This part of the literature overview presents several models that make relevant contributions to ship routing and scheduling. Furthermore, this section discusses some heuristics to solve routing and scheduling problems. We review this literature to give an overview of problems related to barge loading and planning problems. The backbone of this part of the literature review about ship routing and scheduling is the review paper of Christiansen et al. [2012]. They review ship routing and scheduling and related problems published between 1999 and 2012. All literature discussed in this section is also discussed in Christiansen et al. [2012].
The review executed by Christiansen et al. [2012] is divided into four parts: - Liner shipping
- Industrial and tramp shipping
- Sailing speed, bunkering and emissions - Offshore logistics, lightering and stowage
The part about ‘Industrial and tramp shipping’ is interesting for this research, because it presents a basic model for ship routing and scheduling and several extensions for this or similar models. ‘Liner shipping’ is beyond our scope, because it is comparable to public bus services that have a fixed route and a fixed schedule according to which they operate [Christiansen et al., 2012]. This is not applicable to CTT because the route is not fixed but depends on available containers. Furthermore, the paper discusses several papers that discuss heuristics to solve the basic problem and extended problems. From the review, it follows that user experience and interaction of planning and decision supporting tools play a major role. We pay attention to this topic too.
First, we introduce the basic model and some extensions in Section 3.2.1. Next, Section 3.2.1 introduces several heuristics and their performance. Finally, we deal with user experience and interaction in Section 3.2.2.
3.2.1 Ship routing and scheduling methods
This section consists of three parts. First we describe the basic model that is used, adjusted, and extended in several studies. After that, we discuss the extensions and finally, the last part
deals with heuristics that are developed to find solutions for different models.
Basic model The basic cargo routing model discussed by Christiansen et al. [2012] is in- troduced by Christiansen and Ronen [2007]. Specified cargoes are the input for the planning process. Routing becomes a scheduling problem when the time-aspect is taken into account. This model is a routing and scheduling model for tramp shipping, which is a more advanced version of the industrial shipping problem.
Christiansen and Ronen [2007] formulate a model to maximize profit gained by operating a fleet. The model includes different types of loading (mandatory and spot cargo), vessel capacity, and time windows. A considerable amount of problems faced in reality are not included in this model. One important extension is the variability in cargo size. The paragraph below discusses this extension.
Extensions When considering variable cargo, a routing and scheduling problem also includes optimizing the size of cargo. This problem is studied by Brønmo et al. [2006] and Korsvik and Fagerholt [2008]. Brønmo et al. [2006] present a mathematical programming model and a set partitioning approach with a priori column generation to tackle the tramp problem extended with flexible cargo sizes. Brønmo et al. [2006] states that the results of the study show that it has an positive economical effect to use flexible cargo sizes.
In order to be able to save computation time and solve larger problems, Korsvik and Fagerholt [2008] develop a tabu search algorithm with an embedded specialized heuristic for determining optimal cargo quantities in each route. Korsvik and Fagerholt [2008] state that the heuristic is able to give optimal and near-optimal solutions to real-life cases within reasonable time. Furthermore, they also found that using flexible cargoes increases the quality of solutions. Brø nmo et al. [2010] extend their previous approach with a static column generation to an approach with dynamic column generation in which ship routes are generated when needed. In this approach no optimal solutions are guaranteed, but more extensive problems can be solved faster than in the static column generation case. Real world cases are solved optimal or near-optimal.
Heuristics Different heuristics can be used to find solutions to the problems mentioned above. As mentioned before, Brø nmo et al. [2010] use column generation to solve the mathematical programming model. Earlier, Brø nmo et al. [2007] used a multi-start local search, but this was outperformed by the tabu search heuristics by Korsvik and Fagerholt [2008] and Korsvik et al. [2009]. Malliappi et al. [2011] present a variable neighborhood search metaheuristic for this problem. Compared to the multi-start and tabu search heuristics, this heuristic gives better solutions with respect to the quality of the solutions and the computation time.
3.2.2 Decision support
Decision support tools (DST) ‘are computational systems with the purpose of helping decision makers by analyzing information and identifying solutions’ [Perimenis et al., 2011]. A DST consists of a database that can store and manage internal and external information, algorithms
necessary for the analysis, and an interface for communication with the user [Perimenis et al., 2011]. A DST aims to analyze the problem, evaluate the performance of alternatives based on criteria, and express the priorities of the decision makers [Shim et al., 2002]. When Fagerholt et al. [2009] implemented and tested a DST at a shipping company, they experienced that there are constraints and secondary objectives that are hard to model in a proper way. From this experience and from discussion with the planners, they conclude that it is desirable for a DST to provide several high quality solutions that planners can analyze and choose from.
3.2.3 Conclusions on ship routing and scheduling
We see that barge planning and loading only plays a minor role in the literature discussed in this section, while in practice it is an important part of ship routing and scheduling problems. Loading, planning and routing are problems dependent on each other, which makes it too complex to analyze them in total and thus are split into subproblems.
From the part on decision support, we learn that it is desirable to provide planners with a number of solutions from which they can choose and adjust to plans they want to execute.