CP Optimizer is a generic system, largely based on CP, to model and solve real-world combinatorial optimization problems with a particular focus on planning and scheduling applications. It provides an algebraic language with simple mathematical concepts (such as intervals, sequences or functions) to capture the temporal dimension of planning and scheduling problems in a combinatorial optimization framework. CP Optimizer implements a model-and-run paradigm that vastly reduces the burden on the user to understand CP or scheduling algorithms: modeling is by far the most important. The automatic search integrates a large panel of techniques from Artificial Intelligence (constraint programming, temporal reasoning, learning, ...) and Operations Research (mathematical programming, graph algorithms, local search, ...) into an exact algorithm that provides good performance out of the box and is continuously improving. This tutorial gives an overview of CP Optimizer for planning and scheduling: typical applications, modeling concepts with examples, ingredients of the automatic search, tools and performance.
Industrial project and machine scheduling with Constraint ProgrammingPhilippe Laborie
More often than not, project and machine scheduling problems are addressed either by generic mathematical programming techniques (like MILP) or by problem-specific exact or heuristic approaches. MILP formulations are commonly used to describe the problem in mathematical terms and to provide optimal solutions or bounds to small problem instances. As they usually do not scale well, one usually resorts to using heuristics for handling large and complex industrial problems.
Though constraint programming (CP) techniques represent the state of the art in several classical project and machine scheduling benchmarks and have been used for almost 30 years for solving industrial problems, they are still seldom considered as an alternative approach in the scheduling community. A possible reason is that, for years, in the absence of efficient and robust automatic search algorithms, CP techniques have been difficult to use for non-CP experts.
We will explain why we think this time is over and illustrate our arguments with CP Optimizer, a generic system, largely based on CP, for modeling and solving real-world scheduling problems.
CP Optimizer extends linear programming with an algebraic language using simple mathematical concepts (such as intervals, sequences and functions) to capture the temporal dimension of scheduling problems in a combinatorial optimization framework. CP Optimizer implements a model-and-run paradigm that vastly reduces the burden on the user to understand CP or scheduling algorithms: modeling is by far the most important. The automatic search combines a wide variety of techniques from Artificial Intelligence (constraint programming, temporal reasoning, learning etc.) and Operations Research (mathematical programming, graph algorithms, local search, etc.) in an exact algorithm that provides good performance out of the box and which is continuously improving.
Self-Adapting Large Neighborhood Search: Application to single-mode schedulin...Philippe Laborie
Providing robust scheduling algorithms that can solve a large variety of scheduling problems with good performance is one of the biggest challenge of practical schedulers today. In this paper we present a robust scheduling algorithm based on Self-Adapting Large Neighborhood Search and apply it to a large panel of single-mode scheduling problems. The approach combines Large Neighborhood Search with a portfolio of neighborhoods and completion strategies together with Machine Learning techniques to converge on the most efficient neighborhoods and completion strategies for the problem being solved. The algorithm is evaluated on a set of 21 scheduling benchmarks, most of which are well established in the scheduling community. Despite the generality of the approach, for 17 benchmarks out of 21, its mean relative distance to state-of-the-art problem specific algorithms is less than 4%. It even outperforms state-of-the-art problem-specific algorithms on 7 benchmarks clearly showing that our algorithm offers a valuable compromise between robustness and performance.
IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three ProblemsPhilippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new
scheduling language supported by a robust and efficient automatic search. This presentation illustrates both the expressivity of the modelling language and the robustness of the automatic search on three problems recently studied in the scheduling literature. We show that all three problems
can easily be modelled with CP Optimizer in only a few dozen lines (the complete models are provided) and that on average the automatic search outperforms existing problem specific approaches.
This slide deck was presented at CP-AI-OR 2009 conference. Complete reference:
Philippe Laborie. "IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three Problems". Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CP-AI-OR 2009). Lecture Notes in Computer Science. Volume 5547, 2009, pp 148-162.
