Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/116274
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Logistics and Maritime Studies | en_US |
| dc.creator | Yang, Y | en_US |
| dc.creator | Zhang, S | en_US |
| dc.creator | Wang, S | en_US |
| dc.date.accessioned | 2025-12-09T02:56:11Z | - |
| dc.date.available | 2025-12-09T02:56:11Z | - |
| dc.identifier.uri | http://hdl.handle.net/10397/116274 | - |
| dc.language.iso | en | en_US |
| dc.subject | Benders decomposition | en_US |
| dc.subject | Cruise deployment | en_US |
| dc.subject | Integrated optimization | en_US |
| dc.subject | Itinerary scheduling | en_US |
| dc.subject | Simultaneous Magnanti–Wong method | en_US |
| dc.subject | Totally unimodular | en_US |
| dc.title | Integrated cruise fleet deployment and itinerary scheduling problem : an enhanced Benders decomposition approach | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.volume | 201 | en_US |
| dc.identifier.doi | 10.1016/j.trb.2025.103321 | en_US |
| dcterms.abstract | With the growing popularity of cruise tourism, the issue of comprehensive and precise cruise management is emphasized by the industrial field, which demands effective strategies in both tactical-level cruise deployment and operational-level itinerary scheduling. This rising concern and the expectation of integrated decision, however, increase the complexity of the problem and the difficulty of optimization. This paper provides a cohesive framework and scalable algorithms for the integrated cruise fleet deployment and itinerary scheduling problem. First, to address this problem, we propose an integer programming model based on a time-expanded network that captures the movement dynamics of cruises over a planning horizon. Several problem-specific reformulations including cumulative-flow-based variables and route-based time-expanded network representation are introduced, based on which, we prove that the itinerary scheduling problem is totally unimodular and the integer variables can be relaxed. Second, we introduce a tailored Benders decomposition approach augmented by the simultaneous Magnanti–Wong method, where a valid and pre-obtainable Magnanti–Wong bound is designed, yielding Pareto-optimal cuts in small computation time in each iteration. Finally, we validate our approach using extensive numerical experiments on both simulation instances and a real case study. The results demonstrate the effectiveness of our integrated solving scheme and the practical applicability of our advanced decomposition method, marking a significant advancement in the field of cruise fleet management. | en_US |
| dcterms.accessRights | embargoed access | en_US |
| dcterms.bibliographicCitation | Transportation research. Part B, Methodological, Nov. 2025, v. 201, 103321 | en_US |
| dcterms.isPartOf | Transportation research. Part B, Methodological | en_US |
| dcterms.issued | 2025-11 | - |
| dc.identifier.scopus | 2-s2.0-105017427450 | - |
| dc.identifier.artn | 103321 | en_US |
| dc.description.validate | 202512 bchy | en_US |
| dc.description.oa | Not applicable | en_US |
| dc.identifier.SubFormID | G000449/2025-11 | - |
| dc.description.fundingSource | Self-funded | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.date.embargo | 2027-11-30 | en_US |
| dc.description.oaCategory | Green (AAM) | en_US |
| Appears in Collections: | Journal/Magazine Article | |
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