Multi-agent optimization of the intermodal terminal main parameters by using AnyLogic simulation platform: Case study on the Ningbo-Zhoushan Port

Highlights

• We conceptualize a new method to study the uncertainty in the intermodal terminals.

• We theorize a methodology to develop the seaport – dry port system.

• We develop a set of the hybrid agent-based simulation models.

• We validate the developed set by a case study on the Ningbo-Zhoushan port.

• We show that the obtained findings improve the strategic planning of the dry ports.

Abstract

Due to numerous uncertainties such as bad weather conditions, frequent changes in the schedules of vessels, breakdowns of equipment, port managers are aiming at providing adaptive and flexible strategic planning of their facilities, especially intermodal terminals. In this research, we investigate a two-stage optimization of intermodal terminals main parameters via using AnyLogic simulation platform. We have developed a set of hybrid simulation models to optimize the main parameters of intermodal terminals which are also called dry ports. To make an express evaluation of the preliminary implementation of dry ports, we have developed an agent-based system dynamics simulation model to achieve the stable state of the main parameters of intermodal terminals. To clarify the obtained averaged benefits of the main dry ports parameters while the port managers make key decisions on the investments into implementation of intermodal terminals, we have developed an agent-based discrete-event simulation model of a seaport – a dry port system. We show that the combination of the agent-based modeling with other simulation approaches simplifies the process of designing simulation models and increases their visibility. The developed set of models allows us to compute the balanced values of the parameters, while an effective operation of a seaport – intermodal terminal system is achieved. On the basis of the provided case study on one of the busiest ports in China, we prove the adequacy and validity of the developed simulation models. Due to the lack of systematic approach to optimization of the main parameters of intermodal terminals in logistic industry, our findings set herein could improve the decision-making process related to the selection of strategic facility planning in the field of intermodal terminals.

Introduction

Nowadays, one of the global trends is the increase in the number of containerized cargoes in the world and the volume of container shipping in the world transport system (“UNCTAD. Review of Maritime Transport 2019”). However, there has been no increase in container volume at major ports all over the world in the last five years (“World Shipping Council. Top 50 world container ports”). The location of ports in residential zones constrains its development and growth of container volumes (Muravev, Rakhmangulov, Hu, & Zhou, 2019). Consequently, port managers have faced the problem of expanding the port territories around maritime terminals.

Undoubtedly, in order to increase the throughput and handling capacities of maritime container terminals, they have to jointly operate with intermodal terminals which are also called dry ports (Cullinane, Bergqvist, & Wilmsmeier, 2012; Jeevan, Chen, & Cahoon, 2017; Roso, Woxenius, & Lumsden, 2009). A dry port is an inland intermodal terminal directly connected to a seaport, with high capacity traffic modes, preferably rail, where customers can leave and/or collect their goods in intermodal loading units, as if directly to the seaport (Roso et al., 2009). Such kinds of terminals as the elements of supply chains (Andersson & Roso, 2016) also contribute to their development through enhancing the outcomes of cost, responsiveness, security, environmental performance, resilience, and innovation in logistic networks (Khaslavskaya & Roso, 2019). However, the regular changes in the schedules of container vessels and trucks arrival times due to increased traffic volume lead to an increase in container capacity of vessels, customs clearance, and other disruptions, such as breakdowns of equipment and bad weather conditions, contribute to the dynamic development of port facilities (Jeevan & Roso, 2019; Loh & Thai, 2015; Muravev, Rakhmangulov et al., 2019) and supply chains as a whole (Ivanov, Sokolov, & Kaeschel, 2011; Ivanov, Das, & Choi, 2018). It means that these facilities have been facing challenges in operations, such as difficulty in meeting different stakeholder objectives, constraints of the storage capacity and limited availability of transportation modes. Furthermore, the external social and environmental factors constrain the development of logistics facilities. For example, from the social perspective, the negative dynamics of the number of people inhabiting the potential location of the intermodal terminal makes the wages of terminal workers lower because of the reduced demand for job offers. From the environmental perspective, the physical expansion of the container yards could be limited by the conservation areas and irrational land use in the area of the potential dry port location (Muravev & Rakhmangulov, 2016). In other words, these factors affect the parameters of the intermodal terminals.

This complexity, dynamics and the systematic consideration of social and environmental factors call for adaptive and flexible planning in intermodal terminals, i.e. obtaining the optimal values of their parameters. In reality, strategic facility planning (SFP), especially in port management, lies in numerous activities. These activities come into the sphere of the port managers’ responsibility, causing frequent lapses into a reactive mode in order to respond to all the requests, orders, regulations, deadlines and demands of the organization. Port managers know that the need to become more proactive and strategic is important, but finding the time to devote to strategic planning is often a struggle. Strategic planning of logistic facilities is a process that can lead to better, more proactive delivery of services from a port management organization to its stakeholders. Generally, the SFP performance in case of port management is measured by financial indicators, such as general and operational costs of the project, Return on Investment (ROI), Net Present Value (NPV) and Discounted Payback Period (DPP) (Dang & Yeo, 2017; Elentably, 2015; Taneja, Ligteringen, & Walker, 2012). Therefore, the central terminal management problem is to optimize the long term physical and technical parameters of intermodal terminals and the parameters of traffic flows. These optimal values of parameters are characterized by low investments and sustainable social, economic and environmental impacts. In order to evaluate the operational performance of facilities, scholars in the field of logistics and transportation have been mainly applying numerous traditional deterministic methods such as integer linear programming (Li, Tian, Cao, & Ding, 2008) and mixed linear programming (Daham, Yang, & Warnes, 2017), genetic algorithm (Bazzazi, Safaei, & Javadian, 2009; Ng, Mak, & Zhang, 2007; Said & El-Horbaty, 2015), etc.

However, these methods could not consider systematically the rapid dynamic development of logistic facilities and the impact of the external factors on the parameters of the intermodal terminals. One of the effective ways to investigate this impact is a combination of analytical and simulation models, which provide port managers with clear insights into solving such problems as bottlenecks in the system, duration of the long-term life cycle, etc.

This research is twofold. Firstly, in order to provide the express assessment of the dry port construction project, we investigate the systemic analysis of the impact of various external factors on the main parameters of an intermodal terminal. Specifically, we have developed an agent-based system dynamics simulation model (ABSDS model), which optimizes the averaged values of main dry port parameters. Secondly, in order to provide an appropriate detailed assessment of the project, we have developed an agent-based discrete event simulation model (ABDES model) of a seaport – dry port system. Basically, this model ensures the detailed estimation of financial indicators of a seaport – dry port system with the obtained optimal values of the intermodal terminal main parameters.

The rest of the article is organized as follows. Section 2 reviews the existing and relevant literature. Section 3 proposes the framework of a set of combined simulation models in the AnyLogic simulation platform to optimize the main parameters of intermodal terminals. Section 4 presents the case study on validation benefits, results of the optimized dry port main parameters and its financial performance indicators. Section 5 explores theoretical and managerial contributions, limitations and future research perspectives of the developed methodology. In Section 6 conclusions are discussed.