Simulation Modeling Based Forcasting of Container Port Operations

RESEARCH PROPOSAL

SIMULATION MODELING BASED FORCASTING OF CONTAINER PORT OPERATIONS

This paper examines container port performance measurement, ways of its modelling and analysis. Detailed research on the topic demonstrates that there is an urgent need for an innovative performance measurement approach to meet the requirements of port stakeholders and, more importantly, to develop analytic tools capable of quick decision-making affecting complex port operations in dynamic environment. Therefore, this study dwells upon the related questions of ‘what and how to measure’ and ‘how to control and improve’ port performance. The empirical data are gathered directly from port operating companies and information databases managed by the government and credit rating agencies. A decision-making framework built on the principles of prioritising and choosing optimal port performance improvement strategies is resulted due to the concepts of benchmarking approach and the usage of the analytic hierarchy process (AHP). The first proposed model is applied to address the challenges of port performance measurement, while the second one deals with the complex and diverse nature of the port performance indicators (PPIs). On the basis of the results obtained from those performance models, the chosen port is analysed in order to prove the feasibility of the proposed methodology. Acknowledging this method may enable decision makers to detect optimal solutions for improving performance indicators of different terminal operating companies.

Keywords: port performance, port operations forecasting, container cargo throughput, integrated forecasting method, analytic hierarchy process

Introduction

In the course of time the main function of a port system appears to shift from being solely a link between the manufacturer and the final consumer to improving the integration of different transport modes through enhancing container transfers within the port, as well as between the maritime and land modes of transport.

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Providing insight to port management into the operation of key areas, port performance indicators are characterized as measures of various aspects of the port’s operation. Simulation is studied within the general domain of methods that test decision-making alternatives. Discrete event simulation is regarded as the process of depicting the behavior of a complex system as a sequence of well-defined events that allows to highlight the impact of inner processes on the system.

Nowadays technological advances across the sector of maritime transport and its increased importance for national economy have resulted in significant changes in understanding of the term “port”. It is stated that, being an intermodal node, a container port converges different modes of transport (Casaca, 2005). In the future the containerization process is expected to expand apace (Havenga & Van Eeden, 2011) increasing the role of the container shipping industry for the global freight transport system. This tendency explains the necessity of introducing more intelligent decision support tools to meet future challenges of ports. Klodzinski (2015) presents ports as “economic generators” which cannot potentially grow without physical and informational infrastructure improvements. When insight in the features that boost cargo throughput in ports is needed, decision makers prefer to rely on simulation modeling as an alternative approach which trends extrapolation methods (De Langen, 2012). An adequately precise forecast of port operations is crucial as it can have significant influence on the development policy, infrastructure investments, operations management, etc. (Zhang, Huang, & Zhao, 2013). Those investigations make it evident that the interconnection between the processes of cargo storage and delivery is of importance and there are some ways to maintain it including discrete event simulation.

I am researching forecasting of container port operations because I want to find out whether the simulation models of port operations (both shore- and sea-side ones) constitutes an indispensable prerequisite for the sustainable port development and project planning within it, so I can determine the influence of which parameters has to be addressed for the optimum supply of port facilities and services. To what extent is simulation modeling of container port operations useful for the promotion of ship-port interfaces and freight transport chains and for the identification of optimization ways of different container ports in a particular region? I suppose that simulation modeling of container terminals has reached a new stage of development when the research scope was expanded to address not only the operational performance results, but also financial results. Having an adequately high level of accuracy for the decision-making process, discrete event simulation makes it possible to monitor different types of port operations and detect bottlenecks of freight transport chains in general.

Regarding delimitations, the analysis of the container ports in the specified region (north-west) lies in the field of study, because, as it is known, 45 per cent of total freight flow in Russia (without taking into account Kaliningrad region) is concentrated there. Moreover, the research is confined to consideration of discrete-event simulation only, as it offers the most realistic results and remains one of the most popular techniques in port operations modelling, despite the introduction of new methods, such as agent-based modelling, web-based simulation and so on.

With growing shipping sector of containerised freight, the trend of broadening simulation into new domains of applications is going to undoubtedly fulfil a need for future port simulation modeling to be concentrated in developing tools and techniques which would be able to meet the emerging challenges in the management of port operations and development. Hence, speaking about a novelty of the study, it is significant to mention that despite the considerable cargo flow of ports in the north-west region, neither theoretical nor empirical research have been conducted on the impact of container ports on the economic development of the region and the comparison of efficiency rates of the chosen ports has not been made.

The characteristics of the object of research make it seem logical that the specific tasks of further analysis in relation to forecasting of container port operations must necessarily be those of:

• to develop a systematic framework which is going to be connected with the multi-stakeholder dimension in terms of port performance measurement;
• to make a critical review of existing performance improvement strategies;
• to provide a solid basis for the development of an integrated forecasting method of port performance.

