A Tabu Search Heuristic for the Robust Dynamic Bayesian Network Optimisation Problem Under the Supply Chain Ripple Effect
IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021
; 632 IFIP:673-680, 2021.
Article
in English
| Scopus | ID: covidwho-1437179
ABSTRACT
Due to the impact of the global COVID-19, supply chain (SC) risk management under the ripple effect is becoming an increasingly hot topic in both practice and research. In our former research, a robust dynamic bayesian network (DBN) approach has been developed for disruption risk assessment, whereas there still exists a gap between the proposed simulated annealing (SA) algorithm and commercial solver in terms of solution quality. To improve the computational efficiency for solving the robust DBN optimisation model, a tabu search heuristic is proposed for the first time in this paper. We design a novel problem-specific neighborhood move to keep the search in feasible solution space. The computational experiments, conducted on randomly generated instances, indicate that the average gap between our approach and commercial solver is within 0.07 %, which validates the performance of the proposed method. © 2021, IFIP International Federation for Information Processing.
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021
Year:
2021
Document Type:
Article
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