A reactive-proactive approach for solving dynamic scheduling with time-varying number of Tasks

Abello, Manuel Blanco; Michalewicz, Zbigniew; Bui, Lam Thu

doi:10.1109/cec.2012.6256484

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…Note that task cancellation is not considered in this paper. In our previous work [ 14 , 17 ] as well as in this paper, we consider three entities that could change in the environment: resource availability, task duration, and total number of tasks. The change in the total number of tasks is given the major emphasis.…”

Section: Background Knowledgementioning

confidence: 99%

Multiobjective Resource-Constrained Project Scheduling with a Time-Varying Number of Tasks

Abello

Michalewicz

2014

The Scientific World Journal

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In resource-constrained project scheduling (RCPS) problems, ongoing tasks are restricted to utilizing a fixed number of resources. This paper investigates a dynamic version of the RCPS problem where the number of tasks varies in time. Our previous work investigated a technique called mapping of task IDs for centroid-based approach with random immigrants (McBAR) that was used to solve the dynamic problem. However, the solution-searching ability of McBAR was investigated over only a few instances of the dynamic problem. As a consequence, only a small number of characteristics of McBAR, under the dynamics of the RCPS problem, were found. Further, only a few techniques were compared to McBAR with respect to its solution-searching ability for solving the dynamic problem. In this paper, (a) the significance of the subalgorithms of McBAR is investigated by comparing McBAR to several other techniques; and (b) the scope of investigation in the previous work is extended. In particular, McBAR is compared to a technique called, Estimation Distribution Algorithm (EDA). As with McBAR, EDA is applied to solve the dynamic problem, an application that is unique in the literature.

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Section: Background Knowledgementioning

confidence: 99%

Multiobjective Resource-Constrained Project Scheduling with a Time-Varying Number of Tasks

Abello

Michalewicz

2014

The Scientific World Journal

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“…The work of Abello, et. al [18] applies the integrate approach in a dynamic scheduling problem with time-varying number of task. Ping lou, et.…”

Section: Literature Reviewmentioning

confidence: 99%

GA-Based Proactive-Reactive Scheduling Mechanism for a Flexible Job Shop Problem under Multi-Uncertainty

Wang

Zhang

Qin

2014

KEM

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This article investigates the flexible job-shop scheduling problem (FJSP) with multi-uncertain. A proactive-reactive scheduling mechanism is put forward to against the fluctuating process time and equipment breakdowns. This mechanism consists of two stages, including proactive scheduling stage and reactive scheduling stage. In the proactive scheduling stage, the redundancy-based technique is used to generate robust baseline schedules; in the reactive scheduling stage, a reactive scheduling is adopted to rectify the predictive scheduling to adapt to the occurrence of machine failures. Based on this, an integrating algorithm is presented with the goal of the make span. Numerical experiments show that the proposed algorithm has a better performance than Genetic Algorithm (GA) on flexible job-shop scheduling problem under processing time uncertainty and the reactive scheduling algorithm can handle the equipment breakdown with little robustness lost.

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“…Dynamic multi-objective optimization has wide applications in real life and industrial production, such as scheduling [1], planning [2]- [5], machine learning [6], and control [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

An Acceleration-based Prediction Strategy for Dynamic Multi-objective Optimization

Zhang

et al. 2023

Preprint

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This paper addresses the problem of dynamic multiobjective optimization problems (DMOPs), by demonstrating new approaches to change detection and change prediction in an evolutionary algorithm framework. Because the objectives of such problems change over time, the Pareto optimal set (PS) and Pareto optimal front (PF) are also dynamic. First, we propose a new change detection method which achieves greater sensitivity by considering changes in both the PS and the PF, unlike most previous approaches. Second, when changes occur, a secondorder (acceleration-based) prediction strategy is proposed to predictively re-initialize the population close to the new set of optima. We compare the performance of the proposed algorithm against two other state-of-the-art algorithms from the literature, using ten different benchmark dynamic instance test problems. Experimental results show that the proposed method outperforms the other algorithms on most of the test problems.

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A reactive-proactive approach for solving dynamic scheduling with time-varying number of Tasks

Cited by 5 publications

References 24 publications

Multiobjective Resource-Constrained Project Scheduling with a Time-Varying Number of Tasks

Multiobjective Resource-Constrained Project Scheduling with a Time-Varying Number of Tasks

GA-Based Proactive-Reactive Scheduling Mechanism for a Flexible Job Shop Problem under Multi-Uncertainty

An Acceleration-based Prediction Strategy for Dynamic Multi-objective Optimization

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