Materials flow control in hybrid make-to-stock/make-to-order manufacturing

Today’s company competiveness is favoured by product customisation and fast delivery. A strategy to meet this challenge is to manufacture standard items to stock for product customisation. This configures a hybrid environment of make-to-stock and make-to-order. To explore the advantages of this requires good understanding of production control. Thus, we study production under hybrid MTS-MTO, organising the system in two stages. The 1 st manufactures items to inventory, which are then customised in the 2 nd . We analyse how the percentage of tardy orders is affected by the inventory of items required to achieve a given fill rate. The impact of two mechanisms for releasing orders to both stages is also analysed. Results of a simulation study indicate that most of the reduction on the percentage of tardy orders is achieved by a moderate increase in the stock level of semi-finished products. Moreover the percentage of tardy orders decreases if suitable controlled release of orders is exerted.This study had the financial support of FCT-Fundação para a Ciência e Tecnologia of Portugal under the project PEst2015-2020: UID/CEC/ 00319/2013.info:eu-repo/semantics/publishedVersio

A discrete particle swarm optimization algorithm with local search for a production-based two-echelon single-vendor multiple-buyer supply chain

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