Model and algorithms for multi-period sea cargo mix problem

In this paper, we consider the sea cargo mix problem in international ocean container shipping industry. We describe the characteristics of the cargo mix problem for the carrier in a multi-period planning horizon, and formulate it as a multi-dimensional multiple knapsack problem (MDMKP). In particul...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:European journal of operational research Jg. 180; H. 3; S. 1381 - 1393
Hauptverfasser: Ang, James S.K., Cao, Chengxuan, Ye, Heng-Qing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.08.2007
Elsevier
Elsevier Sequoia S.A
Schriftenreihe:European Journal of Operational Research
Schlagworte:
0–1 integer program
Algorithms
Applied sciences
Cargos
Container ships
Exact sciences and technology
Heuristic
Heuristic algorithm
Integer programming
Knapsack problem
Logistics
Operational research. Management science
Optimization algorithms
Sea cargo mix
Shipping industry
Studies
Sea cargo mix
0–1 integer program
Heuristic algorithm
Logistics
Energy analysis
Statistical analysis
Numerical method
Booking
Modeling
Optimization
Knapsack problem
Container
Zero one programming
0-1 integer program
Heuristic method
Profit
Mixed problem
Planning
Large scale system
ISSN:0377-2217, 1872-6860
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, we consider the sea cargo mix problem in international ocean container shipping industry. We describe the characteristics of the cargo mix problem for the carrier in a multi-period planning horizon, and formulate it as a multi-dimensional multiple knapsack problem (MDMKP). In particular, the MDMKP is an optimization model that maximizes the total profit generated by all freight bookings accepted in a multi-period planning horizon subject to the limited shipping capacities. We propose two heuristic algorithms that can solve large scale problems with tens of thousands of decision variables in a short time. Finally, numerical experiments on a wide range of randomly generated problem instances are conducted to demonstrate the efficiency of the algorithms.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2006.05.012