Container fleet-sizing for part transportation and storage in a two-level supply chain

Abstract

This research addresses the fleet-sizing of containers that are used for the protection, transportation, and storage of parts between a component plant and multiple assembly plants. These containers are needed for the storage of completed parts as they are produced, and also for storage of parts as they are used in assembly. The containers are reusable, expensive, occupy a large amount of space when empty or full, and are required to maintain production. An analytical model is developed for the minimum container fleet size that results in no production stoppages due to lack of containers assuming no system variability. The model considers the discrete nature of part production and shipping as well as differences in available production and transportation time. The model is shown to be accurate and provides insight into the factors that affect container fleet size and production stoppage trade-offs.

A MILP Model for Reusable Containers Management in Automotive Plastic Components Supply Chain

“…The paper presents two mathematical models used to determine the cycle time, container size, individual retailer order quantities, and shipping sequence with the intention of minimizing the average total system costs. Park and Kim [6] present an analytical model for fleet-sizing of containers that are used for the protection (storage of finished parts), transportation and storage of parts between a component plant and multiple assembly plants. Atamer et al [7] analyses the pricing and production decisions of a manufacturer selling a single product when using reusable containers with stochastic customer demand, and two supply scenarios are analysed: (1) new containers and (2) con-A MILP Model for Reusable Containers Management in Supply Chain 169 tainers returned by customers.…”

Dynamic Vehicle Scheduling for Working Service Network with Dual Demands

“…Koç et al [16] introduced the fleet size and mix location-routing problem with time windows and developed a powerful hybrid evolutionary search algorithm. Park and Kim [17] addressed the fleet sizing of containers and developed an analytical model for the minimum container fleet size.…”