Intelligent logistics intelligent scheduling algorithm

Abstract

The invention discloses an intelligent logistics intelligent scheduling algorithm which is applied to the logistics field or the information processing field. In order to solve the technical problem of low logistics scheduling efficiency, the adopted technical scheme is an intelligent logistics intelligent scheduling algorithm which comprises the following steps: (S1) constructing an intelligent logistics system, and calculating the cost and time for minimizing reverse logistics of materials; (S2) scheduling and planning a shortest logistics path through global search of materials and corresponding sources by adopting an ABC algorithm; (S3) adopting an AQM algorithm to improve an ABC algorithm, calculating logistics data change in the whole intelligent logistics system, and performing optimization setting on the ABC algorithm; according to the invention, the logistics scheduling work can be carried out at the minimum cost through the ABC algorithm, and the data fluctuation influence generated by the material management is eliminated by using the AQM algorithm, so that the material management is recovered to a queue type management mode, thereby achieving the purpose of optimizing the scheduling stability through the ABC algorithm, and greatly improving the logistics scheduling efficiency.

Description

Technical Field

[0001] The present invention relates to the field of logistics or information processing, and more specifically to an intelligent logistics intelligent scheduling algorithm; Background Art

[0002] In today's high-tech society, which is facing the depletion of natural energy and resources, as well as ecological pollution caused by the disposal of waste and obsolete products, innovative reverse logistics functions play a huge role in giving entities the skills to better use ecological energy and resources and to achieve a sober trade-off between the financial system and the ecological spectrum. Online shopping, mail order and after-sales services have a place in the rate of return of these organizations, which have failed to pay further attention to improving rewards. Given the different legislative measures aimed at protecting ecological and service interests, most organizations have adopted the reverse flow, that is, from customers to recycling centers located within their logistics network. Consumer goods reprocessing is no longer an innovation, waste paper reuse, soft drink bottle collection, metal scrap brokers have clearly become common places in daily life. In turn, reverse logistics is driven by various factors, such as competition and marketing goals, direct financial reasons and growing anxiety about the ecosystem. Importantly, it has an ecological dimension as well as a value retrieval dimension, so the time required for reverse logistics processes is reduced by using optimal scheduling algorithms.

[0003] A heuristic algorithm is proposed in the prior art, which constructs an integer programming model that contains different cost components incurred during the disassembly process to minimize the costs associated with the disassembly procedure. Although this method can give an ideal arrangement, it is not sufficient to handle practical estimation problems due to unreasonable processing time. Another invention proposes an imaginative mixed integer direct programming strategy that is expected to be used to configure and schedule supply chains with reverse flows. Although generation, transmission and reverse logistics capabilities are evaluated simultaneously, errors are often made in the logistics scheduling process. Summary of the invention

[0004] In view of the above problems, the present invention discloses an intelligent logistics intelligent scheduling algorithm, which can deploy network servers and realize the analysis and processing of network data.

[0005] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0006] An intelligent logistics intelligent scheduling algorithm, the steps include:

[0007] (S1) Build an intelligent logistics system to calculate and minimize the cost and time of reverse logistics of materials;

[0008] (S2) Using the ABC algorithm to globally search for materials and their corresponding sources, scheduling and planning the shortest logistics path;

[0009] (S3) The AQM algorithm is used to improve the ABC algorithm, calculate the changes in logistics data in the entire intelligent logistics system, and optimize the ABC algorithm.

[0010] As a further technical solution of the present invention, the calculation of minimizing the cost and time of reverse logistics of component materials is shown in formula (1):

[0011]

[0012] In formula (1), T1 represents the time required to disassemble the i-th destructive component material, T2 represents the time required to disassemble the i-th non-destructive component material; t2 represents the average time used to disassemble the i-th destructive component material; t2 represents the average time used to disassemble the i-th non-destructive component material, E represents the total re-manufacturing of scrapped products, and D is the non-destructive disassembly efficiency; the main goal of component materials is to minimize the total disassembly time and total cost. The function of the total material disassembly time is shown in equation (2):

[0013]

[0014] In formula (2), T3 represents the total time required for material disassembly; the function of the total material cost is shown in equation (3):

[0015]

[0016] In formula (3), M represents the function of total material cost, A i Table 1 shows the unit recovery cost of material i, B i represents the unit purchase cost of material i, C i represents the unit disposal cost of material i, R represents the total quantity of purchased materials, and U represents the total quantity of disposed materials.

