Multi-objective configuration optimization for product-extension service

• An optimization model for configuration of product-extension service (PES) is proposed.
• Service module, service component, and service module instance are introduced.
• Service cost, response time, and service performance are simultaneously optimized.
• Non-dominated sorting genetic algorithm II is used to solve the PES configuration model.
• The proposed method is applied in optimization of elevator service configuration.

This study aims at optimizing the configuration of product-extension service (PES). PES is a new value-added paradigm which aids manufacturers to achieve sustainable growth and profitability. The configuration method is an efficient way for rapid PES customization to enhance customer satisfaction. However, the earlier service configuration approaches can produce a large number of feasible solutions, especially when there are more module instances, less constraints or fewer customers requirements. This will increase the burden of service solution screening and lower the efficiency of service delivery. To solve this problem, a multi-objective optimization model for configuration of the product-extension service is proposed. The optimization model simultaneously considers service performance, service cost and response time, and it is solved by non-dominated sorting genetic algorithm II (NSGA II) to obtain a set of optimal configuration solutions. Finally, an application of this service configuration optimization to elevator service demonstrates the potential of the method.