Method and system for planning and scheduling stockpiling operation of dangerous goods container yard

Abstract

The invention provides a dangerous goods container yard stockpiling operation planning and scheduling method and system, and relates to the technical field of dangerous goods container stockpiling intelligent decision-making, and the method comprises the steps: constructing a risk data set of dangerous goods of a current shift; determining dangerous goods container grouping and stacking safety distance; constructing a dangerous goods container stockpiling decision optimization model; and inputting the data obtained in real time into the dangerous goods container stockpiling decision optimization model to obtain an optimal dangerous goods container stockpiling scheme. The technical problems that in the prior art, a traditional stockpiling planning method mainly depends on artificial experience, operation efficiency is low, the utilization rate of the storage yard is low, and even potential safety hazards exist are solved, and under the condition that the safe stockpiling distance of the dangerous cargo container in the storage yard is guaranteed, the safety of the storage yard is guaranteed. The technical effects that the assigned number of the moving carrying machines is optimized, the operation driving cost and the follow-up box turning cost of the moving carrying machines are reduced, the stockpiling operation efficiency is improved, and the safety risk is reduced are achieved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of intelligent decision-making for dangerous goods container storage, and in particular to a method and system for planning and scheduling storage operations in a dangerous goods container yard. Background Art

[0002] Material Safety Data Sheet (MSDS) is a comprehensive legal document on the characteristics of chemicals that chemical manufacturers or sellers provide to customers in accordance with legal requirements. It generally consists of sixteen chapters.

[0003] TS176-2020 is a Chinese transportation industry standard. Its full name is "Design Specifications for Port Dangerous Goods Container Yards". This specification applies to the design of new, rebuilt, and expanded port dangerous goods container yards in coastal and inland ports. It is a mandatory industry standard for water transport engineering construction.

[0004] Dangerous goods terminals are distribution points throughout the entire hazardous goods logistics chain, generally handling the logistics of dangerous goods and containers at ports. They engage in container loading and unloading, storage, consolidation, and warehousing operations for various types of dangerous goods. Currently, dangerous goods container terminals rely primarily on manual experience, which can lead to low operational efficiency, insufficient yard utilization, and even safety hazards. While safety management information systems have been established, they still fail to comprehensively consider the compatibility of dangerous goods, safe spacing, operating costs of handling machinery, and container turnover, resulting in inefficient yard planning. Therefore, a solution is needed to address these issues. Summary of the Invention

[0005] The present disclosure provides a method for planning and scheduling the storage operations of dangerous goods container yards, which is used to solve the technical problems in the existing technology that the compatibility of dangerous goods, safety spacing, operating costs of handling machinery and container turnover costs cannot be comprehensively considered, the yard planning efficiency is low, and there are even safety hazards.

[0006] According to a first aspect of the present disclosure, a method for planning and scheduling storage operations in a dangerous goods container yard is provided, comprising:

[0007] Through multi-source data, the container information, operation information and current site operation data to be operated on the current shift are obtained, and the multi-source data include data obtained from the terminal operating system, external platform system and customs supervision related system; the obtained data are pre-processed and the dangerous goods container data are screened out. According to the MSDS, the dangerous goods containers are classified according to their physical and chemical properties, health hazards, environmental hazards, and fire and reaction hazards, and the risk data set, target operation requirement data set and emergency treatment data set of the dangerous goods of the current shift are constructed; the dangerous goods container grouping and stacking safety distance are determined according to the MSDS; based on the obtained cargo owner information and dangerous goods dimensions, historical operation data information is obtained, and the historical operation data information includes data on storage location, operation equipment, and whether danger occurs; a dangerous goods container storage decision optimization model is constructed, and the dangerous goods container storage decision optimization model includes a mobile handling machinery quantity decision model and a bay box quantity and mobile handling machinery operation sequence decision model; the real-time acquired data is input into the dangerous goods container storage decision optimization model to obtain the optimal dangerous goods container storage plan.

[0008] According to a second aspect of the present disclosure, a dangerous goods container yard storage operation planning and scheduling system is provided, comprising:

[0009] A multi-source data acquisition module, which is used to obtain container information, operation information, and current site operation data for the current shift through multi-source data, including data obtained from the terminal operating system, external platform system, and customs supervision-related systems;

[0010] A data preprocessing module is used to perform operation preprocessing on the acquired data and filter out dangerous goods container data. According to the MSDS, the dangerous goods container data is classified according to its physical and chemical properties, health hazards, environmental hazards, and fire and reaction hazards, and a risk data set, a target operation requirement data set, and an emergency response data set for the dangerous goods of the current shift are constructed;

[0011] A module for calculating the safe distance between dangerous goods container groups and stacking, wherein the module is used to determine the safe distance between dangerous goods container groups and stacking according to the MSDS;

[0012] A historical operation data acquisition module is used to acquire historical operation data information based on the acquired cargo owner information and dangerous goods dimensions. The historical operation data information includes data on storage location, operating equipment, and whether danger has occurred;

[0013] A dangerous goods container storage decision optimization model construction module is used to construct a dangerous goods container storage decision optimization model, which includes a mobile handling machinery quantity decision model and a bay container quantity and mobile handling machinery operation sequence decision model;

[0014] The optimal dangerous goods container storage plan input module is used to input real-time acquired data into the dangerous goods container storage decision optimization model to obtain the optimal dangerous goods container storage plan.