[0017] As a further technical solution of the present invention, the ABC algorithm steps include:

[0018] (S21) In the initial stage of the ABC algorithm, the material source data is randomly generated, F = {F1, F2, ..., F i}, F represents the material source data, i represents the serial number;

[0019] (S22) In the global search phase of the ABC algorithm, each logistics scheduling plan is randomly changed to obtain a new logistics scheduling plan, which will perform neighborhood search on the material data. The new logistics scheduling plan is obtained by using the differential expression from F i Created as shown in formula (4):

[0020] s i =F i+r×(F i -F k ) (4)

[0021] In formula (4), s i represents a new logistics scheduling plan, k belongs to {1,2,…,i} and k is not equal to i, r represents a uniform random number in the range [-1,1]; once s is obtained i , will be s i To evaluate and compare: If s i The fitness is better than the old logistics scheduling solution x i , the employed bees will forget the old logistics scheduling plan and remember the new logistics scheduling plan; otherwise, the next global search will be carried out;

[0022] (S23) In the interactive stage of the ABC algorithm, when the global domain search is completed, the logistics scheduling plan shares the logistics scheduling plan with the adjacent material source data, and then each material source data will select the logistics scheduling plan in a probabilistic manner, and the material source data picks up the old logistics scheduling plan x i The probability P i As shown in formula (5):

[0023]

[0024] In formula (5), fi is the fitness of the old logistics scheduling plan xi; once the material source data selects the food source x i , the material source data will also be i Perform local search, the same as step (S2), if the modified logistics scheduling scheme has better fitness, then replace x i ;

[0025] (S24) In the logistics scheduling plan generation stage of the ABC algorithm, if the quality of the logistics scheduling plan cannot be improved after the global search of the ABC algorithm, it is assumed that the logistics scheduling plan is abandoned, and the logistics scheduling plan will randomly generate material source data and return to step (S2) until the number of iterations.

[0026] As a further technical solution of the present invention, the AQM algorithm improves the ABC algorithm and calculates the performance index of the entire intelligent logistics system according to the changes in logistics data during the operation of the intelligent logistics system as shown in formula (6):

[0027]

[0028] In formula (6), J represents the operation performance of the intelligent logistics system, Ω represents the trajectory function of the material operation, Y represents the prediction of the material data change law, and U represents the optimal logistics data change performance. According to the amount of material management incoming data, the logistics queue optimization amount is derived as shown in formula (7):

[0029] U=(U1QB+S) -1 BQ[Ω-Γ] (7)

[0030] In formula (7), U1 represents the optimization amount of the queue within the logistics time, Q represents the difference of the logistics scheduling after optimization, B represents the amount of materials to maintain the stable compensation of the intelligent logistics system, S represents the change margin of the intelligent logistics system, and Г represents the total amount of data that needs to be converted during the operation of the intelligent logistics system. By adjusting the trajectory function of the change of material management, the ABC algorithm scheduling is made more reasonable. The change trajectory after improvement is expressed as:

[0031]

[0032] In formula (8), q represents the minimum change of logistics data, β represents the change law function of logistics data, R0 represents the total amount of logistics data, N represents the total number of logistics data transportation batches, and H represents the optimized logistics data division. According to the improved logistics data change trajectory, the actual amount of logistics data is substituted into the calculation, and the change law function is obtained as follows:

[0033] β(k)=β(k-1)+μq(k) (9)

[0034] In formula (9), k represents the logistics data transportation batch, and μ represents the change in the weight of logistics data. The AQM algorithm is used to eliminate the fluctuation effect of material management, so that material management can be restored to the queue management mode, thereby achieving the purpose of ABC algorithm optimization scheduling stability.

[0035] As a further technical solution of the present invention, the intelligent logistics system includes:

[0036] The procurement module is used for enterprise configuration of suppliers and third-party personnel to perform shipping locations for material procurement;

[0037] Inbound dispatch system for intelligent logistics dispatch and inbound call information evaluation;

[0038] An inbound call history database for storing inbound call history data;

[0039] Account information database, used to store account information of purchasing users, suppliers and third-party personnel;

[0040] Inbound call receiving equipment, used to connect external communication signals through optical fiber interfaces, send data queries to pass caller information to the inbound dispatch system, and assign inbound calls to answer queues;

[0041] Answer queue, used to distribute incoming call signals to corresponding user call devices;

[0042] Among them, the procurement module is unidirectionally connected to the inbound scheduling system, the inbound scheduling system, the inbound call history database and the account information database are bidirectionally connected, the optical fiber interface is unidirectionally connected to the inbound telephone receiving device, the inbound telephone receiving device is bidirectionally connected to the inbound evaluation system, the inbound telephone receiving device is unidirectionally connected to the answering queue, and the answering queue is unidirectionally connected to the user call device.

