Method for scheduling automatic feeding of a mixing silo based on model prediction and local exhaustion

Abstract

The application discloses a kind of based on model prediction and local exhaust mixing bin automatic feeding scheduling method, comprising: obtaining the parameter information and state information of system, and according to actual equipment state and parameter, corresponding mathematical model is established, according to the weight of raw material in bin and remaining discharging time, bin is divided into four categories, the bin needing to be added in the nearest time is limited to class I, then according to the established mathematical model, exhaust the combination of charging trolley and material taking machine and predict the bin state brought by each different combination in a certain period of time, the combination that meets the constraint condition is selected using the idea of rolling optimization, that is, the charging trolley and material taking machine needed to be scheduled in the next task are obtained.The mixing bin automatic feeding method provided by the application can solve the scheduling problem of bin automatic feeding in raw material yard mixing and blending process, reduce the operation cost of charging trolley and material taking machine, and can avoid the occurrence of empty bin in mixing bin.

Claims

1. A scheduling method for automatic feeding of mixed material bins based on model prediction and local exhaustive search, characterized in that, include: Obtain the parameter and status information of the mixing silo system, and establish a corresponding mathematical model of the mixing silo system based on the actual equipment status and parameters; Based on the weight of raw materials in the silos and the remaining feeding time, the silos that need to be replenished at the nearest moment are restricted to the target class. Based on the established mathematical model, exhaustively enumerate the combinations of feeding trolley and material handling machine and predict the silo status brought about by each different combination within a preset time period; Using the idea of ​​rolling optimization, the combination with the lowest operating cost among those that meet the constraints is used to determine the next task that requires scheduling the feeding trolley and the reclaimer. A mathematical model of the corresponding mixing silo is established based on the actual equipment status and parameters, including: set up x ( i , j ) indicates the first i The first task j Whether the material hopper is being filled is indicated by 0 (no filling) and 1 (filling). t s ( i ) indicates the first i The starting moment of adding ingredients to each task. t e ( i () indicates the end time of feeding, the first j The initial weight of each hopper is W (0, j ); Depend on Get the first k The start time of the first task's material feeding, the first j Weight of raw materials in each hopper ,in, Indicates the first k -1 task completion time, the first j The weight of raw materials in each silo Indicates the first k -1 The end time of the task addition and the first k The amount of material discharged from the silo during the time from the start of each task's material feeding; Depend on Get the first k At the end of the feeding process for each task, the weight of raw materials in the j-th hopper is... ,in, Indicates the first k The end time of the first task's refueling and the first k The amount of material discharged from the silo during the time from the start of each task's material feeding. Indicates the first k The end time of the first task's refueling and the first k The amount of material added to the silo during the time from the start of each task's material feeding. Indicates the first k The first task j Whether to add material to each hopper: 0 indicates no material is added, 1 indicates material is added; Depend on get t k At that moment, the i The location of the feeding cart ,in, Indicates the first i The initial position of the feeding cart. This indicates the speed at which the feeding trolley moves; Depend on get t k At that moment, the i The location of the material handling machine ,in, Indicates the first i The initial position of the reclaimer. v r This indicates the moving speed of the material handling machine; The time required to transport raw materials from the stockpile to the silo is calculated based on the type of material in each silo, the location of the corresponding stockpile for each type, and the belt speed. To prevent the hopper from becoming empty during the mixing process, the following constraints are set: , Indicates the number of tasks. Indicates the quantity of materials in the silo; The method further includes: With the optimization objective of minimizing the weighted sum of the movement costs of the material handling machine and the feeding trolley, a performance index function is established: ,in s r ( i , j ) and s c ( i , j ) respectively represent the first j No. 1 scraper feeder i Cost of moving a task ω 1 and ω 2 represents the weight of the operating costs of the two different types of equipment.