[0015] One or more technical solutions provided in the present disclosure have at least the following technical effects or advantages: obtaining container information, operation information, and current site operation data to be operated on the current shift; constructing a risk data set, target operation requirement data set, and emergency treatment data set for the current shift's dangerous goods; determining the grouping and safe stacking distance of dangerous goods containers based on the MSDS; obtaining historical operation data information based on the obtained cargo owner information and dangerous goods dimensions; constructing a dangerous goods container storage decision optimization model based on the historical operation data information; inputting the real-time acquired data into the dangerous goods container storage decision optimization model to obtain the optimal dangerous goods container storage plan. The present disclosure can solve the technical problems in the prior art that the compatibility of dangerous goods, safety spacing, handling machinery operation cost and container turnover cost cannot be comprehensively considered, resulting in low yard planning efficiency and even safety hazards. It realizes the optimization decision of the number of mobile handling machinery assigned in the yard, the container storage bay position, and the operation order of the mobile handling machinery. Under the condition of ensuring the safe storage distance of dangerous goods containers in the yard, the number of assigned mobile handling machinery is optimized, the operation cost of the mobile handling machinery and the subsequent container turnover cost are reduced, the storage operation efficiency is improved, and the safety risk is reduced. It should be understood that the contents described in this section are not intended to indicate the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without any creative work.

[0017] Figure 1 A flowchart of a method for planning and scheduling storage operations for a dangerous goods container yard provided by an embodiment of the present disclosure;

[0018] Figure 2 A schematic diagram of the structure of a dangerous goods container yard storage operation planning and scheduling system provided by an embodiment of the present disclosure;

[0019] Figure 3 A multi-model process diagram of a dangerous goods container yard storage operation planning and scheduling method provided in an embodiment of the present disclosure.

[0020] Explanation of the accompanying drawings: multi-source data acquisition module 11, data preprocessing module 12, dangerous goods container grouping and stacking safety distance calculation module 13, historical operation data acquisition module 14, dangerous goods container storage decision optimization model construction module 15, optimal dangerous goods container storage plan input module 16. DETAILED DESCRIPTION

[0021] The following description of exemplary embodiments of the present disclosure is provided in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0022] Example 1

[0023] The embodiment of the present disclosure provides a method for planning and scheduling storage operations of dangerous goods container yards, with reference to Figure 1 For illustration, the methods include:

[0024] Obtaining container information, operational information, and current site operation data for the current shift through multi-source data, including data obtained from the terminal operating system, external platform systems, and customs supervision-related systems;

[0025] Specifically, multi-source data mainly includes data obtained from the terminal operating system, external platform system and customs supervision-related systems. First, the terminal operating system is the core information system within the port, which is used to manage all activities related to port operations, including cargo loading and unloading, container storage, and transport vehicle scheduling. The data obtained from the TOS usually includes detailed information on containers, such as box number, type, weight, dangerous goods classification, and operation plans, such as expected arrival and departure times, site layout and storage status. Secondly, the external platform system includes shipping company information systems, cargo owner information systems, logistics service provider systems, etc. These systems provide upstream and downstream information related to port operations. The data obtained from the external platform system includes ship arrival forecasts, cargo order information, transportation contract details, etc., which are used to predict operation volume and optimize resource allocation. Finally, the customs information system provides data related to cargo clearance, supervision and security inspection, which is crucial to ensuring the legality and safety of goods. The data includes customs declaration information, inspection and quarantine status, dangerous goods declaration forms, etc. By connecting with customs data, the port can ensure that all operations comply with regulatory requirements and avoid risks brought about by illegal operations. The specific contents are as follows:

[0026] Obtain dangerous goods diversion application data and import manifest data, including vessel call sign, voyage number, total number of empty containers, total number of containers, total number of pieces, bill of lading serial number, bill of lading number, port entry date, main cargo name, number of pieces, packaging type, volume (cubic meters), dangerous goods level, dangerous goods code, number of pieces in the container, gross weight of the container, container status, container holder code, temperature measurement unit code, and temperature;

[0027] Obtain data related to ship docking operations, including ship name, import voyage, export voyage, expected berthing time, actual berthing time, expected departure time, actual departure time, number and type of containers unloaded, number and type of containers loaded;

[0028] Obtain container inspection data and inspection appointment data, including container number, scheduled pickup time, and fleet code;

[0029] Obtain registration data for fleets, vehicles, escorts, warehouse customers, bulk trucks, and companies, including fleet code, vehicle number, escort name and contact information, customer name and contact information, company name, and whether it is registered;

[0030] Obtain container pickup and warehousing business appointment data, including container number, fleet code, and bill of lading number;

[0031] Obtain customs-related data, including container number, bill of lading number, release instructions, inspection instructions, and cargo detention instructions;

[0032] Obtain current site operation data, including equipment location, current container location, completed work volume for the current shift, work volume to be completed, warehousing operation progress, and work volume to be deconsolidated;

[0033] Calculate real-time operating efficiency, including the operating efficiency of 20-foot container moving and handling machinery, the operating efficiency of 40-foot container moving and handling machinery, the operating efficiency of 20-foot container empty forklift, and the operating efficiency of 40-foot container empty forklift.

[0034] The acquired data is pre-processed and dangerous goods container data is filtered out. According to the MSDS, the dangerous goods container data is classified according to its physical and chemical properties, health hazards, environmental hazards, and fire and reaction hazards. The risk dataset, target operation requirement dataset, and emergency response dataset of the dangerous goods of the current shift are constructed;

[0035] Specifically, ensure that all data obtained from multiple sources are in the same format to facilitate subsequent processing. For example, unify all date formats, units, etc., remove duplicate, erroneous or incomplete data records, ensure the accuracy and reliability of the data, merge data from different sources, and create a comprehensive database containing detailed information on hazardous materials, work plans and safety requirements. First, classify hazardous materials, including physical and chemical properties, and analyze the physical state (such as solid, liquid, gas), chemical stability, reactivity, etc. of hazardous materials; health hazards, evaluate the potential effects of hazardous materials on human health, including acute toxicity, skin and eye irritation, carcinogenicity, etc.; environmental hazards, consider the impact of hazardous materials on the environment, such as bioaccumulation, aquatic toxicity, etc.; fire and reaction hazards, evaluate the flammability, explosiveness and chemical reactions that may occur under specific conditions of hazardous materials. Then, define the classification risk level, and try to avoid operations or storage that are both high risk or high and medium risk in close proximity;

[0036] 1. Highly dangerous:

[0037] a) Class 2.1 flammable gases and Class 2.3 toxic gases;

[0038] b) Class 3 flammable liquids, with a flash point below -18 degrees and a boiling point below or equal to 35 degrees;

[0039] c) Metallo-organic substances in Class 4.2 and Class 4.3 (UN3395, 3396, 3397, 3398, 3399).

