CN113919688B - Logistics park approach vehicle dynamic scheduling method considering late arrival - Google Patents
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Abstract
The invention relates to a logistics park approach vehicle dynamic scheduling method considering late arrival. In the logistics park approach vehicle queuing sequence under the reservation mechanism, when the condition that the vehicles do not arrive at the site for operation according to the reserved time is considered, a logistics park approach vehicle dynamic scheduling mathematical optimization model considering late is established, the operation cost of the logistics park is minimized, the stability of the logistics system is maximized, a heuristic algorithm is adopted for solving, a new operation plan is issued and executed according to the solving result, and the logistics park operation system can efficiently and orderly perform operation.
Description
Technical Field
The invention relates to the field of logistics scheduling of logistics parks, in particular to a logistics park approach vehicle dynamic scheduling method considering late arrival.
Background
In the logistics park vehicle approach scheduling system of the reservation mechanism, the approach queuing sequence of the vehicles is determined by the reservation rule, the reservation mechanism can greatly relieve the operation pressure, reduce the waiting time of the vehicles and save the operation cost of enterprises. However, in actual operation, due to the influence of various factors, a late arrival phenomenon that some vehicles arrive at the non-reserved time occurs, so that queuing approach of other vehicles is influenced, the field order is disordered, and the operation cost of an enterprise is increased.
The existing vehicle reservation mechanism of the logistics park is faced with the on-site emergency such as the late arrival of vehicles, the field workers usually arrange scheduling tasks by taking self experience as the main part, the scheduling efficiency and fairness are low, and improper treatment generally causes the improvement of the operation cost of the logistics park and the dissatisfaction of customers. And the rescheduling is used as a real-time dynamic scheduling, so that a new scheduling scheme can be effectively generated, vehicles can efficiently run in the garden operation process at a lower cost, the phenomenon that the whole reservation queuing system is unstable due to late arrival of the vehicles is reduced, and the method has strong practical significance and application value.
The Chinese patent application numbers are: CN202010035705.2, name: a method for reordering automobiles based on genetic algorithm. The method is a vehicle reordering method based on a genetic algorithm. And establishing an objective function with least times of violating the sequencing rule in the downstream sequence through the physical reordering of the vehicles and the virtual reordering of the orders, and performing sequencing adjustment on the upstream vehicle sequence. The method does not consider the anti-interference capability of the system, and cannot carry out optimal sequencing in case of emergency.
The Chinese patent application numbers are: CN201510424161.8, name: a medical treatment queuing method based on mobile position information. The method comprises the steps of comparing the dynamic diagnosis time of a patient with the predicted arrival time in real time, judging whether the patient is a late patient, updating the sequence of queues and the dynamic diagnosis time of each patient according to a rescheduling rule, and issuing a real-time diagnosis schedule. The method adopts an estimation method to predict the late-arrival performance of the patient, the prediction condition is easy to be inconsistent with the actual condition, and the stability of the system is damaged.
Disclosure of Invention
The invention aims to provide a logistics park approach vehicle dynamic scheduling method considering late arrival, which is characterized in that in the case of a logistics park approach vehicle queuing sequence under a reservation mechanism, when a late arrival vehicle which does not arrive according to a reservation sequence on time appears, the queuing of subsequent approach vehicles is rescheduled according to known vehicle reservation information, goods position operation information, late arrival vehicle information and other data, the stability of a queuing system is maximized while the operation cost of the logistics park is minimized, and the vehicle queuing system can efficiently and orderly perform interference generated by the late arrival vehicle.
A logistics park approach vehicle reservation mechanism is characterized in that a time period table of vehicle operation and an operation goods taking place are planned in advance, a plan is made in advance according to principles such as a client reservation sequence, logistics park operation cost and the like, and the logistics park performs actual operation according to the plan. In the field operation, the initial scheduling scheme can not be realized as expected due to the emergency that the client does not arrive on time, and a new scheduling plan is temporarily determined manually on the field, so that the efficiency is greatly reduced, and the customer dissatisfaction and the waste of operation resources of the logistics park are caused.
