Single machine due window assignment resource allocation scheduling with job-dependent learning effect

2017 ◽  
Vol 56 (1-2) ◽  
pp. 715-725 ◽  
Author(s):  
Na Yin
Author(s):  
Yu Tian

In this study, the due-window assignment single-machine scheduling problem with resource allocation is considered, where the processing time of a job is controllable as a linear or convex function of amount of resource allocated to the job. Under common due-window and slack due-window assignments, our goal is to determine the optimal sequence of all jobs, the due-window start time, due-window size, and optimal resource allocation such that a sum of the scheduling cost (including weighted earliness/tardiness penalty, weighted number of early and tardy job, weighted due-window start time, and due-window size) and resource consumption cost is minimized. We analyze the optimality properties, and provide polynomial time solutions to solve the problem under four versions of due-window assignment and resource allocation function.


2013 ◽  
Vol 423-426 ◽  
pp. 2224-2227
Author(s):  
Yan Peng Fan ◽  
Chuan Li Zhao

This paper considers single-machine due-window assignment and scheduling with learning effect and resource-dependent processing times. The processing time of a job is a function of its position in a sequence, its starting time, and its resource allocation. The objective is to determine the optimal sequence of jobs and optimal resource allocation so as to minimize the sum of earliness, tardiness, due-windows and resource and operation time cost, the considered problem is molded as an assignment problem and can be solved with a polynomial time algorithm.


2014 ◽  
Vol 31 (05) ◽  
pp. 1450036 ◽  
Author(s):  
Ji-Bo Wang ◽  
Ming-Zheng Wang

We consider a single-machine common due-window assignment scheduling problem, in which the processing time of a job is a function of its position in a sequence and its resource allocation. The window location and size, along with the associated job schedule that minimizes a certain cost function, are to be determined. This function is made up of costs associated with the window location, window size, earliness, and tardiness. For two different processing time functions, we provide a polynomial time algorithm to find the optimal job sequence and resource allocation, respectively.


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