[0043] As a further technical solution of the present invention, the purchasing module includes:

[0044] Evaluation Module, the primary analytical tool for evaluating vendor proposals and determining contract awards;

[0045] Summary module, used to convey the complete contract to the purchasing client and third-party intermediary personnel for review;

[0046] A supplier database to store descriptive information about logistics providers, shipper requirements, and approved suppliers;

[0047] A proposal database for storing descriptive information on business activities and logistics contracts;

[0048] Among them, the summary module is bidirectionally connected to the purchasing user, the third-party personnel are unidirectionally connected to the supplier database, the evaluation module and the summary module, the supplier database is unidirectionally connected to the proposal database, the supplier database is bidirectionally connected to the logistics supplier, the proposal database is unidirectionally connected to the summary module, the proposal database is bidirectionally connected to the evaluation module, and the proposal database is bidirectionally connected to the logistics supplier.

[0049] As a further technical solution of the present invention, the inbound scheduling system includes a scheduling module, a call evaluation module, a modeling module and an intelligent scheduling algorithm.

[0050] As a further technical solution of the present invention, the account information includes co-ownership information, financial strength information, security review information, operational capability information, business information, operational contact information, real ownership information and liability capability information.

[0051] The beneficial positive effects of the present invention are:

[0052] Different from conventional technologies, the present invention can provide an arrangement of efficient, responsive and proactive information systems. Through the ABC algorithm, logistics scheduling can be carried out at the lowest cost. The AQM algorithm is used to eliminate the impact of fluctuations in material management and restore the queue management mode of material management, thereby achieving the purpose of optimizing scheduling stability using the ABC algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0054] Figure 1 A diagram of an intelligent logistics intelligent scheduling algorithm is shown;

[0055] Figure 2 The structure diagram of the intelligent logistics system is shown;

[0056] Figure 3 The internal structure diagram of the procurement module is shown;

[0057] Figure 4 A flow chart showing the scheduling of inbound calls to be responded to by agents;

[0058] Figure 5 A comparison graph of the processing efficiency of the three algorithms is shown. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] like Figure 1 As shown, an intelligent logistics intelligent scheduling algorithm includes the following steps:

[0061] (S1) Build an intelligent logistics system to calculate and minimize the cost and time of reverse logistics of materials;

[0062] In a specific embodiment, the creation of sub-devices is gradually expanding, which leads to a huge electronic waste. This is the result of the current individual expectation to achieve new innovations in a short time. This situation makes manufacturers create new items in a very short time, so there is a need to dispose or dismantle existing items. In this case, manufacturers want to re-manufacture scrapped (re-manufacture scrapped) products to meet the component needs in the production of new products. In this sense, in our previous work, we demonstrated a method to select the optimal amount of recycled products, which can reduce the total cost required for reverse logistics.

[0063] (S2) Using the ABC algorithm to globally search for materials and their corresponding sources, scheduling and planning the shortest logistics path;

[0064] In the specific embodiment, this paper proposes a new reverse logistics operation method for remanufacturing scrapped items from the perspective of optimizing the scheduling algorithm. In previous work, we have found the optimal number of recycled remanufactured scrapped products for disassembly according to orders. This paper shows the procedure for properly scheduling machine disassembly products, which can reduce the total time and total cost required for reverse logistics operations. In the proposed structure, the artificial bee colony (ABC) algorithm is used. Compared with existing scheduling algorithms, the proposed framework has better performance.

[0065] (S3) The Active Queue Management (AQM) algorithm is used to improve the ABC algorithm, calculate the changes in logistics data in the entire intelligent logistics system, and optimize the ABC algorithm.

[0066] In a specific embodiment, order disassembly is a key process in the reverse logistics of remanufacturing scrap products. Through a proper order disassembly process, the total cost of reverse logistics can be reduced. It is troublesome to predict the exact yield of components from remanufacturing scrap. On the other hand, we can foresee the maximum possibility of obtaining yield from remanufacturing scrap. Reverse logic. In addition, in order to meet the demand, parts need to be purchased from external suppliers. If the actual yield is lower than expected, parts need to be purchased to meet the demand. In another case, the yield is higher than expected; too many components must be processed. In this way, if the predicted value is the smallest or most extreme, it will bring misfortune to the remanufacturing scrap reverse logistics operation. Subsequently, the prediction should be close to the real yield.

[0067] In a specific embodiment, the calculation of minimizing the cost and time of reverse logistics of component materials is shown in formula (1):

[0068]

[0069] In formula (1), T1 represents the time required to disassemble the i-th destructive component material, T2 represents the time required to disassemble the i-th non-destructive component material; t2 represents the average time used to disassemble the i-th destructive component material; t2 represents the average time used to disassemble the i-th non-destructive component material, E represents the total re-manufacturing of scrapped products, and D is the non-destructive disassembly efficiency; the main goal of component materials is to minimize the total disassembly time and total cost. The function of the total material disassembly time is shown in equation (2):

[0070]

[0071] In formula (2), T3 represents the total time required for material disassembly; the function of the total material cost is shown in equation (3):

[0072]

[0073] In formula (3), M represents the function of total material cost, A i Table 1 shows the unit recovery cost of material i, B i represents the unit purchase cost of material i, C i represents the unit disposal cost of material i, R represents the total number of purchased materials, and U represents the total number of disposed materials. Equation (3) is the total time required for the order disassembly process in the remanufacturing scrap reverse logistics. Then, the goal of this work is to minimize the total time and total cost required for the disassembly process. Minimizing the total time function is a complex task in general because it may contain multiple products and components. Therefore, optimization is the preferred method to solve the scheduling task. In this work, we choose the artificial bee colony (ABC) algorithm optimization to solve the scheduling problem.

