Multiple Objective
optimisation using Tabu Search
MOTS
MOTS developed by Dr Adil
Baykasoglu to solve complex multiple objective optimization problems. MOTS can
effectively find Pareto optimal solutions in multiple objective optimization and it is able to solve goal programming
models. MOTS has been applied to many diverse complex
optimization problems by the author and his colleagues. Some of the
applications and core MOTS papers are listed below. Upon request these papers
can be obtained from the author.
A TABOO SEARCH BASED
APPROACH TO FIND THE PARETO OPTIMAL SET IN MULTIPLE OBJECTIVE OPTIMISATION
Adil Baykasoglu et al
Taboo search is a stochastic optimisation
technique which works with a population of solutions to optimise
a given objective function. It is generally applied to single objective
optimisation problems. Taboo search has the potential for solving multiple
objective optimisation (MOO) problems, because it works with more than one
solution at a time, and this gives it the opportunity to evaluate multiple
objective functions simultaneously. In this paper, a taboo search based
algorithm is developed to find Pareto optimal solutions in multiple objective
optimisation problems. The developed algorithm has been tested with a number of
problems and compared with other techniques. Results obtained from this work
have proved that a taboo search based algorithm can find Pareto optimal
solutions in MOO effectively.
* The full paper is published in "Journal of
Engineering Optimization, 31(1999), 731-748".
SOLUTION OF GOAL PROGRAMMING
MODELS USING A BASIC TABOO SEARCH ALGORITHM
Adil Baykasoglu et al
Goal programming is a very powerful technique
for solving multiple objective optimisation problems. It has been successfully applied
to numerous diverse real life problems. In this paper a Taboo search based
method is developed to solve preemptive goal
programming problems. The method can easily be applied to any kind of preemptive goal programming problems.
* The full paper is published by "Journal of
Operational Research Society" (1999) 50, 960-973.
GOAL PROGRAMMING USING
MULTIPLE OBJECTIVE TABU SEARCH
Adil Baykasoglu
Goal programming (GP) is one of the most
commonly used mathematical programming tool to model
multiple objective optimization problems. There are numerous multiple objective
optimisation problems of various complexity modelled
using GP in the literature. One of the main difficulty
in GP is to solve their mathematical formulations optimally. Due to
difficulties imposed by the classical solution techniques there is a trend in
the literature to solve mathematical programming formulations including goal
programs using the modern heuristics optimization techniques, namely genetic
algorithms (GA), tabu search (TS) and simulated
annealing (SA). This paper uses the multiple objective tabu search
(MOTS) algorithm proposed previously by the author to solve GP models. For this
purpose, GP models are first converted to their classical multiple objective
optimization equivalent by using some simple
conversion procedures. Then the problem is solved using the MOTS algorithm.
Results obtained from the computational study presents that MOTS can be
considered as a promising candidate for solving GP models.
* The full paper is published by "Journal of
Operational Research Society" (2001) 52-12,
1359-1369.
SOME APPLICATIONS OF MOTS
MANUFACTURING SYSTEMS DESIGN APPLICATIONS
Baykasoglu, A., Gindy, N.N.Z.,
MOCACEF 1.0: Capability
based approach to form part-machine groups for cellular manufacturing
applications. Int. J. of Production Research, 38-5(2000), 1133-1161.
Saad, S. M., Baykasoglu, A., Nabil N.Z. Gindy,
An integrated framework for reconfiguration of cellular manufacturing systems
using virtual cells, Production Planning and Control,13-4 (2002), 381-393.
Baykasoglu,
A., The reconfiguration problem of manufacturing systems, Journal of
Polytechnic, 4-4(2001), 69-80.
Baykasoglu, A., Gindy, N.N.Z.,
Saad, S.M.,
A framework for the reconfiguration of cellular manufacturing systems. IMS-98, 2nd Int. Symposium on Intelligent
Manufacturing Systems, pp.565-574, 6-7 August
1998,
Baykasoglu, A., Multiple Objective Decision Support Framework for
Configuring, Loading and Reconfiguring Manufacturing Cells, PhD Thesis,
University of Nottingham, Department of Manufacturing Engineering and
Operations Management, England, April 1999.
