Solution for Multi-Objective Optimisation Master Production Scheduling Problems Based on Swarm Intelligence Algorithms

Bakar, Mohd Rizam Abu; Abbas, Iraq Tariq; Kalal, Muiead A.; Alsattar, Hassan A.; Bakhayt, Abdul-Gabbar Khaddar; Kalaf, Bayda Atiya

doi:10.1166/jctn.2017.6729

Cited by 12 publications

(6 citation statements)

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“…In comparison to short-term predictions made a day or a few hours later, mid-to long-term predictions are more challenging and often less accurate, as they require the model to capture complex dependencies and robustness against noise in the data over extended periods. Given mid-and long-term forecasting is the major concern for enterprises and institutions, this study investigates various forecasting time horizons (1,3,5,7,10,14,21, and 28 days) and assesses the predictive performance of the models using the MAPE as a metric for forecasting error.…”

Section: Experimental Settingmentioning

confidence: 99%

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SF-Transformer: A Mutual Information-Enhanced Transformer Model with Spot-Forward Parity for Forecasting Long-Term Chinese Stock Index Futures Prices

Mao,

Liu,

Huang

2024

Entropy

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The complexity in stock index futures markets, influenced by the intricate interplay of human behavior, is characterized as nonlinearity and dynamism, contributing to significant uncertainty in long-term price forecasting. While machine learning models have demonstrated their efficacy in stock price forecasting, they rely solely on historical price data, which, given the inherent volatility and dynamic nature of financial markets, are insufficient to address the complexity and uncertainty in long-term forecasting due to the limited connection between historical and forecasting prices. This paper introduces a pioneering approach that integrates financial theory with advanced deep learning methods to enhance predictive accuracy and risk management in China’s stock index futures market. The SF-Transformer model, combining spot-forward parity and the Transformer model, is proposed to improve forecasting accuracy across short and long-term horizons. Formulated upon the arbitrage-free futures pricing model, the spot-forward parity model offers variables such as stock index price, risk-free rate, and stock index dividend yield for forecasting. Our insight is that the mutual information generated by these variables has the potential to significantly reduce uncertainty in long-term forecasting. A case study on predicting major stock index futures prices in China demonstrates the superiority of the SF-Transformer model over models based on LSTM, MLP, and the stock index futures arbitrage-free pricing model, covering both short and long-term forecasting up to 28 days. Unlike existing machine learning models, the Transformer processes entire time series concurrently, leveraging its attention mechanism to discern intricate dependencies and capture long-range relationships, thereby offering a holistic understanding of time series data. An enhancement of mutual information is observed after introducing spot-forward parity in the forecasting. The variation of mutual information and ablation study results highlights the significant contributions of spot-forward parity, particularly to the long-term forecasting. Overall, these findings highlight the SF-Transformer model’s efficacy in leveraging spot-forward parity for reducing uncertainty and advancing robust and comprehensive approaches in long-term stock index futures price forecasting.

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Section: Experimental Settingmentioning

confidence: 99%

“…The long-term stock index futures prediction plays a crucial role in advancing China’s stock index futures market [ 13 , 14 ]. This research topic has garnered significant attention.…”

Section: Introductionmentioning

confidence: 99%

SF-Transformer: A Mutual Information-Enhanced Transformer Model with Spot-Forward Parity for Forecasting Long-Term Chinese Stock Index Futures Prices

Mao,

Liu,

Huang

2024

Entropy

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“…For the MPS at the tactical level, its emphasis is placed on time and spatial disintegration of cumulative planning targets and forecasts, along with the provision and forecast of required resources. This procedure eventually becomes difficult and slows down as the number of considered resources, products and time periods increases [7]. Most of the classic modelling approaches present limitations as the MPS dimension grows, particularly if the MPS is posed as a multi-objective issue.…”

Section: Contribution To Applied Artificial Intelligence Systemsmentioning

confidence: 99%

Digital Twin for Supply Chain Master Planning in Zero-Defect Manufacturing

Serrano

Mula

Poler

2021

IFIP Advances in Information and Communication Technology

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Recently, many novel paradigms, concepts and technologies, which lay the foundation for the new revolution in manufacturing environments, have emerged and make it faster to address critical decisions today in supply chain 4.0 (SC4.0), with flexibility, resilience, sustainability and quality criteria. The current power of computational resources enables intelligent optimisation algorithms to process manufacturing data in such a way, that simulating supply chain (SC) planning performance in real time is now possible, which allows relevant information to be acquired so that SC nodes are digitally interconnected. This paper proposes a conceptual framework based on a digital twin (DT) to model, optimise and prescribe a SC's master production schedule (MPS) in a zero-defect environment. The proposed production technologies focus on the scientific development and resolution of new models and optimisation algorithms for the MPS problem in SC4.0.

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“…At the tactical level, the MPS needs a temporo-spatial disintegration of cumulative planning targets and forecasts, along with the provision and forecasting of required resources. This procedure eventually becomes difficult and slows down as the number of considered resources, products and time periods increases [20][21][22] because feasible solutions exponentially increases space in relation to a growing number of nodes (elements containing a product, period and resource) in the system, which defines it as an NP-hard problem. Most classic modeling approaches (simulation methods, heuristics, metaheuristics, matheuristics) present computational limitations as the MPS problem dimension grows, particularly if the MPS is posed as a multi-objective issue.…”

Section: Introductionmentioning

confidence: 99%

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Serrano-Ruiz

Mula

2021

Computers

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Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to survive in the long term. On the one hand, the antidisruptive potential and the inherent sustainability implications of the zero-defect manufacturing (ZDM) management model should be highlighted. On the other hand, the digitization and virtualization of processes by Industry 4.0 (I4.0) digital technologies, namely digital twin (DT) technology, enable new simulation and optimization methods, especially in combination with machine learning (ML) procedures. This paper reviews the state of the art and proposes a ZDM strategy-based conceptual framework that models, optimizes and simulates the master production schedule (MPS) problem to maximize service levels in SCs. This conceptual framework will serve as a starting point for developing new MPS optimization models and algorithms in supply chain 4.0 (SC4.0) environments.

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Solution for Multi-Objective Optimisation Master Production Scheduling Problems Based on Swarm Intelligence Algorithms

Cited by 12 publications

References 0 publications

SF-Transformer: A Mutual Information-Enhanced Transformer Model with Spot-Forward Parity for Forecasting Long-Term Chinese Stock Index Futures Prices

SF-Transformer: A Mutual Information-Enhanced Transformer Model with Spot-Forward Parity for Forecasting Long-Term Chinese Stock Index Futures Prices

Digital Twin for Supply Chain Master Planning in Zero-Defect Manufacturing

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

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