To address the decarbonization challenge, we consider a tramp ship routing problem with speed optimization where the availability of future cargoes is uncertain. We propose a two-stage stochastic programming model to solve it. The first stage of the model decides on the deterministic cargoes with the known availability, while the second stage decides on the detailed routing and scheduling plans with the available spot cargoes revealed. Since sailing speed heavily influences on the fuel consumption, and hence costs and emissions, we include the ships’ speeds along the different sailing legs as decision variables. The opening of the Northern Sea Route (NSR) provides a shorter alternative in connecting Asia and Europe, which may benefit the shipping industry in reducing $$\mathrm {CO_2}$$ CO 2 emissions. We use our model to evaluate the impact of NSR on $$\mathrm {CO_2}$$ CO 2 emissions in tramp shipping considering speed optimization and possible future carbon tax schemes, such as fixed and progressive carbon taxes. The computational results indicate that using the NSR improves the gross margins and reduces the $$\mathrm {CO_2}$$ CO 2 emissions for tramp operators.

The LNG-ADP problem is a tactical planning problem for creating an annual delivery program (ADP) for a liquefied natural gas (LNG) producer. An ADP specifies the departure dates of the LNG carriers as well as the delivery dates at the different customers for a period of 12–18 months. The problem can be formulated as maritime inventory routing problem, as it is an important requirement to plan the deliveries such that inventory levels are kept within minimum and maximum limits at the customers as well as the LNG production facility. Inspired by the case of Yamal LNG, we propose a novel discrete-time formulation for the LNG-ADP problem with transshipment and intermediate storage. Our formulation also allows for waiting at the unloading ports. The problem is solved using a rolling horizon heuristic (RHH) for a case based on the Yamal LNG project. We study the impact of different RHH configurations on run time and solution quality. The results show that using a central period that is shorter than the forecast period provides the best objective function, whereas a central period that is longer than the forecast period improves run time. We also explore the effect of allowing waiting at the unloading ports. Waiting does not necessarily improve the objective function value, despite increasing the solution space. However, we observe a reduction in run time for instances where waiting is allowed.

The introduction of transshipment ports in the liquefied natural gas (LNG) supply chain in recent years offers additional flexibility, but also challenges to the planning of the annual delivery program. We present a new variant of the LNG-annual delivery program (ADP) planning problem by considering transshipment as well as time-dependent sailing times. We present a continuous time formulation for the LNG-ADP problem and propose a rolling horizon heuristic to solve the problem. Both the model and heuristic were used to solve a case inspired by the Yamal LNG project. The computational results show that the heuristic provides good solutions within a relatively short amount of time, especially compared to the exact solution methods. However, there is a trade-off between computational time and solution quality when designing the rolling horizon heuristic. The results also show the impact storage capacity at the transshipment port has on the total cost.

A supply chain network design (SCND) problem with fuzzy dual objective integer programming is proposed for stochastic transportation lead time. The conflict objectives considered in this paper are to maximize the total net profit associated with the supply chain network design and to minimize the delay probabilities associated with the transportation lead time between the production plant and the distribution center and the distribution center and the customer. It is assumed that the possible risks associated with transport lead times follow a normal distribution with defined mean and standard deviation. The model is tested with different levels of risk to achieve maximum satisfaction in the network. By considering acceptable levels of satisfaction, the results of the analysis of the proposed model will provide different scenarios, which will help decision makers better understand the supply chain network required for the design. LINGO software programming solution, and case analysis. The results show that the lower the risk level, the higher the probability of product delivery to customers ahead of time, and the lower the profit.