Journal ArticleDOI
A theoretic and practical framework for scheduling in a stochastic environment
TLDR
A typology is proposed that distinguishes between proactive, progressive, and revision approaches, and a theoretic model integrating those three approaches is defined, focusing on scheduling and schedule execution.Abstract:
There are many systems and techniques that address stochastic planning and scheduling problems, based on distinct and sometimes opposite approaches, especially in terms of how generation and execution of the plan, or the schedule, are combined, and if and when knowledge about the uncertainties is taken into account. In many real-life problems, it appears that many of these approaches are needed and should be combined, which to our knowledge has never been done. In this paper, we propose a typology that distinguishes between proactive, progressive, and revision approaches. Then, focusing on scheduling and schedule execution, a theoretic model integrating those three approaches is defined. This model serves as a general template to implement a system that will fit specific application needs: we introduce and discuss our experimental prototypes which validate our model in part, and suggest how this framework could be extended to more general planning systems.read more
Citations
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Journal ArticleDOI
Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey
Mitsuo Gen,Lin Lin +1 more
TL;DR: This paper focuses on the design of multiobjective evolutionary algorithms (MOEAs) to solve a variety of scheduling problems, and introduces fitness assignment mechanism and performance measures for solving multiple objective optimization problems.
Journal ArticleDOI
Discrete time–cost–environment trade-off problem for large-scale construction systems with multiple modes under fuzzy uncertainty and its application to Jinping-II Hydroelectric Project
TL;DR: In this paper, a discrete time-cost-environment trade-off problem for large-scale construction systems with multiple modes under fuzzy uncertainty is presented, in which the total project duration is regarded as a fuzzy variable.
Journal ArticleDOI
Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns
Maroua Nouiri,Abdelghani Bekrar,Abderrazak Jemai,Damien Trentesaux,Ahmed Chiheb Ammari,Smail Niar +5 more
TL;DR: The flexible job shop scheduling problem under machine breakdowns is considered and a two stages particle swarm optimization (2S-PSO) is proposed to solve the problem assuming that there is only one breakdown.
Journal ArticleDOI
Dynamic optimization of chemotherapy outpatient scheduling with uncertainty
Shoshana Hahn-Goldberg,Michael W. Carter,J. Christopher Beck,Maureen E. Trudeau,Philomena Sousa,Kathy Beattie +5 more
TL;DR: This work proposes dynamic template scheduling, a novel technique that combines proactive and online optimization and applies it to the chemotherapy outpatient scheduling problem, and finds improvements in makespan of up to 20 % when usingynamic template scheduling compared to current practice.
References
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Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
MonographDOI
Markov Decision Processes
P. Whittle,M. L. Puterman +1 more
TL;DR: Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria, and explores several topics that have received little or no attention in other books.
Book
Principles of Artificial Intelligence
TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.
Journal ArticleDOI
The Shifting Bottleneck Procedure for Job Shop Scheduling
TL;DR: An approximation method for solving the minimum makespan problem of job shop scheduling by sequences the machines one by one, successively, taking each time the machine identified as a bottleneck among the machines not yet sequenced.
Journal ArticleDOI
Project scheduling under uncertainty: survey and research potentials
Willy Herroelen,Roel Leus +1 more
TL;DR: The fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, fuzzy project Scheduling, robust (proactive) scheduling and sensitivity analysis are reviewed.