[0040] 2. Moderate risk:

[0041] a) Category 3 flammable liquids except red risk;

[0042] b) 4.3 type metal organic removal;

[0043] c) Self-reactive substances of Class 4.1.

[0044] 3. Lower risk:

[0045] a) Class 4.1 flammable solids (except desensitized explosives and self-reactive substances);

[0046] b) Class 8 corrosive substances (except hydrofluoric acid, strong acid and strong alkali).

[0047] 4. Low risk:

[0048] Category 9 substances.

[0049] Finally, data sets are constructed, including a risk data set that contains the risk level, safe operating restrictions, and storage conditions for each dangerous good; a target operating requirements data set that develops specific operating guidelines for each dangerous good, including best practices for loading, unloading, storage, and transportation; and an emergency response data set that develops an emergency response plan for each dangerous good, including leak handling, personnel evacuation, medical first aid, and environmental protection measures.

[0050] Determine the grouping and safe stacking distance of dangerous goods containers according to MSDS;

[0051] Specifically, first, collect the MSDS of all hazardous materials. These data sheets contain key information such as the physical and chemical properties, health hazards, environmental hazards, and fire and reaction hazards of hazardous materials. Based on the information in the MSDS, classify hazardous materials according to their characteristics, for example, distinguish between flammable liquids, explosive substances, oxidizers, toxic substances, etc. Conduct a risk assessment for each hazardous material to determine its potential hazard level and safety requirements. This includes considering risks such as leakage, fire, explosion, and release of harmful vapors. Based on the classification of hazardous materials and the results of the risk assessment, determine the stacking requirements for each hazardous material. For example, flammable liquids should be kept away from heat sources and sparks, and explosive materials should be stored separately and not mixed with conflicting materials. Refer to relevant safety regulations and standards to calculate the minimum safe distance between different hazardous materials and between hazardous materials and non-hazardous materials or facilities. For example, according to the General Rules for Storage of Hazardous Chemicals in Warehouses (GB 15603-2022), stacking spacing should meet requirements such as greater than or equal to 200 cm in the main aisle and greater than or equal to 100 cm in the stacking spacing. Develop specific operating instructions and emergency procedures to ensure that hazardous materials can be effectively managed in daily operations and emergency situations. Implement regular safety inspections and reviews to ensure that the storage and handling of all hazardous materials comply with established safety distances and operating requirements. Update the risk data set, target operating requirement data set, and emergency handling data set of hazardous materials in a timely manner as new information emerges or regulations change.

[0052] Based on the obtained cargo owner information and dangerous goods dimensions, historical operation data information is obtained, wherein the historical operation data information includes data on storage location, operation equipment, and whether danger has occurred;

[0053] Specifically, historical operation data is collected and utilized, including cargo owner information, data related to each cargo owner, including but not limited to the cargo owner's name, contact information, and previous transaction records; dangerous goods dimension, recording detailed information about dangerous goods, such as cargo type, hazard level, packaging type, physical and chemical properties, etc. Historical operation data, extracting historical operation records from the terminal operating system (TOS), external platform systems, and customs supervision-related systems. Historical operation data also includes: storage location, recording the previous storage location of the cargo, storage conditions (such as temperature and humidity), and its relationship with surrounding cargo; operating equipment, recording the type of equipment used to handle dangerous goods, frequency of use, maintenance status, etc.; whether there was any danger, recording whether any accidents occurred during the operation, such as leakage, fire, explosion, etc., as well as the handling and impact of the accident; dangerous goods dimension, recording detailed information about dangerous goods, such as cargo type, hazard level, packaging type, physical and chemical properties, etc.; finally, analyzing trends in historical data to identify which types of dangerous goods are prone to problems under specific conditions and which equipment performs poorly when handling specific cargo. Based on the lessons learned from previous accidents, safety training should be strengthened and emergency response plans should be improved.

[0054] Constructing a dangerous goods container storage decision optimization model, the dangerous goods container storage decision optimization model includes a mobile handling machinery quantity decision model, a bay container quantity and mobile handling machinery operation sequence decision model;

[0055] Specifically, the decision-making process for dangerous goods container storage planning is divided into two stages: a decision model for the number of mobile handling machines, and a decision model for the allocation of container capacity to each bay and the order in which the mobile handling machines are operated. A hierarchical and progressive decision plan is developed. Queuing theory and mathematical programming are used to optimize the number of mobile handling machines operating in the container area. Queuing theory is also used to calculate the average vehicle arrival rate and the average operating efficiency of the mobile handling machines.

[0056] The real-time data is input into the dangerous goods container storage decision optimization model to obtain the optimal dangerous goods container storage plan.

[0057] Specifically, after real-time data is fed into the optimization model, it runs based on the latest data, dynamically calculating the number of mobile handling machines, the number of bays per unit, and the order in which these machines should operate. The resulting optimal stockpiling plan is then quickly communicated to on-site operators or automated equipment for execution. During the stockpiling plan execution, real-time data is continuously monitored to ensure the safety and efficiency of stockpiling operations. Simultaneously, execution results and on-site feedback are collected for continuous model improvement and self-adjustment.

[0058] Among them, this embodiment can solve the technical problems in the existing technology that the compatibility of dangerous goods, safety distance, operating cost and container turning cost of handling machinery cannot be comprehensively considered, the yard planning efficiency is low, and there are even safety hazards. It realizes the optimization decision of the number of assigned mobile handling machinery in the yard, the storage position of containers and the operation order of mobile handling machinery. Under the condition of ensuring the safe storage distance of dangerous goods containers in the yard, the number of assigned mobile handling machinery is optimized, the operating cost and subsequent container turning cost of mobile handling machinery are reduced, the storage operation efficiency is improved, and the technical effect of reducing safety risks is achieved.