A (Not So Short) Introduction to CP Optimizer for SchedulingPhilippe Laborie
CP Optimizer is a generic Constraint Programming (CP) based system to model and solve scheduling problems (among other combinatorial problems). It provides an algebraic language with simple mathematical concepts such as intervals or functions to capture the temporal dimension of scheduling problems in a combinatorial optimization framework. From the very beginning, CP Optimizer was designed with the goal to provide a similar experience as Mathematical Programming (MP) tools like CPLEX, with a strong focus on usability. In particular CP Optimizer implements a model & run paradigm that does not require the user to understand Constraint Programming or scheduling algorithms: declarative modeling is the only thing that matters. The automatic search provides good out of the box performance and is continuously improving from version to version. The convergence with MP goes even further, with a convergence of the tools and functionalities around the engine like an input/output format, modeling assistance with warnings and conflict refiner, interactive executable, etc. These tools accelerate the development and maintenance of models for complex industrial scheduling problems that will be efficiently solved by the automatic search. This tutorial, heavily illustrated with examples, gives an overview of CP Optimizer for scheduling. No prior knowledge of Constraint Programming is required.
Modeling and Solving Resource-Constrained Project Scheduling Problems with IB...Philippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new scheduling language supported by a robust and efficient automatic search. We show how the main features of resource-constrained project scheduling such as work-breakdown structures, optional tasks, different types of resources, multiple modes and skills, resource calendars and objective functions such as earliness/tardiness, unperformed tasks or resource costs can be modeled in CP Optimizer. The robustness of the automatic search will be illustrated on some classical resource-constrained project scheduling benchmarks.
This slide deck was presented at EURO 2009 conference (http://www.euro-2009.de/).
Philippe Laborie
Modeling and Solving Scheduling Problems with CP OptimizerPhilippe Laborie
This presentation focuses on using CP Optimizer to address scheduling problems. We will initially cover modeling concepts related with scheduling in CP Optimizer. Using examples we will then provide details on tools, functionalities and tips for speeding-up the development of your scheduling models and improving their efficiency.
Industrial project and machine scheduling with Constraint ProgrammingPhilippe Laborie
More often than not, project and machine scheduling problems are addressed either by generic mathematical programming techniques (like MILP) or by problem-specific exact or heuristic approaches. MILP formulations are commonly used to describe the problem in mathematical terms and to provide optimal solutions or bounds to small problem instances. As they usually do not scale well, one usually resorts to using heuristics for handling large and complex industrial problems.
Though constraint programming (CP) techniques represent the state of the art in several classical project and machine scheduling benchmarks and have been used for almost 30 years for solving industrial problems, they are still seldom considered as an alternative approach in the scheduling community. A possible reason is that, for years, in the absence of efficient and robust automatic search algorithms, CP techniques have been difficult to use for non-CP experts.
We will explain why we think this time is over and illustrate our arguments with CP Optimizer, a generic system, largely based on CP, for modeling and solving real-world scheduling problems.
CP Optimizer extends linear programming with an algebraic language using simple mathematical concepts (such as intervals, sequences and functions) to capture the temporal dimension of scheduling problems in a combinatorial optimization framework. CP Optimizer implements a model-and-run paradigm that vastly reduces the burden on the user to understand CP or scheduling algorithms: modeling is by far the most important. The automatic search combines a wide variety of techniques from Artificial Intelligence (constraint programming, temporal reasoning, learning etc.) and Operations Research (mathematical programming, graph algorithms, local search, etc.) in an exact algorithm that provides good performance out of the box and which is continuously improving.
Self-Adapting Large Neighborhood Search: Application to single-mode schedulin...Philippe Laborie
Providing robust scheduling algorithms that can solve a large variety of scheduling problems with good performance is one of the biggest challenge of practical schedulers today. In this paper we present a robust scheduling algorithm based on Self-Adapting Large Neighborhood Search and apply it to a large panel of single-mode scheduling problems. The approach combines Large Neighborhood Search with a portfolio of neighborhoods and completion strategies together with Machine Learning techniques to converge on the most efficient neighborhoods and completion strategies for the problem being solved. The algorithm is evaluated on a set of 21 scheduling benchmarks, most of which are well established in the scheduling community. Despite the generality of the approach, for 17 benchmarks out of 21, its mean relative distance to state-of-the-art problem specific algorithms is less than 4%. It even outperforms state-of-the-art problem-specific algorithms on 7 benchmarks clearly showing that our algorithm offers a valuable compromise between robustness and performance.
IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three ProblemsPhilippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new
scheduling language supported by a robust and efficient automatic search. This presentation illustrates both the expressivity of the modelling language and the robustness of the automatic search on three problems recently studied in the scheduling literature. We show that all three problems
can easily be modelled with CP Optimizer in only a few dozen lines (the complete models are provided) and that on average the automatic search outperforms existing problem specific approaches.
This slide deck was presented at CP-AI-OR 2009 conference. Complete reference:
Philippe Laborie. "IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three Problems". Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CP-AI-OR 2009). Lecture Notes in Computer Science. Volume 5547, 2009, pp 148-162.
A (Not So Short) Introduction to CP Optimizer for SchedulingPhilippe Laborie
CP Optimizer is a generic Constraint Programming (CP) based system to model and solve scheduling problems (among other combinatorial problems). It provides an algebraic language with simple mathematical concepts such as intervals or functions to capture the temporal dimension of scheduling problems in a combinatorial optimization framework. From the very beginning, CP Optimizer was designed with the goal to provide a similar experience as Mathematical Programming (MP) tools like CPLEX, with a strong focus on usability. In particular CP Optimizer implements a model & run paradigm that does not require the user to understand Constraint Programming or scheduling algorithms: declarative modeling is the only thing that matters. The automatic search provides good out of the box performance and is continuously improving from version to version. The convergence with MP goes even further, with a convergence of the tools and functionalities around the engine like an input/output format, modeling assistance with warnings and conflict refiner, interactive executable, etc. These tools accelerate the development and maintenance of models for complex industrial scheduling problems that will be efficiently solved by the automatic search. This tutorial, heavily illustrated with examples, gives an overview of CP Optimizer for scheduling. No prior knowledge of Constraint Programming is required.
Modeling and Solving Resource-Constrained Project Scheduling Problems with IB...Philippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new scheduling language supported by a robust and efficient automatic search. We show how the main features of resource-constrained project scheduling such as work-breakdown structures, optional tasks, different types of resources, multiple modes and skills, resource calendars and objective functions such as earliness/tardiness, unperformed tasks or resource costs can be modeled in CP Optimizer. The robustness of the automatic search will be illustrated on some classical resource-constrained project scheduling benchmarks.
This slide deck was presented at EURO 2009 conference (http://www.euro-2009.de/).
Philippe Laborie
Modeling and Solving Scheduling Problems with CP OptimizerPhilippe Laborie
This presentation focuses on using CP Optimizer to address scheduling problems. We will initially cover modeling concepts related with scheduling in CP Optimizer. Using examples we will then provide details on tools, functionalities and tips for speeding-up the development of your scheduling models and improving their efficiency.
The slides presents end-to-end example of architecture modeling with EAST-ADL using a PowerWindow example following the V-model. MetaEdit+ tool provides EAST-ADL support for safety analysis, code generation and integration with external tools.
Recent advances on large scheduling problems in CP OptimizerPhilippe Laborie
After recapping the main principles of CP Optimizer, this short talk focuses on recent advances in the automatic search allowing to handle some scheduling problems with hundreds of thousands of activities in a matter of seconds.
The slides presents end-to-end example of architecture modeling with EAST-ADL using a PowerWindow example following the V-model. MetaEdit+ tool provides EAST-ADL support for safety analysis, code generation and integration with external tools.
Recent advances on large scheduling problems in CP OptimizerPhilippe Laborie
After recapping the main principles of CP Optimizer, this short talk focuses on recent advances in the automatic search allowing to handle some scheduling problems with hundreds of thousands of activities in a matter of seconds.
CP Optimizer: a generic optimization engine at the crossroad of AI and OR fo...Philippe Laborie
Classical scheduling problems (like job-shop or RCPSP) are among the most difficult problems studied in combinatorial optimization. Still, they are far from accounting for all the complexity of industrial scheduling applications. Since more than 20 years, our team at ILOG (now IBM) develops and integrates a large panel of techniques from AI (constraint programing, temporal reasoning, learning, ...) and OR (mathematical programming, graph algorithms, local search, ...) to solve our customers most complex scheduling problems. These works have lead to the design of CP Optimizer, a generic solver based on a very expressive (but still, quite concise) mathematical modeling language to formulate complex scheduling problems. The models are solved with an automatic search algorithm that is exact, efficient, robust and continuously improving. This presentation gives an overview of CP Optimizer illustrated on some examples.
