Energy-optimized production planning and scheduling method

Abstract

The energy-optimized production planning and scheduling method of the present invention is implemented through the following steps: a). Setting the processing speed; b). Setting the processing time and energy consumption; c). Setting the machine state and assumptions; d). Establishing a processing energy consumption model; e). Establishing a no-load energy consumption model; f). Establishing a total energy consumption model; g). Establishing an energy-saving scheduling model, and finally, generating prediction data based on the established constraint functions and conditions. The energy-optimized production planning and scheduling method of the present invention is based on the established constraint functions and conditions including the machine processing time, the workpiece processing sequence, the machine is only allowed to process one workpiece at the same time, the total workload of the machine, the completion time of the machine, and the completion time of the process, with the goal of optimizing the total energy consumption and the maximum completion time of the workpiece, generating prediction data, and achieving optimal energy saving while ensuring that the workshop machine processes the workpiece.

Description

Technical Field

[0001] The present invention relates to a production planning and scheduling method, and more specifically, to an energy-optimized production planning and scheduling method. Background Art

[0002] The product manufacturing process is a nonlinear system. On the one hand, all components consume energy. The energy consumption of equipment is related to the startup time and equipment power. On the other hand, different equipment also affect each other due to process and environmental effects. For example, the processing workshop has strict requirements on the ambient temperature. To ensure the temperature balance, the air conditioning system needs to start and stop according to the terminal temperature. The temperature change of the entire space is affected by positive and negative feedback such as external radiation, indoor wind field, environmental insulation, air conditioning heat exchange, processing equipment, and heat emission of lighting equipment. Therefore, the entire manufacturing system needs to be optimized comprehensively from the perspective of energy consumption, carbon emissions, and production output.

[0003] Low-carbon manufacturing is one of the important means to achieve green manufacturing. Energy-saving scheduling can achieve the goals of energy saving, consumption reduction and cost reduction while improving production efficiency by formulating effective scheduling plans. In the production scheduling process, it is necessary to comprehensively consider speed selection and workpiece sorting. By reasonably selecting the speed of the machine and arranging the processing sequence of each workpiece, the goal of energy saving and consumption reduction can be achieved. Therefore, taking the job shop as the object, in order to achieve energy saving and consumption reduction, it is necessary to study an energy-saving scheduling optimization algorithm that simultaneously optimizes the maximum completion time of the workpiece and the total energy consumption. Summary of the invention

[0004] In order to overcome the disadvantages of the above technical problems, the present invention provides an energy-optimized production planning and scheduling method.

[0005] The energy-optimized production planning and scheduling method of the present invention is characterized by being implemented through the following steps:

[0006] a). Setting of processing speed: Assume that the number of machines involved in workpiece processing in the target workshop is m, and the total number of workpieces is n; the processing path of each workpiece is known in advance, and machine k has multiple optional processing speeds, that is, there is a discrete speed set as shown in formula (1):

[0007] V k = {v k1 ,v k2 ,…,v kdk},k=1,2,…,m (1)

[0008] Where dk is the total number of optional processing speeds of machine k;

[0009] b) Setting of processing time and energy consumption, assuming that the process of processing workpiece i on machine k is Q′ ik It means that the average processing time q of workpiece i on machine k is obtained from the optional processing speed of machine k. ik ; Then when the workpiece i moves at a speed v kl When processing on machine k, the actual processing time p ikl It is obtained by formula (2):

[0010]

[0011] Where l = 1, 2, ..., dk;

[0012] The workpiece i has a speed v kl The actual energy consumption during processing on machine k is E kl ;

[0013] c) Machine status and assumptions. To simplify the problem, only the processing and no-load states of the machine are considered. That is, once each machine is started at the initial moment, it is not allowed to stop until all the workpieces assigned to the machine are completed. In addition, the following assumptions are set:

[0014] All machines and workpieces are available at the initial time zero;

[0015] Each machine can only process one workpiece at a time;

[0016] Each workpiece can only be processed by one machine at a time;

[0017] The machining process of the workpiece is not allowed to be interrupted;

[0018] Each workpiece is independent of each other, and different processes of the same workpiece follow certain processing constraints;

[0019] Machine failures and machine adjustment time are not taken into account;

