Due-Window Assignment and Resource Allocation Scheduling with Truncated Learning Effect and Position-Dependent Weights

Abstract EN

This paper studies single-machine due-window assignment scheduling problems with truncated learning effect and resource allocation simultaneously.

Linear and convex resource allocation functions under common due-window (CONW) assignment are considered.

The goal is to find the optimal due-window starting (finishing) time, resource allocations and job sequence that minimize a weighted sum function of earliness and tardiness, due window starting time, due window size, and total resource consumption cost, where the weight is position-dependent weight.

Optimality properties and polynomial time algorithms are proposed to solve these problems.