In practice, manufacturers who offer base and extended warranty should make simultaneous decisions on product pricing, spare part pricing for out-of-warranty products, base and extended warranty policy, and spare part inventory management. Previous studies neither considered the influence of out-of-warranty products as one of the main sources of manufacturers’ profit nor optimized these decisions in an integrated model. In this paper, these challenges are addressed by introducing an optimization model to maximize the manufacturer's profit. A new math-heuristic algorithm is proposed for solving the model using the hybridization of metaheuristic algorithms with a developed dynamic programming algorithm. To generate a new solution within the loop of the metaheuristic algorithm, the product price, spare part price and warranty policy variables are first determined with the aid of the operators of league championship algorithm or improved particle swarm optimization algorithm. The maximum number of failures in each time interval is calculated accordingly, and finally, spare part inventory costs are optimized with the aid of the proposed dynamic programming algorithm. The model is verified by Monte-Carlo simulation and is solved with real data for LED TV product. A sensitivity analysis is conducted to evaluate the influence that various parameters may have on the optimal solution.

实际上，提供基本保修和扩展保修的制造商应在产品定价，保修外产品的备件定价，基本保修和扩展保修政策以及备件库存管理方面同时做出决定。先前的研究既没有将保修外产品的影响视为制造商利润的主要来源之一，也没有在集成模型中优化这些决策。在本文中，通过引入优化模型以最大程度地提高制造商的利润来应对这些挑战。提出了一种新的数学启发式算法，该算法通过将元启发式算法与已开发的动态规划算法混合使用来求解模型。为了在元启发式算法的循环内生成新的解决方案，即产品价格，首先借助联赛冠军算法或改进的微粒群优化算法的运营商确定备件价格和保修政策变量。相应地计算每个时间间隔内的最大故障数，最后，借助提出的动态规划算法来优化备件库存成本。该模型已通过蒙特卡洛仿真验证，并通过LED电视产品的真实数据进行了求解。进行敏感性分析以评估各种参数可能对最佳解决方案的影响。借助提出的动态规划算法优化了备件库存成本。该模型已通过蒙特卡洛仿真验证，并通过LED电视产品的真实数据进行了求解。进行敏感性分析以评估各种参数可能对最佳解决方案的影响。借助提出的动态规划算法优化了备件库存成本。该模型已通过蒙特卡洛仿真验证，并通过LED电视产品的真实数据进行了求解。进行敏感性分析以评估各种参数可能对最佳解决方案的影响。