Literature review

A considerable amount of valuable work on performance measurement in the field of maritime logistics has been done to accomplish the revision of the notion “port performance measurement”. The study of performance indicators in ports and terminals has been attracting academics in the past three decades. This topic can be seen as a well-established segment in port-related literature judging by the number of publications.

The port performance studies of earlier times were commonly concentrated on port efficiency and productivity investigations for purposes of benchmarking at different levels: a single-port (Talley, 2006), a regional (Park & De, 2004) and an international level (Cullinane & Wang, 2006). Nevertheless, these studies were examining the sea side operations losing sight of the landside operations (Bichou, 2006).

Over time ports’ activities and strategies have continuously been adapted to an evolutionary changing environment in order to survive themselves in a highly competitive environment as well as achieve competitive advantages. The port evolutionary changes were introduced by previous studies (Woo, Pettit, & Beresford, 2013) and some of them suggested the eye-catching issues arising in the port industry (Vis & van Anholt, 2010). For instance, the issues such as supply chain integration, lean/agile perspectives, customer-oriented practices, and value-added activities have been addressed (Schellinck & Brooks, 2014).

UNCTAD (1976) suggested productivity and effectiveness indicators have been used by many researchers as a mean of measuring port performance. Furthermore, the suggested port performance indicators are said to be divided in two broad categories, which are financial and operational ones. Financial aspects measure a quantitative contribution on a port’s economic activity, whereas operational aspects evaluate the effectiveness of port operations, such as service time, arrival time and tons per ship-hour at berth.

Studies with regard to port performance measurement have been conducted for making comparisons at a single-port level and at multi-ports level (Talley, 2006). Port performance at the single-port level is generally evaluated by comparing ports’ real throughputs with their optimum throughputs over time (Talley, 2006). In this scope, an engineering optimum approach is typically used to define the maximum throughputs that a port can handle under its capacity (Guldogan, 2010). However, when ports are put in the competitive environment, the economic optimum approach on cargo handling and cargo competing volume, i.e. port charges, cargo handling charges, vessel turnaround time, can be applied since cost related variables are crucial determinants for port users in a port selection (Schellinck & Brooks, 2014)

The need for a sufficiently accurate cargo throughput forecast is not surprising since it can significantly influence the port development strategy, investments in infrastructure and daily operations management (Zhang et al, 2013). A review of the literature of the last 20 years reveals the whole spectrum of scientific papers about forecasting the container throughput. The latter include different simple models, such as exponential smoothing time series models, regression time series models, distributed lag models, which are easier to understand. More sophisticated models were also introduced, for instance, neural networks models, nonlinear time series models, state space models, which apply more complex theory methods, like advanced econometric methods, chaos theory, artificial intelligence, emerging systems (Goulielmos & Kaselimi, 2011).

Besides the approaches mentioned above, some other models for forecasting container throughput were developed, such as an error-correction model (Fung, 2002), six univariate forecasting models (classical decomposition model, trigonometric regression model, regression model with seasonal dummy variables, the grey and hybrid grey model, SARIMA model) (Peng & Chu, 2009), and other.

Simulation modeling techniques are being applied to a wide range of container terminal planning processes and operational analysis of container handling systems. These models have become extremely valuable as decision support tools during the planning and modeling of CT operations.

The delivery of overall system performance measures may be accomplished by resorting to the methodology known as simulation-based optimization (SO) (Fu & Diabat, 2015). SO may provide an adequate setting for supporting logistic decisions and their evaluation in a dynamic and stochastic real framework, starting from mathematical programming-based formulations. To this purpose, a modern SO framework for logistic systems must provide an easy-to-understand language for modeling policies and system dynamics, as well as a tool for describing constraints and objectives of the underlying optimization problem (Angeloudis & Bell, 2011).

SMs have been used extensively in the modelling, planning and analysis of CTs. Many different SMs regarding port operation, especially CTs modelling, have been developed. Simulation optimization models consider the stochastic factor in CT and can tackle the practical assigning and scheduling problem efficiently. Bruzzone and Signorile (1998) developed a collection of simulation tools and used genetic algorithms to make strategic decisions and scheduling for resource allocation and CT organization. Vis and van Anholt (2010) studied the effect of different types of berth configurations on vessel operation times at container terminals and also created SMs for each type of berth in which all relevant logistics processes required for unloading and loading a vessel have been implemented. Guldogen (2010) investigated the effect of different storage policies on the overall performance of a CT in the port of Izmir. Wanke (2011) considered SBL by SM with case study which assessed the impact of different berth allocation problems on main performances including demurrage costs. Ding (2010) presented a SM to estimate the throughput capacities of a CT under different combination patterns of the types of arriving vessels. One can conclude that CT operations have been adequately analyzed and modelled by using different SMs. Various SMs in the field of optimizing CT planning are applied more and more in world CTs.

