{"id":111915,"date":"2025-11-22T12:18:47","date_gmt":"2025-11-22T11:18:47","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=111915"},"modified":"2025-12-05T17:34:02","modified_gmt":"2025-12-05T16:34:02","slug":"electric-trucks-pre-post-carriage","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/electric-trucks-pre-post-carriage\/","title":{"rendered":"Electric Trucks in Intermodal Terminal Pre- and Post-Carriage"},"content":{"rendered":"

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Freight transport in Germany has experienced steady and strong growth in recent decades [1]. Despite the use of various modes of transport, road freight transport remains the dominant form of transport to this day [2]. Within rail freight transport, however, combined transport (CT) is showing above-average, dynamic growth. Forecasts up to 2026 assume continued double-digit growth rates, which underlines the increasing importance of CT as an environment-friendly alternative in the freight transport sector [3]. In 2022, pre and post-carriage in CT [4] accounted for 15% of road transport (303.9 million kilometres …<\/div>\n

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\n\n\n\n<\/thead>\n\n\n\n\n\n\n\n\n<\/tbody>\n<\/table>\n

Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
Digital<\/td>\n−<\/td>\n27,55 \u20ac<\/td>\n
Expert<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
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Competence Assurance for the Verification and Validation of Simulation Models<\/h4>\n
Requirements for AI-supported assistance in production and logistics<\/div>
Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Felix \u00d6zkul<\/a> \n \"ORCID<\/a>, Robin Sutherland<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Can artificial intelligence be used to assess the validity and credibility of simulation models and data for production and logistics? And what requirements must AI assistance meet to effectively support experts in verification and validation while simultaneously safeguarding their competence? In an online survey, simulation experts were asked regarding their expectations and needs. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 24-33 | DOI 10.30844\/I4SE.26.5.3<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Data-Driven\n <\/picture>\n <\/div>\n
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Data-Driven Supplier Selection in Industry 4.0<\/h4>\n
Towards an industrial platform for selection and configuration<\/div>
Purushothaman Ganesh<\/a> \n \"ORCID<\/a>, Baris E. Albayrak<\/a> \n \"ORCID<\/a>, J\u00f6rg Franke<\/a> \n \"ORCID<\/a>, Till Sindel<\/a> \n \"ORCID<\/a>, Jens F\u00fcrst<\/a>, Sebastian Lang<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial supply chains must repeatedly reconfigure sourcing strategies in response to disruptions, yet supplier capability information remains heterogeneous and difficult to operationalize. Existing research addresses supplier selection, simulation, and interoperability standards separately, treating discovery, evaluation, and optimization as disconnected steps requiring manual data transformation. This paper presents a framework for a data-driven industrial platform integrating three components: Asset Administration Shell-based supplier profiles structured by an Actor-Service ontology for semantic discovery, automatic discrete-event simulation model generation from layout data for performance evaluation, and KPI-driven configuration using optimization algorithms. The main contributions are an end-to-end interoperable workflow, a two-tier supplier profile concept separating semantic descriptions from simulation parameters, and a standardized KPI interface maintaining consistency ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 50-61 | DOI 10.30844\/I4SE.26.4.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
\n \t \n
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Digital Factory Planning for Startups<\/h4>\n
A simulation-based production structure design<\/div>
Herwig Winkler<\/a> \n \"ORCID<\/a>, Tobias Isau<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n With the increasing complexity of production and logistics systems, traditional factory planning approaches are reaching their limits. In this context, digital factory planning offers a promising solution for enabling well-informed decisions, particularly during the early planning phases. For startups, the optimal planning of a production facility is challenging, as they often operate with limited financial and infrastructural resources. This paper presents a methodological approach to digital factory planning that utilizes VR simulation for the layout planning of a factory hall for a young company in the solar industry. The proposed approach demonstrates how simulations can support the design of flexible production structures, particularly in startup environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 68-75<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Site\n <\/picture>\n <\/div>\n
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Site Assessment for Flexible Intralogistics in Brownfield Sites<\/h4>\n
Innovative decision support in practice<\/div>
Jolanda Schierbaum<\/a>, Carsten Feldmann<\/a>, Lars Renhof<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Due to its dynamic environment, space planning in intralogistics is not a one-time task but a recurring decision-making process subject to numerous constraints imposed by existing infrastructure. Decisions are often based on incomplete data, resulting in a high risk of poor planning decisions and inefficient use of space. This paper presents a practice-oriented process model for space evaluation in brownfield projects. The proposed approach improves the standardization and consistency of space evaluation and promotes best practices among all stakeholders. By supporting systematic decision-making, the process model contributes to optimized planning and resource allocation, thereby reducing risks and avoiding costly implementation errors.The process model is demonstrated through a case study conducted at a commercial vehicle manufacturer. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 124-133<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Experiencing\n <\/picture>\n <\/div>\n
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\t\t