[0059] In the method provided by the embodiment of the present disclosure, obtaining container information, operation information, and current site operation data to be operated on the current shift further includes:

[0060] Obtain dangerous goods diversion application data and import manifest data;

[0061] Obtain data related to ship docking operations;

[0062] Obtain inspection box data and inspection appointment data;

[0063] Obtain fleet, vehicle, escort, warehouse customer, bulk truck, and enterprise registration data;

[0064] Obtaining container pickup and warehousing service reservation data;

[0065] Connect to the customs system to obtain customs data, including bill of lading numbers, release instructions, inspection instructions, and cargo detention instructions;

[0066] Obtain current site operation data, including equipment location, current container location, completed work volume for the current shift, work volume to be completed, warehousing operation progress, and work volume to be deconsolidated.

[0067] Furthermore, the construction of the risk data set of dangerous goods for the current shift also includes:

[0068] Clean the acquired dangerous goods information to remove incomplete, erroneous or duplicate data to ensure data quality;

[0069] Integrate MSDS information, including hazardous properties of chemicals, safe operating instructions, and emergency procedures;

[0070] Based on the MSDS information, the potential risk level of each dangerous goods is determined using a risk assessment model that includes accident consequence analysis and quantitative risk assessment;

[0071] Calculate safe stacking distance based on the nature and storage requirements of dangerous goods;

[0072] Integrate the information to form a dangerous goods risk dataset.

[0073] Furthermore, the target operation requirement dataset also includes:

[0074] Collecting the operation plan of the current shift, wherein the operation plan includes container information and operation type, wherein the operation type includes loading and unloading, storage, and transshipment;

[0075] Develop specific operating specifications based on the nature of dangerous goods, including operating procedures, safety measures, and equipment requirements;

[0076] Integrate the information to form a target handling requirements dataset for dangerous goods.

[0077] Furthermore, the emergency processing data set also includes:

[0078] Establish a mapping table of emergency response plans for different types of emergencies, including fire, leakage, and explosion, and formulate detailed emergency response plans;

[0079] Configuring the mapping table to match resources required for emergency response, including personnel, equipment, and supplies;

[0080] Collect data on the training of all personnel involved in emergency response and on emergency drills conducted regularly;

[0081] Integrate information to form emergency response data sets.

[0082] Further, refer to Figure 3 For illustration, determining the safe distance between dangerous goods container groups and stacking according to MSDS also includes:

[0083] Collect MSDS information of reservation box group;

[0084] Group dangerous goods containers by different shippers and different goods;

[0085] Determine the safe distance for stacking containers of different classification groups. The safety distance determination method includes the accident consequence method and the quantitative risk assessment method. The accident consequence method determines the safety distance by calculating the impact range in the most serious accident situation. The quantitative risk assessment method determines the safety distance by evaluating the probability of an accident under a certain risk level.

[0086] Further, refer to Figure 3 For illustration, the mobile handling machinery quantity decision model also includes:

[0087] Calculate the average vehicle arrival rate λ and the average operating efficiency μ of mobile handling machinery using queuing theory, wherein the average vehicle arrival rate λ and the average operating efficiency μ of mobile handling machinery calculated by queuing theory are estimated through historical data;

[0088] The obtained average vehicle arrival rate λ and the average operating efficiency μ of mobile handling machinery are used as known parameters of the model;

[0089] Use the exhaustive method to solve the decision model of the number of mobile handling machinery;

[0090] Calculate the minimum average waiting time of vehicles;

[0091] Calculate the maximum average utilization rate of mobile handling machinery.

[0092] Furthermore, minimizing the average waiting time of vehicles also includes:

[0093] Calculate the average waiting time of the vehicle, the formula is expressed as:

[0094]

[0095] Among them, λ: the average number of vehicles arriving per unit time, that is, the average vehicle arrival rate;

[0096] μ: The number of container trucks that can be loaded and unloaded by the mobile handling machinery per unit time, that is, the operating efficiency of the mobile handling machinery, also known as the average service rate;

[0097] k: the total number of bays in the yard that can be used to stack containers of this batch, with k representing the bay number;

[0098] ρ: The ratio of the average vehicle arrival rate to the average service rate of mobile handling machinery, that is,

[0099] I: Total number of mobile handling machines assigned for on-site operations;

[0100] W: average waiting time of vehicles;

[0101] Minimize the average waiting time of vehicles, the variable W is based on the number of mobile handling machines allocated, that is, the decision variable I to solve the objective function: min W;

[0102] The constraints are as follows:

[0103] It is derived based on M / M / I queuing theory, where represents the probability that there is no vehicle in the yard.

[0104] Furthermore, maximizing the average utilization rate of mobile handling machinery also includes:

[0105] The formula for calculating the average utilization rate of mobile handling machinery is:

[0106]

[0107] Among them, R1: average utilization rate of vehicles;

[0108] μ: The number of vehicles that can be loaded and unloaded by mobile handling machinery per unit time, that is, the operating efficiency of mobile handling machinery, also known as the average service rate;

[0109] λ: the average number of vehicles arriving per unit time, i.e. the average vehicle arrival rate;

[0110] I: The total number of mobile handling machines assigned to the site, i.e., the decision variable;

[0111] Constraints:

[0112] Derived based on M / M / I queuing theory.

[0113] I≤I * ,I∈N * ;

[0114] Among them, I: the total number of mobile handling machines assigned to the site, that is, the decision variable;

[0115] N: The total number of arriving box groups during the planning period, n represents the number of arriving box groups, and can take values ​​1, 2, 3, ..., N;

[0116] Maximize the average utilization rate of mobile handling machinery in the field, and solve it based on the number of allocated mobile handling machinery (decision variable I), max R1;

[0117] Constraints:

[0118] The number of mobile handling machines allocated on-site must be a positive integer and less than the maximum number of mobile handling machines.

[0119] Furthermore, the calculation of the average vehicle arrival rate λ through queuing theory also includes:

[0120] Collecting vehicle arrival data over a certain period of time, the data including an exact timestamp of each vehicle's arrival;

[0121] Arrange the collected data in chronological order and group them by hour, minute or other appropriate time intervals;

[0122] The average arrival rate λ of vehicles is the inverse of vehicle arrival;

[0123] Calculate the average arrival interval, where λ is 1 divided by the average arrival interval;

[0124] For each set of data, calculate the average service efficiency of the mobile handling machinery during that period.

[0125] Furthermore, the average operating efficiency μ of mobile handling machinery also includes:

[0126] Collecting operational record data of mobile handling machinery over a period of time, such data should include the timestamps of when the mobile handling machinery starts and ends service, and the number of containers served;

[0127] Arrange the collected data in chronological order and group them according to the time intervals of shifts and working days;

[0128] The average service efficiency of the mobile handling machinery during the period is calculated by dividing the total number of containers completed during the period by the working hours of the mobile handling machinery;

[0129] The average operating efficiency μ of mobile handling machinery is the average of the service efficiency of all operating cycles.

[0130] Further, refer to Figure 3 For illustration, the decision model of the number of boxes per location and the order of mobile handling machinery operations also includes:

[0131] The decision model for the number of boxes per bay and the operating sequence of the mobile handling machinery is used to optimize the number of boxes allocated to each bay and the operating sequence of the mobile handling machinery based on the expected additional box turnover volume and the moving distance of the mobile handling machinery;

[0132] Determine the arrival order of the container groups, container information and other relevant parameters for transferring containers from the yard to the ship;

[0133] Calculate and minimize the moving distance of mobile handling machinery in the yard area and minimize the additional expected container turnover volume;

[0134] The formula for calculating the minimum additional expected turnover volume is:

[0135]

[0136] Among them, minR2: minimize the newly added expected turnover volume;

[0137] a: The ratio of the increase in the turnover rate to the increase in the bay box occupancy rate;

[0138] b: The intercept of the linear regression fitting formula between the box turnover rate and the box space occupancy rate;

[0139] N: The total number of arriving boxes during the planning period. n represents the number of arriving boxes, and the possible values ​​are 1, 2, 3, ..., N.

[0140] p kn : The number of boxes of the nth arriving box group planned to be stacked in the kth bay, that is, the decision variable;

[0141] K: The total number of bays in the yard that can be used to stack containers of this batch, with k representing the bay number;

[0142] m k : The total number of containers stacked at bay k;

[0143] c k : The maximum number of containers that can be stacked in the k-th bay;

[0144] Constraints:

[0145]

[0146] Indicates the planned stacking quantity of all bays that can stack the boxes of this order group, that is, the sum of the loading and unloading tasks is the number of loading and unloading tasks constraint for the arrival order box group;

[0147] Minimize the moving distance of mobile handling machinery in the yard area. The formula is expressed as:

[0148]

[0149] minL: Minimize the moving distance of mobile handling machinery in the yard area;

[0150] a: The ratio of the increase in the turnover rate to the increase in the bay box occupancy rate;

[0151] b: The intercept of the linear regression fitting formula between the box turnover rate and the box space occupancy rate;

[0152] N: The total number of arriving boxes during the planning period. n represents the number of arriving boxes, and the possible values ​​are 1, 2, 3, ..., N.

[0153] K: The total number of bays in the yard that can be used to stack containers of this batch, with k representing the bay number;

[0154] I: Total number of mobile handling machines assigned for on-site operations;

[0155] x ink : 0-1 variable, if the nth operation order of the i-th mobile handling machine (corresponding to the nth arriving box group) is the k-th position, then the value is 1; otherwise it is 0 (decision variable);

[0156] The distance traveled by the mobile handling machinery from the k1 bay to the k2 bay;

[0157] Among them: the planned stacking quantity of all bays for stacking the boxes of this order group, that is, the sum of the loading and unloading tasks is the number constraint of the loading and unloading tasks of the box group of the arrival order:

[0158] where y in: 0-1 variable, if the i-th mobile handling machine performs the stacking task in the n-th operation order (corresponding to the n-th arriving box group), the value is 1; otherwise it is 0 (decision variable);

[0159] The operation sequence of each mobile handling machine corresponds to the storage yard position constraints:

[0160]

[0161] Constraints on the types of container groups planned to be stacked at the yard bay:

[0162] where z kt : 0-1 variable, if the k-th bay is stacked with the t-th type of box group, the value is 1; otherwise, it is 0;

[0163] The planned stacking volume of containers in each storage bay is less than the total volume of containers that can be stacked in that bay:

[0164]

[0165] Maintain a safe distance between the planned stacking locations of different types of dangerous goods containers:

[0166]

[0167] 0-1 variable constraints on decision variables:

[0168]

[0169] Specifically, the present invention relates to the following main parameters:

[0170] a: The ratio of the increase in the turnover rate to the increase in the bay box occupancy rate;

[0171] b: The intercept of the linear regression fitting formula between the box turnover rate and the box space occupancy rate;

[0172] T: The number of types of box groups arriving during the planning period. t represents the box group number and can take values ​​of 1, 2, 3, ..., T;

[0173] N: The total number of arriving boxes during the planning period. n represents the number of arriving boxes, and the possible values ​​are 1, 2, 3, ..., N.

