A Robust Optimisation Approach of a Simultaneous Cost-Risk Reduction under a Just-in-Time Environment Using a Genetic Algorithm

Abstract:

This paper addresses the problem of simultaneously minimising the total costs of the final product produced by systems adopting a JIT approach. A cost which incorporates both the cost of production processes and the cost arising from the many potential risks associated with any reduction. A robust genetic approach is proposed in order to optimise the novel mathematical model published in [1]. Genetic operators adopted to improve the genetic search algorithm are introduced and discussed. Experiments are conducted to evaluate the performance of the proposed algorithm and an illustrative example is given. A comparison of the genetic operators used is made by means of evaluating different rates, to define the most suitable rate of crossover and mutation. The findings illustrate the effectiveness of the proposed approach in the JIT system with focus on simultaneous cost-risk reduction.

• Periodical: Applied Mechanics and Materials (Volume 743)
• Pages: 307-316
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.743.307
• Online since: March 2015
• Authors: Faraj El Dabee*, Romeo Marian, Yousef Amer
• Keywords: Cost-Risk Reduction, Genetic Algorithm (GA), Just-in-Time (JIT), Model, Optimization, Production System

References

[1] F. El Dabee, R. Marian, and Y. Amer, A Novel Optimisation Model for Simultaneous Cost-Risk Reduction in Multi-suppliers Just-in-Time Systems, J. Computer Sci. 9 (2013a) 1778-1792.

DOI: 10.3844/jcssp.2013.1778.1792

[2] K. Wang, Y. Wang, Applying Genetic Algorithms to Optimize the Cost of Multiple Sourcing Supply Chain Systems – An Industry Case Study, in: A. Yang, Y. Shan and L. Bui, Success in Evolutionary Computation, Springer Berlin Heidelberg, 2008, 355-372.

DOI: 10.1007/978-3-540-76286-7_16

[3] R. Malhotra, N. Singh, and Y. Singh, Genetic Algorithms: Concepts, Design for Optimization of Process Controllers, Computer and Information Sci. 4 (2011) 39-54.

DOI: 10.5539/cis.v4n2p39

[4] S. Kannaiah, J. Thangavel, and D. Kothari, A Genetic Algorithm Based Multi Objective Service Restoration in Distribution Systems, J. Computer Sci. 7 (2011) 448, (2011).

DOI: 10.3844/jcssp.2011.448.453

[5] F. El Dabee, R. Marian, and Y. Amer, Development of a model for simultaneous cost-risk reduction in JIT systems using multi-external and local backup suppliers, Automation, Control and Intelligent Systems, 1 (2013b) 42-52.

DOI: 10.11648/j.acis.20130103.12

[6] F. El Dabee, R. Marian, and Y. Amer, A Novel Model for Simultaneously Minimising Costs and Risks in Just-in-Time Systems Using Multi-backup Suppliers: Part 1- Modelling, World Academy of Science, Engineering and Technology (WASET), 7 (2013c) 139-142.

[7] T. Aven, J. Vinnem, Risk management with applications from the offshore petroleum industry: With Applications from the Offshore Petroleum Industry, Springer, London, (2007).

[8] A. Ghadge, S. Dani, and R. Kalawsky, Supply chain risk management, The International Journal of Logistics Management, 23 (2012) 313-339.

DOI: 10.1108/09574091211289200

[9] F. El Dabee, R. Marian, and Y. Amer, A Simultaneous Cost-risk Reduction Optimisation in JIT Systems using Genetic Algorithms, 4th International Conference on Control System, Computing and Engineering, IEEE, 2014 [under reviewed].

DOI: 10.1109/iccsce.2014.7072694

[10] S. Torabi, S. Fatemi Ghomi, and B. Karimi, A Hybrid Genetic Algorithm for the Finite Horizon Economic Lot and Delivery Scheduling in Supply Chains, European Journal of Operational Research, 173 (2006) 173-189.

DOI: 10.1016/j.ejor.2004.11.012

[11] F. El Dabee, R. Hokoma, Just-In-Time for Reducing Inventory Costs throughout a Supply Chain: A Case Study, World Academy of Science, Engineering and Technology (WASET), (2012).

[12] L. Krajewski, L. Ritzman and M. Malhotra, Operations Management: Processes and Supply Chains, 10th ed., Pearson, Upper Saddle River, N. J, (2013).

[13] S. Sivanandam, S. Deepa, Introduction to Genetic Algorithms, Springer Berlin Heidelberg, New York, 2008, 165-209.

[14] R. Rawal, K. Inamdar, Review on Various Optimisation Techniques used for Process Parameters of Resistance Spot Welding, International Journal of Current Engineering and Technology, 3 (2014) 160-164.

[15] R. Marian, Optimisation of Assembly Sequences Using Genetic Algorithms, PhD, School of Advanced Manufacturing Mechanical Engineering, University of South Australia, (2003).

