Two-machine group scheduling problem with blocking and anticipatory setups

1993 ◽  
Vol 69 (3) ◽  
pp. 467-481 ◽  
Author(s):  
Rasaratnam Logendran ◽  
Chelliah Sriskandarajah
Keyword(s):  
Group Scheduling ◽  
Scheduling Problem ◽  
Machine Group

scholarly journals Study on Single-Machine Group Scheduling with Due-Window Assignment and Position-Dependent Weights

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Weiguo Liu ◽  
Xuyin Wang ◽  
Xiaoxiao Wang ◽  
Peizhen Zhao
Keyword(s):  
Single Machine ◽  
Group Scheduling ◽  
Weighted Sum ◽  
Scheduling Problem ◽  
Machine Group ◽  
Due Window ◽  
The Common ◽  
Due Window Assignment

This article considers a single-machine group scheduling problem with due-window assignment, where the jobs are classified into groups and the jobs in the same group must be processed in succession. The goal is to minimize the weighted sum of lateness and due-window assignment cost, where the weights depend on the position in which a job is scheduled (i.e., position-dependent weights). For the common, slack, and different due-window assignment methods, we prove that the problem can be solved polynomially, i.e., in O N log N time, where N is the number of jobs.

scholarly journals Scheduling Jobs Families with Learning Effect on the Setup

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Sergio Fichera ◽  
Antonio Costa ◽  
Fulvio Cappadonna
Keyword(s):  
Completion Time ◽  
Learning Effect ◽  
Flow Shop ◽  
Group Scheduling ◽  
Scheduling Problem ◽  
Learning Effects ◽  
Anova Analysis ◽  
Hybrid Metaheuristic ◽  
Set Up ◽  
Over Time

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.

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