Classical scheduling problems (like job-shop or RCPSP) are among the most difficult problems studied in combinatorial optimization. Still, they are far from accounting for all the complexity of industrial scheduling applications. Since more than 20 years, our team at ILOG (now IBM) develops and integrates a large panel of techniques from AI (constraint programming, temporal reasoning, learning, ...) and OR (mathematical programming, graph algorithms, local search, ...) to solve our customers most complex scheduling problems. These works have lead to the design of CP Optimizer, a generic solver based on a very expressive (but still, quite concise) mathematical modeling language to formulate complex scheduling problems. The models are solved with an automatic search algorithm that is exact, efficient, robust and continuously improving. This talk gives a short overview of CP Optimizer.
Optimization: from mathematical tools to real applicationsPhilippe Laborie
The existence of powerful mathematical optimization engines is a necessary but not a sufficient condition for the pervasion of optimization technologies in the real world. This seminar, presented in 2013, explores some of the challenges related with the development of optimization applications as well as some general guidelines to avoid common pitfalls. It is illustrated with IBM ILOG optimization technologies and solutions.
An Update on the Comparison of MIP, CP and Hybrid Approaches for Mixed Resour...Philippe Laborie
We consider a well known resource allocation and scheduling problem for which different approaches like mixed-integer programming (MIP), constraint programming (CP), constraint integer programming (CIP), logic-based Benders decompositions (LBBD) and SAT-modulo theories (SMT) have been proposed and experimentally compared in the last decade. Thanks to the recent improvements in CP Optimizer, a commercial CP solver for solving generic scheduling problems, we show that a standalone tiny CP model can out-perform all previous approaches and close all the 335 instances of the benchmark. The article explains which components of the automatic search of CP Optimizer are responsible for this success. We finally propose an extension of the original benchmark with larger and more challenging instances.
This presentation introduces CP Optimizer a model & run optimization engine for solving discrete combinatorial problems with a particular focus on scheduling problems.
Conditional interval variables: A powerful concept for modeling and solving c...Philippe Laborie
Scheduling is not only about deciding when to schedule a predefined set of activities. Most of real-world scheduling problems also involve selecting a subset of activities (oversubscribed problems) and a particular way to execute them (resource or mode allocation, alternative recipes, preemptive activity splitting, etc.). We present the notion of conditional interval variable in the context of Constraint Programming and show how this concept can be leveraged to model and solve complex scheduling problems involving both temporal and non-temporal decisions.
This slide deck was presented at the 21st International Symposium on Mathematical Programming (ISMP 2012).
Philippe Laborie
Solving Industrial Scheduling Problems with Constraint ProgrammingPhilippe Laborie
Scheduling problems represent an important class of application for Constraint Programming (CP) in the industry. For more than 20 years, our team at ILOG (now IBM) has been developing CP technology and applying it to solve our customers' scheduling problems. In the first part of the talk, we will present some extensions of CP (interval variables, functions, sequences) that capture the temporal dimension of scheduling and make it possible/easier to design efficient models for complex problems. But a powerful modeling language is not sufficient: in an industrial context, one must also simplify the complete process of model design (connection to data, data processing and consistency checking, model debugging, etc.) and problem resolution (robust automatic search algorithm, warm start, model chaining, etc.), this will be the topic of the second part of the talk. The presentation will be illustrated with examples using IBM ILOG CP Optimizer.
This paper presents the concept of objective landscape in the context of Constraint Programming. An objective landscape is a light-weight structure providing some information on the relation between decision variables and objective values, that can be quickly computed once and for all at the beginning of the resolution and is used to guide the search. It is particularly useful on decision variables with large domains and with a continuous semantics, which is typically the case for time or resource quantity variables in scheduling problems. This concept was recently implemented in the automatic search of CP Optimizer and resulted in an average speed-up of about 50% on scheduling problems with up to almost 2 orders of magnitude for some applications.
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