Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results

AbstractPlanning, scheduling and constraint satisfaction are important areas in artificial intelligence (AI) with broad practical applicability. Many real-world problems can be formulated as AI planning and scheduling (P&S) problems, where resources must be allocated to optimize overall performance objectives. Frequently, solving these problems requires an adequate mixture of planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Constraint satisfaction plays an important role in solving such real-life problems, and integrated techniques that manage P&S with constraint satisfaction are particularly useful. Knowledge engineering supports the solution of such problems by providing adequate modelling techniques and knowledge extraction techniques for improving the performance of planners and schedulers. Briefly speaking, knowledge engineering tools serve as a bridge between the real world and P&S systems.

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Editorial for AI planning and scheduling

Journal of Scheduling ◽

10.1007/s10951-008-0095-3 ◽

2009 ◽

Vol 12 (3) ◽

pp. 225-226

Author(s):

Rong Qu ◽

Maria Fox ◽

Derek Long

Keyword(s):

Ai Planning ◽

Planning And Scheduling

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On-line Planning and Scheduling: An Application to Controlling Modular Printers

Journal of Artificial Intelligence Research ◽

10.1613/jair.3184 ◽

2011 ◽

Vol 40 ◽

pp. 415-468 ◽

Cited By ~ 9

Author(s):

W. Ruml ◽

M. B. Do ◽

R. Zhou ◽

M. P.J. Fromherz

Keyword(s):

Intelligent Systems ◽

High Speed ◽

Ai Planning ◽

Line Planning ◽

Planning And Scheduling ◽

Space Planning ◽

On Line ◽

Control Knowledge ◽

General Architecture ◽

Domain Independent

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge.

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Flexible scheduling of diagnostic tests in automotive manufacturing

Flexible Services and Manufacturing Journal ◽

10.1007/s10696-021-09438-3 ◽

2022 ◽

Author(s):

Simone König ◽

Maximilian Reihn ◽

Felipe Gelinski Abujamra ◽

Alexander Novy ◽

Birgit Vogel-Heuser

Keyword(s):

Assembly Line ◽

Resource Constraints ◽

Mutual Exclusion ◽

Mathematical Formulation ◽

Testing Procedure ◽

Flexible Scheduling ◽

Planning And Scheduling ◽

Automotive Manufacturing ◽

Boolean Satisfiability Problem ◽

Starting Point

AbstractThe car of the future will be driven by software and offer a variety of customisation options. Enabling these customisation options forces modern automotive manufacturers to update their standardised scheduling concepts for testing and commissioning cars. A flexible scheduling concept means that every chosen customer configuration code must have its own testing procedure. This concept is essential to provide individual testing workflows where the time and resources are optimised for every car. Manual scheduling is complicated due to constraints on time, predecessor-successor relationships, mutual exclusion criteria, resources and status conditions on the car engineering and assembly line. Applied methods to handle the mathematical formulation for the corresponding industrial optimisation problem and its implementation are not yet available. This paper presents a procedure for automated and non-preemptive scheduling in the testing and commissioning of cars, which is built on a Boolean satisfiability problem on parallel and identical machines with temporal and resource constraints. The presented method is successfully implemented and evaluated on a variant assembly line of an automotive Original Equipment Manufacturer. This paper is the starting point for an automated workflow planning and scheduling process in automotive manufacturing.

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Robots in Retirement Homes: Applying Off-the-Shelf Planning and Scheduling to a Team of Assistive Robots

Journal of Artificial Intelligence Research ◽

10.1613/jair.5306 ◽

2017 ◽

Vol 58 ◽

pp. 523-590 ◽

Cited By ~ 10

Author(s):

Tony T. Tran ◽

Tiago Vaquero ◽

Goldie Nejat ◽

J. Christopher Beck

Keyword(s):

