Calibrated Route Finder: Improving the Safety, Environmental Consciousness, and Cost Effectiveness of Truck Routing in Sweden

Mikael Rönnqvist (), Gunnar Svenson (), Patrik Flisberg () and Lars-Erik Jönsson ()
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Mikael Rönnqvist: Forestry Research Institute of Sweden, SE-75183 Uppsala, Sweden; and Université Laval, Québec City, Québec G1V 0A6, Canada
Gunnar Svenson: Forestry Research Institute of Sweden, SE-75183 Uppsala, Sweden
Patrik Flisberg: Forestry Research Institute of Sweden, SE-75183 Uppsala, Sweden
Lars-Erik Jönsson: SDC, SE-85183 Sundsvall, Sweden

Interfaces, 2017, vol. 47, issue 5, 372-395

Abstract: Calibrated Route Finder (CRF), an online route generation system, successfully finds the best route when many conflicting objectives are involved by using analytics in a collaborative environment. CRF, which has been in use since 2009, uses many diverse big data sources, which must be revised continuously. One of its key features is its use of an innovative inverse optimization process that establishes more than 100 weights to balance distance, speed, social values, environmental impacts, traffic safety, driver stress, fuel consumption, CO 2 emissions, and costs. The system enables the measurement of hilliness and curvature and incorporates rules that consider legal and practical issues related to routing in and around cities, turning in intersections, time delays, fuel consumption, and CO 2 emissions that result from waiting, acceleration, and braking. The system is used by all major forest companies in Sweden and in 60 percent of the two million annual transports in this sector. It has resulted in a paradigm shift from manual, imprecise, and unilaterally determined routes to automatically determined routes, which the stakeholders determine jointly. It has also enabled standardization, promoted collaboration, and reduced costs, thus strengthening the competitiveness of the Swedish forest industry in the international market.

Keywords: analytics; big data; inverse optimization; optimization; routing; logistics; fuel consumption; environment; decision support system (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:47:y:2017:i:5:p:372-395

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