An order scheduling problem with position-based learning effect

2016 ◽  
Vol 74 ◽  
pp. 175-186 ◽  
Author(s):  
Jianyou Xu ◽  
Chin-Chia Wu ◽  
Yunqiang Yin ◽  
Chuanli Zhao ◽  
Yi-Tang Chiou ◽  
...  
2018 ◽  
Vol 70 (3) ◽  
pp. 487-501 ◽  
Author(s):  
Chin-Chia Wu ◽  
Win-Chin Lin ◽  
Xingong Zhang ◽  
I-Hong Chung ◽  
Tzu-Hsuan Yang ◽  
...  

2021 ◽  
Vol 58 ◽  
pp. 291-305
Author(s):  
Chin-Chia Wu ◽  
Danyu Bai ◽  
Xingong Zhang ◽  
Shuenn-Ren Cheng ◽  
Jia-Cheng Lin ◽  
...  

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jan-Yee Kung ◽  
Jiahui Duan ◽  
Jianyou Xu ◽  
I-Hong Chung ◽  
Shuenn-Ren Cheng ◽  
...  

In recent years, various customer order scheduling (OS) models can be found in numerous manufacturing and service systems in which several designers, who have developed modules independently for several different products, convene as a product development team, and that team completes a product design only after all the modules have been designed. In real-life situations, a customer order can have some requirements including due dates, weights of jobs, and unequal ready times. Once encountering different ready times, waiting for future order or job arrivals to raise the completeness of a batch is an efficient policy. Meanwhile, the literature releases that few studies have taken unequal ready times into consideration for order scheduling problem. Motivated by this limitation, this study addresses an OS scheduling model with unequal order ready times. The objective function is to find a schedule to optimize the total completion time criterion. To solve this problem for exact solutions, two lower bounds and some properties are first derived to raise the searching power of a branch-and-bound method. For approximate solution, four simulated annealing approaches and four heuristic genetic algorithms are then proposed. At last, several experimental tests and their corresponding statistical outcomes are also reported to examine the performance of all the proposed methods.


2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Sergio Fichera ◽  
Antonio Costa ◽  
Fulvio Cappadonna

The present paper aims to address the flow-shop sequence-dependent group scheduling problem with learning effect (FSDGSLE). The objective function to be minimized is the total completion time, that is, the makespan. The workers are required to carry out manually the set-up operations on each group to be loaded on the generic machine. The operators skills improve over time due to the learning effects; therefore the set-up time of a group under learning effect decreases depending on the order the group is worked in. In order to effectively cope with the issue at hand, a mathematical model and a hybrid metaheuristic procedure integrating features from genetic algorithms (GA) have been developed. A well-known problem benchmark risen from literature, made by two-, three- and six-machine instances, has been taken as reference for assessing performances of such approach against the two most recent algorithms presented by literature on the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGSLE problem under investigation.


Sign in / Sign up

Export Citation Format

Share Document