In order to achieve the purpose, the technical scheme of the invention is as follows: a logistics park approach vehicle dynamic scheduling method considering late arrival is characterized in that a logistics park approach vehicle dynamic scheduling mathematical optimization model considering late arrival is established when a situation that vehicles do not arrive at a site according to reserved time to operate is considered in a logistics park approach vehicle queuing sequence under a reservation mechanism, the operation cost of a logistics park is minimized, meanwhile, the stability of a logistics system is maximized, a heuristic algorithm is adopted to solve, a new operation plan is issued and executed according to a solving result, and the logistics park operation system can operate efficiently and orderly.
In an embodiment of the present invention, the method specifically includes the following steps:
1) firstly, establishing a field check-in mechanism, wherein when the vehicle finishes check-in before the preset operation time starts, the vehicle can enter a park to finish an operation task according to a reservation plan, and when the vehicle fails to finish check-in before the preset operation time starts, the vehicle is regarded as late arriving;
2) when a vehicle is late, a late client is contacted to obtain the expected late time, and a new scheduling plan is redesigned according to the late condition of the vehicle and the original scheduling plan and by combining the vehicle information on the spot;
3) when constructing a new dispatching plan, in order to reduce the influence on the operation time of a vehicle which is not late, the time deviation cost of the vehicle in the new dispatching plan needs to be considered, meanwhile, the overtime cost of the operation at a goods taking point caused by the existence of the late vehicle also needs to be considered, and the optimal dynamic dispatching plan is found by minimizing the dynamic dispatching time deviation cost of the vehicle and the overtime operation cost of the goods taking position;
4) the method comprises the following steps of establishing a logistics park approach vehicle dynamic scheduling mathematical optimization model considering late arrival as follows:
the objective function is:
Minf＝Pt+Pn (1)
vehicle dynamic scheduling time offset cost:
the overtime operation cost of the goods taking position is as follows:
the constraint conditions are as follows:
OT＝BTi，i＝max{L} (8)
L∈{1，2，3，...，n} (15)
Q∈{1，2，3，...，n} (16)
V∈{i，i+1，...，n} (17)
the formula (1) is an objective function for minimizing the dynamic scheduling time deviation cost of the vehicle and the overtime operation cost of the goods taking position; the formula (2) is used for calculating the dynamic scheduling time deviation cost of the vehicle; the formula (3) is used for calculating the overtime operation cost of the goods taking position; the formula (4) restricts the operation ending time of each vehicle in the original reservation plan to be larger than the operation starting time; the operation ending time of each vehicle is larger than the operation starting time after constraint rescheduling in the formula (5); the formula (6) restricts that in the original reservation plan, the operation starting time of the rear vehicle is not less than the operation ending time of the front vehicle; the formula (7) restricts the operation time of each vehicle to be unchanged before and after scheduling; the formula (6) and the formula (7) jointly restrict the vehicle operation time of the same goods taking position in the original reservation plan not to be overlapped; equation (8) is to calculate the time at which the job is expected to end; equation (9) constrains the job start time of the rescheduled vehicle to be no greater than the end time of the unscheduled vehicle; equation (10) constrains the rescheduled job start time late for the vehicle to be greater than the original planned job start time plus the late vehicle late time; formula (11) is to calculate the overtime of each pick location; the formulas (12) and (13) restrict that the vehicle operation time after rescheduling is not overlapped; (14) the vehicle is restricted in the rescheduling plan, and if the operation time of the vehicle is scheduled to be within the time of the original reservation plan of the late vehicle, the vehicle is the vehicle which arrives at the scene; the formula (18) represents a 0-1 decision variable, if the vehicle i arrives late at the time t and the vehicle n waits for entering the field, the value is 1, otherwise, the value is 0; the formula (19) represents a 0-1 decision variable, if the goods space j is taken for overtime within t time, the value is 1, otherwise, the value is 0; wherein, L: a set of vehicle numbers; t: a scheduled time period; q: taking a goods position number set; ATij: reserving the operation starting time at the goods taking point j by the vehicle i; BT (BT)ij: reserving the operation ending time at the goods taking point j by the vehicle i; CT (computed tomography)ij: the working time of the vehicle i at the pick-up point j; OT: predicted end time of the job; RT (reverse transcription)j: taking the working overtime time of the goods location j; v: a set of vehicles behind a late vehicle, including late vehicles; kr: a set of vehicles ranked as having arrived on-site after a late vehicle; DTi: late time of vehicle i; RAT (radio access technology)ij: the operation starting time at the pick-up point j after the vehicle i is rescheduled; RBTij: the operation ending time at the goods taking point j after the vehicle i is rescheduled; c1: unit time cost of vehicle dynamic scheduling time deviation; c2: taking overtime unit time cost of the goods space; u shapetin: if the vehicle is late at the time t and the vehicle n waits for the approach on the spot, the value is 1, otherwise the value is 0; ztj: if the goods space j is taken for overtime at the time t, the value is 1, otherwise, the value is 0;
5) solving the logistics park approach vehicle dynamic scheduling mathematical optimization model which is established in the step 4) and takes late consideration by adopting a heuristic algorithm, and solving optimal operation time distribution information and goods taking point distribution information;
6) and 5) according to the optimal operation time distribution information and the optimal goods taking point distribution information obtained in the step 5), after the dispatching plan is determined again, the logistics park enterprise issues a new operation arrangement plan to the client, and meanwhile, vehicle entering dispatching is carried out according to the new operation arrangement plan, so that the logistics park operation system is ensured to be carried out smoothly and orderly.
Compared with the prior art, the invention has the following beneficial effects:
(1) the logistics park entrance vehicle dynamic scheduling method considering late arrival is established, the anti-interference capability of a logistics park entrance vehicle scheduling system is improved, and the entrance plan of the vehicle is dynamically adjusted according to the actual condition that the vehicle on site signs in.
(2) The invention establishes an optimization model of logistics park approach vehicle dynamic scheduling, obtains an optimal solution through a heuristic algorithm, provides support for quickly making a correct strategy for the logistics park, improves customer satisfaction and saves the operation cost of enterprises.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is a diagram of a mathematical model for solving dynamic scheduling of vehicle approach by a heuristic algorithm.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
The invention relates to a logistics park approach vehicle dynamic scheduling method considering late arrival, which is characterized in that a logistics park approach vehicle dynamic scheduling mathematical optimization model considering late arrival is established when a situation that vehicles do not arrive at a site according to reserved time to perform operation is considered in a logistics park approach vehicle queuing sequence under a reservation mechanism, the operation cost of a logistics park is minimized, the stability of a logistics system is maximized, a heuristic algorithm is adopted to solve, a new operation plan is issued and executed according to a solving result, and the logistics park operation system can efficiently and orderly perform operation. FIG. 1 is a schematic flow chart of the present invention. FIG. 2 is a diagram of a mathematical model for solving dynamic scheduling of vehicle approach by a heuristic algorithm.
The following is a specific example of the present invention.
1) The vehicle reservation scheduling plan of a certain day in the plant area is obtained from a certain steel plant, and is shown in table 1. The number of vehicles on the day is 8 in total, the number is L ═ 1,2, …,8, the goods taking positions for carrying out the work are two in total, the number is Q ═ 1,2, the work starting time is AT, the work ending time is BT, and the time for moving the two vehicles into and out of the goods taking positions is calculated in the work time.
TABLE 1
| L | AT | BT | Q |
| 1 | 8:00 | 8:40 | 1 |
| 2 | 8:40 | 9:30 | 1 |
| 3 | 9:30 | 10:10 | 1 |
| 4 | 10:10 | 11:40 | 1 |
| 5 | 8:00 | 8:50 | 2 |
| 6 | 8:50 | 9:25 | 2 |
| 7 | 9:25 | 10:25 | 2 |
| 8 | 10:25 | 11:40 | 2 |
2) When the vehicle 2 does not check in before the job start time, it is determined that the vehicle 2 cannot be reservedAnd the time arrives at the site to carry out operation. At the time of starting the work of the vehicle 2, the vehicle which has arrived at the site and completed the check-in and has not performed the work is KrAnd {4,7}, while confirming to the customer that the late time of the vehicle 2 is about DT ═ 20.
