Human error unplanned downtime inferring and job-operator matching based on inverse optimal value method. (November 2020)

• Record Type: Journal Article
• Authors: Zhang, Lili; Li, Zhengfeng; Yang, Yang; Cui, Menghan
• Is Part Of: Computers & industrial engineering. Volume 149(2020)
• Publication Date: 2020-11
• Subjects: Downtime inferring -- Inverse optimal value method -- 0–1 mixed integer nonlinear bi-level programming -- Hybrid parthenogenetic particle swarm optimization algorithm -- Goal-driven operator assignment; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106840
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: Highlights: An inverse optimal value method is proposed to optimize downtime and assignment. A 0–1 mixed integer nonlinear bilevel programming is constructed. A hybrid parthenogenetic particle swarm optimization algorithm is proposed. 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.