Christian Vad Karsten

Competitive Liner Shipping Network Design

Abstract We present a solution method for the liner shipping network design problem which is a core strategic planning problem faced by container carriers. We propose the first practical algorithm which explicitly handles transshipment time limits for all demands. Individual sailing speeds at each service leg are used to balance sailing speed against operational costs, hence ensuring that the found network is competitive on both transit time and cost. We present a matheuristic for the problem where a MIP is used to select which ports should be inserted or removed on a route. Computational results are presented showing very promising results for realistic global liner shipping networks. Due to a number of algorithmic enhancements, the obtained solutions can be found within the same time frame as used by previous algorithms not handling time constraints. Furthermore, we present a sensitivity analysis on fluctuations in bunker price which confirms the applicability of the algorithm.

Container shipping service selection and cargo routing with transshipment limits

We address the tactical planning problem faced by container liner shipping companies to select a set of sailing services from a given pool of candidate services and route available cargo over the chosen services so as to maximize profit. One of the distinctive features of our model is that it incorporates limits on the number of transshipments for each container, a common service requirement in practice. These limits can vary by shipment attributes such as origin and destination, and cargo priority. We propose a new stage-indexed multi-commodity flow model that is based on an augmented network containing links (representing sub-paths) between every pair of ports visited by a candidate service. This sub-path structure, together with our approach of indexing the flow variables by transportation stage, enables the model to accurately capture transshipment costs and enforce transshipment limits. To reduce the computational time to solve this problem, we develop preprocessing steps that exploit network structure to eliminate variables, describe valid inequalities to strengthen the models linear programming relaxation, and propose an optimization-based heuristic algorithm to generate good initial solutions. We report successful computational results for realistic problem instances from a benchmark suite of liner shipping problems, solved using a standard solver applied to our reduced and strengthened model.

A Matheuristic for the Liner Shipping Network Design Problem with Transit Time Restrictions

We present a mathematical model for the liner shipping network design problem with transit time restrictions on the cargo flow. We extend an existing matheuristic for the liner shipping network design problem to consider transit time restrictions. The matheuristic is an improvement heuristic, where an integer program is solved iteratively as a move operator in a large-scale neighborhood search. To assess the effects of insertions/removals of port calls, flow and revenue changes are estimated for relevant commodities along with an estimation of the change in the vessel cost. Computational results on the benchmark suite LINER-LIB are reported, showing profitable networks for most instances. We provide insights on causes for rejecting demand and the average speed per vessel class in the solutions obtained.

Simultaneous Optimization of Container Ship Sailing Speed and Container Routing with Transit Time Restrictions

We introduce a decision support tool for liner shipping companies to optimally determine the sailing speed and needed fleet for a global network. We incorporate cargo routing decisions with tight transit time restrictions on each container such that we get a realistic picture of the utilization of the network. Furthermore, we show that it is possible to extend the model to include optimal time scheduling decisions such that the time associated with transshipments is also reflected accurately. To solve the speed optimization problem, we propose an exact algorithm based on Benders decomposition and column generation that exploits the separability of the problem. Computational results show that the method is applicable to liner shipping networks of realistic size and that it is important to incorporate cargo routing decisions when optimizing speed. The online appendix is available at https://doi.org/10.1287/trsc.2018.0818.

Big Data Optimization in Maritime Logistics

Seaborne trade constitutes nearly 80 % of the world trade by volume and is linked into almost every international supply chain. Efficient and competitive logistic solutions obtained through advanced planning will not only benefit the shipping companies, but will trickle down the supply chain to producers and consumers alike. Large scale maritime problems are found particularly within liner shipping due to the vast size of the network that global carriers operate. This chapter will introduce a selection of large scale planning problems within the liner shipping industry. We will focus on the solution techniques applied and show how strategic, tactical and operational problems can be addressed. We will discuss how large scale optimization methods can utilize special problem structures such as separable/independent subproblems and give examples of advanced heuristics using divide-and-conquer paradigms, decomposition and mathematical programming within a large scale search framework. We conclude the chapter by discussing future challenges of large scale optimization within maritime shipping and the integration of predictive big data analysis combined with prescriptive optimization techniques.

Optimization in liner shipping

Seaborne trade is the lynchpin in almost every international supply chain, and about 90% of non-bulk cargo worldwide is transported by container. In this survey we give an overview of data-driven optimization problems in liner shipping. Research in liner shipping is motivated by a need for handling still more complex decision problems, based on big data sets and going across several organizational entities. Moreover, liner shipping optimization problems are pushing the limits of optimization methods, creating a new breeding ground for advanced modelling and solution methods. Starting from liner shipping network design, we consider the problem of container routing and speed optimization. Next, we consider empty container repositioning and stowage planning as well as disruption management. In addition, the problem of bunker purchasing is considered in depth. In each section we give a clear problem description, bring an overview of the existing literature, and go in depth with a specific model that somehow is essential for the problem. We conclude the survey by giving an introduction to the public benchmark instances LINER-LIB. Finally, we discuss future challenges and give directions for further research.