Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation

Zahmani, Mohamed Habib; Atmani, Baghdad

doi:10.1007/s10951-020-00664-5

Cited by 14 publications

(5 citation statements)

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“…Thus, the displayed scheduling performance should be similar to the original, which has been verified in the job shop scheduling problems (Li and Olafsson, 2005a; Koonce and Tsai, 2000; Karthikeyan et al ., 2012). In previous studies, the original solution generated by GA was used as a benchmark to compare the generalization performance of dynamic shop scheduling rules (Habib Zahmani and Atmani, 2021; Koonce and Tsai, 2000; Choi et al ., 2011), and the calculation indexes include minimum manufacturing time, average flow time and the number of late parts. The research studies the project scheduling problem of construction industry, which is similar to manufacturing problems such as shop scheduling and belongs to a subclass of combinatorial problems.…”

Section: Discussionmentioning

confidence: 99%

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Knowledge extraction for solving resource-constrained project scheduling problem through decision tree

Xie

Chen

et al. 2023

ECAM

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PurposeProject scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.Design/methodology/approachThe resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.FindingsIn this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.Originality/valueThis paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

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Section: Discussionmentioning

confidence: 99%

“…The three data sets all involve four renewable resources. To further evaluate the generalization performance of the DT classification model, this study measures it by using the parameter index, as shown in formula (5) (Habib Zahmani and Atmani, 2021). …”

Section: Computational Experimentsmentioning

confidence: 99%

Knowledge extraction for solving resource-constrained project scheduling problem through decision tree

Xie

Chen

et al. 2023

ECAM

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“…A method for minimizing the makespan of a scheduling problem by combining concepts from dispatching rules, GA, and data mining is developed. The study findings suggested that the method effectively solves scheduling problems in real-time (Habib Zahmani & Atmani, 2021). SA with GA was used to sequence several courses related to the classroom in education (Czibula et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal job scheduling to minimize total tardiness by dispatching rules and community evaluation chromosomes

Bari¹,

Karande²

2023

Decis. Mak. Appl. Manag. Eng.

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In traditional scheduling, job processing times are assumed to be fixed. However, this assumption may not be applicable in many realistic industrial processes. Using the job processing time of real industrial processes instead of a fixed value converts the deterministic model to a stochastic one. This study provides three approaches to solving the problem of stochastic scheduling: stochastic linguistic, stochastic scenarios, and stochastic probabilistic. A combinatorial algorithm, dispatching rules and community evaluation chromosomes (DRCEC) is developed to generate an optimal sequence to minimize the tardiness performance measure in the scheduling problem. Thirty-five datasets of scheduling problems are generated and tested with the model. The DRCEC is compared to the Genetic Algorithm (GA) in terms of total tardiness, the tendency of convergence, execution time, and accuracy. The DRCEC has been discovered to outperform the GA. The computational results show that the DRCEC approach gives the optimal response in 63 per cent of cases and the near-optimal solution in the remaining 37 per cent of cases. Finally, a manufacturing company case study demonstrates DRCEC's acceptable performance.

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“…The system is supported by a data module and contains a simulation module, a learning module, and an application module, and the functions of each module and the specific relationships between them are described as follows. The learning module is responsible for extracting the knowledge embedded in the scheduling data in database layer 2, containing data preprocessing methods and decision tree algorithms that are often used to solve scheduling problems [35,36]. The data preprocessing methods include missing value padding, duplicate value deletion, and outlier removal [37].…”

Section: Dr Real-time Selection System Proposedmentioning

confidence: 99%

“…The learning module is responsible for extracting the knowledge embedded in the scheduling data in database layer 2, containing data preprocessing methods and decision tree algorithms that are often used to solve scheduling problems [35,36]. The data preprocessing methods include missing value padding, duplicate value deletion, and outlier removal [37].…”

Section: Dr Real-time Selection System Proposedmentioning

confidence: 99%

Real-Time Selection System of Dispatching Rules for the Job Shop Scheduling Problem

Zhao,

Liu,

et al. 2023

Machines

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Personalized market demands make the job shop scheduling problem (JSSP) increasingly complex, and the need for scheduling methods that can solve scheduling strategies quickly and easily has become very urgent. In this study, we utilized the variety and simplicity of dispatching rules (DRs) and constructed a DR real-time selection system with self-feedback characteristics by combining simulation techniques with decision tree algorithms using makespan and machine utilization as scheduling objectives, which are well adapted to the JSSP of different scales. The DR real-time selection system includes a simulation module, a learning module, and an application module. The function of the simulation module is to collect scheduling data in which is embedded a novel mathematical model describing the JSSP; the function of the learning module is to construct a DR assignment model to assign DR combinations to the job shop system, and the function of the application module is to apply the assigned DR combinations. Finally, a series of job shop systems are simulated to compare the DR assignment model with the NSGA-II and PSO algorithms. The aim is to verify the superiority of the DR assignment model and the rationality of the DR real-time selection system.

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Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation

Cited by 14 publications

References 48 publications

Knowledge extraction for solving resource-constrained project scheduling problem through decision tree

Knowledge extraction for solving resource-constrained project scheduling problem through decision tree

Optimal job scheduling to minimize total tardiness by dispatching rules and community evaluation chromosomes

Real-Time Selection System of Dispatching Rules for the Job Shop Scheduling Problem

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