[0020] During the processing of any workpiece, the processing speed of the machine is not allowed to be adjusted;

[0021] d). Establish a processing energy consumption model. The total energy consumption is divided into processing energy consumption and no-load energy consumption. Processing energy consumption refers to the energy consumed by the machine to process the workpiece. The total processing energy consumption is calculated using formula (3):

[0022]

[0023] In the formula, x ikl is a 0-1 variable. If workpiece i is processed on machine k at speed level l, then x ikl =1, otherwise 0; E kl is the actual energy consumption of workpiece i when processed on machine k, pikl is the actual processing time of workpiece i on machine k;

[0024] e). Establish an no-load energy consumption model. No-load energy consumption refers to the energy consumed by the machine when it is running at no load during the time interval between adjacent workpieces. That is, when the previous workpiece has been processed and the next workpiece has not arrived, the energy consumed by the machine when it is running at no load. The total no-load energy consumption is calculated using formula (4):

[0025]

[0026] In the formula, SE K is the average no-load energy consumption of machine k per unit time; MC K is the total time taken to complete the task of machine k; W K is the total load of machine k, i.e., the sum of the processing time of all workpieces on machine k;

[0027] f). Establish a total energy consumption model. The total energy consumption is the sum of processing energy consumption and no-load energy consumption, which is calculated using formula (5):

[0028]

[0029] g). Establish an energy-saving scheduling model. The purpose of energy-saving scheduling is to improve production efficiency while achieving energy saving and consumption reduction. Therefore, a mathematical model is established with the goal of optimizing total energy consumption and maximum completion time of workpieces;

[0030] g-1). Establish the objective function of minimizing the total energy consumption of the workshop as shown in formula (6):

[0031] minF1=EC1+EC2 (6)

[0032] And, establish the function of minimizing the maximum completion time of the workpiece as shown in formula (7):

[0033] minF2=CT max (7)

[0034] g-2). Establish the machine processing time constraint, as shown in formula (8):

[0035]

[0036] Where i = 1, 2, ..., n; k = 1, 2, ..., m; n is the total number of machines in the workshop, m is the total number of workpieces; x ikl is a 0-1 variable. Formula (8) indicates that once the processing speed of the workpiece on the machine is determined, it is not allowed to be changed;

[0037] g-3). Establish the constraints of workpiece processing sequence, as shown in formula (9):

[0038]

[0039] Where, i = 1, 2, ..., n; k = 1, 2, ..., m; C ik Process Q' ik The completion time, p ik Process Q' ik The previous process in workpiece i; C Pik For p ik completion time;

[0040] g-4). Establish the constraint that any machine is only allowed to process one workpiece at the same time, as shown in formula (10):

[0041]

[0042] In the formula, i, j = 1, 2, ..., n; k = 1, 2, ..., m; Q is a set positive number; y ikl is a 0-1 variable;

[0043] g-5). Establish the total workload function of the machine, if as shown in formula (11):

[0044]

[0045] g-6). Establish the completion time function of the machine, if formula (12) is as shown:

[0046] MC k =max{C ik},i=1,2,…,n (12)

[0047] g-7). Establish the constraint function of the process completion time, if formula (13) is as follows:

[0048] C ik ≥0,i=1,2,…,n,k=1,2,…,m (13)

[0049] g-8). Establish a 0-1 variable constraint function, if shown in formulas (14) and (15);

[0050]

[0051] y ijk ∈{0,1},i,j=1,2,…,n,k=1,2,…,m (15)

[0052] Finally, based on the established constraint functions and conditions, prediction data is generated with the goal of optimizing the total energy consumption and the maximum completion time of the workpiece, so as to achieve optimal energy saving while ensuring that the workshop machines can process the workpieces.

[0053] The beneficial effects of the present invention are as follows: the energy-optimized production planning and scheduling method of the present invention first establishes a total energy consumption model formed by the sum of processing energy consumption and no-load energy consumption according to the number of machines m and the number of workpieces to be processed n in the workshop, and then establishes an objective function with minimum total energy consumption and minimum maximum completion time, and based on the established constraint functions and conditions including machine processing time, workpiece processing sequence, the machine is only allowed to process one workpiece at the same time, the total workload of the machine, the completion time of the machine, and the completion time of the process, with the goal of optimizing the total energy consumption and the maximum completion time of the workpiece, generates prediction data, and achieves optimal energy saving while ensuring that the workshop machines process the workpieces. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with the embodiments.