Experiencing Digital Twins in Production and Logistics<\/h4>\n
The fischertechnik\u00ae Learning Factory 4.0 as a development platform for possible expansion stages<\/div>
Jan Schickram<\/a>, Tareq Albeesh<\/a>, Deike Gliem<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The fischertechnik\u00ae Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844\/I4SE.26.2.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Loam\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Loam Construction and Wooden Shelving<\/h4>\n
A contribution to sustainability in warehouse logistics<\/div>
Viviano De Giacomo<\/a> \n \"ORCID<\/a>, Nathalie Fritsch<\/a> \n \"ORCID<\/a>, Jakob Kennert<\/a> \n \"ORCID<\/a>, Dieter Uckelmann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>This study examines the contribution of natural building materials, in particular loam and wood, to the sustainable development of logistics infrastructure, assessing ecological, economic, and technical dimensions across the entire life cycle. Potentials, restrictions, and supportive framework conditions are identified based on literature analyses and expert interviews. Wood proves to be technically mature and ecologically advantageous, especially in high rack construction, while loam offers high potential for energy- and resource-efficient construction. The study concludes with recommendations for research, policy, and practice to establish circular construction methods in logistics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 82-89<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Electric trucks (e-trucks) play an important role in reducing CO\u2082 emissions especially on short distances in pre and post-carriage in combined road-rail freight transport (CT). Using the example of a CT terminal, this article highlights the logistical and energy challenges involved in using e-trucks to establish suitable charging infrastructures and ensuring a reliable power supply.<\/p>\n","protected":false},"featured_media":111812,"menu_order":0,"template":"","categories":[79167,79298],"tags":[],"product_cat":[79304],"topic":[79371],"technology":[79487],"knowhow":[],"industry":[69251,74079],"writer":[82121],"content-type":[83932],"potential":[71227,69462],"solution":[78673],"glossary":[],"class_list":["post-111915","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-typeset","product_cat-articles","topic-logistics","technology-energy-monitoring","industry-automotive-en","industry-renewable-energies","writer-ralf-elbert-en","content-type-article","potential-energy-efficiency","potential-resource-efficiency","solution-logistics-technology","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan.jpeg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-150x150.jpeg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-666x375.jpeg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-768x432.jpeg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-1024x576.jpeg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-1032x320.jpeg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-764x376.jpeg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-392x320.jpeg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-608x496.jpeg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-640x325.jpeg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-274x376.jpeg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-514x292.jpeg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-320x440.jpeg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-514x289.jpeg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-196x180.jpeg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan.jpeg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan.jpeg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-510x510.jpeg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-510x287.jpeg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-100x100.jpeg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/11\/AdobeStock_544561574_Kalyakan-64x36.jpeg",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Electric trucks (e-trucks) play an important role in reducing CO\u2082 emissions especially on short distances in pre and post-carriage in combined road-rail freight transport (CT). Using the example of a CT terminal, this article highlights the logistical and energy challenges involved in using e-trucks to establish suitable charging infrastructures and ensuring a reliable power supply.","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/111915","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/111812"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=111915"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=111915"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=111915"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=111915"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=111915"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=111915"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=111915"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=111915"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=111915"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=111915"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=111915"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=111915"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=111915"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}