Production and transportation cooperative scheduling method for prefabricated building prefabricated parts and related device

Abstract

The invention provides an assembly type building prefabricated part production and transportation cooperative scheduling method and a related device, and belongs to the technical field of assembly type building prefabricated part production and transportation cooperative scheduling. The method comprises the following steps: constructing a multi-objective optimization model of total scheduling cost, maximum flow time and energy consumption; setting constraint conditions of the multi-objective optimization model; improving the sea gull optimization algorithm to obtain an improved sea gull optimization algorithm; based on the set constraint conditions of the multi-objective optimization model, an improved sea gull optimization algorithm is adopted to solve the objective function of the established multi-objective optimization model, and an optimal result is obtained; and according to the obtained optimal result, carrying out production and transportation cooperative scheduling on the fabricated building prefabricated components to obtain a production and transportation cooperative scheduling result. According to the invention, the problem of low scheduling accuracy caused by the fact that only a single factor is considered in an established single-target model in the prior art is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of coordinated scheduling of precast components production and transportation in prefabricated buildings, and specifically relates to a method and related device for coordinated scheduling of precast components production and transportation in prefabricated buildings. Background Art

[0002] Currently, in the existing technologies for different scheduling problems of precast components in prefabricated buildings, when constructing single-objective or multi-objective optimization models, most of them use intelligent optimization algorithms for solution. Although it has laid a solid theoretical foundation for future research, the following problems exist in the existing technologies:

[0003] 1) Under the call of the policy of "vigorously developing prefabricated buildings", few researchers present the precast components and the production scheduling optimization problem at the same time, and most of them are optimized under ideal mathematical models, which do not conform well to the actual production and transportation process;

[0004] 2) In the existing technologies, the research on coordinated scheduling of production and transportation with time windows is relatively less;

[0005] 3) In the existing technologies, most of them solve the coordinated scheduling problem of production and transportation by establishing a single-objective model. The single-objective model usually can only optimize one or a few of the objectives, and it is difficult to take into account all relevant factors. In the actual production and transportation process, there may be conflicts or trade-off relationships between some objectives. Considering only a single factor will lead to low accuracy of scheduling. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and related device for coordinated scheduling of precast components production and transportation in prefabricated buildings, which are used to solve the problem that the single-objective model established in the existing technology only considers a single factor, resulting in low accuracy of scheduling.

[0007] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0008] In the first aspect, the present invention provides a method for coordinated scheduling of precast components production and transportation in prefabricated buildings, including the following steps:

[0009] Construct a multi-objective optimization model of total scheduling cost, maximum flow time and energy consumption, and the multi-objective optimization model includes a minimum total scheduling cost objective function, a minimum maximum flow time objective function and a minimum energy consumption objective function;

[0010] Set the constraint conditions of the multi-objective optimization model;

[0011] Improve the seagull optimization algorithm to obtain an improved seagull optimization algorithm;

[0012] Based on the constraint conditions of the established multi-objective optimization model, an improved seagull optimization algorithm is used to solve the objective function of the established multi-objective optimization model, and the optimal result is obtained;

[0013] According to the obtained optimal result, the production and transportation collaborative scheduling of precast components of the prefabricated building is carried out, and the production and transportation collaborative scheduling result is obtained.

[0014] A further improvement of the present invention lies in that the expression of the objective function for minimizing the total scheduling cost is:

[0015]

[0016] where, minZ 1 is the objective function for minimizing the total scheduling cost, c ijk is the unit time processing cost of component i in process k of workshop j, x ijkt is whether component i is the unit time processing cost in process k of workshop j at time t (1 means yes, 0 means no), d jj' is the transportation distance from workshop j to construction site j′, y ijj'vt is whether vehicle v arrives at construction site j′ from workshop j at time t (1 means yes, 0 means no), h i is the unit production cost of component i, w ijkt is the completion time of component i in process k of workshop j, D i is the demand of component i, α is the production cost weight, θ is the delivery delay penalty coefficient, and I, J, K, and T are respectively the set of precast component types, the set of production workshops, the set of processes (N1, N2, N3, N4, N5, N6, N7, N8, and N9), and the set of time periods;

[0017] The expression of the objective function for minimizing the maximum flow time is:

[0018]

[0019] where, minZ 2 is the objective function for minimizing the maximum flow time, x ijkt is whether component i is the unit time processing cost in process k of workshop j at time t (1 means yes, 0 means no), p k,i is the processing time of component i in process k;;

[0020] The expression of the objective function for minimizing energy consumption is:

[0021]

[0022] where, minZ 3 is the objective function for minimizing energy consumption, x ijkt$D_{ijk}$ is the unit time processing cost of component $i$ at process $k$ in workshop $j$ at time $t$ (1 means yes, 0 means no). i $D_i$ is the demand quantity of component $i$. r $PD$ is the machine energy consumption during the actual processing of components. r $PE$ is the energy consumption of the machine in the no-load state.

[0023] A further improvement of the present invention lies in that the constraint conditions of the multi-objective optimization model include demand satisfaction constraint, vehicle load constraint, vehicle flow balance constraint, batch transportation constraint, transportation-vehicle association constraint, time window constraint, process constraint, working time constraint, and variable value constraint.