Studies on port performance measurement have traditionally focused on efficiency and productivity of the port (terminal) operations. In such studies various research scopes and approaches are used for productivity comparisons or engineering and economic optimums. However, ports are treated as isolated nodes that provide a basic ship-shore operation with an emphasis on cost and technical efficiency rather than as a crucial part of international supply chains. Accordingly, these studies fail to make a link between quayside operations and shoreside systems (Bichou, 2006).

Methodology

This part of the proposal is mainly focused on the analysis of data describing the methods to be used in carrying out the study. First of all, it is noteworthy that the research methodology, at least to this point, is an evolving one, which is expected to be fully developed as the study is being improved.

Empirical data to be used will be based on the port’s annual cargo turnover and the amount of loading equipment of different types at its disposal. The study is intended to consider port’s performance indicators over the period 2000-2017. This data primarily given by the Federal State Statistics Service of the Russian Federation appears to be relevant for the study as it provides an opportunity to identify whether positive changes in the dynamics of the operational results can correlate with the development of discrete-event simulation as the technique of port operations modelling.

 The Big port Saint Petersburg is designated as the research subject, since it is regarded as the European gateway of Russia and as the maritime transportation hub that establishes strong linkages between the East and the West. It is located on the eastern shore of the Gulf of Finland of the Baltic Sea region. Big port of Saint-Petersburg is also comprised of berths of Bronka, Kronshtadt, Lomonosov. First analysis of statistics on the port leads to the conclusion that it is the leader in providing dry cargo transshipment when the North-West of the country is considered.

The choice of appropriate methodological considerations is driven by the research question and objectives of the study. This study will conduct two forms of the data collection methods: online and offline documentation research (secondary data of financial and operational measures of the port under consideration) and questionnaire surveys (primary data collection). The need for the usage of both quantitative and qualitative methods is dictated by the fact that, in order to offer diagnostic instruments to decision makers, multiple dimensions of analysis should be involved.

The secondary data of the quantitative PPIs will be collected from information databases managed by credit rating agencies to test the validity of the proposed performance frameworks. However, port authorities are reluctant to provide the quantitative data due to the confidentiality of information and the possibility of threat to business activity.

As for surveys, three groups of port activities’ participants: terminal operators, port users, and port administration will become questionnaire respondents. Therefore, due to the assessment of PPIs for port performance measurement from their point of view the qualitative PPIs will be collected. Terminal operators will be invited to assess terminal supply chain integration, the supporting activities, safety measures. Port users will offer their way of looking on customers’ satisfaction and terminal supply chain integration, while administrators will evaluate sustainable growth. The survey will be conducted through an online survey tool and distributed by e-mails.

It is important to note that some limitations have to be made in order to reduce unnecessary details from the gathered information. It can be described as follows:

•                      container yard is of infinite capacity;

•                      container yard storage level is equal to 5000 containers;

•                      personnel of the port and technical equipment are predetermined;

•                      weather and some other external factors do not influence the port.

The analysis of the collected information will be intended to lead to the statement that port performance measurement can be viewed as a typical multicriteria decision making (MCDM) problem. Hence, the study will be based on a MCDM approach as a data analysis technique.

According to the MCDM method, PPIs and their importance are evaluated through their separate conduction and their synthesis. MCDM approach is aimed at selecting performance improvement and maintenance strategies. We can identify the strengths and weaknesses of the container ports through the proposed port performance measurement models. Therefore, the framework for modelling PPI improvement strategies needs to be developed to improve their performance. The weights assigned to criteria are affected by subjective judgements and the scores are figured out for each alternative to find the best solution.

Thus, the study adopts conceptual, mathematical, empirical methods and questionnaire survey approach.

Anticipated results

This part of the proposal is going to demonstrate that the main success factors of the port performance, according to the questionnaire surveys, are highly qualified personnel, sufficient technical equipment, sustainable process of cargo handling, convenient location and highly-developed territory infrastructure. Therefore, they will be examined in details in order to be used as the main parameters for the simulation model.

Regarding the resources of the port, the utilization levels will probably vary from one process to another. Nevertheless, it is expected that the RTGs reflect the highest utilization for container stacking operations in the yard as well as transport loading and unloading.

Each container taken out of the port will be assigned the parameter of average flow time, which is defined as the time between the arrival to the stacked container yard and the departure from the system via different modes of transport. It will be pointed out to what extent the flow time for the containers varies in accordance to its transportation scheme.