3) And determining the unit time cost of the dynamic scheduling time deviation of the enterprise and the overtime unit time cost of the goods taking point. Generally, in order to reduce the overtime time of the pick-up point, the unit time cost of the overtime time needs to be set higher, and the unit time cost of the dynamic scheduling time deviation in the embodiment is C1The unit time cost of overtime of the goods pick-up point is C2＝15。
4) The effect of the late vehicle approach dynamic scheduling method provided by the invention is tested according to the group of values, and a differential evolution algorithm is selected as a heuristic algorithm for solving a rescheduling optimization model. The specific steps of solving the problem by the differential evolution algorithm are as follows:
5) the method comprises the following steps: and initializing a candidate solution population. The maximum evolution algebra MAXGEN is set to 500, the job start time, the job end time, and the pick-up bit number are real number coded, and 50 individuals are randomly generated as one population. The scaling factor F is set to a fixed value of 0.5 and the recombination probability Cr is set to a fixed value of 0.7.
6) Step two: and calculating the fitness value of each individual in the population, namely the objective function value. And judging whether the termination condition is reached, namely the minimum scheduling cost and overtime cost or the maximum evolution algebra is reached, namely the evolution algebra reaches 500 times. If so, terminating the evolution, and obtaining the optimal individual, the average fitness and the like; if not, continuously searching for the optimal individual.
7) Step three: selecting base vector X of differential variation by adopting random compensation selection methodr1(j) Wherein j is the jth individual, and the current population is subjected to differential variation to obtain a variation individual Ui(j) Wherein i is the ith generation population. As shown in the following formula, r1, r2 and r3 are randomly generated integer values different from each other.
Ui(j)＝xr1(j)+F(xr2(j)-Xr3(j))
8) Step four: combining the current population with the current populationCombining different individuals, and obtaining a test population Y by a binomial distribution crossing methodi(j) Is represented by the following formula, wherein jrandIs [1,50 ]]Random integer of r betweenj＝rand(0,1)。
9) Step five: and obtaining a new generation of population by adopting a one-to-one survivor selection method between the current population and the test population, calculating the target functions of the test population and the current population, and reserving the corresponding vector with a smaller target function to the next generation, wherein f is the target function as shown in the following formula.
10) Step six: and returning to the step two.
11) In this example, the result of solving the mathematical optimization model for the logistics park vehicle approach scheduling considered late by using the differential evolution algorithm is shown in table 2, where the number of the vehicle is L, the start time of the rescheduled job is RAT, the end time of the rescheduled job is RBT, the number of the rescheduled pickup location is Q, and the objective function value is f is 130.
TABLE 2
12) The new scheduling plan can be obtained, the rescheduled plan does not generate overtime cost, and meanwhile, the rescheduling operation time of other vehicles and the original reserved operation time have the minimum deviation, so that the cost is saved for the logistics operation of the steel plant, the stability of a queuing system is ensured, and the satisfaction degree of the customer goods taking operation is improved.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (1)
1. A logistics park approach vehicle dynamic scheduling method considering late arrival is characterized in that a logistics park approach vehicle dynamic scheduling mathematical optimization model considering late arrival is established when a situation that vehicles do not arrive at a site for operation according to reserved time is considered in a logistics park approach vehicle queuing sequence under a reservation mechanism, the operation cost of a logistics park is minimized, the stability of a logistics system is maximized, a heuristic algorithm is adopted for solving, a new operation plan is issued and executed according to a solving result, and the logistics park operation system can operate efficiently and orderly; the method comprises the following concrete implementation steps:
1) firstly, establishing a field check-in mechanism, wherein when the vehicle finishes check-in before the preset operation time starts, the vehicle can enter a park to finish an operation task according to a reservation plan, and when the vehicle fails to finish check-in before the preset operation time starts, the vehicle is regarded as late arriving;
2) when a vehicle is late, a late client is contacted to obtain the expected late time, and a new scheduling plan is redesigned according to the late condition of the vehicle and the original scheduling plan and by combining the vehicle information on the spot;