[0024] A further improvement of the present invention lies in that the calculation formula of the demand satisfaction constraint is as follows:

[0025]

[0026] where $x_{ijk}$ ij(N9)t is the transportation volume of component $i$, and $D_i$ i is the demand quantity of component $i$;

[0027] The calculation formula of the vehicle load constraint is as follows:

[0028]

[0029] where $q_{iv}$ iv is the quantity of component $i$ transported by vehicle $v$, and $g_v$ v is the load capacity of vehicle $v$;

[0030] The calculation formula of the vehicle flow balance constraint is as follows:

[0031]

[0032] where $z_{vjj't}$ ijj'vt is whether vehicle $v$ arrives at the construction site $j'$ from workshop $j$ at time $t$ (1 means yes, 0 means no), and $s_{jj'}$ jj' is the transportation time from workshop $j$ to the construction site $j'$;

[0033] The calculation formula of the batch transportation constraint is as follows:

[0034]

[0035] where $q_{iv}$ iv is the quantity of component $i$ transported by vehicle $v$, and $x_{ijk}$ ijkt is the unit time processing cost of component $i$ at process $k$ in workshop $j$ at time $t$ (1 means yes, 0 means no);

[0036] The calculation formula of the transportation-vehicle association constraint is as follows:

[0037]

[0038] where y ijj′vt indicates whether vehicle v transports from workshop j to construction site j' at time t (1 means yes, 0 means no), and z jj'vt' indicates whether vehicle v arrives at construction site j' from workshop j at time t (1 means yes, 0 means no);

[0039] The calculation formula for the time window constraint is as follows:

[0040]

[0041] where E ij is the earliest time for precast component i to be shipped from workshop j, and l ijj'vt indicates whether component i is on the way from workshop j to construction site j' at time t (1 means yes, 0 means no), and L ij is the latest time for the precast component to reach the construction site;

[0042] The calculation formula for the process constraint is as follows:

[0043]

[0044] where U kt indicates whether process k can be interrupted at time t (1 means yes, 0 means no), and x ijkt indicates whether component i is the unit time processing cost of process k in workshop j at time t (1 means yes, 0 means no);

[0045] The calculation formula for the working time constraint is as follows:

[0046]

[0047] where H jt indicates whether workshop j is in normal working hours at time t, and x ijkt indicates whether component i is the unit time processing cost of process k in workshop j at time t (1 means yes, 0 means no), and w ijkt is the completion time of component i for process k in workshop j, and p ijk is the processing time of component i for process k in workshop j;

[0048] The calculation formula for the variable value constraint is as follows:

[0049]

[0050] where x ijkt indicates whether component i is the unit time processing cost of process k in workshop j at time t (1 means yes, 0 means no), and yijj′vt Let \(z\) be whether vehicle \(v\) is transported from workshop \(j\) to construction site \(j'\) at time \(t\) (1 means yes, 0 means no). ijj'vt Let \(l\) be whether vehicle \(v\) arrives at construction site \(j'\) from workshop \(j\) at time \(t\) (1 means yes, 0 means no). ijj'vt Let \(I\) be whether component \(i\) is on the way from workshop \(j\) to construction site \(j'\) at time \(t\) (1 means yes, 0 means no). \(I\), \(J\), \(K\), \(V\), and \(T\) are respectively the set of precast component types, the set of production workshops, the set of processes, the set of transport vehicles, and the set of time periods.

[0051] A further improvement of the present invention lies in that, in the step of improving the seagull optimization algorithm to obtain the improved seagull optimization algorithm, the specific content of improving the seagull optimization algorithm includes:

[0052] a) Initialize the position of the seagull population using the chaotic mapping strategy;

[0053] b) Combine the multi-objective particle swarm optimization algorithm with the seagull optimization algorithm;

[0054] c) Calculate the new position of the seagull using an additional variable with a cosine change.

[0055] A further improvement of the present invention lies in that, in the content b) of the improvement, the specific content of combining the multi-objective particle swarm optimization algorithm with the seagull optimization algorithm includes:

[0056] A. Replace the particles in the multi-objective particle swarm optimization algorithm with seagulls in the seagull optimization algorithm;

[0057] B. Replace the movement of the particles in the multi-objective particle swarm optimization algorithm with the long-distance migration behavior and spiral attack behavior of seagulls during hunting in the seagull optimization algorithm.

[0058] A further improvement of the present invention lies in that, in the content c) of the improvement, the calculation formula of the additional variable is:

[0059]

[0060] where \(A\) is the additional variable, \(f\) c is the control factor, \(f\) c \( = 2\), \(x\) is the current iteration number executed by the improved seagull optimization algorithm, and \(MAX\) iteration is the maximum iteration number executed by the improved seagull optimization algorithm.

[0061] In the second aspect, the present invention provides a collaborative scheduling system for the production and transportation of precast components of an assembled building, including a multi-objective optimization model construction module, a constraint condition setting module, a seagull optimization algorithm improvement module, a multi-objective optimization model objective function solving module, and a production and transportation collaborative scheduling module;

[0062] The multi-objective optimization model construction module is used to construct a multi-objective optimization model for the total scheduling cost, the maximum flow time, and the energy consumption. The multi-objective optimization model includes an objective function for minimizing the total scheduling cost, an objective function for minimizing the maximum flow time, and an objective function for minimizing the energy consumption;

[0063] The constraint condition setting module is used to set the constraint conditions of the multi-objective optimization model;

[0064] The seagull optimization algorithm improvement module is used to improve the seagull optimization algorithm to obtain an improved seagull optimization algorithm;

[0065] The multi-objective optimization model objective function solving module is used to solve the objective function of the established multi-objective optimization model by using the improved seagull optimization algorithm based on the set constraint conditions of the multi-objective optimization model to obtain the optimal result;

[0066] The production and transportation collaborative scheduling module is used to perform production and transportation collaborative scheduling on the precast components of the prefabricated building according to the obtained optimal result to obtain the production and transportation collaborative scheduling result.

