin Pesch,et al.  Learning in Automated Manufacturing: A Local Search Approach , 1994 .

[21]  Klaus H. Ecker,et al.  Scheduling Computer and Manufacturing Processes , 2001 .

[22]  Holger H. Hoos,et al.  An Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem , 2002, Ant Algorithms.

[23]  Jacek Blazewicz,et al.  The job shop scheduling problem: Conventional and new solution techniques , 1996 .

[24]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .