An Optimization Model for Port Service Network of Refrigerated Containers Based on Transportation Time Reliability
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International Journal of Engineering and Technology, Vol. 9, No. 5, October 2017 An Optimization Model for Port Service Network of Refrigerated Containers Based on Transportation Time Reliability Xiangqun Song, Qianli Ma, Wenyuan Wang, and Shibo Li Abstract—In order to develop the transshipment and temperature, when the temperature get control, time processing capacity of port nodes in the container port service reliability can be decisive to evaluate the efficiency of cold network and meet the increasing demand for cold chain chain logistics. Domestic and foreign researchers have logistics transportation, container flow quantity should be conducted a lot of research in the past which can be reasonably distributed in port nodes which undertake primarily divided into two aspects. The first aspect is the functions of transportation and transfer in the cold chain. evaluation and calculation method of time reliability. For Through the study of refrigerated container service network, this paper establishes an optimization model for port service example, Chang J. S. [7] defined time reliability as the network of refrigerated container with minimizing the total difference between actual arrival time and schedule time, cost as the objective, transportation time reliability and storage Liang C. Y. [8] established the time reliability calculation capacity of the refrigerated container in port nodes as model from two aspects of the running time and the waiting constraints to achieve the optimal container flow allocation in time, and Namazi-Rad M. R. [9] introduced time window to transshipment ports under different supply and demand evaluate the reliability, Jong G. D. [10] took the method of quantity. preference survey to assess the transportation time reliability. The second aspect is the optimization problems constrained Index Terms—Port service network, transportation time by time reliability. Yao B. [11] took the influence of the reliability, storage capacity, refrigerated containers travel time randomness on the reliability into consideration and proposed optimization model of public transport network, and Xu Wangtu [12] took arrival time reliability as I. INTRODUCTION main constraint to solve maximum transport capacity of freight network. Most of the existing studies take In the global shipping market, getting the right goods at point-to-point single transport line as the object to research the right time and place is as important as reducing costs. In its time reliability, but less researchers take into account that order to meet the high level of requirement of food transportation time uncertainty of each line in the different transportation, cold chain logistics has gained rapid stage impacts the reliability of the whole transportation development in the past few years. Refrigerated containers network. On the other hand, refrigerated containers must get play an increasingly important role in the cold chain into specialized refrigerated slots for storage after arriving at logistics because of its advantages of flexibility in loading the port, so storage capacity of refrigerated containers of the and unloading, stability in goods transport temperature and port has important influence on the assignment of transport low pollution. At present, the refrigerated container quantity. In the current study, the method of fixed value [13] transportation is mainly based on maritime transportation or statistical data analysis [14] is used to determine the port and chooses transshipment port for affiliation in the process capacity constraints, and the uncertainty of port capacity is of ocean transportation according to loading, unloading and not considered. In view of the above analysis, this paper will transshipment requirements, thus forming the large shipping divide the transport process of each route into three stages: network with ports as nodes and multi parallel routes. The maritime transportation, transfer work, and following optimization of multi routes and ports has attracted the maritime transportation, considering the effects of interest of many scholars, such as optimization problems transportation time randomness of each stage on the time aimed at port container transportation demand segmentation reliability of the whole shipping transportation network and [1], optimal transportation route design problems between random variable to describe the port’s storage capacity of seaports and hinterland ports [2], optimal transport mode the refrigerated container, and finally establish an combination problems of multimodal container optimization model for port service network of refrigerated transportation [3], allocation optimization problems of containers to ensure the optimal transport quantity of each multimodal container transportation [4]; heuristic algorithm route. [5], ant colony algorithm [6] and etc. are applied in the course of the study to solve the optimization model. Since the quality of cargo is sensitive to time and II. PROBLEM DESCRIPTION AND MODELING A. Problem Description Manuscript received July 21, 2016; revised October 23, 2016. Shown in Fig. 1, a number of refrigerated containers need Xiangqun Song, Qianli Ma, Wenyuan Wang and Shibo Li are with to be transported overseas from several starting ports-SP to Dalian University of Technology, Dalian City, Liaoning Province, China (e-mail: [email protected], [email protected], several destination ports-DP while there are a number of [email protected], DOI: 10.7763/IJET.2017.V9.1007 405 International Journal of Engineering and Technology, Vol. 9, No. 5, October 2017 transshipment ports-TP that can bear the task of transferring C. Symbols and Concept Description refrigerated containers between starting ports and 1) The decision variables destination ports. Transshipment ports are divided into two The decision variables are shown in Table I. types: one is transshipment port without cargo demand and the other is transshipment port with some little cargo TABLE I: DECISION VARIABLES demand. When refrigerated containers from starting ports Decision variable Description arrive at a transshipment port, they are divided into two The transport quantity of the refrigerated parts: one part is the refrigerated containers that the container from starting port SPi to q transshipment port demands and then will be shipped from ij transshipment port TPj (unit : TEU), i SP , the port to the hinterland; the other part is the refrigerated j TPD TPN containers for transferring and will enter the refrigerated The transport quantity of the refrigerated container from transshipment port TPj to container yard for storage and be shipped by the q transshipment port to the destination ports afterwards. Due jk destination port DPk (unit : TEU), to the difference in cold chain infrastructure of j TPD TPN , k DP transshipment ports such as refrigerated container yard area and specialized reefer plugs, the storage capacity of 2) The parameters refrigerated container of different transshipment ports is different. In consideration of the randomness of yard use, Some relative parameters are shown in Table II. storage capacity is denoted by random variables. In addition, the transportation cost between various ports, the failure TABLE II: PARAMETERS Parameter Description quality cost of cargo in the transport process, the carbon SP The set of all starting ports emission cost and the loading or unloading cost of The set of all transshipment ports with cargo transshipment ports are different. The transportation time TPD demand between each port and the storage time of the refrigerated TPN The set of all transshipment ports without cargo container at each transshipment port are both random demand variables. DP The set of all destination ports The total supply quantity of the refrigerated container shipped from starting port SPi (unit : Qi TEU), i SP The storage capacity of the refrigerated container Pj () at transshipment port TPj (unit : TEU), which is a random variable, j TPD TPN The demand of the refrigerated container at S j transshipment port with cargo demand TPj (unit : TEU), j TPD The maritime transportation time of the ij refrigerated container from starting port SPi to tmt transshipment port TPj (unit : day), which is a random variable, i SP , j TPD TPN The storage time of the refrigerated container at j transshipment port TPj (unit : day), which is a td random variable, j TPD TPN The specified transportation time of the T refrigerated container (unit : day) The transportation distance from starting port SPi dij to transshipment port TPj (unit : KM), i SP j TPD TPN Fig. 1. Sketch map of port service network. The transportation distance from transshipment port TPj to destination port DPk (unit : KM), d jk j TPD TPN , k DP B. Hypothesis of the Model The transportation cost of the refrigerated 1) Considering that the requirement of transportation time container from starting port SPi to transshipment in cold chain logistics is higher, it is specified that the ij port TPj (unit : yuan/TEU·KM), i SP , refrigerated container only be transshipped once in the j TPD TPN process of transportation. The transportation cost of the refrigerated 2) Assuming that the transportation cost between different container from starting port TPj to transshipment jk port DPk (unit : yuan/TEU·KM), ports, the failure quality cost of cargo in the transportation j TPD TPN , k DP process are known. The failure quality cost of cargo in the 3) Due to the short storage time of the refrigerated transportation process