[0067] In a third aspect, the present invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above-introduced production and transportation collaborative scheduling method for precast components of prefabricated buildings are implemented.

[0068] In a fourth aspect, the present invention provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-introduced production and transportation collaborative scheduling method for precast components of prefabricated buildings are implemented.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention belongs to an improved invention. Compared with the existing production and transportation collaborative scheduling methods for precast components of prefabricated buildings, on the one hand, the present invention improves the seagull optimization algorithm to obtain an improved seagull optimization algorithm. On the other hand, the present invention uses the improved seagull optimization algorithm to solve the objective function of the established multi-objective optimization model. Compared with other complex optimization algorithms, the seagull optimization algorithm has relatively simple parameter settings, and users do not need to perform cumbersome parameter adjustments. The seagull optimization algorithm also has strong search capabilities when solving complex optimization problems. Moreover, when establishing the optimization model, the present invention comprehensively considers three factors: the total scheduling cost, the maximum flow time, and the energy consumption, which helps to maximize the overall benefit, thereby effectively solving the problem that the single-objective model established in the prior art only considers a single factor, resulting in low scheduling accuracy.

[0071] Furthermore, the present invention also discloses that the constraint conditions of the multi-objective optimization model include demand satisfaction constraint, vehicle load constraint, vehicle flow balance constraint, batch transportation constraint, transportation-vehicle association constraint, time window constraint, process constraint, working time constraint, and variable value constraint. Considering various constraint conditions comprehensively can more fully reflect the actual situation of the scheduling problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 is a flowchart of the production and transportation collaborative scheduling method for precast components of prefabricated buildings according to the present invention;

[0073] Figure 2 is a schematic diagram of the production and transportation collaborative scheduling system for precast components of prefabricated buildings according to the present invention;

[0074] Figure 3 is a conceptual model diagram of the seagull migration and attack behaviors of the traditional seagull optimization algorithm according to the present invention;

[0075] Figure 4 is a trend graph of the non-linear additional variable changing with the number of iterations according to the present invention;

[0076] Figure 5 is a schematic structural diagram of the electronic device according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] To further understand the content of the present invention, the following provides a detailed description of the present invention with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention rather than limiting it.

[0078] The production and transportation collaborative scheduling method for precast components of prefabricated buildings proposed by the present invention improves the seagull optimization algorithm to obtain an improved seagull optimization algorithm. Based on the constraint conditions of the set multi-objective optimization model, the improved seagull optimization algorithm is used to solve the objective function of the established multi-objective optimization model to obtain the optimal result. According to the obtained optimal result, the production and transportation collaborative scheduling of precast components of prefabricated buildings is carried out to obtain the production and transportation collaborative scheduling result. Compared with the prior art, the present invention effectively solves the problem that the single-objective model established in the prior art only considers a single factor, resulting in low scheduling accuracy.

[0079] Example 1:

[0080] The flowchart of the production and transportation collaborative scheduling method for precast components of prefabricated buildings according to the present invention is as Figure 1 shown. The production and transportation collaborative scheduling method for precast components of prefabricated buildings according to the present invention includes the following steps:

[0081] S1. Build a multi-objective optimization model for the total scheduling cost, the maximum flow time, and the energy consumption. The multi-objective optimization model includes an objective function for minimizing the total scheduling cost, an objective function for minimizing the maximum flow time, and an objective function for minimizing the energy consumption.

[0082] S2. Set the constraint conditions of the multi-objective optimization model.

[0083] S3. Improve the Seagull Optimization Algorithm to obtain an improved Seagull Optimization Algorithm.

[0084] S4. Based on the set constraint conditions of the multi-objective optimization model, use the improved Seagull Optimization Algorithm to solve the objective functions of the established multi-objective optimization model to obtain the optimal results.

[0085] S5. Perform production and transportation collaborative scheduling on the precast components of the prefabricated building according to the obtained optimal results to obtain the production and transportation collaborative scheduling results.

[0086] Example 2:

[0087] The schematic diagram of the production and transportation collaborative scheduling system for the precast components of the prefabricated building of the present invention is as Figure 2 shown. The production and transportation collaborative scheduling system for the precast components of the prefabricated building of the present invention includes a multi-objective optimization model construction module, a constraint condition setting module, a Seagull Optimization Algorithm improvement module, a multi-objective optimization model objective function solving module, and a production and transportation collaborative scheduling module.

[0088] Among them, the multi-objective optimization model construction module is used to build a multi-objective optimization model for the total scheduling cost, the maximum flow time, and the energy consumption. The multi-objective optimization model includes an objective function for minimizing the total scheduling cost, an objective function for minimizing the maximum flow time, and an objective function for minimizing the energy consumption.

[0089] The constraint condition setting module is used to set the constraint conditions of the multi-objective optimization model.

[0090] The Seagull Optimization Algorithm improvement module is used to improve the Seagull Optimization Algorithm to obtain an improved Seagull Optimization Algorithm.

