Optimal myopic policies and index policies for stochastic scheduling problems

1994 ◽  
Vol 40 (1) ◽  
pp. 75-89 ◽  
Author(s):  
J�rgen Weishaupt
Keyword(s):  
Stochastic Scheduling ◽  
Scheduling Problems ◽  
Index Policies ◽  
Myopic Policies

scholarly journals Optimality of index policies for stochastic scheduling with switching penalties

1992 ◽  
Vol 29 (04) ◽  
pp. 957-966 ◽  
Author(s):  
Mark P. Van Oyen ◽  
Dimitrios G. Pandelis ◽  
Demosthenis Teneketzis
Keyword(s):  
Optimal Policy ◽  
Time Delays ◽  
Stochastic Scheduling ◽  
Optimal Scheduling ◽  
Scheduling Policies ◽  
Parallel Queues ◽  
Lump Sum ◽  
Index Policies ◽  
Service Policy ◽  
The Impact

We investigate the impact of switching penalties on the nature of optimal scheduling policies for systems of parallel queues without arrivals. We study two types of switching penalties incurred when switching between queues: lump sum costs and time delays. Under the assumption that the service periods of jobs in a given queue possess the same distribution, we derive an index rule that defines an optimal policy. For switching penalties that depend on the particular nodes involved in a switch, we show that although an index rule is not optimal in general, there is an exhaustive service policy that is optimal.

Overview of Stochastic Scheduling Problems

Scheduling ◽  
2011 ◽  
pp. 607-610 ◽  
Author(s):  
Michael L. Pinedo
Keyword(s):  
Stochastic Scheduling ◽  
Scheduling Problems

Strategy evaluation for stochastic scheduling problems with order constraints

1991 ◽  
Vol 23 (01) ◽  
pp. 86-104 ◽  
Author(s):  
K. D. Glazebrook
Keyword(s):  
Single Machine ◽  
Stochastic Scheduling ◽  
Theoretical Framework ◽  
Precedence Constraints ◽  
Scheduling Problems ◽  
Strategy Evaluation ◽  
Order Constraints

A single machine is available to process a collection J of jobs. The machine is free to switch between jobs at any time, but processing must respect a set Γof precedence constraints. Jobs evolve stochastically and earn rewards as they are processed, not otherwise. The theoretical framework of forwards induction/Gittins indexation is used to develop approaches to strategy evaluation for quite general (J,Γ). The performance of both forwards induction strategies and a class of quasi-myopic heuristics is assessed.

scholarly journals Some indexable families of restless bandit problems

2006 ◽  
Vol 38 (3) ◽  
pp. 643-672 ◽  
Author(s):  
K. D. Glazebrook ◽  
D. Ruiz-Hernandez ◽  
C. Kirkbride
Keyword(s):  
Index Theory ◽  
Stochastic Scheduling ◽  
Gittins Index ◽  
Scheduling Problems ◽  
Bandit Problems ◽  
Index Policy ◽  
Restless Bandit ◽  
Machine Maintenance ◽  
State Evolution ◽  
Strong Performance

In 1988 Whittle introduced an important but intractable class of restless bandit problems which generalise the multiarmed bandit problems of Gittins by allowing state evolution for passive projects. Whittle's account deployed a Lagrangian relaxation of the optimisation problem to develop an index heuristic. Despite a developing body of evidence (both theoretical and empirical) which underscores the strong performance of Whittle's index policy, a continuing challenge to implementation is the need to establish that the competing projects all pass an indexability test. In this paper we employ Gittins' index theory to establish the indexability of (inter alia) general families of restless bandits which arise in problems of machine maintenance and stochastic scheduling problems with switching penalties. We also give formulae for the resulting Whittle indices. Numerical investigations testify to the outstandingly strong performance of the index heuristics concerned.

scholarly journals Performance comparison of approximate dynamic programming techniques for dynamic stochastic scheduling

2021 ◽  
Vol 11 (2) ◽  
pp. 178-185
Author(s):  
Yasin Göçgün
Keyword(s):  
Dynamic Programming ◽  
Approximate Dynamic Programming ◽  
Relevant Literature ◽  
Stochastic Scheduling ◽  
Performance Comparison ◽  
Direct Search ◽  
Basis Functions ◽  
Scheduling Problems ◽  
Programming Techniques ◽  
Dynamic Stochastic

This paper focuses on the performance comparison of several approximate dynamic programming (ADP) techniques. In particular, we evaluate three ADP techniques through a class of dynamic stochastic scheduling problems: Lagrangian-based ADP, linear programming-based ADP, and direct search-based ADP. We uniquely implement the direct search-based ADP through basis functions that differ from those used in the relevant literature. The class of scheduling problems has the property that jobs arriving dynamically and stochastically must be scheduled to days in advance. Numerical results reveal that the direct search-based ADP outperforms others in the majority of problem sets generated.

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