Word Burstiness: ¿La clave para encontrar palabras clave?

Notably, the complexity and heterogeneity of contract types and the lack of data are major hurdles in formulating a fully behavioural freight transport model covering the full range of decisions and contexts. Understanding a holistic freight transport market requires an appropriate degree of aggregation and abstraction. In order to mathematically model the logistics choices and empirically test them, generalization and simplification are required, while the traceability of causes and effects and the variability in the behaviours are also maintained.

This research is an effort to propose some empirical tools and approaches at the operational level, including advanced choice models and agent-based simulation, to answer the research questions. The work is broken down into the following subsections which state how and in which papers the abovementioned areas are addressed.

1.4.1 The choices of shipment size and vehicle type

Chapter 2 presents an attempt to model the joint decisions of shipment size and vehicle type in an urban area using a copula–based continuous–discrete choice model, as summarised in Figure 6. Models are estimated from a sample of 550 ancillary shippers’ observations and 1,484 for–hire carriers’ observations in Mashhad, Iran. This research contributes to the state-of-the-art by considering the continuous nature of the choice of shipment size in a copula-based model.

Considering the differences in decision–making between carriers and shippers, two different models are estimated, while the assumption of pure utility maximization is relaxed via a hybrid utility–regret specification. Results show that differences existed between shippers’ and carriers’ preferences. The results also prove the importance of considering the two decisions jointly as well as the relevance of using a hybrid utility–regret formulation for the cost of transport.

21 Figure 6 – Modeling of shipment size and vehicle type

1.4.2 The choices of using container terminal and dwell time

Considering the growth in maritime containerised trade, limited availability of land around ports, and the increase in vessel size, it is important to understand the circumstances under which freight operators use CTs in order to allocate their resources effectively. The analysis of preferences for the use of CTs by importers in the hinterland of the Port of Brisbane (Australia), is studied in Chapter 3. However, a considerable number of observations in this case study have missing information regarding the weight and timestamp(s) for the shipment. In most choice models, records with missing data are removed prior to analysis, a practice that causes the parameter estimates of the models to be biased when the

22

percentage of missing data is significant. The main body of literature on the non-response problem concerns imputation (Ramalho and Smith, 2002), but latent variable models to address non-response to attitudinal items have been applied in some social science studies (Knott et al., 1991; Albanese and Knott, 1992; Muircheartaigh and Moustaki, 1999). Notably, these studies were unable to handle more than two latent variables due to computational difficulties. Ramalho and Smith (2002) proposed a likelihood-based approach to deal with missing data in discrete choice models when there is either “unit non-response” or “item non-response”. Sanko et al. (2014) addressed missing responses for household income in travel surveys with hybrid choice models. To avoid producing unbiased estimates for the choice model in this study, missing information is treated as a latent variable using a hybrid choice model to compensate for the missing observations. That is how the second research question is answered in this case study.

According to the previous discussion on the interrelation between decisions of using transshipment points and the dwell time at these facilities (i.e. the staging duration), Chapter 3 is extended to cover the endogeneity and simultaneity of these decisions for both import and export container movements, as shown in Figure 7. Accordingly, Chapter 4 investigates the relationship between shipment characteristics and the decision to use CTs as well as the duration of the dwell time at CTs, either as an intermediate stop or as a location for unpacking and separate distribution by presenting a joint hybrid discrete–discrete choice model.

23

Figure 7 – Modeling of using container terminal and dwell time

1.4.3 Impact analysis of cooperation in hinterland container delivery

Chapters 5, 6 and 7 consider the effects of cooperation between freight agents to answer the last research question, as shown in Figure 8. Chapter 5 presents an agent-based simulation in order to analyse the heterogeneous choice of freight actors to implement truck–sharing strategies in the import container movements, using a Q-learning algorithm.

Chapter 6 presents an optimisation problem (a dynamic capacitated vehicle routing problem with time windows) for both import and export movements integrated with empty container repositioning (street-turn strategy). Given that only full container movements are paid, empty container repositioning is directly linked to profits. Accordingly, the demand of

24

an exporter for empty containers can be connected to the presence of nearby empty containers stored by an importer. This concept is termed “street-turn” and is an important objective from the shipping lines’ perspective to the point that coordination between shipping lines would not only reduce the number of empty container movements but also increase profits. This coordination can be provided through an online market supported by a port authority, where information about the containers becomes available to all involved actors.

This web-based information exchange platform allows shipping lines to match empty container needs without storing them in the ECP. This concept is also sometimes referred to as a “virtual container yard (VCY)” or “triangulation” and has been successfully applied as either a module of a Port Community System (e.g., Virtuele Haven in the Port of Rotterdam), or a standalone market (e.g., Ports of Oakland, Los Angeles, Long Beach, and Montreal) (Maguire et al., 2010).

This research contributes to the state-of-the-art of vehicle routing and allocation problems by considering a two–dimensional capacity, including the weight and size of the container, and of dynamic travel times of links. Considering a multi–dimensional capacity is imperative for container movement because it is important to consider that a 40–foot container does not violate the weight constraint imposed by either the vehicle itself or road authorities. Moreover, real–time network dynamics assure the optimum strategy is considered in the vehicle routing problem, where the total transport cost considers both time–based and distance–based operational costs.

Furthermore, emissions reduction for the most important pollutants as a result of inland empty container repositioning and truck-sharing is presented. Specifically, average speed is calculated for every route segment of every vehicle, and ecological footprints are estimated according to the COPERT model calibrated for Australia (EMISIA;

Commonwealth of Australia, 2016). The model is a function of the average speed of travelled links and the Australian fleet vintage configuration registered in Queensland (Queensland Government, 2013).

Chapter 7 presents an extension of the previous chapters by relaxing the time-windows constraint and using a probability matching reinforcement algorithm in order to evaluate the effects of cooperation in import and export container delivery. In this study we consider two main reinforcement learning strategies: (i) freight agents diversify in their first few choices and gradually converge to a single preferred option; (ii) freight agents learn the

25

probabilities of different outcomes, and ultimately the actions that were successful in the past are more likely to be adopted in the future. In the latter approach, agents predict their future reward in a multi-step task while learning from their previous experiences.

Figure 8 – Modelling the cooperation in hinterland container delivery