Applying Simheuristics to Minimize Overall Costs of an MRP Planned Production System

Seiringer, Wolfgang; Castaneda, Juliana; Altendorfer, Klaus; Panadero, Javier; Juan, Ángel A.

doi:10.3390/a15020040

Cited by 4 publications

(1 citation statement)

References 29 publications

(42 reference statements)

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“…They could support planning, such as material resource planning and job scheduling. For example, Seiringer et al (2022) combined simulated annealing and simulation to support material planning Juan, Li, Ammouriova, Panadero, and Faulin in terms of specifying lot size, safety stock, as well as lead time. Researchers have studied different versions of the flow shop problem using simheuristics.…”

Section: Production and Manufacturingmentioning

confidence: 99%

Simheuristics: An Introductory Tutorial

Juan¹,

Ammouriova

et al. 2022

2022 Winter Simulation Conference (WSC)

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Both manufacturing and service industries are subject to uncertainty. Probability techniques and simulation methods allow us to model and analyze complex systems in which stochastic uncertainty is present. When the goal is to optimize the performance of these stochastic systems, simulation by itself is not enough and it needs to be hybridized with optimization methods. Since many real-life optimization problems in the aforementioned industries are NP-hard and large scale, metaheuristic optimization algorithms are required. The simheuristics concept refers to the hybridization of simulation methods and metaheuristic algorithms. This paper provides an introductory tutorial to the concept of simheuristics, showing how it has been successfully employed in solving stochastic optimization problems in many application fields, from production logistics and transportation to telecommunication and insurance. Current research trends in the area of simheuristics, such as their combination with fuzzy logic techniques and machine learning methods, are also discussed. INTRODUCTIONSince uncertainty is present in most real-life systems, simulation methods are frequently employed to analyze complex systems in industries such as manufacturing and production logistics, transportation, health care, finance, smart cities, and telecommunication. Usually, the uncertainty in these systems is modeled via probability distributions, either theoretical or empirical ones. Then, a logical model of the system is built, and computer simulation is utilized to get insight on the system performance under different scenarios, each of which represent a possible system configuration. As pointed out by some authors, the advances in computer power and simulation software have transformed simulation into a 'first-resource' method for analyzing complex systems under uncertainty (Lucas et al. 2015).Still, simulation by itself is not an optimization tool, since it does not contain a mechanism to efficiently explore the vast solution spaces that arise in combinatorial optimization problems. Therefore, if our goal is

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Section: Production and Manufacturingmentioning

confidence: 99%

Simheuristics: An Introductory Tutorial

Juan¹,

Ammouriova

et al. 2022

2022 Winter Simulation Conference (WSC)

Self Cite

View full text Add to dashboard Cite

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Potential of Simulation Effort Reduction by Intelligent Simulation Budget Management for Multi-Item and Multi-Stage Production Systems

Seiringer

Altendorfer

Castaneda

et al. 2022

2022 Winter Simulation Conference (WSC)

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RETRACTED: Public Spending, Green Finance, and Zero Carbon for Sustainable Development: A Case of Top 10 Emitting Countries

Hong

Farooq

Nadeem

et al. 2022

Front. Environ. Sci.

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In general, the public expenditure on schooling, science, and research and development (R&D) is thought to have a positive effect on the development and sustainability of an economy, but such evidence is lacking in the developing and developed countries, especially in the top 10 CO2-emitting countries. This study investigates the impact of public spending and green finance on environmental sustainability, using the ordinary least square method and data envelopment analysis, which uses the panel data from selected countries from 2008 to 2018. Results reveal a fluctuating green economic growth index, which was due to the non-serious existence of government policies. More precisely, a 1% increase in gross domestic product (GDP) growth increases the carbon emissions by about 0.40%, whereas the rise in coal consumption decreases environmental efficiency by about 0.88%. We also concluded that 0.95% GDP growth and economic development significantly enhance environmental emissions, whereas 0.5% of renewable energy consumption decreases the negative impact of environmental pollution. Furthermore, a 1% growth in renewable energy consumption improved environmental efficiency by 0.58%. Furthermore, the analysis demonstrates that the public expenditure on human capital and renewable energy (R&D) leads to a productive green economy through labor, and technically advance developmental practices, with varying consequences in distinctive countries.

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Applying Simheuristics to Minimize Overall Costs of an MRP Planned Production System

Cited by 4 publications

References 29 publications

Simheuristics: An Introductory Tutorial

Simheuristics: An Introductory Tutorial

Potential of Simulation Effort Reduction by Intelligent Simulation Budget Management for Multi-Item and Multi-Stage Production Systems

RETRACTED: Public Spending, Green Finance, and Zero Carbon for Sustainable Development: A Case of Top 10 Emitting Countries

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