[0074] The ABC algorithm consists of three types of artificial bees, which are employed bees, employed bees, bystanders, and scouts. Employed bees collect food sources from global searches. Employed bees provide data to onlookers, who stand by in the hive and watch the dance of employed bees. The onlookers will then choose a food source that may correspond to the nature of the food source. In this way, good food sources attract more bees than bad ones. Scout bees arbitrarily scan new food sources in the hive area. When a scout bee or onlooker bee finds a food source, it is hired. When a food source is completely abused, all employed bees associated with it will abandon the position and may become scouts again. Therefore, scout bees engage in the "investigation" occupation, while employed bees and onlooker bees engage in the "abuse" occupation. This further encourages the work of the scout bee stage. The steps of the ABC algorithm include:

[0075] (S21) In the initial stage of the ABC algorithm, the material source data is randomly generated, F = {F1, F2, ..., F i}, F represents the material source data, i represents the serial number;

[0076] (S22) In the global search phase of the ABC algorithm, each logistics scheduling plan is randomly changed to obtain a new logistics scheduling plan, which will perform neighborhood search on the material data. The new logistics scheduling plan is obtained by using the differential expression from F i Created as shown in formula (4):

[0077] s i =Fi +r×(F i -F k ) (4)

[0078] In formula (4), s i represents a new logistics scheduling plan, k belongs to {1,2,…,i} and k is not equal to i, r represents a uniform random number in the range [-1,1]; once s is obtained i , will be s i To evaluate and compare: If s i The fitness is better than the old logistics scheduling solution x i , the employed bees will forget the old logistics scheduling scheme and remember the new logistics scheduling scheme; otherwise, the next global search will be carried out; in the proposed algorithm, the food source is related to the conceivable solution of the optimization problem, and the nectar measurement of the food source is compared with the fitness of the relevant solution. In ABC, the first large part of the bee colony is the employed bees, and the other half are the bystanders. Since only one bee is expected for each food source, the number of employed bees is equivalent to the number of food sources. Therefore, the number of bystander bees is also equal to the number of solutions under consideration. The ABC algorithm first collects randomly generated food sources. The main method of ABC can be described as follows.

[0079] (S23) In the interactive stage of the ABC algorithm, when the global domain search is completed, the logistics scheduling plan shares the logistics scheduling plan with the adjacent material source data, and then each material source data will select the logistics scheduling plan in a probabilistic manner, and the material source data picks up the old logistics scheduling plan x i The probability P i As shown in formula (5):

[0080]

[0081] In formula (5), fi is the fitness of the old logistics scheduling plan xi; once the material source data selects the food source x i , the material source data will also be i Perform local search, the same as step (S2), if the modified logistics scheduling scheme has better fitness, then replace x i ;

[0082] (S24) In the logistics scheduling plan generation stage of the ABC algorithm, if the quality of the logistics scheduling plan cannot be improved after the global search of the ABC algorithm, it is assumed that the logistics scheduling plan is abandoned, and the logistics scheduling plan will randomly generate material source data and return to step (S2) until the number of iterations.

[0083] In a specific embodiment, the AQM algorithm improves the ABC algorithm and calculates the performance index of the entire intelligent logistics system according to the changes in logistics data during the operation of the intelligent logistics system as shown in formula (6):

[0084]

[0085] In formula (6), J represents the operation performance of the intelligent logistics system, Ω represents the trajectory function of the material operation, Y represents the prediction of the material data change law, and U represents the optimal logistics data change performance. According to the amount of material management incoming data, the logistics queue optimization amount is derived as shown in formula (7):

[0086] U=(U1QB+S) -1 BQ[Ω-Γ] (7)

[0087] In formula (7), U1 represents the optimization amount of the queue within the logistics time, Q represents the difference of the logistics scheduling after optimization, B represents the amount of materials to maintain the stable compensation of the intelligent logistics system, S represents the change margin of the intelligent logistics system, and Г represents the total amount of data that needs to be converted during the operation of the intelligent logistics system. By adjusting the trajectory function of the change of material management, the ABC algorithm scheduling is made more reasonable. The change trajectory after improvement is expressed as:

