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A Fuzzy Programming Method for Modeling Demand Uncertainty in the Capacitated Road–Rail Multimodal Routing Problem with Time Windows
School of Management Science and Engineering, Shandong University of Finance and Economics.
School of Management Science and Engineering, Shandong University of Finance and Economics.
Institute of Technology, Shandong TV University.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0003-4057-4124
2019 (English)In: Symmetry, ISSN 2073-8994, E-ISSN 2073-8994, Vol. 11, no 1, article id 91Article in journal (Refereed) Published
Abstract [en]

Demand uncertainty is an important issue that influences the strategic, tactical, and operational-level decision making in the transportation/logistics/supply chain planning. In this study, we explore the effect of demand uncertainty on the operational-level freight routing problem in the capacitated multimodal transportation network that consists of schedule-based rail transportation and time-flexible road transportation. Considering the imprecise characteristic of the demand, we adopt fuzzy set theory to model its uncertainty and use trapezoidal fuzzy numbers to represent the fuzzy demands. We set multiple transportation orders as the optimization object and employ soft time windows to reflect the customer requirement on on-time transportation. Under the above situation, we establish a fuzzy mixed integer nonlinear programming (FMINLP) model to formulate the capacitated road–rail multimodal routing problem with demand uncertainty and time windows. We first use the fuzzy expected value model and credibility measure based fuzzy chance-constrained programming to realize the defuzziness of the model and then adopt linearization technique to reformulate the crisp model to finally generate an equivalent mixed integer linear programming (MILP) model that can be solved by standard mathematical programming software. Finally, a numerical case is presented to demonstrate the feasibility of the proposed method. Sensitivity analysis and fuzzy simulation are combined to quantify the effect of demand uncertainty on the routing problem and also reveal some helpful insights and managerial implications.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2019. Vol. 11, no 1, article id 91
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-243906DOI: 10.3390/sym11010091ISI: 000459739500091Scopus ID: 2-s2.0-85061075416OAI: oai:DiVA.org:kth-243906DiVA, id: diva2:1287399
Note

QC 20190213

Available from: 2019-02-11 Created: 2019-02-11 Last updated: 2019-03-13Bibliographically approved

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