[16] R. Z. Farahani, M. Elahipanah, A genetic algorithm to optimise the total cost and service level for just-in-time distribution in a supply chain, International Journal of Production Economics, 111 (2008) 229-243.

DOI: 10.1016/j.ijpe.2006.11.028

[17] W. Wong, C. Kwong, P. Mock and W., Genetic Optimization of JIT Operation Schedules for Fabric-cutting Process in Apparel Manufacture, Journal of Intelligent Manufacturing, 17 (2006) 341-354.

DOI: 10.1007/s10845-005-0007-8

[18] S. Ghasimi, R. Ramli and N. Saibani, A Genetic Algorithm for Optimizing Defective Goods Supply Chain Costs Using JIT Logistics and Each-Cycle Lengths, Applied Mathematical Modelling, 38 (2013) 1534-1547.

DOI: 10.1016/j.apm.2013.08.023

[19] W. Jianhua, H. Xianfeng, A hybrid genetic algorithm for agile supply chain scheduling optimization, presented at the 2010 2nd International Conference on Future Computer and Communication, 1 (2010) 396-400.

DOI: 10.1109/icfcc.2010.5497760

[20] S. A. Mansouri, A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines, European Journal of Operational Research, vol. 167, pp.696-716, (2005).

DOI: 10.1016/j.ejor.2004.07.016

[21] A. Azadeh, V. Ebrahimipour and P. Bavar, A hybrid GA-Simulation Approach to Improve JIT Systems. International Journal of Production Research, 48 (2010) 2323-2344.

DOI: 10.1080/00207540802676441

[22] P. Murphy, and D. Wood, Contemporary Logistics, Tenth ed. USA: Pearson Education, Inc., (2011).

[23] PL. Palaniappan, N. Jawahar, A Genetic Algorithm for Simultaneous Optimisation of Lot Sizing and Scheduling in a Flow Line Assembly, International Journal of Production Research, 49 (2011) 375-400.

DOI: 10.1080/00207540903471478

[24] D. Gupta, S. Ghafir, An Overview of Methods Maintaining Diversity in Genetic Algorithms, International Journal of Emerging Technology and Advanced Engineering, 2 (1012) 56-60.

[25] Zhu, G and S. Kwong, S 2010, Gbest-Guided Artificial Bee Colony Algorithm for Numerical Function Optimization, Applied Mathematics and Computation, 217: 3166-3173.

DOI: 10.1016/j.amc.2010.08.049

[26] W. Mahmudy, R. Marian, and L. Luong, Solving part type selection and loading problem in flexible manufacturing system using real coded genetic algorithms: Part I: Modelling, World Academy of Science, Engineering and Technology (WASET), 6 (2012).

DOI: 10.1109/kst.2013.6512791

[27] T. Kumar, S. R. Singh and C. B. Gupta, Genetic Algorithm Based Multi Product and Multi Agent Inventory Optimization in Supply Chain Management, International Journal of Modelling and Optimization, 2 (2012) 653-657.

DOI: 10.7763/ijmo.2012.v2.203

[28] M. Gen, R. Cheng, Genetic Algorithms and Engineering Optimization. New York: John Wiley & Sons, Inc., (2000).

[29] P. Radhakrishnan, V. Prasad and M. Gopalan, Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management, J. Comp. Sci., 5 (2009) 233-241.

DOI: 10.3844/jcs.2009.233.241

[30] P. Garg, Evolutionary Computation Algorithms for Cryptanalysis: A Study, International Journal of Computer Science and Information Security, 7 (2010).

[31] T. Abdelmaguid, M. Dessouky, A Genetic Algorithm Approach to the Integrated Inventory-Distribution Problem, Int. J. Production Resource, 44 (2006) 4445-4464.

DOI: 10.1080/00207540600597138

[32] N. A. Mohd-Lair, An integrated model for optimising manufacturing and distribution network scheduling, PhD thesis, University of South Australia, (2008).

[33] A. Leuveano, F. Jafarand and M. Muhamad, Development of Genetic Algorithm on Multi-Vendor Integrated Procurement-Production System under Shared Transportation and Just-in-Time Delivery System, 2nd Int. Conference on Uncertainty Reasoning and Knowledge Eng., 2012, 78-81.

DOI: 10.1109/urke.2012.6319589

[34] N. Radcliffe, Equivalence Class Analysis of Genetic Algorithms, Complex Systems, 5 (1991) 183-205.

[35] H. Mühlenbein, D. Schlierkamp-Voosen, Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization, Evolutionary Computation, 1 (1993) 25-49.

DOI: 10.1162/evco.1993.1.1.25

[36] G. Zhou, H. Min, H and M. Gen, The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach, Computers & Industrial Engineering, 43 (2002) 251-261.

DOI: 10.1016/s0360-8352(02)00067-0

[37] M. Gen, R. Cheng, Genetic Algorithms and Engineering Design. New York: John Wiley & Sons, Inc., (1997).