2. The scheduling method according to claim 1, characterized in that, The parameters of the mixing silo system include: the number of mixing silos and the upper limit of the raw material weight in the mixing silos. W max The lower limit of the weight of raw materials in the silo W min Distance between adjacent silos d Number of feeding carts n c The number of reclaimers corresponding to each feeding trolley m r The length of the conveyor belt between the silo and the corresponding material pile.

3. The scheduling method according to claim 2, characterized in that, The status information of the mixing silo system includes: the type of raw material in the silo, the weight W of the raw material in the silo, and the discharge flow rate of the silo. f - The speed of the feeding trolley v c The feeding flow rate of the feeding trolley f + The moving speed of the reclaimer v r The location of the feeding cart p c The location of the material handling machine p r .

4. The scheduling method according to claim 3, characterized in that, Based on the weight of raw materials in the silo and the remaining feeding time, silos are classified into several categories, including: The remaining time for raw materials is calculated based on the weight of raw materials in each bin and the feeding speed of the bins. The weight cutoff line for the raw materials in each bin is then set based on the remaining time. W L Divider line between remaining time t L The silos are divided into four categories. Category I represents low weight and low remaining time, meaning the weight of the raw material in the silo is less than [amount missing]. W L And the remaining time of raw materials is less than t L Category II indicates high weight and low remaining time, meaning the weight of the raw material in the silo is not less than [amount missing]. W L And the remaining time of raw materials is less than t L Category III indicates low weight and high remaining time, meaning the weight of the raw material in the silo is less than [a certain value]. W L And the remaining time of raw materials is not less than t L Category IV indicates high weight and high remaining time, meaning the weight of the raw material in the silo is not less than [amount missing]. W L And the remaining time of raw materials is not less than t L .

5. The scheduling method according to claim 4, characterized in that, Limit the stockpiles that need to be replenished in the near future to the target class, including: Set the scheduling period to T s Every time T s Update the system status and check if there is a Type I silo, which is a silo with a low weight of raw materials and a short remaining feeding time. If it does not exist, wait for the next scheduling. If it exists, proceed to the next step.

6. The scheduling method according to claim 5, characterized in that, Based on the established mathematical model, the combinations of feeding trolleys and reclaimers are exhaustively enumerated, and the resulting silo status for each different combination within a preset time period is predicted, including: Set the prediction task period to m Predict based on the current state m The status of each task cycle is used to select the feeding trolley and the reclaimer using an exhaustive method, feeding the current type I silo, and predicting the status of all silos in the task cycle based on the established mathematical model. m If a situation arises where the constraints are not met, then the combination of the feeding trolley and the reclaimer that does not meet the constraints is excluded. If the constraints are met, then the performance index of the feeding trolley and the reclaimer that meet the constraints is calculated and stored according to the performance index function.

7. The scheduling method according to claim 6, characterized in that, Using the concept of rolling optimization, the combination with the lowest operating cost among those satisfying the constraints is selected to determine the next task that requires scheduling the feeding trolley and the reclaimer, including: If none of the exhaustive combinations satisfy the constraints, then modify the dividing line for the raw material weight in the silo. W L Divider line between remaining time t L Then, the steps of exhaustively enumerating the combinations of feeding trolley and reclaimer based on the established mathematical model and predicting the silo state brought about by each different combination within a preset time period are repeated. If a combination of feeding trolley and reclaimer satisfies the constraints, then compare the performance index functions of all combinations that satisfy the constraints, and select the combination with the minimum weighted sum of the moving costs of the feeding trolley and reclaimer as the current combination. m The optimal solution within the period; Introducing the concept of rolling optimization, after obtaining a set of m After finding the optimal solution within each task cycle, in order to reduce model errors and the impact of subsequent state changes, only the combination of the feeding trolley and the reclaimer of the first task is selected as the scheduling of the trolley and the reclaimer for the next task. Repeat the above steps to complete the real-time scheduling of automatic material supply to the mixing silo until the mixing and stacking plan is completed.

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