[0088]

[0089] In formula (8), q represents the minimum change of logistics data, β represents the change law function of logistics data, R0 represents the total amount of logistics data, N represents the total number of logistics data transportation batches, and H represents the optimized logistics data division. According to the improved logistics data change trajectory, the actual amount of logistics data is substituted into the calculation, and the change law function is obtained as follows:

[0090] β(k)=β(k-1)+μq(k) (9)

[0091] In formula (9), k represents the logistics data transportation batch, and μ represents the change in the weight of logistics data. The AQM algorithm is used to eliminate the fluctuation effect of material management, so that material management can be restored to the queue management mode, thereby achieving the purpose of ABC algorithm optimization scheduling stability.

[0092] In a specific embodiment, Figure 2As shown, the intelligent logistics system includes a procurement module, an inbound scheduling system, an inbound call history database, an account information database, an inbound call receiving device and an answering queue. Among them, the procurement module is used for suppliers and third-party personnel to perform enterprise configuration of the shipping location for material procurement; the inbound scheduling system is used for intelligent logistics scheduling and inbound call information evaluation; the inbound call history database is used to store inbound call history data; the account information database is used to store the account information of the purchasing user, supplier and third-party personnel; the inbound call receiving device is used to connect the external communication signal through the optical fiber interface, send data query to pass the caller information to the inbound scheduling system, and assign the inbound call to the answering queue; the answering queue is used to assign the inbound call signal to the corresponding user call device. The procurement module is unidirectionally connected to the inbound scheduling system, the inbound scheduling system, the inbound call history database and the account information database are bidirectionally connected, the optical fiber interface is unidirectionally connected to the inbound call receiving device, the inbound call receiving device is bidirectionally connected to the inbound evaluation system, the inbound call receiving device is unidirectionally connected to the answering queue, and the answering queue is unidirectionally connected to the user call device.

[0093] In a specific embodiment, an inbound dispatch system dispatches inbound telephone calls to be responded to by agents in an order based in part on the predicted outcome of the inbound telephone calls. The inbound dispatch system includes a dispatch module, a call evaluation submodule, and a modeling module, and is connected to an inbound call history database and an account information database. The modeling module builds one or more models to predict the outcome of an inbound call using account information in the inbound call history database in the database. The dispatch module applies the model to predict the outcome of pending inbound calls and places orders for agents to respond to pending inbound calls based on the call evaluation submodule. The modeling module 14 builds a statistical model and the call evaluation submodule calculates a priority value used by the dispatch module. The priority value is a result of the model calculation and a result of the solution of an optimization problem defined based on the model calculation. The inbound dispatch system and the inbound call receiving device can be integrated in a single computing platform, or can be based on a separate computing platform connected to a proprietary application program interface of the inbound call receiving device, or connected to a commercial application middleware. Inbound call reception is receiving inbound telephone calls through a fiber optic interface. Inbound telephone call analog signals received by the inbound call receiving equipment through the fiber optic interface are distributed to the answering queue for response by the agent operating the telephone equipment.

[0094] In a specific embodiment, the inbound call receiving device transmits the caller information to the inbound dispatch system by means of data query, etc., and waits for the response of the inbound dispatch system before assigning the inbound call to the answering queue. In addition, the inbound call receiving device provides the inbound dispatch system with agent activity and capacity. For example, the inbound call receiving device can provide agent activity information. When the inbound call receiving device has sufficient capacity to respond, the inbound dispatch system immediately returns the call signal to the inbound call receiving device. In other words, if there is sufficient capacity to handle the inbound call, the inbound call is not delayed but routed to be answered immediately. When the capacity of the inbound call receiving device is insufficient, the inbound dispatch system delays the call on virtual reservation until the appropriate time based on the priority value calculated by the call evaluation submodule. Regardless of whether the inbound call is delayed, the inbound dispatch system 10 collects the data of the inbound call and stores it in the inbound call history database. The results of the inbound call and the predicted results will also be collected and stored to provide a detailed call-by-call history record for future modeling and verification of the comparison of the predicted results with the actual results. For example, once the inbound call is completed, the results are generated. For example, successful connections to agents, abandoned calls, purchases, or customer churn in billing records are all related to inbound calls.

[0095] In a specific embodiment, Figure 3 As shown, the procurement module includes an evaluation module, a summary module, a supplier database and a proposal database. The evaluation module is used as the main analytical tool for evaluating supplier proposals and determining contract awards; the summary module is used to convey the complete contract to procurement customers and third-party intermediaries for review; the supplier database is used to store descriptive information about logistics suppliers, shipper requirements, and approved suppliers; and the proposal database is used to store descriptive information about business activities and logistics contracts. The summary module is bidirectionally connected to procurement users, third-party personnel are unidirectionally connected to the supplier database, the evaluation module and the summary module, the supplier database is unidirectionally connected to the proposal database, the supplier database is bidirectionally connected to the logistics supplier, the proposal database is unidirectionally connected to the summary module, the proposal database is bidirectionally connected to the evaluation module, and the proposal database is bidirectionally connected to the logistics supplier.