[0091] The multi-objective optimization model objective function solving module is used to, based on the set constraint conditions of the multi-objective optimization model, use the improved Seagull Optimization Algorithm to solve the objective functions of the established multi-objective optimization model to obtain the optimal results.

[0092] The production and transportation collaborative scheduling module is used to perform production and transportation collaborative scheduling on the precast components of the prefabricated building according to the obtained optimal results to obtain the production and transportation collaborative scheduling results.

[0093] Example 3:

[0094] S1. Build a multi-objective optimization model for the total scheduling cost, the maximum flow time, and the energy consumption. The multi-objective optimization model includes an objective function for minimizing the total scheduling cost, an objective function for minimizing the maximum flow time, and an objective function for minimizing the energy consumption.

[0095] Among them, the expression of the objective function for minimizing the total scheduling cost is:

[0096]

[0097] Among them, minZ 1 is the objective function for minimizing the total scheduling cost, c ijk is the unit time processing cost of component i in process k of workshop j, x ijkt is whether component i is processed at unit time in process k of workshop j at time t (1 means yes, 0 means no), d jj' is the transportation distance from workshop j to construction site j′, y ijj'vt is whether vehicle v arrives at construction site j′ from workshop j at time t (1 means yes, 0 means no), h i is the unit production cost of component i, w ijkt is the completion time of component i in process k of workshop j, D i is the demand for component i, α is the production cost weight, θ is the delivery delay penalty coefficient, and I, J, K, and T are the sets of precast component types, production workshops, processes (N1, N2, N3, N4, N5, N6, N7, N8, and N9), and time periods, respectively.

[0098] The expression of the objective function for minimizing the maximum flow time is:

[0099]

[0100] Among them, minZ 2 is the objective function for minimizing the maximum flow time, x ijkt is whether component i is processed at unit time in process k of workshop j at time t (1 means yes, 0 means no), p k,i is the processing time of component i in process k.

[0101] The expression of the objective function for minimizing the energy consumption is:

[0102]

[0103] Among them, minZ 3 is the objective function for minimizing the energy consumption, x ijkt is whether component i is processed at unit time in process k of workshop j at time t (1 means yes, 0 means no), D i is the demand for component i, pr is the machine energy consumption during the actual processing of components, PD r is the energy consumption of the machine in the no-load state.

[0104] S2. Set the constraint conditions of the multi-objective optimization model.

[0105] The constraint conditions of the multi-objective optimization model in this step include demand satisfaction constraint, vehicle load constraint, vehicle flow balance constraint, batch transportation constraint, transportation-vehicle association constraint, time window constraint, process constraint, working time constraint, and variable value constraint.

[0106] The calculation formula for the demand satisfaction constraint is:

[0107]

[0108] where x ij(N9)t is the transportation volume of components, D i is the demand for component i.

[0109] The calculation formula for the vehicle load constraint is:

[0110]

[0111] where q iv is the number of components i transported by vehicle v, g v is the load capacity of vehicle v.

[0112] The calculation formula for the vehicle flow balance constraint is:

[0113]

[0114] where z ijj'vt indicates whether vehicle v arrives at the construction site j' from workshop j at time t (1 means yes, 0 means no), s jj' is the transportation time from workshop j to the construction site j'.

[0115] The calculation formula for the batch transportation constraint is:

[0116]

[0117] where q iv is the number of components i transported by vehicle v, x ijkt indicates whether component i is the unit time processing cost of process k in workshop j at time t (1 means yes, 0 means no).

[0118] The calculation formula for the transportation-vehicle association constraint is:

[0119]

[0120] where yijj′vt Whether vehicle v is transported from workshop j to the construction site j' at time t (1 means yes, 0 means no), z jj'vt' Whether vehicle v arrives at the construction site j' from workshop j at time t (1 means yes, 0 means no).

[0121] The calculation formula for the time window constraint is as follows:

[0122]

[0123] Among them, E ij is the earliest time for precast component i to be shipped from workshop j, l ijj'vt indicates whether component i is on the way from workshop j to the construction site j' at time t (1 means yes, 0 means no), L ij is the latest time for the precast component to reach the construction site.

[0124] The calculation formula for the process constraint is as follows:

[0125]

[0126] Among them, U kt indicates whether process k can be interrupted at time t (1 means yes, 0 means no), x ijkt indicates whether component i is the unit time processing cost of process k in workshop j at time t (1 means yes, 0 means no).

[0127] The calculation formula for the working time constraint is as follows:

[0128]

[0129] Among them, H jt indicates whether workshop j is in normal working hours at time t, x ijkt indicates whether component i is the unit time processing cost of process k in workshop j at time t (1 means yes, 0 means no), w ijkt is the completion time of component i in process k in workshop j, p ijk is the processing time of component i in process k in workshop j.

[0130] The calculation formula for the variable value constraint is as follows:

[0131]

[0132] Among them, x ijkt indicates whether component i is the unit time processing cost of process k in workshop j at time t (1 means yes, 0 means no), y ijj′vt indicates whether vehicle v is transported from workshop j to the construction site j' at time t (1 means yes, 0 means no), z ijj'vtWhether vehicle v arrives at construction site j' from workshop j at time t (1 means yes, 0 means no), l ijj'vt Whether component i is on the way from workshop j to construction site j’ at time t (1 means yes, 0 means no), I, J, K, V, and T are respectively the set of precast component types, the set of production workshops, the set of processes, the set of transport vehicles, and the set of time periods.

