This paper formulates a two-echelon singleproducer multi-buyer supply chain model, while a single product is produced and transported to the buyers by the producer. The producer and the buyers apply vendormanaged inventory mode of operation. It is assumed that the producer applies economic production quantity policy, which implies a constant production rate at the producer. The operational parameters of each buyer are sales quantity, sales price and production rate. Channel profit of the supply chain and contract price between the producer and each buyer is determined based on the values of the operational parameters. Since the model belongs to nonlinear integer programs, we use a discrete particle swarm optimization algorithm (DPSO) to solve the addressed problem; however, the performance of the DPSO is compared utilizing two well-known heuristics, namely genetic algorithm and simulated annealing. A number of examples are provided to verify the model and assess the performance of the proposed heuristics. Experimental results indicate that DPSO outperforms the rival heuristics, with respect to some comparison metrics.
A discrete particle swarm optimization algorithm with local search for a production-based two-echelon single-vendor multiple-buyer supply chain
04.05.2015
oai:zenodo.org:3813543
Journal of Industrial Engineering International 12(1) 29-43
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Supplier selection , Contract design , SCOR , Multi-echelon , Lost sales , Competitive location , NSGA-II , Genetic Algorithm , Economic production quantity , Vendor Managed Inventory , Vendor-managed inventory , Closed-loop supply chain network design , Quota allocation , Tabu search , Heuristic algorithm , Supply chain , Sustainable supply chain network design , Facility disruptions , Maximal covering location problem , Particle swarm optimization , Supply chain management , Location-allocation problem , Social responsibility , Fuzzy logic
An Adaptive Particle Swarm Optimization Algorithm with Local Search
British Library Online Contents | 2015
|An integrated vendor–buyer model with stock-dependent demand
Online Contents | 2010
|British Library Conference Proceedings | 2022
|