[0055] The energy-optimized production planning and scheduling method of the present invention is implemented by the following steps:

[0056] a). Setting of processing speed: Assume that the number of machines involved in workpiece processing in the target workshop is m, and the total number of workpieces is n; the processing path of each workpiece is known in advance, and machine k has multiple optional processing speeds, that is, there is a discrete speed set as shown in formula (1):

[0057] V k = {v k1 ,v k2 ,…,v kdk},k=1,2,…,m (1)

[0058] Where dk is the total number of optional processing speeds of machine k;

[0059] b) Setting of processing time and energy consumption, assuming that the process of processing workpiece i on machine k is Q′ ik It means that the average processing time q of workpiece i on machine k is obtained from the optional processing speed of machine k. ik ; Then when the workpiece i moves at a speed v kl When processing on machine k, the actual processing time p ikl It is obtained by formula (2):

[0060]

[0061] Where l = 1, 2, ..., dk;

[0062] The workpiece i has a speed v kl The actual energy consumption during processing on machine k is E kl ;

[0063] c) Machine status and assumptions. To simplify the problem, only the processing and no-load states of the machine are considered. That is, once each machine is started at the initial moment, it is not allowed to stop until all the workpieces assigned to the machine are completed. In addition, the following assumptions are set:

[0064] All machines and workpieces are available at the initial time zero;

[0065] Each machine can only process one workpiece at a time;

[0066] Each workpiece can only be processed by one machine at a time;

[0067] The machining process of the workpiece is not allowed to be interrupted;

[0068] Each workpiece is independent of each other, and different processes of the same workpiece follow certain processing constraints;

[0069] Machine failures and machine adjustment time are not taken into account;

[0070] During the processing of any workpiece, the processing speed of the machine is not allowed to be adjusted;

[0071] d). Establish a processing energy consumption model. The total energy consumption is divided into processing energy consumption and no-load energy consumption. Processing energy consumption refers to the energy consumed by the machine to process the workpiece. The total processing energy consumption is calculated using formula (3):

[0072]

[0073] In the formula, x ikl is a 0-1 variable. If workpiece i is processed on machine k at speed level l, then x ikl =1, otherwise 0; E kl is the actual energy consumption of workpiece i when processed on machine k, p ikl is the actual processing time of workpiece i on machine k;

[0074] e). Establish an no-load energy consumption model. No-load energy consumption refers to the energy consumed by the machine when it is running at no load during the time interval between adjacent workpieces. That is, when the previous workpiece has been processed and the next workpiece has not arrived, the energy consumed by the machine when it is running at no load. The total no-load energy consumption is calculated using formula (4):

[0075]

[0076] Where, SE K is the average no-load energy consumption of machine k per unit time; MC K is the total completion time of machine k; W K is the total load of machine k, i.e., the sum of the processing time of all workpieces on machine k;

[0077] f). Establish a total energy consumption model. The total energy consumption is the sum of processing energy consumption and no-load energy consumption, which is calculated using formula (5):

[0078]

[0079] g). Establish an energy-saving scheduling model. The purpose of energy-saving scheduling is to improve production efficiency while achieving energy saving and consumption reduction. Therefore, a mathematical model is established with the goal of optimizing total energy consumption and maximum completion time of workpieces;

[0080] g-1). Establish the objective function of minimizing the total energy consumption of the workshop as shown in formula (6):

[0081] minF1=EC1+EC2 (6)

[0082] And, establish the function of minimizing the maximum completion time of the workpiece as shown in formula (7):

[0083] minF2=CT max (7)

[0084] g-2). Establish the machine processing time constraint, as shown in formula (8):

[0085]

[0086] Where i = 1, 2, ..., n; k = 1, 2, ..., m; n is the total number of machines in the workshop, m is the total number of workpieces; x ikl is a 0-1 variable. Formula (8) indicates that once the processing speed of the workpiece on the machine is determined, it is not allowed to be changed;

[0087] g-3). Establish the constraints of workpiece processing sequence, as shown in formula (9):

