Optimal Stochastic Dynamic Scheduling in Multi-Class Queues with Tardiness and/or Earliness Penalties

1994 ◽  
Vol 8 (4) ◽  
pp. 491-509 ◽  
Author(s):  
Dimitrios G. Pandelis ◽  
Demosthenis Teneketzis
Keyword(s):  
Dynamic Scheduling ◽  
Infinite Horizon ◽  
Optimal Strategies ◽  
Stochastic Dynamic ◽  
Preemptive Scheduling ◽  
Due Date ◽  
Discounted Cost ◽  
Earliness And Tardiness ◽  
Scheduling Strategies ◽  
Multi Server

Tasks belonging to N classes arrive for processing in a multi-server facility. Each arriving task has a due date (deterministic or random) associated with the completion of its service. If the service of a task is completed at a time other than the task's due date, an earliness or tardiness penalty is incurred. We determine properties of dynamic nonidling nonpreemptive and dynamic nonidling preemptive scheduling strategies that minimize an infinite horizon expected discounted cost due to the earliness and tardiness penalties. We provide examples that illustrate the properties of the optimal strategies.

scholarly journals Scheduling jobs with stochastically ordered processing times on parallel machines to minimize expected flowtime

1986 ◽  
Vol 23 (03) ◽  
pp. 841-847 ◽  
Author(s):  
R. R. Weber ◽  
P. Varaiya ◽  
J. Walrand
Keyword(s):  
Processing Time ◽  
Parallel Machines ◽  
Preemptive Scheduling ◽  
Total Reward ◽  
Processing Times ◽  
Identical Machines ◽  
Scheduling Strategies

A number of jobs are to be processed using a number of identical machines which operate in parallel. The processing times of the jobs are stochastic, but have known distributions which are stochastically ordered. A reward r(t) is acquired when a job is completed at time t. The function r(t) is assumed to be convex and decreasing in t. It is shown that within the class of non-preemptive scheduling strategies the strategy SEPT maximizes the expected total reward. This strategy is one which whenever a machine becomes available starts processing the remaining job with the shortest expected processing time. In particular, for r(t) = – t, this strategy minimizes the expected flowtime.

Two Informed Dynamic Scheduling Strategies for Iterative LDPC Decoders

2013 ◽  
Vol 61 (3) ◽  
pp. 886-896 ◽  
Author(s):  
Huang-Chang Lee ◽  
Yeong-Luh Ueng ◽  
Shan-Ming Yeh ◽  
Wen-Yen Weng
Keyword(s):  
Dynamic Scheduling ◽  
Scheduling Strategies

scholarly journals Optimal stochastic dynamic scheduling for managing community recovery from natural hazards

2020 ◽  
Vol 193 ◽  
pp. 106627 ◽  
Author(s):  
Saeed Nozhati ◽  
Yugandhar Sarkale ◽  
Edwin K.P. Chong ◽  
Bruce R. Ellingwood
Keyword(s):  
Natural Hazards ◽  
Dynamic Scheduling ◽  
Stochastic Dynamic ◽  
Community Recovery
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