Stochastic scheduling problem with varying weight for each job

2010 ◽  
Vol 5 (4) ◽  
pp. 681-689 ◽  
Author(s):  
Manzhan Gu ◽  
Xiwen Lu
Keyword(s):  
Stochastic Scheduling ◽  
Scheduling Problem

Optimal Policy for a Stochastic Scheduling Problem with Applications to Surgical Scheduling

2016 ◽  
Vol 25 (7) ◽  
pp. 1194-1202 ◽  
Author(s):  
Harish Guda ◽  
Milind Dawande ◽  
Ganesh Janakiraman ◽  
Kyung Sung Jung
Keyword(s):  
Optimal Policy ◽  
Stochastic Scheduling ◽  
Scheduling Problem ◽  
Surgical Scheduling

Research on Production Scheduling Problems in Process Industry Based on Ant Colony System

2010 ◽  
Vol 108-111 ◽  
pp. 519-524
Author(s):  
Lie Ping Zhang ◽  
Yun Sheng Zhang
Keyword(s):  
Production Scheduling ◽  
Stochastic Scheduling ◽  
Ant Colony ◽  
Process Industry ◽  
Process Time ◽  
Ant Colony System ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Process Step ◽  
Scheduling Model

In order to improve the production of process industry, the ant colony system(ACS) was applied to the production scheduling problem. Based on the analysis of the production scheduling problem for process industry, a production scheduling model was established, whose goal was to obtain the shortest total process time. The search strategy, heuristic information rules, pheromone updating mechanism, process step starting time and detailed algorithm implementation of ACS were discussed. Using a practical production scheduling problem as an example, the established model and designed algorithm were applied to implement the scheduling simulation. The simulation results show that the scheduling model and algorithm are feasible, and have a better scheduling performance than the stochastic scheduling method, and can be applied to solve practical production scheduling problem for process industry.

Customer Scheduling with Incomplete Information

1995 ◽  
Vol 9 (2) ◽  
pp. 269-284 ◽  
Author(s):  
Ulrich Rieder ◽  
Jürgen Weishaupt
Keyword(s):  
Dynamic Programming ◽  
Incomplete Information ◽  
Bayesian Approach ◽  
Stochastic Scheduling ◽  
Partial Ordering ◽  
Scheduling Problem ◽  
Gittins Index ◽  
Scheduling Model ◽  
Bayesian Dynamic Programming ◽  
Monotonicity Results

A stochastic scheduling model with linear waiting costs and unknown routing probabilities is considered. Using a Bayesian approach and methods of Bayesian dynamic programming, we investigate the finite-horizon stochastic scheduling problem with incomplete information. In particular, we study an equivalent nonstationary bandit model and show the monotonicity of the total expected reward and of the Gittins index. We derive the monotonicity and well-known structural properties of the (greatest) maximizers, the so-called stay-on-a-winnerproperty and the stopping-property. The monotonicity results are based on a special partial ordering on .

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