[0088]

[0089] Where, i = 1, 2, ..., n; k = 1, 2, ..., m; C ik Process Q' ik The completion time, p ik Process Q' ik The previous process in workpiece i; C Pik For p ik completion time;

[0090] g-4). Establish the constraint that any machine is only allowed to process one workpiece at the same time, as shown in formula (10):

[0091]

[0092] In the formula, i, j = 1, 2, ..., n; k = 1, 2, ..., m; Q is a set positive number; y ikl is a 0-1 variable;

[0093] g-5). Establish the total workload function of the machine, if as shown in formula (11):

[0094]

[0095] g-6). Establish the completion time function of the machine, if formula (12) is as shown:

[0096] MC k =max{C ik},i=1,2,…,n (12)

[0097] g-7). Establish the constraint function of the process completion time, if formula (13) is as follows:

[0098] C ik ≥0,i=1,2,…,n,k=1,2,…,m (13)

[0099] g-8). Establish a 0-1 variable constraint function, if shown in formulas (14) and (15);

[0100]

[0101] y ijk ∈{0,1},i,j=1,2,…,n,k=1,2,…,m (15)

[0102] Finally, according to the established constraint functions and conditions, with the goal of optimizing total energy consumption and maximum completion time of workpieces, prediction data is generated to achieve optimal energy saving while ensuring that the workshop machines process workpieces. Through the goals and constraints, the system model is established and solved to obtain the corresponding production plan and energy efficiency quota satisfactory solution.

[0103] Traditional energy consumption analysis is a time-delayed passive response system, and the correlation between production scheduling-equipment startup-air conditioning energy consumption-comprehensive energy consumption is not considered during production planning and scheduling. Energy saving and consumption reduction are limited to the reduction of leakage, and the laissez-faire energy scheduling method cannot meet the requirements of energy saving and consumption reduction in the operation mechanism of the manufacturing system. At the same time, since the machining workshops of the manufacturing system mostly adopt a two- or three-shift work system, the traditional energy saving and cost reduction method of peak shaving and valley filling is actually ineffective. Therefore, under the conditions of the established process method, the only way to achieve system energy saving is to find the optimal combination parameters through the comprehensive optimization of multiple factors in the microenvironment.

[0104] Due to the universality of deviations from production plans, and the fact that the prediction accuracy of energy and loads gradually decreases with the expansion of the time scale, simply solving the problem once according to the daily production plan cannot achieve the dynamic optimality of the system. Therefore, it is necessary to build an optimal scheduling mechanism with multi-time scale coordination, eliminate the impact of prediction errors and random fluctuations step by step through multi-time scale coordination, and optimize the parameters dynamically at different time scales to achieve the comprehensive optimality of the system.

[0105] In terms of time scale, dispatching involves coordinated optimization of multiple time scales such as annual, monthly, daily, and hourly. Load forecasting can reduce the error of dispatch optimization schemes and improve dispatch accuracy. Accordingly, prediction systems based on different time scales also need to include multi-time scale predictions. The system's optimized dispatching and operation control are divided into daily, hourly, and minute levels, corresponding to daily planned dispatching, intraday rolling optimization, and real-time correction, respectively.

[0106] The dispatch center generates forecast data for the 24 hours before the day based on the daily demand plan and energy consumption quota of the production plan, and uses an optimization model to generate a day-ahead dispatch plan. Based on the day-ahead forecast data, the center performs rolling optimization to correct the errors in the day-ahead plan according to the latest load and production performance of the day to optimize the dispatch plan. Based on the rolling forecast results, the center implements forecasting and monitoring to solve the control parameter data for the next moment.