[0174] A n : The number of the nth arriving box group during the planning period, A n The value range is 1, 2, 3, ..., T;

[0175] N n : The number of boxes arriving at the nth box group during the planning period, that is, the number of loading and unloading tasks for the box group arriving in this order;

[0176] N nt : The quantity of the t-type box of the n-th arriving box group during the planning period, if A n =t, N nt is a positive integer; otherwise it is 0;

[0177] S n : The set of yard bays that can store the nth arriving container group within the planning period;

[0178] K: The total number of bays in the yard that can be used to stack containers of this batch, with k representing the bay number;

[0179] m k : The total number of containers stacked at bay k;

[0180] c k : The maximum number of containers that can be stacked in the k-th bay;

[0181] I: Total number of mobile handling machines assigned for on-site operations;

[0182] The distance traveled by the mobile handling machinery from the k1 bay to the k2 bay;

[0183] The number of bays of safety interval between the t1 type box group and the t2 type box group;

[0184] M: A sufficiently large positive number.

[0185] Specifically, the main variables involved are as follows:

[0186] R2: the estimated additional container turnover volume in the yard;

[0187] L: The sum of the moving distances of the mobile handling machinery;

[0188] p kn : The number of boxes of the nth arriving box group planned to be stacked in the kth bay (decision variable);

[0189] x ink : 0-1 variable, if the nth operation order of the i-th mobile handling machine (corresponding to the nth arriving box group) is the k-th position, then the value is 1; otherwise it is 0 (decision variable);

[0190] y in : 0-1 variable, if the i-th mobile handling machine performs the stacking task in the n-th operation order (corresponding to the n-th arriving box group), the value is 1; otherwise it is 0 (decision variable);

[0191] z kt : 0-1 variable, if the k-th bay is stacked with the t-th type of box group, the value is 1; otherwise it is 0.

[0192] The decision model of the number of bay boxes and the order of mobile handling machinery operations is an integer programming model. The branch and bound method is used to solve it and the decision variable p is obtained. kn , x ink and y in , and then through the decision variable p kn The quantity of containers allocated to each container bay can be obtained, thereby determining the optimal bay bay quantity allocation plan and the operation order of mobile handling machinery;

[0193] Through the decision variable x ink and y in The order of loading and unloading operations of each mobile handling machinery can be obtained.

[0194] Minimize the average waiting time of vehicles, maximize the average utilization rate of mobile handling machinery, minimize the newly expected container turnover volume, and minimize the distance traveled by mobile handling machinery within the yard. Determine the basic structure of the operation plan and set a reasonable time window for each operation task. Based on the results of the mobile handling machinery quantity decision model and the container volume and mobile handling machinery operation sequence decision model, clarify the specific operation tasks of each mobile machinery within each time window, including specific operational steps such as container loading and unloading, storage, and handling. Rationally allocate resources such as mobile machinery and manpower based on the requirements of the operation tasks. Analyze efficiency bottlenecks in the operation plan, such as machinery waiting time and operation conflicts, and improve operation efficiency by adjusting the operation sequence and optimizing resource allocation.

[0195] Taking the three key operational indicators of dangerous goods container terminals—safety, operation, and service—as optimization targets, this paper proposes an optimization decision-making method to optimize dangerous goods container storage plans. This method achieves an overall optimal production and operation schedule, resolving the primary contradiction of dangerous goods terminals simultaneously meeting the ever-increasing demands for safety, operation, and service.

[0196] Example

[0197] Based on the same inventive concept as the method for planning and scheduling dangerous goods container yard storage operations in the aforementioned embodiment, Figure 2 As shown, the present disclosure also provides a dangerous goods container yard storage operation planning and scheduling system, the system comprising:

[0198] A multi-source data acquisition module, which is used to obtain container information, operation information, and current site operation data for the current shift from multiple sources, including data obtained from the terminal operating system, external platform system, and customs supervision-related systems;

[0199] A data preprocessing module is used to perform operation preprocessing on the acquired data and filter out dangerous goods container data. According to the MSDS, the dangerous goods container data is classified according to its physical and chemical properties, health hazards, environmental hazards, and fire and reaction hazards, and a risk data set, a target operation requirement data set, and an emergency response data set for the dangerous goods of the current shift are constructed;

[0200] A module for calculating the safe distance between dangerous goods container groups and stacking, wherein the module is used to determine the safe distance between dangerous goods container groups and stacking according to the MSDS;

[0201] A historical operation data acquisition module is used to acquire historical operation data information based on the acquired cargo owner information and dangerous goods dimensions. The historical operation data information includes data on storage location, operating equipment, and whether danger has occurred;

[0202] A dangerous goods container storage decision optimization model construction module is used to construct a dangerous goods container storage decision optimization model, which includes a mobile handling machinery quantity decision model and a bay container quantity and mobile handling machinery operation sequence decision model;

[0203] The optimal dangerous goods container storage plan input module is used to input real-time acquired data into the dangerous goods container storage decision optimization model to obtain the optimal dangerous goods container storage plan.

[0204] The specific example of the method for planning and scheduling the storage operation of a dangerous goods container yard in the aforementioned embodiment 1 is also applicable to the system for planning and scheduling the storage operation of a dangerous goods container yard in this embodiment. Through the detailed description of the method for planning and scheduling the storage operation of a dangerous goods container yard, those skilled in the art can clearly understand the system for planning and scheduling the storage operation of a dangerous goods container yard in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0205] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of this disclosure can be achieved. This is not a limitation herein.

[0206] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for planning and scheduling storage operations of dangerous goods container yards, characterized in that: Methods include: Obtain container information, operation information and current site operation data to be operated in the current shift through multi-source data, including data obtained from the terminal operation system, external platform system and customs supervision related system; The acquired data is pre-processed and the dangerous goods container data is screened out. According to the MSDS, the dangerous goods container data is classified according to its physical and chemical properties, health hazards, environmental hazards, and fire and reaction hazards, and the risk data set, target operation requirement data set, and emergency treatment data set of dangerous goods for the current shift are constructed; Determine the grouping and safe stacking distance of dangerous goods containers according to MSDS; According to the obtained cargo owner information and dangerous goods dimension, historical operation data information is obtained, and the historical operation data information includes data on storage location, operation equipment, and whether danger occurs; Constructing a dangerous goods container storage decision optimization model, wherein the dangerous goods container storage decision optimization model includes a mobile handling machinery quantity decision model and a bay container quantity and mobile handling machinery operation sequence decision model; The real-time data is input into the dangerous goods container storage decision optimization model to obtain the optimal dangerous goods container storage plan.

