Laborie

AAAI Conferences

In version 2.0, IBM ILOG CP Optimizer has been extended by the introduction of scheduling support based on the concept of optional interval variables. This paper formally describes the new modeling language features available to the users of CP Optimizer for resource-based scheduling. We show that the new language is flexible enough to model problems never before addressed by CP scheduling engines, as well as naturally describing classical scheduling problems found in the literature. This modeling power is based on a small number of general concepts such as intervals, sequences and functions. This makes the modeling language simple, clear and easy to learn, while maintaining the high-level structural aspects of the scheduling model.


Reasoning with Conditional Time-Intervals. Part II: An Algebraical Model for Resources

AAAI Conferences

In version 2.0, IBM ILOG CP Optimizer has been extended by the introduction of scheduling support based on the concept of optional interval variables. This paper formally describes the new modeling language features available to the users of CP Optimizer for resource-based scheduling. We show that the new language is flexible enough to model problems never before addressed by CP scheduling engines, as well as naturally describing classical scheduling problems found in the literature. This modeling power is based on a small number of general concepts such as intervals, sequences and functions. This makes the modeling language simple, clear and easy to learn, while maintaining the high-level structural aspects of the scheduling model.


On constraint programming for a new flexible project scheduling problem with resource constraints

arXiv.org Artificial Intelligence

Real-world project scheduling often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simultaneous scheduling of multiple lots is mandatory in many production planning applications. To meet these requirements, a new flexible resource-constrained multi-project scheduling problem is introduced where both decisions (activity selection flexibility and time flexibility) are integrated. Besides the minimization of makespan, two alternative objectives inspired by a steel industry application case are presented: maximization of balanced length of selected activities (time balance) and maximization of balanced resource utilization (resource balance). New mixed integer and constraint programming (CP) models are proposed for the developed integrated flexible project scheduling problem. The real-world applicability of the suggested CP models is shown by solving large steel industry instances with the CP Optimizer of IBM ILOG CPLEX. Furthermore, benchmark instances on flexible resource-constrained project scheduling problems (RCPSP) are solved to optimality.


Google vs IBM: A Constraint Solving Challenge on the Job-Shop Scheduling Problem

arXiv.org Artificial Intelligence

The job-shop scheduling is one of the most studied optimization problems from the dawn of computer era to the present day. Its combinatorial nature makes it easily expressible as a constraint satisfaction problem. In this paper, we compare the performance of two constraint solvers on the job-shop scheduling problem. The solvers in question are: OR-Tools, an open-source solver developed by Google and winner of the last MiniZinc Challenge, and CP Optimizer, a proprietary IBM constraint solver targeted at industrial scheduling problems. The comparison is based on the goodness of the solutions found and the time required to solve the problem instances. First, we target the classic benchmarks from the literature, then we carry out the comparison on a benchmark that was created with known optimal solution, with size comparable to real-world industrial problems.


SIGMAN96-006.pdf

AAAI Conferences

An Approach to Resource Constrained Project Scheduling JRmes M. Crawford CIRL 1269 University of Oregon Eugene, OR 97403-1269 Abstract This paper gives an overview of a new approach to Resource Constrained Project Scheduling. The approach is based on the combination of a novel optimization technique with limited discrepancy search, and generates the best known solutions to benchmark problems of realistic size and character. Introduction Historically there has often been a mismatch between the types of scheduling problems that have been studied academically and the needs of the manufacturing community. The most obvious difference is that many real problems are much larger than common academic benchmarks. A second, and equally important, difference is that real problems generally involve constraints that have a more complex structure than can be expressed within a limited framework like job shop scheduling.