Online over time processing of combinatorial problems

In an online environment, jobs arrive over time and there is no information in advance about how many jobs are going to be processed and what their processing times are going to be. In this paper, we study the online scheduling of Boolean Satisfiability (SAT) and Mixed Integer Programming (MIP) inst...

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Vydáno v:Constraints : an international journal Ročník 23; číslo 3; s. 310 - 334
Hlavní autoři: Duque, Robinson, Arbelaez, Alejandro, Díaz, Juan F.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2018
Springer Nature B.V
Springer Verlag
Témata:
Artificial Intelligence
Boolean algebra
Combinatorial analysis
Completion time
Computer Science
Integer programming
Interruption
Lateness
Linear programming
Machine learning
Mixed integer
Operations Research
Operations Research/Decision Theory
Optimization
Production scheduling
Scheduling
Topical Collection on Integration of Constraint Programming
Regression models
Runtime estimation
Online scheduling
Machine learning
Combinatorial problems
Mixed integer programming
Classification models
Mixed Integer Programming
Constraint Programming
SAT Problem
ISSN:1383-7133, 1572-9354
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Shrnutí:In an online environment, jobs arrive over time and there is no information in advance about how many jobs are going to be processed and what their processing times are going to be. In this paper, we study the online scheduling of Boolean Satisfiability (SAT) and Mixed Integer Programming (MIP) instances that are well-known NP-complete problems. Typical online machine scheduling approaches assume that jobs are completed at some point in order to minimize functions related to completion time (e.g., makespan, minimum lateness, total weighted tardiness, etc). In this work, we formalize and present an online over time problem where arriving instances are subject to waiting time constraints. We propose computational approaches that combine the use of machine learning, MIP, and instance interruption heuristics. Unlike other approaches, we attempt to maximize the number of solved instances using single and multiple machine configurations. Our empirical evaluation with well-known SAT and MIP instances, suggest that our interruption heuristics can improve generic ordering policies to solve up to 21.6x and 12.2x more SAT and MIP instances. Additionally, our hybrid approach observed up to 90% of solved instances with respect to a semi clairvoyant policy (SCP).
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ISSN:1383-7133
1572-9354
DOI:10.1007/s10601-018-9287-4