Optimization of a dynamic supply portfolio considering risks and discount’s constraints

Masoud Rabbani, S.M Khalili, H Janani, M Shiripour

Abstract


Purpose: Nowadays finding reliable suppliers in the global supply chains has become so important for success, because reliable suppliers would lead to a reliable supply and besides that orders of customer are met effectively . Yet, there is little empirical evidence to support this view, hence the purpose of this paper is to fill this need by considering risk in order to find the optimum supply portfolio.

Design/methodology/approach: This paper proposes a multi objective model for the supplier selection portfolio problem that uses conditional value at risk (CVaR) criteria to control the risks of delayed, disrupted and defected supplies via scenario analysis. Also we consider discount’s constraints which are common assumptions in supplier selection problems. The proposed approach is capable of determining the optimal supply portfolio by calculating value-at-risk and minimizing conditional value-at-risk. In this study the Reservation Level driven Tchebycheff Procedure (RLTP) which is one of the reference point methods, is used to solve small size of our model through coding in GAMS. As our model is NP-hard; a meta-heuristic approach, Non-dominated Sorting Genetic Algorithm (NSGA) which is one of the most efficient methods for optimizing multi objective models, is applied to solve large scales of our model.

Findings and Originality/value: In order to find a dynamic supply portfolio, we developed a Mixed Integer Linear Programming (MILP) model which contains two objectives. One objective minimizes the cost and the other minimizes the risks of delayed, disrupted and defected supplies. CVaR is used as the risk controlling method which emphases on low-probability, high-consequence events. Discount option as a common offer from suppliers is also implanted in the proposed model. Our findings show that the proposed model can help in optimization of a dynamic supplier selection portfolio with controlling the corresponding risks for large scales of real word problems.

Practical implications: To approve the capability of our model various numerical examples are made and non-dominated solutions are generated. Sensitive analysis is made for determination of the most important factors. The results shows that how a dynamic supply portfolio would disperse the allocation of orders among the suppliers combined with the allocation of orders among the planning periods, in order to hedge against the risks of delayed, disrupted and defected supplies.

Originality/value: This paper provides a novel multi objective model for supplier selection portfolio problem that is capable of controlling delayed, disrupted and defected supplies via scenario analysis. Also discounts, as an option offered from suppliers, are embedded in the model. Due to the large size of the real problems in the field of supplier selection portfolio a meta-heuristic method, NSGA II, is presented for solving the multi objective model. The chromosome represented for the proposed solving methodology is unique and is another contribution of this paper which showed to be adaptive with the essence of supplier selection portfolio problem.


Keywords


Supplier selection, Dynamic supply portfolio, Conditional value-at-risk, Mixed integer programming, RLTP, NSGA II.

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DOI: http://dx.doi.org/10.3926/jiem.880


Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2020

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience