Discrete event simulation as a strategic decision instrument for a CO<sub>2</sub>– and cost-efficient distribution chain applied in the FMCG sector

Rabe, Markus; Sari, Mehmet Umut; Fechteler, Till; Ruini, Luca

doi:10.1080/2287108x.2015.1014306

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Rabe et al ( 2016 ) presented a DES model for collaborative route planning in supply chains in order to reduce transportation distance. Rabe et al ( 2015 ) utilised DES for strategically designing supply chain distribution for minimisation of emissions and costs, while improving service level. Miranzadeh et al ( 2015 ) utilised DES for simulating a three-echelon supply chain aiming to determine the echelon having the longest waiting time and number of requests.…”

Section: Results Of the Systematic Literature Reviewmentioning

confidence: 99%

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Wofuru-Nyenke

Briggs

Aikhuele

2022

Process Integr Optim Sustain

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Managers at various echelons of supply chains are continuously faced with problems of examining and improving supply chain processes with the aim of improving productivity and customer service level, while reducing emissions and total costs simultaneously. This study is aimed at presenting the trends in sustainable manufacturing supply chain modelling in order to establish the modelling approaches which have predominantly been used for improving manufacturing supply chains from years 2010 to 2020. The study employs the systematic literature review methodology for reviewing articles published within the 11-year period. We proffer a classification approach for manufacturing supply chain models, grouping these models into mathematical models, simulation models, hybrid models, and their subcategories. The results showed that though there is a rising trend in the use of simulation and hybrid models, mathematical models have been used more for sustainable manufacturing supply chain modelling. The rise in the use of simulation and hybrid models can be explained by the fact that these models tend to handle uncertain and stochastic data better than mathematical models, which perform better with deterministic data. This research will aid other researchers in recognising the current gaps in manufacturing supply chain modelling in order to identify future research directions.

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Section: Results Of the Systematic Literature Reviewmentioning

confidence: 99%

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Wofuru-Nyenke

Briggs

Aikhuele

2022

Process Integr Optim Sustain

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“…BothDanloup et al[72], Hoffa-Dabrowska and Grzybowska[73], Rabe et al[74], Van Der Vorst et al[75], Lokhandwala and Cai[76] …”

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confidence: 99%

“…Recently, Hoffa-Dabrowska and Grzybowska[73] develop a simulation model of a supply chain to show how consolidation of transportation orders help the economical and environmental pillars of sustainability. In another study, Rabe et al[74] use a supply chain simulation tool called SimChain[83] to compare two different supply chain network structures in terms of their costs and CO 2 emissions. In the context of roads and hence decrease environmental pollution.…”

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confidence: 99%

Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications

et al. 2021

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The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners.

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Gutenschwager,

Rabe,

Kuhl

et al. 2023

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Discrete event simulation as a strategic decision instrument for a CO₂– and cost-efficient distribution chain applied in the FMCG sector

Cited by 6 publications

References 4 publications

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications

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Discrete event simulation as a strategic decision instrument for a CO2– and cost-efficient distribution chain applied in the FMCG sector

Cited by 6 publications

References 4 publications

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications

Retail

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Product

Resources

About

Discrete event simulation as a strategic decision instrument for a CO₂– and cost-efficient distribution chain applied in the FMCG sector