Constraint Programming for LNG Ship Scheduling and Inventory Management

Abstract We propose a constraint programming approach for the optimization of inventory routing in the liquefied natural gas industry. We present two constraint programming models that rely on a disjunctive scheduling representation of the problem. We also propose an iterative search heuristic to generate good feasible solutions for these models. Computational results on a set of largescale test instances demonstrate that our approach can find better solutions than existing approaches based on mixed integer programming, while being 4 to 10 times faster on average

A binary particle swarm optimization algorithm for ship routing and scheduling of liquefied natural gas transportation

With the increasing global demands for energy, fuel supply management is a challenging task of today’s industries in order to decrease the cost of energy and diminish its adverse environmental impacts. To have a more environmentally friendly fuel supply network, Liquefied Natural Gas (LNG) is suggested as one of the best choices for manufacturers. As the consumption rate of LNG is increasing dramatically in the world, many companies try to carry this product all around the world by themselves or outsource it to third-party companies. However, the challenge is that the transportation of LNG requires specific vessels and there are many clauses in related LNG transportation contracts which may reduce the revenue of these companies, it seems essential to find the best option for them. The aim of this paper is to propose a meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to come with an optimized solution for ship routing and scheduling of LNG transportation. The application demonstrates what sellers need to do to reduce their costs and increase their profits by considering or removing some obligations

Optimisation of vendor-managed inventory systems

Imperial Users onl

An operational model for liquefied natural gas spot and arbitrage sales

As more buyers become interested in the spot purchase of liquefied natural gas (LNG), the share of spot trade in LNG business increases. This means that the cash flowing into the upstream of LNG projects is a combination of that generated by deliveries to long-term contract (LTC) customers and uncommitted product and arbitrage spot sales. LTC cash flows are more predictable while uncommitted product and arbitrage cash flows, affected by the dynamics of supply and demand, are more volatile and therefore less predictable. In this research, we formulate an inventory routing problem (IRP) which maximizes the profit of an LNG producer with respect to uncommitted product and arbitrage spot sales, and also LTC deliveries at an operational level. Using the model, the importance of arbitrage, interest rates and compounding frequency in profit maximization, and also the significance of interest rates and fluctuation in spot prices in decision-making for spot sales of uncommitted product are studied. It is proven that writing traditional LTCs with relaxed destination clauses which assist in arbitrage is beneficial to the LNG producer. However, in contrast to what was predicted neither the interest rate nor the compounding frequency has any importance in profit maximization when no change of selling strategy is observed. Apart from these, it is shown that there is a trade-off between the expectation of higher spot prices and the inventory and shipping costs in decision-making for spot sales of uncommitted product in the LNG industry. Finally, it is observed that the interest rate can affect the set of decisions on spot sales of uncommitted product, although the importance of such changes in profit remains to be further explored

Scheduling of unloading, inventory and delivery operations at an LNG receiving terminal

Master'sMASTER OF ENGINEERIN

A Literature Review, Container Shipping Supply Chain: Planning Problems and Research Opportunities

This paper provides an overview of the container shipping supply chain (CSSC) by taking a logistics perspective, covering all major value-adding segments in CSSC including freight logistics, container logistics, vessel logistics, port/terminal logistics, and inland transport logistics. The main planning problems and research opportunities in each logistics segment are reviewed and discussed to promote further research. Moreover, the two most important challenges in CSSC, digitalization and decarbonization, are explained and discussed in detail. We raise awareness of the extreme fragmentation of CSSC that causes inefficient operations. A pathway to digitalize container shipping is proposed that requires the applications of digital technologies in various business processes across five logistics segments, and change in behaviors and relationships of stakeholders in the supply chain. We recognize that shipping decarbonization is likely to take diverse pathways with different fuel/energy systems for ships and ports. This gives rise to more research and application opportunities in the highly uncertain and complex CSSC environment.</jats:p

Liquefied Natural Gas Ship Route Planning Model Considering Market Trend Change

We consider a new biannual liquefi ed natural gas (LNG) ship routing and scheduling problem and a stochastic extension under boil-off gas (BOG) uncertainty while serving geographically dispersed multiple customers using a fl eet of heterogeneous vessels. We are motivated not only by contract trend changes to shorter ones but also by technological advances in LNG vessel design. The mutual coincidence of both transitions enables developing a new LNG shipping strategy to keep up with emerging market trend. We fi rst propose a deterministic LNG scheduling model formulated as a multiple vehicle routing problem (VRP). The model is then extended to consider BOG using a two-stage stochastic modeling approach in which BOG is a random variable. Since the VRP is typically a combinatorial optimization problem, its stochastic extension is much harder to solve. In order to overcome this computational burden, a Monte Carlo sampling optimization is used to reduce the number of scenarios in the stochastic model while ensuring good quality of solutions. The solutions are evaluated using expected value of perfect information (EVPI) and value of stochastic solution (VSS). The result shows that our proposed model yields more stable solutions than the deterministic model. The study was made possible by the NPRP award [NPRP 4-1249-2-492] from the Qatar National Research Fund (a member of the Qatar Foundation)

Stochastic ship fleet routing with inventory limits

This thesis describes a stochastic ship routing problem with inventory management. The problem involves finding a set of least costs routes for a fleet of ships transporting a single commodity when the demand for the commodity is uncertain. Storage at consumption and supply ports is limited and inventory levels are monitored in the model. Consumer demands are at a constant rate within each time period in the deterministic problem, and in the stochastic problem, the demand rate for a period is not known until the beginning of that period. The demand situation in each time period can be described by a scenario tree with corresponding probabilities. Several possible solution approaches for solving the problem are studied in the thesis. This problem can be formulated as a mixed integer programming (MIP) model. However solving the problem this way is very time consuming even for a deterministic problem with small problem size. In order to solve the stochastic problem, we develop a decomposition formulation and solve it using a Branch and Price framework. A master problem (set partitioning with extra inventory constraints) is built, and the subproblems, one for each ship, involve solving stochastic dynamic programming problems to generate columns for the master problem. Each column corresponds to one possible tree of schedules for one ship giving the schedule for the ship for all demand scenarios. In each branch-and-bound node, the node problem is solved by iterating between the master problem and the subproblems. Dual variables can be obtained solving the master problem and are used in the subproblems to generate the most promising columns for the master problem. Computational results are given showing that medium sized problems can be solved successfully. Several extensions to the original model are developed, including a variable speed model, a diverting model, and a model which allows ships to do extra tasks in return for a bonus. Possible solution approaches for solving the variable speed and the diverting model are presented and computational results are given