This research addresses the sustainability and safety related challenges associated with the complex, practical, and real-time maritime transportation problem, and proposes a multiobjective mathematical model integrating different shipping operations. A mixed integer nonlinear programming (MINLP) model is formulated considering different maritime operations, such as routing and scheduling of ships, time window concept considering port's high tidal scenario, discrete planning horizon, loading/unloading operation, carbon emission from the vessel, and ship's draft restriction for maintaining the vessel's safety at the port. The relationship between fuel consumption and vessel speed optimization is included in the model for the estimation of the total fuel consumed and carbon emission from each vessel. Time window concept considered in the problem aims to improve the service level of the port by imposing different penalty charges associated with the early arrival of the vessel before the starting of the time window and vessel failing to finish its operation within the allotted time window. Another practical aspect of the maritime transportation such as high tide scenario is included in the model to depict the vessel arrival and departure time at a port. Two novel algorithms—Nondominated sorting genetic algorithm II (NSGA-II) and Multiobjective particle swarm optimization have been applied to solve the multiobjective mathematical model. The illustrative examples inspired from the real-life problems of an international shipping company are considered for application. The experimental results, comparative, and sensitivity analysis demonstrate the robustness of the proposed model.

This paper endeavors to explore the sustainable container shipping problem considering fuel bunker management and provide adequate recovery policies for countering disruption within maritime transportation. This paper addresses the environmental concerns related to fuel consumption and carbon emission within shipping operations and simultaneously presents strategies for countering disruption within the maritime transportation domain. Several studies addressed bunker fuel management strategies, but overlooked the need for integrating it with shipping operations. This paper aims to bridge this research gap by proposing a novel mathematical model and presenting a heuristic procedure combined with a variable neighborhood search algorithm for maximizing the shipping company's profitability, while addressing the vessel routing and scheduling decisions, container loading and unloading operations, selection of bunkering ports, and determining bunkered amount for heavy fuel oil and marine diesel oil. Recovery strategies such as port swapping and rescheduling of vessel route are considered to deal with disruptions related to weather adversities. An illustrative example is presented depicting the realistic scenario and providing results associated with ship routes, vessel speed, bunkering ports, bunkered amounts, fuel consumed by the vessel on each sailing leg, arrival and departure time of the ships, etc. Insights obtained from the analysis performed based on the fuel price, ship's bunkering capacity, adverse weather conditions on various routes, port closure, carbon tax, and fuel consumption provide useful information for shipping company managers. Managerial implications are presented with regard to the impact of fuel prices and carbon tax on shipping operation from the perspective of overall operational cost. Moreover, the results provide important policy insights for shipping company managers in terms of possessing alternate vessel route options for normal scenario and disrupted scenarios including weather adversities on sailing leg or port closure.