TY - JOUR
T1 - Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization
AU - De, Arijit
AU - Mamanduru, Vamsee Krishna Reddy
AU - Gunasekaran, Angappa
AU - Subramanian, Nachiappan
AU - Tiwari, Manoj Kumar
PY - 2016/6/30
Y1 - 2016/6/30
N2 - Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A mixed integer non-linear programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4-10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions.
AB - Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A mixed integer non-linear programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4-10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions.
KW - Carbon emission
KW - Maritime transportation
KW - Mixed integer non-linear programming
KW - Particle swarm Optimization-composite particle
KW - Ship routing
KW - Carbon emission
KW - Maritime transportation
KW - Mixed integer non-linear programming
KW - Particle swarm Optimization-composite particle
KW - Ship routing
U2 - 10.1016/j.cie.2016.04.002
DO - 10.1016/j.cie.2016.04.002
M3 - Article
SN - 0360-8352
VL - 96
SP - 201
EP - 215
JO - Computers & Industrial Engineering
JF - Computers & Industrial Engineering
ER -