2. The method according to claim 1, characterized in that Obtaining container information, operation information and current site operation data for the current shift also includes: Obtain dangerous goods diversion application data and import manifest data; Obtain data related to the ship's berthing operations; Obtain inspection box data and inspection appointment data; Obtain fleet, vehicle, escort, warehouse customer, bulk truck, and enterprise filing data; Obtaining booking data for container pickup and warehousing services; Connect to the customs system to obtain customs data, including bill of lading number, release instructions, inspection instructions, and cargo detention instructions; Obtain current site operation data, including equipment location, current container location, completed work volume of the current shift, work volume to be completed, warehousing operation progress, and work volume to be deconsolidated and consolidated.

3. The method according to claim 1, characterized in that The risk data set for constructing the dangerous goods of the current shift also includes: Clean the acquired dangerous goods information to remove incomplete, erroneous or duplicate data to ensure data quality; Integrate MSDS information, including hazardous properties of chemicals, safe operation instructions, and emergency procedures; Based on the MSDS information, the potential risk level of each dangerous goods is determined using a risk assessment model, which includes accident consequence analysis and quantitative risk assessment; Calculate safe stacking distance based on the properties and storage requirements of dangerous goods; The information is integrated into a dangerous goods risk dataset.

4. The method according to claim 1, characterized in that The target operation requirement data set also includes: Collecting the operation plan of the current shift, the operation plan includes dangerous goods container information and operation types, the operation types include loading and unloading, storage, and transshipment; Formulate specific operating specifications based on the nature of dangerous goods, including operating sequence, safety measures, and equipment requirements; Integrate the information to form a target handling requirements data set for dangerous goods.

5. The method according to claim 1, characterized in that The emergency processing data set also includes: For different types of emergency situations, a mapping table of emergency response plans for the emergency situations is established; Configuring the mapping table to match resources required for emergency processing; Collect data on the training of all personnel involved in emergency response and on emergency drills conducted regularly; Integrate information to form emergency response data sets.

6. The method according to claim 1, characterized in that Determining the grouping and stacking safety distance of dangerous goods containers according to MSDS also includes: Collect the MSDS information of the reserved dangerous goods container group; Group dangerous goods containers according to different shippers and different goods; Determine the safe distance for stacking containers of different classification groups. The method for determining the safe distance includes an accident consequence method and a quantitative risk assessment method. The accident consequence method determines the safe distance by calculating the impact range under the most serious accident situation, and the quantitative risk assessment method determines the safe distance by evaluating the probability of an accident under a certain risk level.

7. The method of claim 1, wherein: The mobile handling machinery quantity decision model also includes: Calculate the average vehicle arrival rate λ and the average operating efficiency μ of mobile handling machinery by queuing theory, wherein the average vehicle arrival rate λ and the average operating efficiency μ of mobile handling machinery calculated by queuing theory are obtained by estimating historical data; The obtained average vehicle arrival rate λ and average operating efficiency μ of mobile handling machinery are used as known parameters of the model; The exhaustive method is used to solve the decision-making model of the number of mobile handling machinery; Calculate the minimum average waiting time of vehicles; Calculate the maximum average utilization of mobile handling machinery.

8. The method according to claim 7, characterized in that Minimizing the average waiting time of vehicles also includes: Calculate the average waiting time of the vehicle, the formula is expressed as: Among them, λ: the average number of vehicles arriving per unit time, that is, the average vehicle arrival rate; μ: the number of containers that can be loaded and unloaded by the mobile handling machinery per unit time, that is, the operating efficiency of the mobile handling machinery, also known as the average service rate; k: the total number of bays in the yard that can be used to stack the containers of this batch, with k representing the bay number; ρ: The ratio of the average vehicle arrival rate to the average service rate of mobile handling machinery, that is, I: Total number of mobile handling machines assigned for on-site operations; W: average waiting time of vehicles; Minimize the average waiting time of vehicles, variable W is based on the number of mobile handling machines allocated, that is, decision variable I to solve the objective function: min W; Constraints: It is derived based on M / M / I queuing theory, where represents the probability that there is no vehicle in the yard.

9. The method according to claim 7, characterized in that Maximizing the average utilization of mobile handling machinery also includes: Calculate the average utilization rate of vehicles, the formula is expressed as: Among them, R1: average utilization rate of vehicles; μ: the number of vehicles that can be loaded and unloaded by mobile handling machinery per unit time, that is, the operating efficiency of mobile handling machinery, also known as the average service rate; λ: the average number of vehicles arriving per unit time, that is, the average vehicle arrival rate; I: The total number of mobile handling machines assigned to the site, i.e., the decision variable; Constraints: It is derived based on M / M / I queuing theory. I≤I * ,I∈N * ; Among them, I: the total number of mobile handling machines assigned to the site, i.e., the decision variable; N: the total number of arrival boxes in the planning period, n represents the number of arrival boxes, and the possible values ​​are 1, 2, 3, ..., N; Maximize the average utilization rate of mobile handling machinery and solve max R1 based on the number of mobile handling machinery assigned to the site (decision variable I); Constraints: The number of mobile handling machines allocated within the site must be a positive integer and less than the maximum number of mobile handling machines.

