Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem

Shahzad, Atif; Mebarki, Nasser

doi:10.1016/j.engappai.2012.04.001

Cited by 64 publications

(22 citation statements)

References 33 publications

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“…As a brand-new research direction in artificial intelligence field since 1990s, Data Mining (DM), also known as Knowledge Discovery in Database (KDD), has been widely used in manufacturing industry, such as engineering design, manufacturing systems, decision support systems, shop floor control and scheduling, fault detection and quality improvement, maintenance, and customer relationship management (Reyes et al, 2015;Wang, 2007;Harding and Shahbaz, 2006;Köksal et al, 2011;Ismail et al, 2009;Shahzad and Mebarki, 2012). Moreover, with the rapid development and great improvement of DM technology in mass data processing, mining efficiency, knowledge processing capability, and robustness of algorithms, the application of DM technology to the acquisition of process knowledge becomes possible.…”

Section: Introductionmentioning

confidence: 99%

Integrating granular computing and bioinformatics technology for typical process routes elicitation: A process knowledge acquisition approach

Zhou

Dai

2015

Engineering Applications of Artificial Intelligence

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Section: Introductionmentioning

confidence: 99%

Integrating granular computing and bioinformatics technology for typical process routes elicitation: A process knowledge acquisition approach

Zhou

Dai

2015

Engineering Applications of Artificial Intelligence

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“…Centralized, priority rules (e.g., heuristics-based) are defined and used on the fly, that is, whenever a decision must be taken. The choice of the rule to apply can also be decided dynamically (Shahzad and Mebarki 2012). Distributed, control decisions are distributed among a set of cooperative control entities, being typically agents or holons in the literature, with or without hierarchical relationships among them.…”

Section: Introductionmentioning

confidence: 99%

Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges

et al. 2015

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International audienceNowadays, industrials are seeking for models and methods that are not only able to provide efficient overall production performance, but also for reactive systems facing a growing set of unpredicted events. One important research activity in that field focuses on holonic/multi-agent control systems that couple predictive/proactive and reactive mechanisms into agents/holons. Meanwhile, not enough attention is paid to the optimization of this coupling. The aim of this paper is to depict the main research challenges that are to be addressed before expecting a large industrial dissemination. Relying on an extensive review of the state of the art, three main challenges are highlighted: the estimation of the future performances of the system in reactive mode, the design of efficient switching strategies between predictive and reactive modes and the design of efficient synchronization mechanisms to switch back to predictive mode. indicator of the production system, discrete-event observer, flexible manufacturing system

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“…Prior literature offers different approaches to exploit the training examples for building a DT. Shahzad and Mebarki (2012) generate the training examples using an optimisation module that solves instances based on tabu search. Olafsson and Li (2010) transform dispatching lists, created by a simulated scheduler in combination with a weighted earliest due date rule (EDD), into a training set.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking

et al. 2019

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In proposing a machine learning approach for a flow shop scheduling problem with alternative resources, sequence-dependent setup times, and blocking, this paper seeks to generate a tree-based priority rule in terms of a well-performing decision tree (DT) for dispatching jobs. Furthermore, generating a generic DT and RF that yields competitive results for instance scenarios that structurally differ from the training instances was another goal of our research. The proposed DT relies on high quality solutions, obtained using a constraint programming (CP) formulation. Novel aspects include a unified representation of job sequencing and machine assignment decisions, as well as the generation of random forests (RF) to counteract overfitting behaviour. To show the performance of the proposed approaches, different instance scenarios for two objectives (makespan and total tardiness minimisation) were implemented, based on randomised problem data. The background of this approach is a real-world physical system of an industrial partner that represents a typical shop floor for many production processes, such as furniture and window construction. The results of a comparison of the DT and RF approach with two priority dispatching rules, the original CP solutions and tight lower bounds retrieved from a strengthened mixed-integer programming (MIP) formulation show that the proposed machine learning approach performs well in most instance sets for the makespan objective and in all sets for the total tardiness objective.

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Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem

Cited by 64 publications

References 33 publications

Integrating granular computing and bioinformatics technology for typical process routes elicitation: A process knowledge acquisition approach

Integrating granular computing and bioinformatics technology for typical process routes elicitation: A process knowledge acquisition approach

Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges

A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking

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