[0133] S3. Improve the seagull optimization algorithm to obtain an improved seagull optimization algorithm.

[0134] The specific content of improving the seagull optimization algorithm in this step includes:

[0135] a) Initialize the position of the seagull population using the chaotic mapping strategy;

[0136] b) Combine the multi-objective particle swarm optimization algorithm with the seagull optimization algorithm;

[0137] c) Calculate the new position of the seagull using an additional variable with cosine variation.

[0138] The traditional seagull optimization algorithm is described in detail below:

[0139] Seagulls are social animals. As the seasons change, the population will carry out large-scale long-distance migrations, from one place to another, in search of the most abundant food. During the migration process, seagull individuals will fly at different positions to avoid colliding with each other. In a group, each seagull individual will move towards the position with the most abundant food and continuously change its position during the movement. Seagulls will attack prey on the way to obtain more food. The two most important characteristics in the seagull optimization algorithm are the long-distance migration of seagulls and the behavior of spiral attacking prey. The conceptual models of these behaviors are as Figure 3 shown, corresponding to the global exploration and local exploitation parts of the seagull optimization algorithm respectively, and can be described by the following mathematical models.

[0140] (1) Long-distance migration

[0141] Long-distance migration is a mathematical model designed by imitating the migration behavior of seagull groups, including three constraint conditions: avoiding seagull individuals from colliding, approaching the best seagull, and moving towards the optimal seagull position.

[0142] C s = A × P s (x)

[0143]

[0144] where x is the current iteration number, f c is the control factor, f c = 2, I maxis the maximum number of iterations, C s is the new position that does not conflict with other seagull individuals, P s (x) is the current position of the seagull, and A is a non - linear additional variable. The seagull's position is updated through the non - linear additional variable A to avoid colliding with other seagulls. A decreases from f c to 0. After fc = 2 to avoid collisions, the seagull will move closer to the best individual position.

[0145] M S = B[P bs (x)-P s (x)]

[0146] B = 2×A 2 ×rand

[0147] where M S is the direction of the current seagull individual's position relative to the optimal seagull individual, B is a random number, P bs (x) is the optimal seagull individual's position, and rand is a random number in (0, 1). If the seagull moves in the direction of the optimal individual, the formula for the new position D s is:

[0148] D s = |C s + M s |

[0149] where D s is the distance between the current seagull individual and the optimal seagull individual (i.e., the optimal seagull individual with a smaller fitness value).

[0150] (2) Spiral attack on prey

[0151] After reaching the new position, the seagull uses its own advantages to continuously change the attack angle and speed to attack the prey, making an attack behavior to obtain more resources beneficial to the survival of the population. In three - dimensional space:

[0152] x' = r×cosθ

[0153] y' = r×sinθ

[0154] z' = r×k

[0155] r = u×e θ

[0156] P s (x) = D s ×x'×y'×z' + P bs (x)

[0157] Among them, r is the radius of each turn of the helix, θ is the attack angle, θ ∈ [0, 2π], both u and k are constant 1, and x′, y′, and z′ are the three components of the spiral attack of the seagull in the three-dimensional spaces of X, Y, and Z respectively.

[0158] The following details the improvements a), b), and c) to the seagull optimization algorithm:

[0159] a) Initialize the seagull population position using the chaotic mapping strategy

[0160] The traditional seagull optimization algorithm uses a random initialization strategy, randomly generating seagull positions within the set upper and lower bounds. This initialization method has a strong blindness in the initial points and cannot cover the better points, which will lead to the premature convergence of the algorithm and reduce the optimization ability of the algorithm.

[0161] Due to its properties such as randomness and ergodicity, chaotic mapping is used in the initialization of the population of swarm intelligence algorithms. In this step, the Tent chaotic mapping strategy is used to initialize the seagull population position.

[0162] Specifically, a completely random variable is randomly added during initialization to perturb the Tent chaotic mapping to achieve the initialization of the population. Through the Tent chaotic mapping, the types of the initial population can be enriched, the probability of the seagull optimization algorithm falling into local optima can be reduced, and the initial population can be screened through the Tent chaotic mapping to retain high-quality population individuals, improving the initial convergence iteration speed and quality of the seagull optimization algorithm.

[0163] The functional expression of the Tent chaotic mapping to generate a chaotic particle sequence is:

[0164]

[0165] Among them, y ij is the chaotic variable, y ij ∈(0, 1), i is the individual serial number in the population, j is the chaotic variable serial number, is the chaotic parameter, The larger the Iq value, the better the chaos. According to the functional expression of the Tent chaotic mapping to generate a chaotic particle sequence, d initial values can be selected to obtain d chaotic variables y Iq , and they are mapped into the search space through the following formula to obtain the initialized population, that is:

[0166] N Iq = Ib I +(ub I -Ib I )y Iq

[0167] Among them, Ib I and ubI For the chaotic variable y Iq The upper and lower bounds of the search.

[0168] b) Combine the multi-objective particle swarm optimization algorithm with the seagull optimization algorithm

[0169] The specific content of combining the multi-objective particle swarm optimization algorithm with the seagull optimization algorithm in the improvement content b) includes:

[0170] A. Replace the particles in the multi-objective particle swarm optimization algorithm with seagulls in the seagull optimization algorithm;

[0171] B. Replace the movement of the particles in the multi-objective particle swarm optimization algorithm with the long-distance migration behavior and spiral attack behavior of seagulls during hunting in the seagull optimization algorithm.