The hybrid approach will provide the following results: the terminals operations will be ranked corresponding to their overall performance and multiple PPIs. Moreover, the strengths and weaknesses of the port will be identified. Therefore, this feature offers insights into how to determine optimal strategies of improving the performance of the port operating companies.

Conclusion

The present proposal proves that the influence of both operational and financial parameters has to be addressed in order to maintain optimal performance of port facilities and services. Hence, the successful application of simulation models of container port operations allows to achieve the objectives of promoting of sustainable freight transportation chains and recognizing the optimization strategies for different container ports in relation to a particular region.

This thesis is intended to develop frameworks in three measurements: pre-performance (related to the question of what to measure), performance (related to the question of how to measure) and post-performance (related to the question of how to monitor and improve) phases to answer the research question.

The frameworks are going to conduct several decision-making tools and procedures and propose different hybrid approaches for each phase. The methods and techniques are demonstrated as follows: the selection of Port Performance Indicators (PPIs) is discussed in conceptual terms using deductive approach (literature review and industrial best practices), industrial real data (secondary data), semistructured interview (primary data).

In order to reach the goal of the paper a discrete event simulation model of the flow of processes is required to be build. Furthermore, the outcome of the port flow will be studied, and different scenarios will be simulated to study both the complexity of operations involved and the utilization of resources.

This study is the initial work for a larger scope in which the influence of other port functions that impact the port processes (for example, customs) will be studied and simulated.

Reference list

• Angeloudis, P., & Bell, M. G. (2011). A review of container terminal simulation models. Maritime Policy & Management, 38(5), 523–540.
• Bichou, K. (2006). Review of port performance approaches and a supply chain framework to port performance benchmarking. Research in Transportation Economics, 17(2), 567-598.
• Bruzzone, A., & Signorile, R. (1998). Simulation and genetic algorithms for ship planning and shipyard layout. Simulation, 71(2), 74–83.
• Cullinane, K., & Wang, T.-F. (2006). Data envelopment analysis and improving container port efficiency. Research in Transportation Economics, 17(2), 517-566.
• Ding, Y.Z. (2010). Throughput capacity of a container terminal considering the combination patterns of the types of arriving vessels. Journal of Shanghai Jiaotong University (Science), 15(1), 124–128.
• Frankel, E. G. (1987). Port planning and development. New York: Wiley.
• Fu, Y.-M., & Diabat, A. (2015). A lagrangian relaxation approach for solving the integrated quay crane assignment and scheduling problem. Applied Mathematical Modelling, 39 (3–4), 1194–1201.
• Goulielmos, A. M. & Kaselimi, E. (2011). A nonlinear forecasting of container traffic: The case study of the Port of Piraeus. International Journal of Shipping and Transport Logistics, 3(1), 79-99.
• Guldogan, E. U. (2010). Simulation-based analysis for hierarchical storage assignment policies in a container terminal. Simulation, 87(6), 523–537.
• Henesey, L. (2008). Enhancing container terminals: A multi-agent systems approach. Blekinge Institute of Technology, Blekinge, Sweden.
• Lee, D.-H., Cao, Z., & Meng, Q. (2007). Scheduling of two-transtainer systems for loading outbound containers in port container terminals with simulated annealing algorithm. International Journal of Production Economics, 107(1), 115–124.
• Park, R.-K., & De, P. (2004). An alternative approach to efficiency measurement of seaports. Maritime Economics & Logistics, 6(2), 53-69.
• Peng, W., & Chu, C. (2009). A comparison of univariate methods for forecasting container throughput volumes. Mathematical and Computer Modelling, 50(6), 1045-1057.
• Schellinck, T., & Brooks, M. R. (2014). Improving port effectiveness through determinance/performance gap analysis. Maritime Policy & Management, 41(5), 328–345.
• Talley, W. K. (2006). Port performance: An economics perspective. Research in Transportation Economics, 17(3), 499-516.
• UNCTAD. (1976). Port performance indicators. New York, US: UNCTAD.
• Vis, I. F., & van Anholt, G. (2010). Performance analysis of berth configurations at container terminals. OR Spectrum, 32(3), 453–476.
• Wanke, F.P. (2011). Ship-berth link simulation: impact on total demurrage costs. International Journal of Shipping and Transport Logistics, 3(3), 261–294.
• Woo, S.-H., Pettit, S. J., & Beresford, A. K. (2013). An assessment of the integration of seaports into supply chains using a structural equation model. Supply Chain Management: An International Journal, 18(1), 235-252.
• Zhang, C., Huang, L., & Zhao, Z. (2013). Research on combination forecast of port cargo throughput based on time series and causality analysis. Journal of Industrial Engineering and Management, 6(1), 124-134.