[0096] In a specific embodiment, the inbound dispatch system includes a dispatch module, a call evaluation module, a modeling module, and an intelligent dispatch algorithm. The account information includes co-ownership information, financial strength information, security review information, operational capability information, business information, operational contact information, real ownership information, and liability capability information. The dispatch module holds the inbound call in a queue that acts as a virtual hold until a response is required, and then releases the inbound call to be placed in an answer queue. Thus, the inbound dispatch system responds to queries from the inbound call receiving device according to the priority of the inbound call, in effect, creating an ordered queue on the inbound call receiving device by delaying responses to lower priority inbound calls. In an alternative embodiment, the dispatch module can reorder the queue directly within the inbound call receiving device to allow real-time sorting of the inbound telephone call queue. The dispatch module obtains data applied to the caller model by performing a lookup based on the caller information received from the inbound call receiving device. The caller's information may include an account number, a zip code, an area code, a telephone exchange, a reservation number, or other relevant information obtained from the inbound caller, such as information obtained through a VRU or by the inbound call receiving device through an inbound call, such as ANI or DNIS information. The nature of the caller information depends on the implementation of the inbound dispatch system and is usually configurable through a graphical user interface provided with the traditional receiving equipment. In addition to the caller information, the dispatch module can query and connect data from other sources, such as Zip-4 and credit bureau sources, as well as other demographic information that can be derived from the caller's information.

[0097] In a particular embodiment, the modeling module creates a caller model by performing statistical analysis on appropriate data from the inbound call history database and the account information database. The statistical analysis performed by the modeling module builds models by relating call outcomes (i.e., dependent variables) to information available at the time the call is received (i.e., independent variables). The end result of each model is an equation that, when calculated, can predict the associated outcome. The application of the caller model to the caller and / or call information can be performed when the call arrives or can be preformed so that potential action codes are calculated at the beginning of the time period in order to provide a faster response when the situation permits.

[0098] In a specific embodiment, Figure 4The flow chart describes the process of scheduling inbound calls to be responded to by agents, which builds a caller model from inbound call history records, the inbound call history records used to model the relevant outcome may be a sample drawn from historical inbound calls of the same nature as the outcome to be modeled, or may be specially designed during the testing phase. For example, a television advertisement that airs in a single or limited number of television markets may be representative of the total target audience and used to generate inbound calls with a volume within the capacity constraints of the call center. The inbound call results for the sample advertisement can then be used to create a model specific to the nature of the product sold by the advertisement. The advertisement-specific model is then used to display the advertisement to a wider audience during the time period so that inbound calls with a greater likelihood of resulting in a purchase will have a higher priority for agent response. Thereafter, the receiving device receives the inbound call. Typically, the inbound calls continue to arrive at the receiving device at a rate that varies over time. The receiving device answers the inbound call in a conventional manner and determines the call and / or caller information. The call and caller information is determined by analyzing the logistics information, which arrives with the inbound call.

[0099] In a specific embodiment, the inbound call is next scheduled for response by an agent interfaced with the receiving device, and the inbound call with a lower priority value is placed on virtual hold, while the outbound call with a higher priority value is returned to the receiving device and placed in a queue for the agent to respond. The virtual hold length of the inbound call depends on the number of inbound calls, the capacity of the receiving device, the agent talk time for each call, and the priority value of the inbound call relative to other pending inbound calls. Based on these factors, when agent resources are available and / or the maximum hold time parameter is exceeded, the inbound call is placed on virtual hold time and forwarded to the receiving device in order of priority value. Alternatively, in an embodiment where the receiving device can sort or change the order of the inbound queue based on available data including priority value, the inbound queue of the receiving device can be reordered in real time as additional queries are received. Thereafter, the results of the inbound call are stored in an inbound call history database. The inbound history database tracks factors S, such as call success or abandonment and the final call outcome. The call outcome may include directly quantifiable factors S such as purchase decisions or less quantifiable factors S such as account usage, cancellations, and customer satisfaction reflected by related information available from the account database and other sources.

[0100] In a specific embodiment, the present invention uses Simulink software to establish a material management experimental model, and experiments are carried out under this model and data is recorded to verify the practicality of the intelligent scheduling algorithm. The computer used in the experiment of the present invention adopts a CPU memory of 64+256G, and the hardware parameters are Intel core i89700KF. The model environment parameter sets the data acquisition accuracy to 96%, the algorithm calculation error is not less than 2.0%, and the data communication method adopts Unicom 5G wireless communication. This research experiment uses a Windows x86 computer system, the material management model is an EPC management network, and the parameter setting meets the GEN-2 label. Experiments are carried out on this basis. By comparing the integer programming model (Scheme 1) and the mixed integer direct programming strategy model (Scheme 2), the data results are analyzed using a computer program, and the test data table is finally displayed as shown in Table 1.

