Abstract In order to solve the problem that even the optimal objective value cannot reach the target cost, this paper constructs an inverse optimal value model for operator assignment. Given a forward operator assignment program, a target optimal operator-induced cost value, and a set of feasible downtime vectors, determine a downtime vector such that the corresponding optimal objective value of the forward operator assignment model is closest to the target cost. This research transforms the inverse optimal value model of operator assignment into a corresponding 0–1 mixed integer nonlinear bilevel programming model. Considering the two kinds of decision variables, a hybrid parthenogenetic particle swarm optimization algorithm is proposed. Then the combination of downtime and “operator-job” assignment pair such that the corresponding optimal objective value of the assignment problem is closest to the target cost is obtained. The application results show that, the inverse optimal value method can reach the target cost, reduces total operator caused loss by 5%, reduces production loss by 34.3%, reduced scrap by 5.1%, while the labor costs are increased by 8.3%. It solves a kind of problem when the optimal value still cannot reach the target goal and the parameters are adjustable. It provides a decision tool for goal driven operator assignment.

中文翻译：

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基于逆最优值法的人为错误计划外停机推断及作业-操作者匹配

摘要 为了解决即使是最优目标值也无法达到目标成本的问题，本文构建了算子分配的逆最优值模型。给定前向操作员分配程序、目标最优操作员诱导成本值和一组可行的停机时间向量，确定停机时间向量，使得前向操作员分配模型的相应最优目标值最接近目标成本。本研究将算子赋值的逆最优值模型转化为对应的0-1混合整数非线性双层规划模型。考虑到这两种决策变量，提出了一种混合孤雌生殖粒子群优化算法。然后结合停机时间和“操作员-作业”分配对，使得分配问题对应的最优目标值最接近目标成本。应用结果表明，逆最优值法可以达到目标成本，减少操作人员总损失5%，生产损失减少34.3%，废品减少5.1%，人工成本增加8.3%。它解决了最优值仍不能达到目标目标且参数可调时的一类问题。它为目标驱动的操作员分配提供了一个决策工具。3%，废品减少5.1%，人工成本增加8.3%。它解决了最优值仍不能达到目标目标且参数可调时的一类问题。它为目标驱动的操作员分配提供了一个决策工具。3%，废品减少5.1%，人工成本增加8.3%。它解决了最优值仍不能达到目标目标且参数可调时的一类问题。它为目标驱动的操作员分配提供了一个决策工具。