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

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.

A note on parallel-machine scheduling with controllable processing times and job-dependent learning effects

“…Furthermore, let X ijr be a 0/1 variable such that X ijr = 1 if job J j is scheduled in position r on machine M i , and X jr = 0, otherwise. As in Lin [19], the optimal matching of jobs to positions can be obtained by the following assignment problem:…”

“…Recently, there has been increasing attention to scheduling problems involving both controllable processing times and learning effects (see [15,19,20,34,35]). Yin and Wang [39] considered single machine scheduling problem with controllable processing times and learning effects, i.e., the actual processing time P j of job J j in position r is P j (x j , r) = (p j −x j )r a , where p j is normal processing time of J j , x j is compression of the processing time of job J j , 0 ≤ x j ≤ m j , m j is maximum reduction in processing time of job J j , and a ≤ 0 is a learning effect.…”

Penyelesaian Penjadwalan Flexible Job Shop Untuk Minimasi Due Windows dengan Algoritma Genetika

“…Suatu pekerjaan diharapkan dapat diselesaikan pada suatu rentang waktu. Penalti akan diberikan apabila pekerjaan diselesaikan tidak berada pada rentang waktu yang diberikan (Lin, 2020).…”