Hybrid Approach for Machine Scheduling Optimization in Custom Furniture Industry

Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain thus reducing accuracy of schedules. The solution described in this paper combines evolutionary computing and neural networks to reduce the impact of (i) the huge search space that the multi-objective optimization must deal with and (ii) the inherent problem of computing the processing times in a domain like custom manufacturing. Our hybrid approach obtains near optimal schedules through the non-dominated sorting genetic algorithm II (NSGA-II) combined with time estimations based on multilayer perceptron networks.

[1]  Geoffrey Boothroyd,et al.  Product design for manufacture and assembly , 1994, Comput. Aided Des..

[2]  Satyandra K. Gupta,et al.  A systematic approach for analyzing the manufacturability of machined parts , 1993 .

[3]  Carlos Alberto,et al.  Genetic Algorithms for Shop-scheduling Problems : Partial Enumeration and Stochastic Heuristics , 2001 .

[4]  L. Jain,et al.  Evolutionary multiobjective optimization : theoretical advances and applications , 2005 .

[5]  Dvir Shabtay,et al.  A survey of scheduling with controllable processing times , 2007, Discret. Appl. Math..

[6]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[7]  Pierre Borne,et al.  EVOLUTIONARY ALGORITHMS FOR JOB-SHOP SCHEDULING , 2004 .

[8]  Carlos A. Coello Coello,et al.  Recent Trends in Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[9]  Andrew Kusiak,et al.  Design of components for schedulability , 1994 .

[10]  Lakhmi C. Jain,et al.  Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[11]  Ioannis Minis,et al.  A generative approach for concurrent manufacturability evaluation and subcontractor selection , 1999 .

[12]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[13]  Stephen I. Gallant,et al.  Perceptron-based learning algorithms , 1990, IEEE Trans. Neural Networks.

[14]  Bertrand M. T. Lin,et al.  A concise survey of scheduling with time-dependent processing times , 2004, Eur. J. Oper. Res..

[15]  Jeffrey W. Herrmann,et al.  Reducing throughput time during product design , 2001 .

[16]  Dana S. Nau,et al.  Systematic approach to analysing the manufacturability of machined parts , 1995, Comput. Aided Des..

[17]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.