MANUFACTURING SYSTEMS LOADING AND SCHEDULING
APPLICATIONS
Baykasoglu, A., Saad, S.M.,
Gindy, N., A loading approach for cellular
manufacturing systems, FAIM'1998: 8th
International Conference on Flexible Automation and Intelligent Manufacturing,
July 1-3 1998, Portland, Oregon, USA, pp. 215-226.
Baykasoglu, A., Gindy, N.N.Z.,
Loading flexible cell production systems: A tabu search based multiple
objective simulation optimisation approach, 15th International
Conference on Production Research, pp.1441-1444
(Vol-2), Editors: M.T. Hillery
and H.J. Lewis, Publisher: Gemini Int. Limited, 9-13, August-1999, University
of Limerick, Limerick, Ireland.
Saad, S.M., Baykasoglu, A., Gindy, N., A new integrated system for loading and
scheduling in cellular manufacturing, Int. J. of Computer Integrated
Manufacturing, 15-1 (2002), 37-49.
Baykasoglu, A., A Multiobjective
Tabu Search Based Simulation Optimisation Approach for Loading of Cellular Manufacturing Systems, Industrial
Engineering, 12(1) 2-24, 2001.
Baykasoglu, A., Özbakýr, L., Sönmez, A. I., Dil Teorisi Ve Tabu Arama Yaklaþýmý
Ýle Esnek Çok Objektifli Atölye Çizelgeleme Problemlerinin Modellenmesi Ve Çözümü, XXIII Yöneylem Araþtýrmasý
ve Endüstri Mühendisliði Kongresi, 3-5 Temmuz 2002, Ýstanbul,
Turkey.
Baykasoglu, A., Özbakýr L., Sönmez
A. I., Determining optimal due dates in earliness-tardiness flexible job shop
scheduling, 2nd International Conference on Responsive
Manufacturing, 26-28, June 2002, Gaziantep, Turkey.
Baykasoglu, A., Özbakýr L., Sönmez
A. I., Analysing
the effect of dispatching rules on job shops with varying flexibility levels,
2nd International Conference on Responsive Manufacturing, 26-28,
June 2002, Gaziantep, Turkey.
Baykasoglu, A., Özbakýr L., Sönmez
A. I., A Multiple Objective Tabu Search Approach to Solve Flexible Job Shop
Scheduling Problems by Employing Grammars, 2nd International
Conference on Responsive Manufacturing, 26-28, June 2002, Gaziantep, Turkey.
PRODUCTION PLANNING APPLICATIONS
Baykasoglu, A., MOAPPS 1.0: Aggregate production
planning using the multiple objective tabu search, Int.
J. of Prod. Res., 39-16 (2001), 3685-3702.
Baykasoglu, A., Çok objektifli üretim planlamasi problemlerinin çok objektifli yasakli tarama
yöntemi
ile çözümü., 3.
GAP Mühendislik
Kongresi, pp. 181-189,
24-26 Mayis 2000 Sanliurfa
DESIGN APPLICATIONS
Baykasoglu, A., Applying
the multiple objective tabu search to continuous optimization problems with a
simple neighborhood strategy, International Journal
for Numerical Methods in Engineering, 65, 406-424,2006.
Baykasoglu,
A., Design Of Mechanical Components Using The
Multiple Objective Tabu Search, ICMCA-2002:
3rd International Conference on Mathematical and Computational
Applications, September 4-6 2002,
Multiple Objective
optimisation using Simulated
Annealing
MOSA
MOSA
is developed by Dr Adil Baykasoglu to solve complex multiple objective goal
programs. The research is under development.
PREEMPTIVE GOAL PROGRAMMING USING SIMULATED ANNEALING
Adil Baykasoglu
Goal
programming is a commonly used technique for modelling and solving multiple
objective optimisation problems. It has been successfully applied to many
diverse real life problems in engineering design and optimization. One of the
first attempts is made in this paper to solve preemptive
goal programming problems by using a simulated annealing algorithm. The
developed algorithm can be applied to nonlinear, linear, integer and
combinatorial goal programs. However the main concentration is on nonlinear
programs, mainly due to the difficulty in solving these programs with the
classical approaches. Several test problems are solved in order to test the
suitability of the simulated annealing in solving preemptive
goal programs. It is observed that the simulated annealing algorithm is a
suitable candidate to solve goal programs. The method can easily be applied to
any kind of preemptive goal programming problem.
*
The full paper is published in "Journal of Engineering Optimization, 37(2005), 49-63".