Reliability-based optimization of economic life forward design for complex systems in uncertain task environments

This study investigates a forward design approach for optimizing the economic life of complex systems by minimizing the time-averaged life-cycle cost, while accounting for their uncertain task environments and physical structures. Conceptual optimization models are developed for the economic life forward design of individual products, complex systems, and short-term systems. Furthermore, an uncertain task environment-oriented economic life forward selection problem (UTE-ELFSP) is proposed and formulated as a mixed-integer linear programming (MILP) model based on a novel ε-accurate linearization approach for the ratio of two non-negative variables. Additionally, a MILP-based economic life stepping optimization (MILP-ELSO) heuristic is devised to solve large-scale problem instances. Computational experiments are conducted to validate the performance of the proposed model and algorithm. The numerical results demonstrate that the MILP model effectively produces optimal solutions for small- and medium-scale UTE-ELFSPs. The proposed MILP-ELSO algorithm exhibits high computational efficiency in large-scale applications and provides valuable insights for the economic design of complex systems with diverse lifespans and cost requirements.