Crowdsourcing in manufacturing enables collaborative product fulfillment through supply contracting, aligned with task derivation and decomposition. Effective coordination of product family planning (PFP) with manufacturing processes is crucial for manufacturer load balancing (MLB). This paper focuses on game-theoretic decisions in crowdsourcing task derivation and decomposition, proposing a bilevel coordinated optimization model formulated as a Stackelberg game. PFP decisions are treated as profit maximization at the upper level, while MLB decisions are formulated as cost minimization at the lower level. A nested-bilevel genetic algorithm is developed for efficient model solution. A case study of tank trailer crowdsourced manufacturing demonstrates the benefits of employing the bilevel game-theoretic decision model, achieving optimal task derivation and decomposition and coordinated solutions for both the platform and crowdsourced manufacturers.

中文翻译：

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通过产品族规划和制造商负载平衡双层协调优化众包制造中的任务推导和分解

制造业中的众包通过供应合同与任务派生和分解相结合，实现协作产品履行。产品系列规划 (PFP​​) 与制造流程的有效协调对于制造商负载平衡 (MLB) 至关重要。本文重点研究众包任务推导和分解中的博弈论决策，提出一种以 Stackelberg 博弈形式表示的双层协调优化模型。 PFP 的决策被视为上层的利润最大化，而 MLB 的决策则被视为下层的成本最小化。开发了嵌套双层遗传算法以实现高效的模型求解。罐式拖车众包制造的案例研究证明了采用双层博弈论决策模型的好处，为平台和众包制造商实现最佳任务推导和分解以及协调解决方案。