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

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  • Shan-Shan Lin

Abstract

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.

Suggested Citation

  • Shan-Shan Lin, 2020. "Due-Window Assignment and Resource Allocation Scheduling with Truncated Learning Effect and Position-Dependent Weights," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-7, October.
    • Handle: RePEc:hin:jnddns:9260479
    DOI: 10.1155/2020/9260479
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