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Due-window assignment scheduling with learning and deterioration effects

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This paper considers single machine due-window assignment scheduling problems with position-dependent weights. Under the learning and deterioration effects of jobs processing times, our goal is to minimize the weighted sum… Click to show full abstract

This paper considers single machine due-window assignment scheduling problems with position-dependent weights. Under the learning and deterioration effects of jobs processing times, our goal is to minimize the weighted sum of earliness-tardiness, starting time of due-window, and due-window size, where the weights only depends on their position in a sequence (i.e., position-dependent weights). Under common due-window (CONW), slack due-window (SLKW) and different due-window (DIFW) assignments, we show that these problems remain polynomial-time solvable.

Keywords: window; due window; deterioration effects; learning deterioration; window assignment; assignment scheduling

Journal Title: Journal of Industrial and Management Optimization
Year Published: 2021

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