Heuristics for minimizing maximum lateness on a single machine with family-dependent set-up times

2008 ◽  
Vol 35 (6) ◽  
pp. 2018-2033 ◽  
Author(s):  
Reha Uzsoy ◽  
Juan Diego Velásquez
Keyword(s):  
Single Machine ◽  
Maximum Lateness ◽  
Set Up

A Forward-Backward Procedure for the Single Machine Problem to Minimize Maximum Lateness

1985 ◽  
Vol 17 (3) ◽  
pp. 252-260 ◽  
Author(s):  
R. E. Larson ◽  
M. I. Dessouky ◽  
R. E. Devor
Keyword(s):  
Single Machine ◽  
Maximum Lateness ◽  
Backward Procedure ◽  
Machine Problem

On the scheduling of alternative stochastic jobs on a single machine

1987 ◽  
Vol 19 (4) ◽  
pp. 955-973 ◽  
Author(s):  
K. D. Glazebrook ◽  
N. A. Fay
Keyword(s):  
Resource Allocation ◽  
Single Machine ◽  
Stochastic Scheduling ◽  
Major Area ◽  
Research Planning ◽  
Stochastic Resource Allocation ◽  
Standard Models ◽  
Set Up ◽  
Area Of Application

Standard models in stochastic resource allocation concern the economic processing of all jobs in some set J. We consider a set up in which tasks in various subsets of J are deemed to be alternative to one another, in that only one member of such a subset of alternative tasks will be completed during the evolution of the process. Existing stochastic scheduling methodology for single-machine problems is developed and extended to this novel class of models. A major area of application is in research planning.

On the scheduling of alternative stochastic jobs on a single machine

1987 ◽  
Vol 19 (04) ◽  
pp. 955-973 ◽  
Author(s):  
K. D. Glazebrook ◽  
N. A. Fay
Keyword(s):  
Resource Allocation ◽  
Single Machine ◽  
Stochastic Scheduling ◽  
Major Area ◽  
Research Planning ◽  
Stochastic Resource Allocation ◽  
Standard Models ◽  
Set Up ◽  
Area Of Application

Standard models in stochastic resource allocation concern the economic processing of all jobs in some set J. We consider a set up in which tasks in various subsets of J are deemed to be alternative to one another, in that only one member of such a subset of alternative tasks will be completed during the evolution of the process. Existing stochastic scheduling methodology for single-machine problems is developed and extended to this novel class of models. A major area of application is in research planning.

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