Agent-based Modelling And Simulation For Freight Transportation Systems Of Contestable Hinterlands Among A Cluster Of Ports

This paper presents an agent-based model for freight transportation systems of contestablehinterlands among a cluster of ports, which is composed of three parts: At first, by comparingthe advantages and disadvantages of the traditional solution of collection and distributionsystem related issues, to explain why using the agent based modeling and simulation;secondly, based on the practice of international logistics and supply chain processes, researchcollection and distribution system structural characteristics, conditions and limitations ofagent behavior, and then build an integrated simulation platform; thirdly, make experiment onDongguan hinterland to test the model, and pointed out their optimization framework.In the literature review chapter, the paper first discusses the hinterland of the port groupshared Research Survey found that scholars rarely discuss in depth from collecting anddistributing micro perspective; further review of the relevant literature collection anddistribution system, pointing out that the current system constitutes a major problemunderstanding narrow, the scarcity of quantitative research methods lack sufficient basis, andso on. To this end, the researchers were from the mathematical modeling and systemsimulation cut, drawing related fields of logistics, supply chain and transportation analysis,system deficiencies summarized various methods analytical model, and simulation modelingmissing in content, and pointed out that the collection and distribution system for a typicalCAS complex system architecture, proved the necessity and innovative Agent-basedsimulation of the hinterland of the port cluster sharing and distribution systems.Then around the secondary research data and systems theory, international logistics theory,the collection and distribution system structure is analyzed in detail. Referring first to importand export supply chain processes, factory orders received by inductive loading dockdeparting8Cargo export sector; followed by multi-agent-based decision-making perspective,the general framework proposed CAS cluster shared port hinterland transport systems, as wellas ports, international sea, road, waterway, railway transportation, import and exportregulation, the owner of seven subsystems structure; thus cut from the body of the conditionsand limitations of intelligent behavior, pointed out the various subsystems of the core Agent,decision-making content, affect the relationship with time important constraint costs and otheraspects, including: election harbor booking, Cargo way to do cabinets time, Cargo route4bigdecision; directly or indirectly related factories, ports, shipping companies inter/services;clearance decision-making process; barge/trailer/sea-rail intermodal transportation costs threecategories set estimation formula; fixed costs and even trailers, railway transport,"one price", sea THC, THCS, O/F, Local Charges and counter check costs, liner sailing barge routes, trainfrequency, etc. practical experience, for the following simulation modeling and experimentaldata provide a solid foundation.MAS modeling stage, clustered around a shared hinterland of the port of CAS structure anddistribution system is proposed to MAS agent simulation core, generated by the ASM agent,BA intelligent behavior algorithms, SRM simulation system is running, IM/OM input, outputand SSCM simulation control management sub-module consists of6ISPHT open integrationplatform/model framework, and then describe the main gathering system simulation modeling"core classes" and "constraint class" agent from the association, Multi-agent model andintegration platform relations, and key design elements ISPHT each module. On this basis, thehinterland of the port group for Cargo Agent Systems in selected booking and Cargo Portmanner, time, route choice of four decision, specific algorithms were constructed, including:subject of delivery, ports and sailing "Select Port decision model selectPort do cabinetremaining time" Cargo export"overall costs","smooth degree" with the main act"inertia"factors such constraints, by machine learning theory developed Smart Choice Harbor Qalgorithm qLearningPort(); reference collection and distribution system cost structures, theformula for estimating road transport roadTransPricing(), water transport waterTransPricing(),Railway Cargo railTransPricing() quotes module, and set transport mode decision modelselectTransMode(); analysis do learn Cargo export process Gantt chart time cabinet decisionmodel selectVanningTime();road network and the hierarchical design method, Floydalgorithm transform static line transport decision model selectStaticTransPath(), and toDijkstra’s algorithm as the core of the real-time routing update model rtTransRouteUpd().Most of the models and algorithms based on the “linear" function and rules build, effectivelycompensate for the difficult analytical modeling, logistics and limitations to considerincomplete, solving complex defects such as high, reflecting the Intelligent Agent from themicro perspective of body simulation research collection and distribution system innovativeand value.Finally, in the experimental stage, achieved by the program code ISPHT platform HighwayCargo module, on this basis, to the hinterland of the port group for the object, Dongguan City,expand the computational experiments. Experimental results show that the model can beconstructed to facilitate real-time observation and cumulative distribution of values for eachcontainer traffic to specific sections of the trunk road and is consistent with the reality of thesituation, confirmed that this article is designed to share the hinterland of the port clusterdistribution system simulation model of MAS reasonable, and simulation-based experimental study issues related to agent feasibility.