[0172] Specifically, the improved seagull optimization algorithm introduces an external archive Archive to store the optimal individuals in each iteration on the basis of the traditional seagull optimization algorithm, and continuously updates the internal population of Archive according to the dominance relationship. After each seagull updates its position, judge the dominance of the new individual. If it is dominated by the individuals in Archive, it is better to join Archive. If the new individual dominates one or more individuals in Archive, the new individual replaces the dominated individuals in Archive. If the new individual and all individuals in the Archive population do not dominate each other, add the individual to Archive. During each update process, the number of individuals in Archive will become more and more, but Archive is limited. Therefore, it is necessary to refer to the concept of crowding degree in NAGA-II to prune the Archive population. Calculate the objective function values of all individuals in Archive and calculate the crowding degree of each individual. When the Archive population reaches the limit value, randomly eliminate the individuals with small crowding degree to form the final Archive population.

[0173] The calculation formula for the crowding degree of each individual is:

[0174]

[0175] where D i is the crowding degree of each individual, f i max and f i min are respectively the maximum and minimum values of the individual objective function, f i (j + 1) and f i (j - 1) are the i-th objective function values of the two individuals adjacent to individual j.

[0176] c) Calculate the new position of the seagull using an additional variable with cosine variation

[0177] Long-distance migration is an important part of the Seagull Optimization Algorithm (SOA). In the traditional SOA, long-distance migration behavior updates the positions of seagulls through additional variables to avoid collisions with other seagulls. Selecting larger additional variables can ensure the global optimization ability of the SOA. Selecting smaller additional variables can also improve the convergence accuracy of the SOA, endowing it with strong local search ability.

[0178] In the traditional SOA, the additional variable decreases linearly, which has defects. Therefore, in this embodiment, the additional variable with cosine variation is used to calculate the new position of the seagull. The calculation formula for the additional variable with cosine variation is:

[0179]

[0180] where A is the additional variable, f c is the control factor, f c = 2, x is the current iteration number of the improved SOA, and MAX iteration is the maximum iteration number of the improved SOA.

[0181] The variation trend of the non-linear additional variable with the iteration number is as Figure 4 shown. It can be seen from Figure 4 that the improved SOA will keep the additional variable A at a small value in the later stage of iteration, thus improving the local search ability and convergence accuracy of the improved SOA in the later stage. On the contrary, the additional variable is decreased suddenly in the early stage of iteration to expand the search range, ensuring the global optimization ability and convergence speed of the improved SOA.

[0182] The specific implementation steps of the improved SOA in this step are described as follows:

[0183] Step1: Input the parameters of the traditional SOA;

[0184] Step2: Initialize the population through Tent chaotic mapping according to the function expression of generating chaotic particle sequences by Tent chaotic mapping and N Iq = Ib I +(ub I -Ib I )y Iq ;

[0185] Step3: Calculate the migration position of each seagull, migrate and attack the seagull individuals according to the calculation formula in the traditional SOA, and update the search coordinates P s (x);

[0186] Step4: Calculate the fitness (i.e., the objective function value) and find the optimal coordinate P bs(x), store the non-dominated solutions into the Archive population according to the Pareto dominance relationship;

[0187] Step5: Generate a random number r between 0 and 1 1 , and judge r 1 Whether it is less than 0.5. If it is less than 0.5, execute Step6; otherwise, execute Step7;

[0188] Step6: Obtain the non-linear additional variable A according to the calculation formula of the additional variable with cosine change, and update the seagull position P s (x);

[0189] Step7: When x is less than the maximum iteration time T max , return to Step3 for iteration; otherwise, output the result and the improved seagull optimization algorithm ends.

[0190] S4. Based on the constraint conditions of the established multi-objective optimization model, use the improved seagull optimization algorithm to solve the objective function of the established multi-objective optimization model to obtain the optimal result.

[0191] In this step, the methods proposed in the present invention are respectively adopted: the improved seagull optimization algorithm (MOSOA, Multi-objective Seagull Optimization Algorithm), the seagull optimization algorithm (SOA, Seagull Optimization Algorithm), and the multi-objective particle swarm optimization algorithm (MOPSO) to solve the objective function of the established multi-objective optimization model respectively. The comparison results of the solutions obtained by the three algorithms are shown in Table 1.

[0192] Table 1 Comparison results of the solutions obtained by the three algorithms

[0193]

[0194] The data in Table 1 show that the optimal total scheduling cost calculated by using the improved seagull optimization algorithm is 246,976 yuan, the total process time is 990.5 hours, the energy consumption is 91,720 kWh, and the calculation time is 14.26 seconds.

[0195] The optimal total scheduling cost calculated by using the seagull optimization algorithm is 250,151 yuan, the total process time is 1,032 hours, the energy consumption is 92,070 kWh, and the calculation time is 21.15 seconds.

[0196] The optimal total scheduling cost calculated by using the multi-objective particle swarm optimization algorithm is 2,781,306 yuan, the total process time is 1,289 hours, the energy consumption is 94,840 kWh, and the calculation time is 25.61 seconds.

[0197] It can be seen that the calculation results of the method (improved seagull optimization algorithm) proposed by the present invention are better, and the improved seagull optimization algorithm has a shorter running time and a faster convergence speed compared with the seagull optimization algorithm and the multi-objective particle swarm optimization algorithm.