10. The method according to claim 7, characterized in that The calculation of the average vehicle arrival rate λ by queuing theory also includes: collecting vehicle arrival data for a certain period, the data including an exact timestamp of each vehicle's arrival; Arrange the collected data in chronological order and group them by hour, minute or other appropriate time intervals; The average arrival rate λ of vehicles is the inverse of vehicle arrival; Calculate the average arrival interval, where λ is 1 divided by the average arrival interval; For each set of data, calculate the average service efficiency of the mobile handling machinery during that period.

11. The method of claim 7, wherein: The average operating efficiency μ of mobile handling machinery also includes: Collecting data on the operation records of mobile handling machines over a period of time, the data should include timestamps of when the mobile handling machine starts and ends service, and the number of containers served; Arrange the collected data in chronological order and group them according to the time intervals of shifts and working days; The average service efficiency of the mobile handling machinery during the period is calculated by dividing the total number of containers completed during the period by the working hours of the mobile handling machinery; The average operating efficiency μ of mobile handling machinery is the average of the service efficiencies of all operating cycles.

12. The method of claim 1, wherein: The decision model of the number of containers per unit and the order of operation of mobile handling machinery also includes: The decision model for the number of boxes per bay and the operating order of the mobile handling machinery is used to optimize the number of boxes allocated to each bay and the operating order of the mobile handling machinery with the estimated additional box turnover volume and the moving distance of the mobile handling machinery as the target; Determine the arrival order and container information of the container group for transferring from the yard to the ship; Calculate and minimize the moving distance of mobile handling machinery in the yard area and minimize the additional estimated container turnover volume; Calculate the minimum additional expected box turnover volume, the formula is: Among them, minR2: minimize the additional estimated box turnover; a: The ratio of the increase in the turnover rate to the increase in the occupancy rate of the bay box slots; b: The intercept of the linear regression fitting formula between the box turnover rate and the box occupancy rate; N: the total number of arrival boxes in the planning period, n represents the number of arrival boxes, and the possible values ​​are 1, 2, 3, ..., N; p kn : The number of boxes of the nth arriving box group planned to be stacked in the kth bay, i.e., the decision variable; K: the total number of bays in the yard that can be used to stack the containers of this batch, with k representing the bay number; m k : The total number of containers stacked at bay k; c k : The maximum number of containers that can be stacked in the kth bay; Constraints: Indicates the planned stacking quantity of all bays that can stack the boxes of this order group, that is, the sum of the loading and unloading tasks is the number of loading and unloading tasks constraint for the arrival order box group; Minimize the moving distance of mobile handling machinery in the yard area. The formula is: minL: Minimize the moving distance of mobile handling machinery in the yard area; a: The ratio of the increase in the turnover rate to the increase in the occupancy rate of the bay box slots; b: The intercept of the linear regression fitting formula between the box turnover rate and the box occupancy rate; N: the total number of arrival boxes in the planning period, n represents the number of arrival boxes, and the possible values ​​are 1, 2, 3, ..., N; K: the total number of bays in the yard that can be used to stack the containers of this batch, with k representing the bay number; I: Total number of mobile handling machines assigned for on-site operations; x ink :0-1 variable, if the nth operation order of the i-th mobile handling machine (corresponding to the nth arriving box group) is the kth position, then the value is 1; otherwise it is 0 (decision variable); The distance travelled by the mobile handling machinery from k1 to k2; Among them: the planned stacking quantity of all bays for stacking the boxes of this order group, that is, the sum of the loading and unloading tasks is the number of loading and unloading tasks constraint for the arrival order box group: where y in :0-1 variable, if the i-th mobile handling machine performs the storage task in the n-th operation order (corresponding to the n-th arriving box group), the value is 1; otherwise it is 0 (decision variable); The operation sequence of each mobile handling machine corresponds to the storage yard position constraints: Constraints on the types of containers to be stacked at the yard bay: where z kt :0-1 variable, if the k-th bay is stacked with the t-th type of box group, the value is 1; otherwise, it is 0; The planned stacking volume in each storage bay is less than the total volume that can be stacked in that bay: The planned stacking locations of different types of dangerous goods containers should maintain safe distances between them: 0-1 variable constraints on decision variables: The decision model of the number of bay boxes and the operation sequence of mobile handling machinery is an integer programming model. The branch and bound method is used to solve the decision variable p. kn , x ink and in , and then through the decision variable p kn Then the number of containers in this group allocated to each storage yard can be obtained; Through the decision variable x ink and in The order of loading and unloading operations of the mobile handling machinery can be obtained, and the box location plan can be output.

13. A dangerous goods container yard storage operation planning and scheduling system, characterized in that: A method for planning and scheduling dangerous goods container yard storage operations for implementing any one of claims 1 to 12, the system comprising: A multi-source data acquisition module, which is used to obtain container information, operation information and current site operation data to be operated in the current shift through multi-source data, and the multi-source data includes data obtained from the terminal operation system, external platform system and customs supervision related system; A data preprocessing module, which is used to perform job preprocessing on the acquired data and filter out dangerous goods container data. According to the MSDS, the dangerous goods container data is classified according to its physical and chemical properties, health hazards, environmental hazards, and fire and reaction hazards, and a risk data set, a target operation requirement data set, and an emergency processing data set for dangerous goods of the current shift are constructed; A module for calculating the safe distance between dangerous goods container groups and stacking, wherein the module is used to determine the safe distance between dangerous goods container groups and stacking according to the MSDS; A historical operation data acquisition module, which is used to acquire historical operation data information according to the acquired cargo owner information and dangerous goods dimensions, wherein the historical operation data information includes data on storage location, operation equipment, and whether danger occurs; A dangerous goods container storage decision optimization model construction module, the dangerous goods container storage decision optimization model construction module is used to construct a dangerous goods container storage decision optimization model, the dangerous goods container storage decision optimization model includes a mobile handling machinery quantity decision model, a bay box quantity and mobile handling machinery operation sequence decision model; The optimal dangerous goods container storage plan input module is used to input real-time acquired data into the dangerous goods container storage decision optimization model to obtain the optimal dangerous goods container storage plan.

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