Transfer-robot task scheduling in job shop

Ham, Andy

doi:10.1080/00207543.2019.1709671

Cited by 42 publications

(21 citation statements)

References 32 publications

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“…Although it was initially developed to find high-quality feasible solutions within a reasonable time, the current commercial CP solvers can find optimal solutions and prove the optimality [21] . This approach has successfully been applied in various optimization problems, more noticeably in production scheduling and sequencing (e.g., Safarzadeh and Kianfar [44] , Mokhtarzadeh et al [45] , Ham [46] ). In this paper, IBM ILOG CP Optimizer is used to model and solve the problem.…”

Section: Constraint Programming (Cp) Approachmentioning

confidence: 99%

Workforce planning and production scheduling in a reconfigurable manufacturing system facing the COVID-19 pandemic

Vahedi-Nouri¹,

Tavakkoli–Moghaddam²,

Hanzálek³

et al. 2022

Journal of Manufacturing Systems

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Section: Constraint Programming (Cp) Approachmentioning

confidence: 99%

Workforce planning and production scheduling in a reconfigurable manufacturing system facing the COVID-19 pandemic

Vahedi-Nouri¹,

Tavakkoli–Moghaddam²,

Hanzálek³

et al. 2022

Journal of Manufacturing Systems

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“…As Ham [41] pointed out, there is no standard in CP formulation, unlike a similar MILP formulation. erefore, this paper formulates the model using generic keywords and syntaxes as we refer to the CP formulations by Laborie et al [40] and IBM ILOG CP Optimizer [42].…”

Section: Constraint Programmingmentioning

confidence: 99%

Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming

Ham

Park

Kim

2021

Mathematical Problems in Engineering

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Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain the same productivity while significantly reducing energy consumption. This paper addresses a flexible job shop scheduling problem with a shutdown (on/off) strategy aiming to minimize makespan and total energy consumption. First, an alternative mixed integer linear programming model is proposed. Second, a novel constraint programming is proposed. Third, practical operational scenarios are compared. Finally, we provide benchmarking instances, CPLEX codes, and genetic algorithm codes, in order to promote related research, thus expediting the adoption of energy-efficient scheduling in manufacturing facilities. The computational study demonstrates that (1) the proposed models significantly outperform other benchmark models and (2) we can maintain maximum productivity while significantly reducing energy consumption by 14.85% (w/o shutdown) and 15.23% (w/shutdown) on average.

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“…For instance, in [57], based on the demand for resources in the manufacturing process, using a slicing paradigm and AI, resources are allotted for robotic systems in Industry 4.0 environment. In [58], simultaneous scheduling of tasks in the production sector for mobile robotic systems is carried out in a job shop environment proving flexible transfer of materials, providing an optimal benchmark performance. Customer Service: AI-enabled robots are also used to entertain customers' queries about products or services, such as retail, in smart industries.…”

Section: Ai For Industry 40mentioning

confidence: 99%

The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions

Jagatheesaperumal¹,

Rahouti²,

Ahmad³

et al. 2021

Preprint

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The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers, has paved the way for Artificial Intelligence (AI) and Big Data in support of smart manufacturing. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, Big data, Blockchain, 5G communications, in support of smart manufacturing and the dynamical processes in modern industries. In this paper, we provide a comprehensive overview of different aspects of AI and Big Data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective towards deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and Big Data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and Big Data methods in smart industries with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. In a nutshell, we have explored the significance of AI and Big data towards Industry 4.0 applications through panoramic reviews and discussions. We believe, this work will provide a baseline for future research in the domain.

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Transfer-robot task scheduling in job shop

Cited by 42 publications

References 32 publications

Workforce planning and production scheduling in a reconfigurable manufacturing system facing the COVID-19 pandemic

Workforce planning and production scheduling in a reconfigurable manufacturing system facing the COVID-19 pandemic

Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming

The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions

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