[0198] Therefore, this step finally selects the improved seagull optimization algorithm to solve the objective function of the established multi-objective optimization model, and obtains the optimal result.

[0199] S5. Perform production and transportation collaborative scheduling on the precast components of the prefabricated building according to the obtained optimal result, and obtain the production and transportation collaborative scheduling result.

[0200] Perform production and transportation collaborative scheduling on the precast components of the prefabricated building according to the obtained optimal result, and obtain the production and transportation collaborative scheduling result (the production scheduling plan and transportation scheduling plan of the precast components), that is, the production order of the components in the distributed workshop. The specific scheduling plan is shown in Table 2.

[0201] Table 2 Optimal scheduling results of each mold table

[0202]

[0203]

[0204] Example 4:

[0205] Please refer to Figure 5 As shown, the present invention also provides an electronic device 100 for the production and transportation collaborative scheduling method of precast components of a prefabricated building; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and operable on the at least one processor 102, and at least one communication bus 104.

[0206] The memory 101 can be used to store the computer program 103. The processor 102 realizes the steps of the collaborative scheduling method for the production and transportation of prefabricated components of the prefabricated building in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device 100 (such as audio data, etc.). In addition, the memory 101 may include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0207] The at least one processor 102 can be a Central Processing Unit (CPU), or can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 can be a microprocessor or the processor 102 can also be any conventional processor, etc. The processor 102 is the control center of the electronic device 100 and connects various parts of the entire electronic device 100 through various interfaces and lines.

[0208] The memory 101 in the electronic device 100 stores multiple instructions to implement the collaborative scheduling method for the production and transportation of prefabricated components of the prefabricated building. The processor 102 can execute the multiple instructions to thereby implement:

[0209] Construct a multi-objective optimization model for the total scheduling cost, the maximum flow time, and the energy consumption. The multi-objective optimization model includes an objective function for minimizing the total scheduling cost, an objective function for minimizing the maximum flow time, and an objective function for minimizing the energy consumption;

[0210] Set the constraint conditions of the multi-objective optimization model;

[0211] Improve the seagull optimization algorithm to obtain an improved seagull optimization algorithm;

[0212] Based on the constraint conditions of the established multi-objective optimization model, an improved seagull optimization algorithm is used to solve the objective function of the established multi-objective optimization model to obtain the optimal result;

[0213] According to the obtained optimal result, the production and transportation collaborative scheduling of prefabricated components of the prefabricated building is carried out to obtain the production and transportation collaborative scheduling result.

[0214] Example 5:

[0215] If the modules / units integrated in the electronic device 100 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above method embodiments of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0216] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0217] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0218] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the flows Figure 1 a flow or flows and / or boxes Figure 1 a box or boxes.

[0219] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the flows Figure 1 a flow or flows and / or boxes Figure 1 a box or boxes.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for coordinated scheduling of production and transportation of prefabricated components for assembled buildings, characterized in that: The following steps are involved: Constructing a multi-objective optimization model of total scheduling cost, maximum process time and energy consumption, wherein the multi-objective optimization model includes an objective function of minimizing total scheduling cost, an objective function of minimizing maximum process time and an objective function of minimizing energy consumption; Set constraints for multi-objective optimization models; Improve the Seagull optimization algorithm to obtain an improved Seagull optimization algorithm; Based on the constraints of the multi-objective optimization model, the improved Seagull optimization algorithm is used to solve the objective function of the established multi-objective optimization model and obtain the optimal result. According to the optimal results obtained, the production and transportation of prefabricated building components are coordinated and scheduled to obtain the production and transportation coordinated scheduling results.

2. The method for coordinated production and transportation scheduling of prefabricated building components according to claim 1 is characterized in that: The expression of the objective function of minimizing the total scheduling cost is: Among them, minZ1 is the objective function of minimizing the total scheduling cost, c ijk is the unit time processing cost of component i in process k of workshop j, x ijkt is the unit time processing cost of whether component i is in process k of workshop j at time t (1 for yes, 0 for no), d jj' is the transportation distance from workshop j to construction site j′, y ijj'vt is whether vehicle v arrives at construction site j′ from workshop j at time t (1 for yes, 0 for no), h i is the unit production cost of component i, w ijkt is the completion time of process k of component i in workshop j, D i is the demand for component i, α is the production cost weight, θ is the delivery delay penalty coefficient, I, J, K and T are the set of prefabricated component types, the set of production workshops, the set of processes (N1, N2, N3, N4, N5, N6, N7, N8 and N9) and the set of time periods respectively; The expression of the objective function of minimizing the maximum process time is: Among them, minZ2 is the objective function of minimizing the maximum process time, x ijkt is the unit time processing cost of component i in process k of workshop j at time t (1 for yes, 0 for no), p k,i is the processing time of component i in process k; The expression of the energy consumption minimization objective function is: Among them, minZ3 is the objective function of minimizing energy consumption, x ijkt is the unit time processing cost of component i in process k of workshop j at time t (1 for yes, 0 for no), D i is the demand for component i, p r is the energy consumption of the machine during the actual processing of the component, PD r It is the energy consumption of the machine when it is not loaded.

3. The method for coordinated scheduling of production and transportation of prefabricated components for assembled buildings according to claim 1 is characterized in that: The constraints of the multi-objective optimization model include demand satisfaction constraints, vehicle load constraints, vehicle flow balance constraints, batch transportation constraints, transportation and vehicle association constraints, time window constraints, process constraints, working time constraints and variable value constraints.

