Portfolio optimisation of material purchase considering supply risk – A multi-objective programming model

For the sake of better coping with the problem of material procurement, a multi-objective optimisation model was established in a systematic analysis framework for material procurement considering supply risk. First, this paper combs and identifies supply risk factors and constructs a supply risk ev...

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Veröffentlicht in:International journal of production economics Jg. 230; S. 107803
Hauptverfasser: Hao, Jun, Li, Jianping, Wu, Dengsheng, Sun, Xiaolei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2020
Schlagworte:
Material purchase
Multi-objective optimisation
NSGA-II
Supply risk
NSGA-II
Material purchase
Supply risk
Multi-objective optimisation
ISSN:0925-5273, 1873-7579
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Zusammenfassung:For the sake of better coping with the problem of material procurement, a multi-objective optimisation model was established in a systematic analysis framework for material procurement considering supply risk. First, this paper combs and identifies supply risk factors and constructs a supply risk evaluation system from the dimensions of quality, price, delivery, service and technology. Second, based on the linguistic scale and fuzzy theory, this paper measures the supply risk of candidate suppliers and estimates the relevant parameters of the multi-objective optimisation model by using the triangular fuzzy numbers. In addition, an improved non-dominated sorting genetic algorithm II (NSGA-II) is utilized in this paper to solve the multi-objective model, since traditional intelligent algorithms have slow convergence speed and are easily trapping into local optimisation. Finally, this paper conducts simulation experiments by setting three types of decision-makers with different risk preferences and provides material procurement combination schemes in different scenarios. Through numerical simulation experiments, it was verified that the optimisation model established in this paper was feasible and useful for the selection of candidate suppliers and the portfolio optimisation of material procurement.
ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2020.107803