Research on common due window assignment flowshop scheduling with learning effect and resource allocation

2019 ◽  
Vol 52 (4) ◽  
pp. 669-686 ◽  
Author(s):  
Hui-Bin Shi ◽  
Ji-Bo Wang
Keyword(s):  
Resource Allocation ◽  
Learning Effect ◽  
Flowshop Scheduling ◽  
Due Window ◽  
Due Window Assignment

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

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Shan-Shan Lin
Keyword(s):  
Resource Allocation ◽  
Learning Effect ◽  
Window Size ◽  
Scheduling Problems ◽  
Polynomial Time Algorithms ◽  
Consumption Cost ◽  
Optimality Properties ◽  
Due Window ◽  
Due Window Assignment ◽  
Time Resource

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.

Single-Machine Due-Window Assignment Scheduling with Resource Allocation and Generalized Earliness/Tardiness Penalties

2021 ◽  
pp. 2150041
Author(s):  
Yu Tian
Keyword(s):  
Resource Allocation ◽  
Single Machine ◽  
Window Size ◽  
Single Machine Scheduling ◽  
Start Time ◽  
Optimal Sequence ◽  
Optimal Resource ◽  
Due Window ◽  
Weighted Earliness ◽  
Due Window Assignment

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.

Single-Machine Due-Window Assignment and Scheduling with Learning Effect and Resource-Dependent Processing Times

2014 ◽  
Vol 31 (05) ◽  
pp. 1450036 ◽  
Author(s):  
Ji-Bo Wang ◽  
Ming-Zheng Wang
Keyword(s):  
Resource Allocation ◽  
Single Machine ◽  
Processing Time ◽  
Window Size ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Earliness And Tardiness ◽  
Processing Times ◽  
Due Window ◽  
Due Window Assignment

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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