Abstract
In a supply chain, procurement of items is done on the basis of individual performance, whereas the performance of supply chain can be improved by using scientific techniques. In this chapter, we discuss the manufacturer’s problem of procuring several items from the available suppliers; where, supplies from each supplier are constrained. The manufacturer needs to determine which item is to be procured from which supplier and in what quantity. The allocation of order amongst suppliers is done on the basis of multiple criteria such as unit price, quality, supply capacity, delivery time, and unit transportation cost. To demonstrate the scenario, we formulate the mathematical model, which leads to a multi-objective optimization problem. The optimization is done using multi-objective genetic algorithm, which gives a set of Pareto-optimal solutions, then we utilize 3D-RadVis technique to get the best solution. To validate the model, numerical example is presented.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Amin SH, Zhang G (2012) An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Syst Appl 39(8):6782–6791
Basnet C, Weintraub A (2009) A genetic algorithm for a bicriteria supplier selection problem. Int Trans Oper Res 16(2):173–187
Cao Y, Luo X, Kwong C, Tang J (2014) Supplier pre-selection for platform-based products: a multi-objective approach. Int J Prod Res 52(1):1–19
Deb K (2012) Optimization for engineering design: Algorithms and examples. PHI Learning Pvt Ltd
Golberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addion Wesley, Reading
He Z, Yen GG (2016) Visualization and performance metric in many-objective optimization. IEEE Trans Evol Comput 20(3):386–402
Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence
Ibrahim A, Rahnamayan S, Martin MV, Deb K (2016) 3d-radvis: Visualization of pareto front in many-objective optimization. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 736–745
Ibrahim A, Rahnamayan S, Martin MV, Deb K (2018) 3d-radvis antenna: visualization and performance measure for many-objective optimization. Swarm Evol Comput 39:157–176
Izadikhah M, Saen RF, Ahmadi K (2017) How to assess sustainability of suppliers in the presence of dual-role factor and volume discounts? A data envelopment analysis approach. Asia-Pac J Oper Res 34(03):1740016
Izadikhah M, Saen RF, Roostaee R (2018) How to assess sustainability of suppliers in the presence of volume discount and negative data in data envelopment analysis? Ann Oper Res 1–27
Li M, Zhen L, Yao X (2017) How to read many-objective solution sets in parallel coordinates [educational forum]. IEEE Comput Intell Mag 12(4):88–100
Michalewicz Z (1996) Evolution strategies and other methods. In: Genetic Algorithms+ Data Structures = Evolution Programs. Springer, pp 159–177
Murata T, Ishibuchi H, Tanaka H (1996) Multi-objective genetic algorithm and its applications to flowshop scheduling. Comput Ind Eng 30(4):957–968
Obayashi, S. and Sasaki, D. (2003). Visualization and data mining of pareto solutions using self-organizing map. In International Conference on Evolutionary Multi-Criterion Optimization, pages 796–809. Springer
Parks GT, Miller I (1998) Selective breeding in a multiobjective genetic algorithm. In: International conference on parallel problem solving from nature. Springer, pp 250–259
Pryke A, Mostaghim S, Nazemi (2007) Heatmap visualization of population based multi objective algorithms. In: International conference on evolutionary multi-criterion optimization. Springer, pp 361–375
Sakawa M (2012) Genetic algorithms and fuzzy multiobjective optimization, vol 14. Springer Science & Business Media
Schaffer JD (1984) Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Seifbarghy M, Esfandiari N (2013) Modeling and solving a multi-objective supplier quota allocation problem considering transaction costs. J Intell Manuf 24(1):201–209
Shaw K, Shankar R, Yadav SS, Thakur LS (2012) Supplier selection using fuzzy ahp and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Syst Appl 39(9):8182–8192
Srinivasan N, Deb K (1994) Multi-objective function optimisation using non-dominated sorting genetic algorithm. Evol Comput 2(3):221–248
Timmerman E (1986) An approach to vendor performance evaluation. J Purch Mater Manag 22(4):2–8
Tušar T, Filipič B (2015) Visualization of pareto front approximations in evolutionary multiobjective optimization: a critical review and the prosection method. IEEE Trans Evol Comput 19(2):225–245
von Lücken C, Brizuela C, Barán B (2019) An overview on evolutionary algorithms for many-objective optimization problems. Wiley interdisciplinary reviews: data mining and knowledge discovery 9(1):e1267
Weber CA, Current J, Desai A (2000) An optimization approach to determining the number of vendors to employ. Supply Chain Manag Int J 5(2):90–98
Weber CA, Current JR (1993) A multiobjective approach to vendor selection. Eur J Oper Res 68(2):173–184
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Shaikh, A., Mishra, P., Talati, I. (2020). Allocation of Order Amongst Available Suppliers Using Multi-objective Genetic Algorithm. In: Shah, N., Mittal, M. (eds) Optimization and Inventory Management. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-13-9698-4_17
Download citation
DOI: https://doi.org/10.1007/978-981-13-9698-4_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9697-7
Online ISBN: 978-981-13-9698-4
eBook Packages: Business and ManagementBusiness and Management (R0)