Publication: Minimising safety stock and lead time in production systems under guaranteed-service time models by swarm intelligence
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2016-01-01
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Springer
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Abstract
In this chapter, we address the problem of placing safety and in-transit inventory over a multi-stage manufacturing supply chains (SC) in which one or more products are manufactured, subject to a stochastic demand . The first part of the problem is to configure the SC given that manufacturers have one or more options to perform every supplying, assembly, and delivery stage. Then, a certain amount of inventory should be placed on each stage to ensure products are delivered to customers just in the stages' service time. We tested a new nature-inspired swarm-based meta-heuristic called Intelligent Water Drop (IWD) which imitates some of the processes that happen in nature between the water drops of a river and the soil of the river bed. The proposed approach is based on the creation of artificial water drops, which adapt to their environment to find the optimum path from a river/lake to the sea. This idea is embedded into our proposed algorithm to find the cheapest cost of supplying components, assembling, and delivering products subject to the stages' service time. We tested our approach using four instances, used widely as test bed in literature. We compared the results computed to the ones computed by Ant Colony Meta-heuristic and provided some metrics as well as graphical results of the outputs.
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Keywords
Supply chain, Lead time, Generational distance, Water drop, Inventory cost
Bibliographic citation
Recio Isasi, Gustavo; Moncayo Martinez, Luis A.; Zhang, David (2016). Minimising safety stock and lead time in production systems under guaranteed-service time models by swarm intelligence. Metaheuristics for production systems. : Springer. Pp. 149-166