Two Clause Learning Approaches for Disjunctive Scheduling

Siala, Mohamed; Artigues, Christian; Hébrard, Emmanuel

doi:10.1007/978-3-319-23219-5_28

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…These strategies achieve the goal of obtaining the best-known result for a given instance but, unfortunately, cannot be replicated on other instances. For example, Vilím et al [65] present the optimal solution value for the instances TAI27 and TAI28 (among others), but the optimal solution value for the TAI29 problem was proposed by Siala et al [66].…”

Section: Quality Of Solutions Analysismentioning

confidence: 99%

Intelligent Scheduling with Reinforcement Learning

et al. 2021

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In this paper, we present and discuss an innovative approach to solve Job Shop scheduling problems based on machine learning techniques. Traditionally, when choosing how to solve Job Shop scheduling problems, there are two main options: either use an efficient heuristic that provides a solution quickly, or use classic optimization approaches (e.g., metaheuristics) that take more time but will output better solutions, closer to their optimal value. In this work, we aim to create a novel architecture that incorporates reinforcement learning into scheduling systems in order to improve their overall performance and overcome the limitations that current approaches present. It is also intended to investigate the development of a learning environment for reinforcement learning agents to be able to solve the Job Shop scheduling problem. The reported experimental results and the conducted statistical analysis conclude about the benefits of using an intelligent agent created with reinforcement learning techniques. The main contribution of this work is proving that reinforcement learning has the potential to become the standard method whenever a solution is necessary quickly, since it solves any problem in very few seconds with high quality, approximate to the optimal methods.

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Section: Quality Of Solutions Analysismentioning

confidence: 99%

Intelligent Scheduling with Reinforcement Learning

et al. 2021

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“…Furthermore, disjunctive scheduling problems, a subfamily of RCPSP addressing unary resources (in our terms global resources), have been effectively solved, e.g. by lazy clause generation [33].…”

Section: Introductionmentioning

confidence: 99%

Time-Aware Test Case Execution Scheduling for Cyber-Physical Systems

Mossige

Gotlieb

Spieker

et al. 2017

Lecture Notes in Computer Science

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Testing cyber-physical systems involves the execution of test cases on target-machines equipped with the latest release of a software control system. When testing industrial robots, it is common that the target machines need to share some common resources, e.g., costly hardware devices, and so there is a need to schedule test case execution on the target machines, accounting for these shared resources. With a large number of such tests executed on a regular basis, this scheduling becomes difficult to manage manually. In fact, with manual test execution planning and scheduling, some robots may remain unoccupied for long periods of time and some test cases may not be executed. This paper introduces TC-Sched, a time-aware method for automated test case execution scheduling. TC-Sched uses Constraint Programming to schedule tests to run on multiple machines constrained by the tests' access to shared resources, such as measurement or networking devices. The CP model is written in SICStus Prolog and uses the Cumulatives global constraint. Given a set of test cases, a set of machines, and a set of shared resources, TC-Sched produces an execution schedule where each test is executed once with minimal time between when a source code change is committed and the test results are reported to the developer. Experiments reveal that TC-Sched can schedule 500 test cases over 100 machines in less than 4 minutes for 99.5% of the instances. In addition, TC-Sched largely outperforms simpler methods based on a greedy algorithm and is suitable for deployment on industrial robot testing.

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Solution Repair by Inequality Network Propagation in LocalSolver

Blaise

Artigues

Benoist³

2020

Parallel Problem Solving From Nature – PPSN XVI

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This paper focuses on optimization problems whose constraints comprise a network of binary and ternary linear inequalities. These constraints are often encountered in the fields of scheduling, packing, layout, and mining. Alone, small-neighborhood local search algorithms encounter difficulties on these problems. Indeed, moving from a good solution to another requires small changes on many variables, due to the tight satisfaction of the constraints.The solution we implemented in LocalSolver is a kind of constraint propagation: when the solution obtained after a local transformation is infeasible, we gradually repair it, one constraint at a time. In order to extend the local transformation rather than cancel it, we impose never to go back on the decision to increase or decrease the value of a variable. We show that the success of this repair procedure is guaranteed for a large class of constraints.We apply this method to several scheduling problems, characterized by precedences and disjunctive resource constraints. We give numerical results on the Job Shop, Open Shop and Unit Commitment Problems, and show that our repair algorithm dramatically improves the performance of our local search algorithms.

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Two Clause Learning Approaches for Disjunctive Scheduling

Cited by 5 publications

References 18 publications

Intelligent Scheduling with Reinforcement Learning

Intelligent Scheduling with Reinforcement Learning

Time-Aware Test Case Execution Scheduling for Cyber-Physical Systems

Solution Repair by Inequality Network Propagation in LocalSolver

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