scholarly journals A Heuristic Procedure for a Ship Routing and Scheduling Problem with Variable Speed and Discretized Time Windows

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Krystel K. Castillo-Villar ◽  
Rosa G. González-Ramírez ◽  
Pablo Miranda González ◽  
Neale R. Smith
Keyword(s):  
Time Windows ◽  
Waiting Times ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Computational Results ◽  
Solution Quality ◽  
Ship Routing ◽  
Routing And Scheduling ◽  
Ship Routing And Scheduling ◽  
Tramp Shipping

This paper develops a heuristic algorithm for solving a routing and scheduling problem for tramp shipping with discretized time windows. The problem consists of determining the set of cargoes that should be served by each ship, the arrival, departure, and waiting times at each port, while minimizing total costs. The heuristic proposed is based on a variable neighborhood search, considering a number of neighborhood structures to find a solution to the problem. We present computational results, and, for comparison purposes, we consider instances that can be solved directly by CPLEX to test the performance of the proposed heuristic. The heuristics achieves good solution quality with reasonable computational times. Our computational results are encouraging and establish that our heuristic can be utilized to solve large real-size instances.

scholarly journals A hybrid GRASP-VNS for ship routing and scheduling problem with discretized time windows

2015 ◽  
Vol 45 ◽  
pp. 350-360 ◽  
Author(s):  
Jesica de Armas ◽  
Eduardo Lalla-Ruiz ◽  
Christopher Expósito-Izquierdo ◽  
Dario Landa-Silva ◽  
Belén Melián-Batista
Keyword(s):  
Time Windows ◽  
Scheduling Problem ◽  
Ship Routing ◽  
Routing And Scheduling ◽  
Ship Routing And Scheduling

scholarly journals A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry

2021 ◽  
Vol 11 (11) ◽  
pp. 4840
Author(s):  
Apichit Maneengam ◽  
Apinanthana Udomsakdigool
Keyword(s):  
Time Window ◽  
Set Covering ◽  
Scheduling Problem ◽  
Ship Routing ◽  
Routing And Scheduling ◽  
Weighted Sum Method ◽  
Ship Routing And Scheduling ◽  
Green Ship ◽  
Covering Model ◽  
Time Window Constraints

This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO2 equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the ε-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.

scholarly journals Tramp Ship Routing and Scheduling with Speed Optimization Considering Carbon Emissions

2019 ◽  
Vol 11 (22) ◽  
pp. 6367 ◽  
Author(s):  
Houming Fan ◽  
Jiaqi Yu ◽  
Xinzhe Liu
Keyword(s):  
Carbon Emissions ◽  
Simulated Annealing Algorithm ◽  
Neighborhood Search ◽  
Solution Quality ◽  
Ship Routing ◽  
Routing And Scheduling ◽  
Genetic Simulated Annealing Algorithm ◽  
Speed Optimization ◽  
Ship Scheduling ◽  
The Impact

The International Maritime Organization (IMO) proposed to reduce the total CO2 emissions of the maritime sector by 50% by 2050, and strive to gradually achieve the zero-carbon target. Therefore, shipping companies need to consider environmental impacts while pursuing benefits. In view of the tramp ship scheduling with speed optimization problem, considering carbon emissions, the configuration of owner ships and charter ships, and the impact of sailing speed on ship scheduling with the target of minimizing the total costs of shipping companies, multi-type tramp ship scheduling and speed optimization considering carbon emissions is established. A genetic simulated annealing algorithm based on a variable neighborhood search is proposed to solve the problem. Firstly, the ship type is matched with the cargo. Then the route is generated according to the time constraint, and finally, the neighborhood search strategy is adopted to improve the solution quality. The effectiveness of the proposed model and algorithm is verified by an example, which also confirms that ship scheduling and sailing speed joint optimization can reduce costs and carbon emissions. Research results can not only deepen the study of the theory of tramp scheduling but also to effectively solve the tramp shipping schedule considering carbon emissions problems faced by companies to provide theoretical guidance.

scholarly journals Ship routing and scheduling systems: forecasting, upscaling and viability

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Said El Noshokaty
Keyword(s):  
Commodity Trade ◽  
Ship Routing ◽  
Content Type ◽  
Routing And Scheduling ◽  
Ship Routing And Scheduling ◽  
Time Problem ◽  
Forecasting System ◽  
Tramp Shipping ◽  
Cleaning Cost ◽  
Practical Implications

Purpose The purpose of this paper is to resolve three problems in ship routing and scheduling systems. Problem 1 is the anticipation of the future cargo transport demand when the shipping models are stochastic based on this demand. Problem 2 is the capacity of these models in processing large number of ships and cargoes within a reasonable time. Problem 3 is the viability of tramp shipping when it comes to real problems. Design/methodology/approach A commodity-trade forecasting system is developed, an information technology platform is designed and new shipping elements are added to the models to resolve tramp problems of en-route ship bunkering, low-tide port calls and hold-cleaning cost caused by carrying incompatible cargoes. Findings More realistic stochastic cargo quantity and freight can now be anticipated, larger number of ships and cargoes are now processed in time and shipping systems are becoming more viable. Practical implications More support goes to ship owners to make better shipping decisions. Originality/value New norms are established in forecasting, upscaling and viability in ship routing and scheduling systems.

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