Flexible job-shop scheduling with learning and forgetting effect by Multi-Agent System

Renna, Paolo

doi:10.5267/j.ijiec.2019.3.003

Cited by 12 publications

(3 citation statements)

References 24 publications

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“…Chiu (2018) determine optimal runtime for fabrication system with backorder, stochastic breakdown, service level constraint and scrap. Renna (2019) proposed a scheduling approach to support manufacturing system under the learning and forgetting impact.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Enabled Inventory Control for Deteriorating Items with Carbon Emission and Trade Credit under Learning and Forgetting

Sushil Bhawaria

2024

cana

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This research introduces a novel approach to inventory control, leveraging machine learning techniques to optimize inventory decisions for deteriorating items while accounting for carbon emissions and trade credit policies. In contemporary supply chain management, sustainability considerations have become increasingly vital. The carbon footprint associated with inventory management practices necessitates innovative solutions to minimize environmental impact. Moreover, trade credit offers financial flexibility but requires careful management to maintain profitability. To address these complex challenges, our proposed framework integrates machine learning algorithms into inventory control to enhance decision-making precision. The model incorporates dynamic learning and forgetting effects, allowing the system to adapt to changing demand patterns over time. This adaptability is particularly critical when dealing with deteriorating items that exhibit non-constant demand rates. Carbon emissions are assessed throughout the supply chain, and environmentally conscious decisions are made to minimize the carbon footprint. Additionally, trade credit terms are optimized to strike a balance between financial constraints and inventory performance. Our approach demonstrates superior performance in terms of minimizing costs, reducing carbon emissions, and enhancing supply chain resilience compared to traditional inventory management methods. Real-world case studies and simulations validate the effectiveness of our machine learning-enabled inventory control system, showcasing its practical applicability. This research contributes to the advancement of sustainable supply chain management by providing a comprehensive framework that combines AI-driven inventory control, carbon emission reduction, and trade credit optimization, ultimately fostering environmentally responsible and financially viable inventory decisions.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Enabled Inventory Control for Deteriorating Items with Carbon Emission and Trade Credit under Learning and Forgetting

Sushil Bhawaria

2024

cana

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“…They solved the problem without presenting a mathematical model in order to minimize the range of operations by introducing a hybrid algorithm. Renna [9] studied flexible job shop scheduling with learning and forgetting effects. Okolowski and Gawiejnowicz [10] introduced a novel learning curve in a parallel machine environment, called DeJong's Learning Curve, in which they proposed an incompressibility index to separate the manual and automatic parts of jobs.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel bi-objective model for a job shop scheduling problem with consideration of Fuzzy parameters, modified learning effects and multiple preventive maintenance activities

2021

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This paper aims at introducing a novel bi-objective model for a Job Shop Scheduling Problem (JSSP) in order to minimize makespan and maximum tardiness simultaneously. Some realistic assumptions, i.e. Fuzzy processing times and due dates involving triangular possibility distributions, transportation times, availability constraints, modified position-based learning effects on processing times, and sum-of-processing-time based learning effects on duration of maintenance activities have been considered, to provide a more general and practical model for the JSSP. Based on the learning effects, Processing times decrease as a machine performs an operation frequently, and workers gain working skills and experiences. In this paper based on DeJong's learning effect a novel and modified formulation has been proposed for this effect. According to the above-mentioned assumptions, a novel mixed-integer linear programming (MILP) model for the JSSP is suggested. The proposed model is first converted to an auxiliary crisp model, given that model is a possibilistic programming, it is then solved by the TH and εconstraint methods for small instances, and the results are compared. For medium and large instances, five metaheuristic algorithms, including NSGA-ΙΙΙ, PESA-ΙΙ, SPEA-ΙΙ, NSGA-ΙΙ, and MOEA/D are utilized, and the results are finally compared on the basis of three performance metrics.

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“…Another important limitation of our study is that we consider workers to be fully interchangeable thereby neglecting potential differences in worker efficiency. We are currently working on the modelling of more complex transfers rule that could take into consideration different factors such as worker's different efficiency, heterogeneous labour, learning and forgetting (Renna, 2019), and different degrees of labour flexibility. However, this research paper has been intended to open a new field of research aiming at investigating labour flexibility as a new output control mechanism that improves performances without increasing the overall capacity of the system.…”

Section: Conclusion Limitations and Future Researchmentioning

confidence: 99%

The use of labour flexibility for output control in workload controlled flow shops: A simulation analysis

Staudacher¹,

Costa²,

Thürer³

2020

10.5267/j.ijiec

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Workload control theory seeks to align capacity and demand to improve delivery performance. However, workload control researchers mainly focused on input control, which regulates the input of work to the production system, thereby neglecting output control, which uses capacity adjustments to regulate the outflow of the work. Moreover, few existing studies on output control investigate a temporarily increase in capacity. This paper introduces a new search direction for output control which does not require an increase in capacity-labour flexibility. Idle operators can move from their workstation to another, thus temporarily increasing the output of that workstation without extra capacity. Using simulation of a five workstations flow shop line, we highlight the positive performance effect of labour flexibility. However, this comes at the cost of high labour movement. Introducing a load-based constraint on when workers are allowed to move significantly reduces labour movement, while realizing most of the performance improvement observed for unconstrained labour movement. This has important implications for future research and practice.

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Flexible job-shop scheduling with learning and forgetting effect by Multi-Agent System

Cited by 12 publications

References 24 publications

Machine Learning Enabled Inventory Control for Deteriorating Items with Carbon Emission and Trade Credit under Learning and Forgetting

Machine Learning Enabled Inventory Control for Deteriorating Items with Carbon Emission and Trade Credit under Learning and Forgetting

A novel bi-objective model for a job shop scheduling problem with consideration of Fuzzy parameters, modified learning effects and multiple preventive maintenance activities

The use of labour flexibility for output control in workload controlled flow shops: A simulation analysis

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