[0101] Table 1 Experimental data of three logistics models

[0102]

[0103] Through the data analysis of Table 1, it is found that the logistics model of the present invention has a material data volume of 2879MB, a material data processing efficiency of up to 4.49MB / s, and a logistics scheduling control strength of 95.4%; the total data volume of the integer programming model proposed in Scheme 1 is 2434MB, the material data processing efficiency is up to 3.12MB / s, and the control strength is 86.2%; the total material data volume of the mixed integer direct programming strategy model proposed in Scheme 2 is 2341MB, the material data processing efficiency is up to 2.14MB / s, and the control strength is only 77.6%. By comparison, it is found that the logistics model of the present invention has superior performance in all aspects.

[0104] The superiority of the intelligent scheduling algorithm of the present invention can be further verified by displaying the data processing efficiency of each logistics model. The processing efficiency of the three algorithms is simulated using MATLAB, and the efficiency comparison simulation is obtained. Figure 5 As shown, by comparison, it is found that the maximum data processing efficiency of the intelligent scheduling algorithm of the present invention is 5.0MB / s, and the growth rate is relatively fast, reaching a stable processing efficiency when the material data is 2300MB; the maximum data processing efficiency of the scheduling algorithm of scheme 1 is 3.2MB / s, and the growth rate is accelerated when the material data exceeds 2600MB; the maximum data processing efficiency of the scheduling algorithm of scheme 2 is 2.5MB / s, and the growth rate is relatively slow. Analysis shows that the processing efficiency of the intelligent scheduling algorithm of the present invention is the best.

[0105] Although specific embodiments of the present invention are described above, those skilled in the art should understand that these specific embodiments are merely illustrative, and those skilled in the art may make various omissions, substitutions and changes to the details of the above methods and systems without departing from the principles and essence of the present invention; for example, combining the above method steps to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention; therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. An intelligent logistics intelligent scheduling algorithm, characterized by: The following steps are involved: (S1) Build an intelligent logistics system to calculate and minimize the cost and time of reverse logistics of materials; (S2) Using the ABC algorithm to globally search for materials and their corresponding sources, scheduling and planning the shortest logistics path; (S3) The AQM algorithm is used to improve the ABC algorithm, calculate the changes in logistics data in the entire intelligent logistics system, and optimize the ABC algorithm.

2. According to claim 1, the intelligent logistics intelligent scheduling algorithm is characterized by: The calculation to minimize the cost and time of reverse logistics of component materials is shown in formula (1): In formula (1), T1 represents the time required to disassemble the i-th destructive component material, and T2 represents the time required to disassemble the i-th non-destructive component material; t2 represents the average time used to dismantle the i-th destructive component material; t2 represents the average time used to disassemble the i-th non-destructive component material, E represents the total re-manufacturing of scrapped products, and D is the non-destructive disassembly efficiency; the main goal of component materials is to minimize the total disassembly time and total cost. The function of the total material disassembly time is shown in equation (2): In formula (2), T3 represents the total time required for material disassembly; the function of the total material cost is shown in equation (3): In formula (3), M represents the function of total material cost, A i Table 1 shows the unit recovery cost of material i, B i represents the unit purchase cost of material i, C i represents the unit disposal cost of material i, R represents the total quantity of purchased materials, and U represents the total quantity of disposed materials.

3. According to claim 1, the intelligent logistics intelligent scheduling algorithm is characterized by: The ABC algorithm steps include: (S21) In the initial stage of the ABC algorithm, the material source data is randomly generated, F = {F1, F2, ..., F i }, F represents the material source data, i represents the serial number; (S22) In the global search phase of the ABC algorithm, each logistics scheduling plan is randomly changed to obtain a new logistics scheduling plan, which will perform neighborhood search on the material data. The new logistics scheduling plan is obtained by using the differential expression from F i Created as shown in formula (4): s i =F i +r×(F i -F k ) (4) In formula (4), s i represents a new logistics scheduling plan, k belongs to {1,2,…,i} and k is not equal to i, r represents a uniform random number in the range [-1,1]; once s is obtained i , will be s i To evaluate and compare: If s i The fitness is better than the old logistics scheduling solution x i , the employed bees will forget the old logistics scheduling plan and remember the new logistics scheduling plan; otherwise, the next global search will be carried out; (S23) In the interactive stage of the ABC algorithm, when the global domain search is completed, the logistics scheduling plan shares the logistics scheduling plan with the adjacent material source data, and then each material source data will select the logistics scheduling plan in a probabilistic manner, and the material source data picks up the old logistics scheduling plan x i The probability P i As shown in formula (5): In formula (5), f i It is the old logistics scheduling plan x i fitness; once the material source data selects the food source x i , the material source data will also be i Perform local search, the same as step (S2), if the modified logistics scheduling scheme has better fitness, then replace x i ; (S24) In the logistics scheduling plan generation stage of the ABC algorithm, if the quality of the logistics scheduling plan cannot be improved after the global search of the ABC algorithm, it is assumed that the logistics scheduling plan is abandoned, and the logistics scheduling plan will randomly generate material source data and return to step (S2) until the number of iterations.

