Fleet routing and scheduling problem based on constraints of chance

Tramp shipping transport is an important part of ocean transportation. However, facing the spot market with many uncertain conditions, it is not easy for fleet operators to plan vessel’s routes and schedule in the later period time, especially considering the situation that loading time window for a...

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Main Authors: Feng Guan, Zixuan Peng, Chao Chen, Zhen Guo, Shaoqiang Yu
Format: Article
Language:English
Published: SAGE Publishing 2017-12-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1177/1687814017743026
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spelling doaj-3033c98465bd41818d42e04e690f61142020-11-25T03:43:48ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402017-12-01910.1177/1687814017743026Fleet routing and scheduling problem based on constraints of chanceFeng Guan0Zixuan Peng1Chao Chen2Zhen Guo3Shaoqiang Yu4School of Transportation Engineering, Shenyang Jianzhu University, Shenyang, ChinaTransportation Management College, Dalian Maritime University, Dalian, ChinaAutomotive Engineering College, Dalian University of Technology, Dalian, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing, ChinaTransportation Management College, Dalian Maritime University, Dalian, ChinaTramp shipping transport is an important part of ocean transportation. However, facing the spot market with many uncertain conditions, it is not easy for fleet operators to plan vessel’s routes and schedule in the later period time, especially considering the situation that loading time window for a lot of cargoes has strong randomness. This article designed a linear programming model with chance constraints for the time window of loading cargo. Before the optimization, a survey for the waiting time of ships for berths is carried out in some of the ports with large export volume. Combined with the degree of acceptance how long ship owners can wait for the berth, the uncertain time window constraints can be transformed into deterministic constraints. The model is solved by column generation optimization technique. The model and algorithm are verified by a case of Panamax bulker fleet planning in real market. The results show that the model and the algorithm proposed in the article can well work on large-scale problem and can achieve good precision. Also, via sensitivity analysis, we provide decision makers good reference to balance profit and risks coming from randomness.https://doi.org/10.1177/1687814017743026
collection DOAJ
language English
format Article
sources DOAJ
author Feng Guan
Zixuan Peng
Chao Chen
Zhen Guo
Shaoqiang Yu
spellingShingle Feng Guan
Zixuan Peng
Chao Chen
Zhen Guo
Shaoqiang Yu
Fleet routing and scheduling problem based on constraints of chance
Advances in Mechanical Engineering
author_facet Feng Guan
Zixuan Peng
Chao Chen
Zhen Guo
Shaoqiang Yu
author_sort Feng Guan
title Fleet routing and scheduling problem based on constraints of chance
title_short Fleet routing and scheduling problem based on constraints of chance
title_full Fleet routing and scheduling problem based on constraints of chance
title_fullStr Fleet routing and scheduling problem based on constraints of chance
title_full_unstemmed Fleet routing and scheduling problem based on constraints of chance
title_sort fleet routing and scheduling problem based on constraints of chance
publisher SAGE Publishing
series Advances in Mechanical Engineering
issn 1687-8140
publishDate 2017-12-01
description Tramp shipping transport is an important part of ocean transportation. However, facing the spot market with many uncertain conditions, it is not easy for fleet operators to plan vessel’s routes and schedule in the later period time, especially considering the situation that loading time window for a lot of cargoes has strong randomness. This article designed a linear programming model with chance constraints for the time window of loading cargo. Before the optimization, a survey for the waiting time of ships for berths is carried out in some of the ports with large export volume. Combined with the degree of acceptance how long ship owners can wait for the berth, the uncertain time window constraints can be transformed into deterministic constraints. The model is solved by column generation optimization technique. The model and algorithm are verified by a case of Panamax bulker fleet planning in real market. The results show that the model and the algorithm proposed in the article can well work on large-scale problem and can achieve good precision. Also, via sensitivity analysis, we provide decision makers good reference to balance profit and risks coming from randomness.
url https://doi.org/10.1177/1687814017743026
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AT zixuanpeng fleetroutingandschedulingproblembasedonconstraintsofchance
AT chaochen fleetroutingandschedulingproblembasedonconstraintsofchance
AT zhenguo fleetroutingandschedulingproblembasedonconstraintsofchance
AT shaoqiangyu fleetroutingandschedulingproblembasedonconstraintsofchance
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