Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Juni 2015
Genre:	Wirtschaft / Recht
	B / Business and Management / Decision Making / International Economics / Management decision making / Management of specific areas / Management science / Operations Management / Operations Research / Operations Research and Decision Theory / Operations Research/Decision Theory / Production management mehr...
ISBN:	9783319176642
EAN-Code:	9783319176642
Verlag:	Springer EN
Einband:	Gebunden
Sprache:	English
Serie:	#57 - Operations Research/Computer Science Interfaces Series
Dimensionen:	H 235 mm / B 155 mm / D
Gewicht:	4492 gr
Seiten:	182
Illustration:	VIII, 182 p. 49 illus., 20 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

This monograph addresses several critical problems to the operations of shipping lines and ports, and provides algorithms and mathematical models for use by shipping lines and port authorities for decision support. One of these problems is the repositioning of container ships in a liner shipping network in order to adjust the network to seasonal shifts in demand or changes in the world economy. We provide the first problem description and mathematical model of repositioning and define the liner shipping fleet repositioning problem (LSFRP). The LSFRP is characterized by chains of interacting activities with a multi-commodity flow over paths defined by the activities chosen. We first model the problem without cargo flows with a variety of well-known optimization techniques, as well as using a novel method called linear temporal optimization planning that combines linear programming with partial-order planning in a branch-and-bound framework. We then model the LSFRP with cargo flows, using several different mathematical models as well as two heuristic approaches. We evaluate our techniques on a real-world dataset that includes a scenario from our industrial collaborator. We show that our approaches scale to the size of problems faced by industry, and are also able to improve the profit on the reference scenario by over US$14 million.