4. The intelligent logistics intelligent scheduling algorithm according to claim 1 is characterized by: The AQM algorithm improves the ABC algorithm and calculates the performance index of the entire intelligent logistics system according to the changes in logistics data during the operation of the intelligent logistics system as shown in formula (6): In formula (6), J represents the operation performance of the intelligent logistics system, Ω represents the trajectory function of the material operation, Y represents the prediction of the material data change law, and U represents the optimal logistics data change performance. According to the amount of material management incoming data, the logistics queue optimization amount is derived as shown in formula (7): U=(U1QB+S) -1 BQ[Ω-C] (7) In formula (7), U1 represents the optimization amount of the queue within the logistics time, Q represents the difference of the logistics scheduling after optimization, B represents the amount of materials to maintain the stable compensation of the intelligent logistics system, S represents the change margin of the intelligent logistics system, and Г represents the total amount of data that needs to be converted during the operation of the intelligent logistics system. By adjusting the trajectory function of the change of material management, the ABC algorithm scheduling is made more reasonable. The change trajectory after improvement is expressed as: In formula (8), q represents the minimum change of logistics data, β represents the change law function of logistics data, R0 represents the total amount of logistics data, N represents the total number of logistics data transportation batches, and H represents the optimized logistics data division. According to the improved logistics data change trajectory, the actual amount of logistics data is substituted into the calculation, and the change law function is obtained as follows: β(k)=β(k-1)+μq(k) (9) In formula (9), k represents the logistics data transportation batch, and μ represents the change in the weight of logistics data. The AQM algorithm is used to eliminate the fluctuation effect of material management, so that material management can be restored to the queue management mode, thereby achieving the purpose of ABC algorithm optimization scheduling stability.

5. The intelligent logistics intelligent scheduling algorithm according to claim 1 is characterized by: The intelligent logistics system comprises: The procurement module is used for enterprise configuration of suppliers and third-party personnel to perform shipping locations for material procurement; Inbound dispatch system for intelligent logistics dispatch and inbound call information evaluation; An inbound call history database for storing inbound call history data; Account information database, used to store account information of purchasing users, suppliers and third-party personnel; Inbound call receiving equipment, used to connect external communication signals through optical fiber interfaces, send data queries to pass caller information to the inbound dispatch system, and assign inbound calls to answering queues; Answer queue, used to distribute incoming call signals to corresponding user call devices; Among them, the procurement module is unidirectionally connected to the inbound scheduling system, the inbound scheduling system, the inbound call history database and the account information database are bidirectionally connected, the optical fiber interface is unidirectionally connected to the inbound telephone receiving device, the inbound telephone receiving device is bidirectionally connected to the inbound evaluation system, the inbound telephone receiving device is unidirectionally connected to the answering queue, and the answering queue is unidirectionally connected to the user call device.

6. The intelligent logistics system according to claim 5, characterized in that: The procurement module includes: Evaluation Module, the primary analytical tool for evaluating vendor proposals and determining contract awards; Summary module, used to convey the complete contract to the purchasing client and third-party intermediary personnel for review; A supplier database to store descriptive information about logistics providers, shipper requirements, and approved suppliers; A proposal database for storing descriptive information on business activities and logistics contracts; Among them, the summary module is bidirectionally connected to the purchasing user, the third-party personnel are unidirectionally connected to the supplier database, the evaluation module and the summary module, the supplier database is unidirectionally connected to the proposal database, the supplier database is bidirectionally connected to the logistics supplier, the proposal database is unidirectionally connected to the summary module, the proposal database is bidirectionally connected to the evaluation module, and the proposal database is bidirectionally connected to the logistics supplier.

7. The intelligent logistics system according to claim 5, characterized in that: The inbound dispatching system includes a dispatching module, a call evaluation module, a modeling module and an intelligent dispatching algorithm.

8. The intelligent logistics system according to claim 5, characterized in that: The account information includes co-ownership information, financial strength information, security review information, operational capability information, business information, operational contact information, true ownership information and liability capability information.

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