Optimization of cost to customer of power train in commercial vehicle using knapsack dynamic programming influenced by vehicle IoT data

Abstract

The automotive original equipment manufacturers (OEM) current challenge of deriving the optimized cost to customer for the product when the product is configured dynamically. For every OEM the product they sell is bounded by warranty terms, thus the product configuration they offer should be reliable to withstand the warranty period. This paper discusses about the optimization of cost of the power train configuration which is offered to the customer is incorporated with the product cost and the provisional warranty cost.  For a target cost the product planner must configure a power train configuration which should adhere to the target cost but selecting the power train configuration only based on cost will defeat the performance of the vehicle. Thus, power train configuration is governed based on the reliability factor of the power train components which is derived using a vehicle IoT data derived from live running vehicles. The cost to customer is calculated as the sum of product cost and provisional-warranty cost calculated based on the dynamic reliability predicted using the vehicle Internet of Things (IoT) data. In this paper, for the target cost   to customer set by the product planner to select the best fit power train configuration for the product line, is formulated as a 0-1 knapsack problem, and dynamic programming is used to find the optimized cost to customer which is the sum of two variables the product cost and provisional warranty cost. The findings using this method is encouraging as the use of combinatorial optimization techniques and the vehicle IoT data model for deriving the dynamic reliability data are working in tandem to provide an optimum cost output.