Dynamic Constraint Models for Planning and Scheduling Problems (original) (raw)
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Mixing planning and scheduling to model complex process environments
In most current APS (Advanced Planning and Scheduling) systems the planning and scheduling tasks are handled separately using different methods and technology from the areas like Artificial Intelligence and Operations Research. Recently, Constraint Programming becomes a roof over several solving technologies that allow us to solve both planning and scheduling tasks within single declarative framework. In the paper, we suggest mixing planning and scheduling tasks within single system that is capable to solve more complicated scheduling problems in complex process environments. We analyse different views of planning and scheduling, identify the similarities and the differences of both tasks and we propose a general structure of scheduler with some planning capabilities. In the second part of the paper, we compare various modelling approaches from the mixed planning and scheduling point of view. We concentrate on capabilities of the models to capture typical problems in complex process environments primarily but the conclusions are applicable to other (non-process) problem areas. The presented results make the basics of a generic scheduling engine that is currently implemented within the VisOpt project.
Computer Science and Software Engineering, 2014
1 The conventional wisdom and practice is that planning and scheduling tasks are solved separately using different methods and approaches. However, recent development in industrial planning and scheduling demands for mixing both tasks to allow modelling of wider class of problems. The purpose of this paper is to present a framework for mixing planning and scheduling tasks within single system. We analyse traditional views of planning and scheduling and we highlight the drawbacks of separating both tasks when applied to modelling complex process environments. We give some real-life examples where the mixed approach helps to model the problems and we propose a generic framework for such mixture. We also argue for using constraint programming as the underlying solving technology and, finally, we describe some constraint models based on the proposed framework. Although, we concentrate on planning and scheduling in complex process environments we believe that the results contribute to both planning and scheduling communities in general.
Toward mixed planning and scheduling
2014
Abstract. 1 The conventional wisdom and practice is that planning and scheduling tasks are solved separately using different methods and approaches. However, recent development in industrial planning and scheduling demands for mixing both tasks to allow modelling of wider class of problems. The purpose of this paper is to present a framework for mixing planning and scheduling tasks within single system. We analyse traditional views of planning and scheduling and we highlight the drawbacks of separating both tasks when applied to modelling complex process environments. We give some real-life examples where the mixed approach helps to model the problems and we propose a generic framework for such mixture. We also argue for using constraint programming as underlying solving technology and, finally, we describe some constraint models based on the proposed framework. Although, we concentrate on planning and scheduling in complex process environments we believe that the results contribute...
Towards mixed planning and scheduling
1 The conventional wisdom and practice is that planning and scheduling tasks are solved separately using different methods and approaches. However, recent development in industrial planning and scheduling demands for mixing both tasks to allow modelling of wider class of problems. The purpose of this paper is to present a framework for mixing planning and scheduling tasks within single system. We analyse traditional views of planning and scheduling and we highlight the drawbacks of separating both tasks when applied to modelling complex process environments. We give some real-life examples where the mixed approach helps to model the problems and we propose a generic framework for such mixture. We also argue for using constraint programming as underlying solving technology and, finally, we describe some constraint models based on the proposed framework. Although, we concentrate on planning and scheduling in complex process environments we believe that the results contribute to both planning and scheduling communities in general.
Conceptual Models for Combined Planning and Scheduling
Electronic Notes in Discrete Mathematics, 2000
Planning and scheduling attracts an unceasing attention of computer science community. Several research areas like Artificial Intelligence, Operations Research and Constraint Programming joined their power to tackle the problems brought by real industrial life. Among them Constraint Programming plays the integrating role because it provides nice declarative capabilities for modelling and, at the same time, it can exploit directly the successful methods developed in AI and OR. In this paper we analyse the problems behind industrial planning and scheduling. In particular we give a survey of possible conceptual models for scheduling problems with some planning features. We compare their advantages and drawbacks and we explain the industrial background. These models were studied within the VisOpt project whose task is to develop a generic scheduling engine for complex production environments.
Introduction to planning, scheduling and constraint satisfaction
Journal of Intelligent Manufacturing, 2010
Planning, scheduling and constraint satisfaction are important areas in Artificial Intelligence (AI). Many real-world problems are known as AI planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. Therefore, solving these problems requires an adequate mixture of planning, scheduling and resource allocation to competing goal activities over time in the presence of complex statedependent constraints. Constraint satisfaction plays also an important role to solve real-life problems, so that integrated techniques that manage planning and scheduling with constraint satisfaction remains necessary.
Dynamic constraint models for complex production environments
Planning and scheduling attracts an unceasing attention of computer science community. However, despite of similar character of both tasks, in most current systems planning and scheduling problems are usually solved independently using different methods. Recent development of Constraint Programming brings a new breeze to these areas. It allows using the same techniques for modelling planning and scheduling problems as well as exploiting successful methods developed in Artificial Intelligence and Operations Research. Currently, scheduling is the most successful application area of constraint programming. In the paper we analyse the problems behind planning and scheduling in complex production environments. We give a survey of three conceptual models developed to model such environments. We discuss their industrial background and compare their advantages and disadvantages. The models were studied within the VisOpt project whose goal is to developed a generic scheduling engine applicable to various complex production environments. However the proposed conceptual models can be applied to other (non-production) problem areas where joined scheduling and planning capabilities are required.
Constraint programming for dynamic scheduling problems
Scheduling problems considered in the literature are often static (activities are known in advance and constraints are fixed). However, every real-life schedule is subject to unexpected events. In these cases, a new solution is needed in a preferably short time and as close as possible to the current solution. In this paper, we present an exact approach for solving dynamic Resource-Constrained Project Scheduling Problems or RCPSP. This approach combines explanation-based constraint programming and operational research techniques. We present our first experimental results that show impressive improvements in both computation time and stability when comparing our approach to a re-execution from scratch.
Slot Models for Schedulers Enhanced by Planning Capabilities
Scheduling is one of the most successful application areas of constraint programming and, recently, many scheduling problems were modelled and solved by means of constraints. Most of these models confine to a conventional formulation of a constraint satisfaction problem that requires all the variables and the constraints to be specified in advance. However, many application areas like complex process environments require a dynamic model where new activities are introduced during scheduling. In the paper we propose a framework for constraint modelling of dynamic scheduling problems with activities generated during scheduling. We also show how some typical scheduling sub-problems like alternatives, setups and processing of byproducts can be modelled in this framework.