The impact of transportation optimisation on assembly line feeding

In the era of mass customisation, feeding parts to mixed-model assembly lines has proven to be a complex task since customers increasingly demand personalised end products. Consequently, the number of parts required at a single assembly line is sharply increasing. On the one hand, part supply must be done with the aim of avoiding excessive logistical handling activities while managing space at the border of line carefully. Hence, different line feeding policies can be exploited. On the other hand, shortages in parts supply, which may result in line stoppage, must be avoided. To this end, different vehicle types such as forklifts, automated guided vehicles and tow trains must be orchestrated carefully. This study is the first to propose a mixed integer programming model that efficiently assigns each part at the same time to a feeding policy and a vehicle type, with the goal to minimise total feeding costs. To accurately quantify costs, the model selects specific routes and determines the fleet size of every vehicle type used. The model is complemented by valid inequalities and validated by solving artificial problem instances. Within the analysis, we demonstrate that optimal selection of vehicle types is superior to heuristic approaches and show that this optimisation-based approach is around 8% cheaper than the industrial standard.