Constraint Programming ◽

Real World ◽

Ai Planning ◽

Retirement Home ◽

Line Planning ◽

Planning And Scheduling ◽

Planning Time ◽

Assistive Robots ◽

Real World Application ◽

Modelling Assumptions

This paper investigates three different technologies for solving a planning and scheduling problem of deploying multiple robots in a retirement home environment to assist elderly residents. The models proposed make use of standard techniques and solvers developed in AI planning and scheduling, with two primary motivations. First, to find a planning and scheduling solution that we can deploy in our real-world application. Second, to evaluate planning and scheduling technology in terms of the ``model-and-solve'' functionality that forms a major research goal in both domain-independent planning and constraint programming. Seven variations of our application are studied using the following three technologies: PDDL-based planning, time-line planning and scheduling, and constraint-based scheduling. The variations address specific aspects of the problem that we believe can impact the performance of the technologies while also representing reasonable abstractions of the real world application. We evaluate the capabilities of each technology and conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application. PDDL-based planning is able to find mostly low quality solutions while the timeline approach was unable to model the full problem without alterations to the solver code, thus moving away from the model-and-solve paradigm. It would be misleading to conclude that constraint programming is ``better'' than PDDL-based planning in a general sense, both because we have examined a single application and because the approaches make different assumptions about the knowledge one is allowed to embed in a model. Nonetheless, we believe our investigation is valuable for AI planning and scheduling researchers as it highlights these different modelling assumptions and provides insight into avenues for the application of AI planning and scheduling for similar robotics problems. In particular, as constraint programming has not been widely applied to robot planning and scheduling in the literature, our results suggest significant untapped potential in doing so.

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The 3rd International Planning Competition: Results and Analysis

Journal of Artificial Intelligence Research ◽

10.1613/jair.1240 ◽

2003 ◽

Vol 20 ◽

pp. 1-59 ◽

Cited By ~ 39

Author(s):

D. Long ◽

M. Fox

Keyword(s):

Statistical Analysis ◽

Ai Planning ◽

Relative Difficulty ◽

Comparative Performance ◽

Planning And Scheduling ◽

International Conference ◽

The Third ◽

The Future ◽

Problem Instances ◽

International Planning

This paper reports the outcome of the third in the series of biennial international planning competitions, held in association with the International Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to describing the domains, the planners and the objectives of the competition, the paper includes analysis of the results. The results are analysed from several perspectives, in order to address the questions of comparative performance between planners, comparative difficulty of domains, the degree of agreement between planners about the relative difficulty of individual problem instances and the question of how well planners scale relative to one another over increasingly difficult problems. The paper addresses these questions through statistical analysis of the raw results of the competition, in order to determine which results can be considered to be adequately supported by the data. The paper concludes with a discussion of some challenges for the future of the competition series.

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Advanced planning and scheduling based on precedence and resource constraints for e-plant chains

International Journal of Production Research ◽

10.1080/00207540410001691956 ◽

2004 ◽

Vol 42 (15) ◽

pp. 2941-2955 ◽

Cited By ~ 36

Author(s):

C. Moon * ◽

J. S. Kim ◽

M. Gen

Keyword(s):

Resource Constraints ◽

Planning And Scheduling ◽

Advanced Planning And Scheduling ◽

Advanced Planning

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Validation and verification issues in a timeline-based planning system

The Knowledge Engineering Review ◽

10.1017/s0269888910000160 ◽

2010 ◽

Vol 25 (3) ◽

pp. 299-318 ◽

Cited By ~ 8

Author(s):

Amedeo Cesta ◽

Alberto Finzi ◽

Simone Fratini ◽

Andrea Orlandini ◽

Enrico Tronci

Keyword(s):

Resource Constraints ◽

Knowledge Engineering ◽

Original System ◽

Planning System ◽

Direct Access ◽

Validation And Verification ◽

Planning And Scheduling ◽

Evaluation Functions ◽

Engineering Environment ◽

Effective Use

AbstractTo foster effective use of artificial intelligence planning and scheduling (P&S)systems in the real world, it is of great importance to both (a) broaden direct access to the technology for the end users and (b) significantly increase their trust in such technology. AutomatedP&Ssystems often bring solutions to the users that are neither ‘obvious’ nor immediately acceptable to them. This is because these tools directly reason on causal, temporal, and resource constraints; moreover, they employ resolution processes designed to optimize the solution with respect to non-trivial evaluation functions. Knowledge engineering environments aim at simplifying direct access to the technology for people other than the original system designers, while the integration of validation and verification (V&V) capabilities in such environments may potentially enhance the users’ trust in the technology. Somehow,V&Vtechniques may represent a complementary technology, with respect toP&S, that contributes to developing richer software environments to synthesize a new generation of robust problem-solving applications. The integration ofV&VandP&Stechniques in a knowledge engineering environment is the topic of this paper. In particular, it analyzes the use of state-of-the-artV&Vtechnology to support knowledge engineering for a timeline-based planning system called MrSPOCK. The paper presents the application domain for which the automated solver has been developed, introduces the timeline-based planning ideas, and then describes the different possibilities to applyV&Vto planning. Hence, it continues by describing the step of addingV&Vfunctionalities around the specialized planner, MrSPOCK. New functionalities have been added to perform both model validation and plan verification. Lastly, a specific section describes the benefits as well as the performance of such functionalities.

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