Loading Methodology for a Capacity Constrained Job-Shop

Authors: Viraj Tyagi, Ajai Jain, P. K. Jain, Aarushi Jain

Abstract:

This paper presents a genetic algorithm based loading methodology for a capacity constrained job-shop with the consideration of alternative process plans for each part to be produced. Performance analysis of the proposed methodology is carried out for two case studies by considering two different manufacturing scenarios. Results obtained indicate that the methodology is quite effective in improving the shop load balance, and hence, it can be included in the frameworks of manufacturing planning systems of job-shop oriented industries.

Keywords: Manufacturing planning, loading, genetic algorithm, Job-Shop

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126293

References:

[1] Anderson E J and Ferris M C (1994), “Genetic algorithms for combinatorial optimization assembly line balancing problem”, ORSA Journal of Computing, Vol. 6, pp. 161-173.
[2] Chen K and Ji P (2007), “A mixed integer programming model for advanced planning and scheduling (APS)”, European Journal of Operational Research, Vol. 181, pp. 515-522.
[3] Deb K (2006), “Optimization for Engineering Design, Algorithms and Examples”, Prentice- Hall of India, ND.
[4] De Jong K A (1975), “An analysis of the behavior of a class of genetic adaptive systems”, Doctoral Dissertation, University of Michigan, Dissertation Abstracts International, 36(10), 5140 B (University Microfilms No. 76-9381)
[5] Ebadian M, Rabbani M, Torabi S A and Jolai F (2009), “Hierarchical production planning and scheduling in make-to-order environments: reaching short and reliable delivery dates”, International Journal of Production Research, Vol. 47, No. 20, pp. 5761-5789.
[6] Jain A (2003), “Integration of process planning and scheduling”, Ph. D Dissertation, Unpublished, Faculty of Engineering and Technology, Kurukshetra University, Kurukshetra.
[7] Kumar N and Shanker K (2000), “A genetic algorithm for FMS part-type selection and machine loading”, International Journal of Production Research, Vol. 38, No. 16, pp. 3861-3887.
[8] Li W D and McMahon C A (2007), “A simulated annealing based optimization approach for integrated process planning and scheduling”, International Journal of Computer Integrated Manufacturing, Vol. 20, No. 1, pp. 80-95.
[9] Mitchell M (2002), “An Introduction to Genetic Algorithms”, Prentice-Hall of India, ND.
[10] Moon C, Seo Y, Yun Y and Gen M (2006), “Adaptive genetic algorithm for advanced planning in manufacturing supply chain”, Journal of Intelligent Manufacturing, Vol.17, pp.509-522.
[11] Ozturk C and OrnekA M (2014), “Operational Extended Model Formulations for Advanced Planning and Scheduling Systems”, Applied Mathematical Modelling, Vol. 38, No. 1, pp. 181-195.
[12] Tiwari M K and Vidyarthi N K (2000), “Solving machine loading problem in a flexible manufacturing system using a genetic algorithm based heuristic approach”, International Journal of Production Research, Vol. 38, No. 14, pp. 3357-3384.
[13] Tyagi V (2013) “Integration of manufacturing planning functions” Ph.D Dissertation, Unpublished, Mechanical Engineering Department, National Institute of Technology, Kurukshetra.
[14] Vinod V and Sridharan R (2008), “Scheduling a dynamic job shop production system with sequence-dependent setups: An experimental study”, Robotics and Computer Integrated Manufacturing, Vol. 24, pp. 435-449.
[15] Zhang X D and Yan H S (2005), “Integrated optimization of production planning and scheduling for a kind of job shop”, International Journal of Advanced Manufacturing Technology, Vol. 26, pp. 876-886.
[16] Zijm W H M (2000), “Towards Intelligent Manufacturing Planning and Control Systems”, OR Spectrum, Vol. 22, pp. 313-345.