4. The method for coordinated scheduling of production and transportation of prefabricated components for assembled buildings according to claim 3 is characterized in that: The calculation formula for the requirement satisfaction constraint is: Among them, x ij(N9)t is the transportation volume of components, D i is the demand for component i; The calculation formula of the vehicle load constraint is: Among them, q iv is the number of components i transported by vehicle v, g v is the load of vehicle v; The calculation formula of the vehicle flow balance constraint is: Among them, z ijj'vt is whether vehicle v arrives at construction site j' from workshop j at time t (1 for yes, 0 for no), s jj' is the transportation time from workshop j to construction site j'; The calculation formula for the batch transportation constraint is: Among them, q iv is the number of components i transported by vehicle v, x ijkt is the unit time processing cost of whether component i is in process k of workshop j at time t (1 for yes, 0 for no); The calculation formula for the transport and vehicle association constraints is: Among them, y ijj′vt is whether vehicle v is transported from workshop j to construction site j' at time t (1 for yes, 0 for no), z jj'vt' is whether vehicle v arrives at construction site j' from workshop j at time t (1 for yes, 0 for no); The calculation formula of the time window constraint is: Among them, E ij is the earliest time that prefabricated component i is shipped from workshop j, l ijj'vt is whether component i is in the process of being transported from workshop j to construction site j' at time t (1 for yes, 0 for no), L ij The latest time for the prefabricated components to arrive at the construction site; The calculation formula of the process constraint is: Among them, U kt Is process k interruptible at time t (1 for yes, 0 for no), x ijkt is the unit time processing cost of whether component i is in process k of workshop j at time t (1 for yes, 0 for no); The calculation formula of the working time constraint is: Among them, H jt is whether workshop j is working normally at time t, x ijkt is the unit time processing cost of component i in process k of workshop j at time t (1 for yes, 0 for no), w ijkt is the completion time of process k of component i in workshop j, p ijk is the processing time of component i in process k of workshop j; The calculation formula of the variable value constraint is: Among them, x ijkt is the unit time processing cost of component i in process k of workshop j at time t (1 for yes, 0 for no), y ijj′vt is whether vehicle v is transported from workshop j to construction site j' at time t (1 for yes, 0 for no), z ijj'vt is whether vehicle v arrives at construction site j' from workshop j at time t (1 for yes, 0 for no), l ijj'vt is whether component i is in transit from workshop j to construction site j' at time t (1 for yes, 0 for no), I, J, K, V and T are the set of prefabricated component types, the set of production workshops, the set of processes, the set of transport vehicles and the set of time periods, respectively.

5. The method for coordinated production and transportation scheduling of prefabricated building components according to claim 1, characterized in that: In the step of improving the Seagull optimization algorithm to obtain the improved Seagull optimization algorithm, the specific contents of improving the Seagull optimization algorithm include: a) Using chaotic mapping strategy to initialize the position of seagull population; b) Combine the multi-objective particle swarm optimization algorithm with the Seagull optimization algorithm; c) Calculate the new position of the seagull using the additional variable of cosine variation.

6. The method for coordinated scheduling of production and transportation of prefabricated components for assembled buildings according to claim 5 is characterized in that: The improved contents b) specifically combine the multi-objective particle swarm algorithm with the Seagull optimization algorithm, including: A. Replace the particles in the multi-objective particle swarm algorithm with the seagulls in the seagull optimization algorithm; B. Replace the movement of particles in the multi-objective particle swarm algorithm with the long-distance migration behavior and spiral attack behavior of seagulls when hunting in the seagull optimization algorithm.

7. The method for coordinated scheduling of production and transportation of prefabricated components for assembled buildings according to claim 5, characterized in that: The calculation formula of the additional variable in the improved content c) is: Among them, A is an additional variable, f c is the control factor, f c =2, x is the number of iterations currently executed by the improved Seagull optimization algorithm, MAX iteration The maximum number of iterations to perform for the improved Seagull optimization algorithm.

8. A production and transportation coordination scheduling system for prefabricated building components, characterized in that: It includes a multi-objective optimization model building module, a constraint condition setting module, a Seagull optimization algorithm improvement module, a multi-objective optimization model objective function solving module, and a production and transportation collaborative scheduling module; The multi-objective optimization model building module is used to build a multi-objective optimization model of total scheduling cost, maximum process time and energy consumption, and the multi-objective optimization model includes a total scheduling cost minimization objective function, a maximum process time minimization objective function and an energy consumption minimization objective function; The constraint condition setting module is used to set the constraint conditions of the multi-objective optimization model; The Seagull optimization algorithm improvement module is used to improve the Seagull optimization algorithm to obtain an improved Seagull optimization algorithm; The multi-objective optimization model objective function solving module is used to solve the objective function of the established multi-objective optimization model based on the set constraints of the multi-objective optimization model using the improved Seagull optimization algorithm to obtain the optimal result; The production and transportation collaborative scheduling module is used to perform production and transportation collaborative scheduling on prefabricated building components according to the obtained optimal results to obtain production and transportation collaborative scheduling results.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for coordinated scheduling of production and transportation of prefabricated building components according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for coordinated scheduling of production and transportation of prefabricated building components according to any one of claims 1 to 7 are implemented.

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