Claims

1. A production planning and scheduling method for energy optimization, characterized in that: This is accomplished by following these steps: a). Setting of processing speed: Assume that the number of machines involved in workpiece processing in the target workshop is m, and the total number of workpieces is n; the processing path of each workpiece is known in advance, and machine k has multiple optional processing speeds, that is, there is a discrete speed set as shown in formula (1): V k ={v k1 ,v k2 ,…,v kdk },k=1,2,…,m (1) Where dk is the total number of optional processing speeds of machine k; b). Setting of processing time and energy consumption. Assume that the process of processing workpiece i on machine k is Q i ' k It means that the average processing time q of workpiece i on machine k is obtained from the optional processing speed of machine k. ik ; Then when the workpiece i moves at a speed v kl When processing on machine k, the actual processing time p ikl It is obtained by formula (2): Where l = 1, 2, ..., dk; The workpiece i has a speed v kl The actual energy consumption during processing on machine k is E kl ; c) Machine status and assumptions. To simplify the problem, only the processing and no-load states of the machine are considered. That is, once each machine is started at the initial moment, it is not allowed to stop until all the workpieces assigned to the machine are completed. In addition, the following assumptions are set: All machines and workpieces are available at the initial time zero; Each machine can only process one workpiece at a time; Each workpiece can only be processed by one machine at a time; The machining process of the workpiece is not allowed to be interrupted; Each workpiece is independent of each other, and different processes of the same workpiece follow certain processing constraints; Machine failures and machine adjustment time are not taken into account; During the processing of any workpiece, the processing speed of the machine is not allowed to be adjusted; d). Establish a processing energy consumption model. The total energy consumption is divided into processing energy consumption and no-load energy consumption. Processing energy consumption refers to the energy consumed by the machine to process the workpiece. The total processing energy consumption is calculated using formula (3): In the formula, x ikl is a 0-1 variable. If workpiece i is processed on machine k at speed level l, then x ikl =1, otherwise 0; E kl is the actual energy consumption of workpiece i when processed on machine k, p ikl is the actual processing time of workpiece i on machine k; e). Establish an no-load energy consumption model. No-load energy consumption refers to the energy consumed by the machine when it is running at no load during the time interval between adjacent workpieces. That is, when the previous workpiece has been processed and the next workpiece has not arrived, the energy consumed by the machine when it is running at no load. The total no-load energy consumption is calculated using formula (4): In the formula, SE K is the average no-load energy consumption of machine k per unit time; MC K is the total time taken to complete the task of machine k; W K is the total load of machine k, i.e., the sum of the processing time of all workpieces on machine k; f). Establish a total energy consumption model. The total energy consumption is the sum of processing energy consumption and no-load energy consumption, which is calculated using formula (5): g). Establish an energy-saving scheduling model. The purpose of energy-saving scheduling is to improve production efficiency while achieving energy saving and consumption reduction. Therefore, a mathematical model is established with the goal of optimizing total energy consumption and maximum completion time of workpieces; g-1). Establish the objective function of minimizing the total energy consumption of the workshop as shown in formula (6): minF1=EC1+EC2 (6) And, establish the function of minimizing the maximum completion time of the workpiece as shown in formula (7): minF2=CT max (7) g-2). Establish the machine processing time constraint, as shown in formula (8): Where i = 1, 2, ..., n; k = 1, 2, ..., m; n is the total number of machines in the workshop, m is the total number of workpieces; x ikl is a 0-1 variable. Formula (8) indicates that once the processing speed of the workpiece on the machine is determined, it is not allowed to be changed; g-3). Establish the constraints of workpiece processing sequence, as shown in formula (9): Where, i = 1, 2, ..., n; k = 1, 2, ..., m; C ik Process Q' ik The completion time, P ik Process Q' ik The previous process in workpiece i; P ik completion time; g-4). Establish the constraint that any machine is only allowed to process one workpiece at the same time, as shown in formula (10): In the formula, i, j = 1, 2, ..., n; k = 1, 2, ..., m; Q is a set positive number; y ijk is a 0-1 variable; g-5). Establish the total workload function of the machine, if as shown in formula (11): g-6). Establish the completion time function of the machine, if formula (12) is as shown: MC k =max{C ik },i=1,2,…,n (12) g-7). Establish the constraint function of the process completion time, if formula (13) is as follows: C ik ≥0,i=1,2,…,n,k=1,2,…,m (13) g-8). Establish a 0-1 variable constraint function, if shown in formulas (14) and (15); y ijk ∈{0,1},i,j=1,2,…,n,k=1,2,…,m (15) Finally, based on the established constraint functions and conditions, prediction data is generated with the goal of optimizing the total energy consumption and the maximum completion time of the workpiece, so as to achieve optimal energy saving while ensuring that the workshop machines can process the workpieces.

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