Energy Price and Workload Related Dispatching Rule: Balancing Energy and Production Logistics Costs

In response to the escalating need for sustainable manufacturing practices amid fluctuating energy prices, this study introduces a novel dispatching rule that integrates energy price and workload considerations with Material Requirement Planning (MRP) to optimize production logistics and energy costs. The dispatching rule effectively adjusts machine operational states, i.e. turn the machine on or off, based on current energy prices and workload. By developing a stochastic multi-item multi-stage job shop simulation model, this research evaluates the performance of the dispatching rule through a comprehensive full-factorial simulation. Findings indicate a significant enhancement in shop floor decision-making through reduced overall costs. Moreover, the analysis of the Pareto front reveals trade-offs between minimizing energy and production logistics costs, aiding decision-makers in selecting optimal configurations.

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Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/wsc63780.2024.10838840
Citations
4
Primary Topic
Renewable energy and sustainable power systems
Type
article
Field-Weighted Citation Impact
0.41
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article

Energy Price and Workload Related Dispatching Rule: Balancing Energy and Production Logistics Costs

Felberbauer Thomas, Wolfgang Seiringer, Altendorfer Klaus, Balwin Bokor
4 citations
Renewable energy and sustainable power systems
0.41
article

Energy Price and Workload Related Dispatching Rule: Balancing Energy and Production Logistics Costs

Felberbauer Thomas, Wolfgang Seiringer, Altendorfer Klaus, Balwin Bokor
article en
4 citations

Abstract

In response to the escalating need for sustainable manufacturing practices amid fluctuating energy prices, this study introduces a novel dispatching rule that integrates energy price and workload considerations with Material Requirement Planning (MRP) to optimize production logistics and energy costs. The dispatching rule effectively adjusts machine operational states, i.e. turn the machine on or off, based on current energy prices and workload. By developing a stochastic multi-item multi-stage job shop simulation model, this research evaluates the performance of the dispatching rule through a comprehensive full-factorial simulation. Findings indicate a significant enhancement in shop floor decision-making through reduced overall costs. Moreover, the analysis of the Pareto front reveals trade-offs between minimizing energy and production logistics costs, aiding decision-makers in selecting optimal configurations.

University of Applied Sciences St Pölten (AT), University of Applied Sciences Upper Austria (AT)
Openalex Percentile: Top 46%
Renewable energy and sustainable power systems
0.41
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