Multi-Objective for a Partial Flexible Open
Shop Scheduling Problem using Hybrid Based
Particle Swarm Algorithm and Ant Colony
Optimization

N. Jananeeswari; Dr.S.Jayakumar; Dr.M.Nagamani

doi:https://doi.org/10.14445/22315373/IJMTT-V44P520

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 44 | Number 2 | Year 2017 | Article Id. IJMTT-V44P520 | DOI : https://doi.org/10.14445/22315373/IJMTT-V44P520

Multi-Objective for a Partial Flexible Open Shop Scheduling Problem using Hybrid Based Particle Swarm Algorithm and Ant Colony Optimization

N. Jananeeswari, Dr.S.Jayakumar, Dr.M.Nagamani

Citation :

N. Jananeeswari, Dr.S.Jayakumar, Dr.M.Nagamani, "Multi-Objective for a Partial Flexible Open Shop Scheduling Problem using Hybrid Based Particle Swarm Algorithm and Ant Colony Optimization," International Journal of Mathematics Trends and Technology (IJMTT), vol. 44, no. 2, pp. 100-107, 2017. Crossref, https://doi.org/10.14445/22315373/IJMTT-V44P520

Abstract

Objectives: Using the multi-objective method is to solve the partial flexible open-shop scheduling problem (PFOSP), this paper optimizes the three objectives of minimizing the makespan, the maximum workload and the total workload of machine, Methods and Statistical Analysis: analyzes the relations between the three optimization objectives in detail, and decides to Findings: minimize the makespan and the maximum workload during the process route selection and to minimize the total workload of machine processing time during the process scheduling. According to the characteristics of multi-objective optimization, the author redesigns the update mode and state transition probabilistic formula for local meta-heuristic information of ant in the optimized ant colony algorithm. Application /Improvements: The simulation experiment proves the based effectiveness of the mixed optimization algorithm of ant colony algorithm and particle swarm algorithm.

Keywords

multi-objective optimization, partial flexible open-shop scheduling, ant colony algorithm, particle swarm algorithm, process scheduling.

References

[1] Ahmadi M.H., Hosseinzade H., Sayyaadi H., Mohammadi A.H., Kimiaghalam F. (2013). Application of the multi-objective optimization method for designing a powered stirling heat engine: design with maximized power, thermal efficiency and minimized pressure loss. Renewable Energy, 60(4), 313-322.
[2] Huang H.Z., Gu Y.K., Du X. (2006). An interactive fuzzy multi-objective optimization method for engineering design. Engineering Applications of Artificial Intelligence, 19(5), 451-460.
[3] Kuroda K., Magori H., Ichimura T., Yokoyama R. (2015). A hybrid multi-objective optimization method considering optimization problems in power distribution systems. Journal of Modern Power Systems and Clean Energy, 3(1), 41-50.
[4] Kesavan E., Gowthaman N., Tharani S., Manoharan S., Arunkumar E. (2016). Design and Implementation of Internal Model Control and Particle Swarm Optimization Based PID for Heat Exchanger System, International Journal of Heat and Technology, 34(3): 386- 390.
[5] Laukkanen T., Tveit T.M., Ojalehto V., Miettinen K., Fogelholm C.J. (2012). Bilevel heat exchanger network synthesis with an interactive multi-objective optimization method. Applied Thermal Engineering, 48(26), 301–316.
[6] Liu C., Peng W., Shi H. (2010). The study of scheduling rules for spraying and drying processes scheduling in painting workshop, 2, 605-608.
[7] Marler R.T., Arora J.S. (2004). Survey of multi-objective optimization methods for engineering. Structural & Multidisciplinary Optimization, 26(6), 369-395.
[8] Maringanti C., Chaubey I., Arabi M., Engel B. (2011). Application of a multi-objective optimization method to provide least cost alternatives for nps pollution control. Environmental Management, 48(3), 448-461.
[9] Nicolaou C. A., Brown N. (2013). Multi-objective optimization methods in drug design. Drug Discovery Today Technologies, 10(3), 427-35.
[10] Nicolaou C.A., Kannas C.C. (2011). Molecular library design using multi-objective optimization methods. Methods in Molecular Biology, 685(685), 53-69.
[11] Nicolaou C.A., Kannas C., Loizidou E. (2012). Multi-objective optimization methods in de novo drug design. Mini Reviews in Medicinal Chemistry, 12(10), 979-987(9).
[12] Rafiee S.E., Sadeghiazad M.M. (2016). Three-Dimensional CFD Simulation of Fluid Flow inside a Vortex Tube on Basis of an Experimental Model- The Optimization of Vortex Chamber Radius, International Journal of Heat and Technology, 34(23): 236-244.
[13] Rupp B., Pauli D., Feller S., Skyttä M. (2010). Workshop scheduling in the mro context, 1281(1), 2000-2003.
[14] Sa´nchez-Caballero S., Boronat-Vitoria T., Marti´nez-Sanz A.V., Colomer-Romero V., Sa´nchez-Caballero S., Boronat-Vitoria T., (2009). Mechanical workshop scheduling using genetic algorithms, 1181(1), 775-785.
[15] Sedenka V., Raida Z. (2010). Critical comparison of multi-objective optimization methods: genetic algorithms versus swarm intelligence. Radio engineering, 19(3), 369-377.
[16] Seyed E.R., Sadeghiazad M.M. (2016). Heat and Mass Transfer Between Cold and Hot Vortex Cores inside Ranque-Hilsch Vortex Tube-Optimization of Hot Tube Length, International Journal of Heat and Technology, 34(1): 31-38.
[17] Weglarz J., Jozefowska J. (1998). Fifth international workshop on project management and scheduling. European Journal of Operational Research, 107(2), 247-249.
[18] Xun J., Ning B., Li K.P. (2008). Multi-objective optimization method for the auto system using cellular automata. Computers in Railways XI, 173-182.
[19] Zhang R., Xie W.M., Yu H.Q., Li W.W. (2014). Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method. Bioresource Technology, 157(157), 161-165.
[20] ZhangY., Squillante M. S., Sivasubramaniam A., Sahoo R. K. (2004). Performance Implications of Failures in Large-Scale Cluster Scheduling. Job Scheduling Strategies for Parallel Processing, International Workshop, 3277, 233-252.
[21] Zheng F., Zecchin A. (2014). An efficient decomposition and dual-stage multi-objective optimization method for water distribution systems with multiple supply sources. Environmental Modelling &Software, 55(C), 143-154