Dispatching Rules for Manufacturing Job-Shop Operations

Tsiushuang, Chen

doi:10.1016/s1474-6670(17)48616-2

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…Instead, dispatching rules have been widely employed in the industry owing to their high computational efficiency and robustness to uncertainty [9]. However, existing studies make simplified assumptions in the definition of the problems, thus hindering their application in the context of business process management.…”

Section: Resource Allocation In Business Processesmentioning

confidence: 99%

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Optimizing Resource Allocation Based on Predictive Process Monitoring

Park

Song

2023

IEEE Access

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Recent breakthroughs in predictive business process monitoring equip process analysts with predictions on running process instances, supporting the elicitation of proactive measures to mitigate risks that can be caused by the process instance. However, contrary to active research on providing various predictions and improving the accuracy of prediction models, the practical application of such predictions has been left to the subjective judgment of domain experts. In this work, we explore the exploitation of the insights from predictive information for the actual process improvement in practice. Concretely, we focus on improving resource allocation in business processes where the goal is to allocate appropriate resources to tasks at the proper time. Based on design science methodology, we develop a two-phase method to improve resource allocation by leveraging predictions. Based on the method, we instantiate an algorithm to optimize total-weighted completion time and evaluate its effectiveness and efficiency. From an academic standpoint, our work demonstrates the combination of predictions using machine learning and optimizations based on scheduling. From a practical standpoint, our work provides a general approach to optimize resource allocations for different objectives using predictions.

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Section: Resource Allocation In Business Processesmentioning

confidence: 99%

“…Existing techniques for resource allocation require information such as the release time, service time, and sequence of required tasks of a process instance [9]. However, in reallife business processes, we often have limited information on process instances (so-called non-clairvoyant resource allocation problems).…”

Section: Introductionmentioning

confidence: 99%

Optimizing Resource Allocation Based on Predictive Process Monitoring

Park

Song

2023

IEEE Access

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“…However, the application of these approaches to practical usages is somewhat limited because most of these techniques are not amenable to actual utilization in real job shops [36]. Dispatching rules, on the other hand, have been widely adopted in the industry as they are computationally efficient and robust to uncertainty [12].…”

Section: Job Shop Schedulingmentioning

confidence: 99%

“…Resource allocation in business process management shares some commonalities with Job Shop Scheduling Problem (JSSP) in operations research [10], which is to find the job sequences on machines to achieve a goal (e.g., minimize makespans), while the machine sequence of the jobs is fixed [11]. Thus, the techniques for JSSP such as dispatching rules [12] can be deployed to efficiently deal with resource allocation in business process management.…”

Section: Introductionmentioning

confidence: 99%

Prediction-based Resource Allocation using LSTM and Minimum Cost and Maximum Flow Algorithm

Park

Song

2019

2019 International Conference on Process Mining (ICPM)

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Predictive business process monitoring aims at providing predictions about running instances by analyzing logs of completed cases in a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates the offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using Bayesian Neural Networks (BNNs) with the online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.

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Steel Coils Scheduling in a Flow-Shop with Parallel Tools and Tight Constraints

Portmann

Camps²

1993

Advances in Factories of the Future, CIM and Robotics

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Dispatching Rules for Manufacturing Job-Shop Operations

Cited by 8 publications

References 2 publications

Optimizing Resource Allocation Based on Predictive Process Monitoring

Optimizing Resource Allocation Based on Predictive Process Monitoring

Prediction-based Resource Allocation using LSTM and Minimum Cost and Maximum Flow Algorithm

Steel Coils Scheduling in a Flow-Shop with Parallel Tools and Tight Constraints

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