3) when constructing a new dispatching plan, in order to reduce the influence on the operation time of a vehicle which is not late, the time deviation cost of the vehicle in the new dispatching plan needs to be considered, meanwhile, the overtime cost of the operation at a goods taking point caused by the existence of the late vehicle also needs to be considered, and the optimal dynamic dispatching plan is found by minimizing the dynamic dispatching time deviation cost of the vehicle and the overtime operation cost of the goods taking position;
4) the method comprises the following steps of establishing a logistics park approach vehicle dynamic scheduling mathematical optimization model considering late arrival as follows:
the objective function is:
Minf＝Pt+Pn (1)
vehicle dynamic scheduling time offset cost:
the overtime operation cost of the goods taking position is as follows:
the constraint conditions are as follows:
OT＝BTi，i＝max{L} (8)
L∈{1，2，3，...，n} (15)
Q∈{1，2，3，...，n} (16)
V∈{i，i+1，...，n} (17)
the formula (1) is an objective function for minimizing the vehicle dynamic scheduling time deviation cost and the goods-taking position overtime operation cost; the formula (2) is used for calculating the dynamic scheduling time deviation cost of the vehicle; the formula (3) is used for calculating the overtime operation cost of the goods taking position; the formula (4) restricts the operation ending time of each vehicle in the original reservation plan to be larger than the operation starting time; the operation ending time of each vehicle is larger than the operation starting time after constraint rescheduling in the formula (5); the formula (6) restricts that in the original reservation plan, the operation starting time of the rear vehicle is not less than the operation ending time of the front vehicle; the formula (7) restricts the operation time of each vehicle to be unchanged before and after scheduling; the formula (6) and the formula (7) jointly restrict the vehicle operation time of the same goods taking position in the original reservation plan not to be overlapped; equation (8) is to calculate the time at which the job is expected to end; formula (9)The job start time of the vehicle subjected to rescheduling is constrained to be not more than the end time of the vehicle not subjected to rescheduling; equation (10) constrains the rescheduled job start time late for the vehicle to be greater than the original planned job start time plus the late vehicle late time; formula (11) is to calculate the overtime of each pick location; the formulas (12) and (13) restrict that the vehicle operation time after rescheduling is not overlapped; (14) the vehicle is restricted in the rescheduling plan, and if the operation time of the vehicle is scheduled to be within the time of the original reservation plan of the late vehicle, the vehicle is the vehicle which arrives at the scene; the formula (18) represents a 0-1 decision variable, if the vehicle i arrives late at the time t and the vehicle n waits for entering the field, the value is 1, otherwise, the value is 0; the formula (19) represents a 0-1 decision variable, if the goods space j is taken for overtime within t time, the value is 1, otherwise, the value is 0; wherein, L: a set of vehicle numbers; t: a scheduled time period; q: taking a goods position number set; ATij: reserving the operation starting time at the goods taking point j by the vehicle i; BT (BT)ij: reserving the operation ending time at the goods taking point j by the vehicle i; CTij: the working time of the vehicle i at the pick-up point j; OT: an expected end time of the job; RT (reverse transcription)j: taking the working overtime time of the goods location j; v: a set of vehicles behind a late vehicle, including late vehicles; kr: a set of vehicles ranked as having arrived on-site after a late vehicle; DTi: late time of vehicle i; RAT (radio access technology)ij: the operation starting time at the pick-up point j after the vehicle i is rescheduled; RBTij: the operation ending time at the goods taking point j after the vehicle i is rescheduled; c1: unit time cost of vehicle dynamic scheduling time deviation; c2: taking overtime unit time cost of the goods space; u shapetin: if the vehicle is late at the time t and the vehicle n waits for the approach on the spot, the value is 1, otherwise the value is 0; ztj: if the goods space j is taken for overtime at the time t, the value is 1, otherwise, the value is 0;
5) solving the logistics park approach vehicle dynamic scheduling mathematical optimization model which is established in the step 4) and takes late consideration by adopting a heuristic algorithm, and solving optimal operation time distribution information and goods taking point distribution information;
6) and 5) according to the optimal operation time distribution information and the optimal goods taking point distribution information obtained in the step 5), after the dispatching plan is determined again, the logistics park enterprise issues a new operation arrangement plan to the client, and meanwhile, vehicle entering dispatching is carried out according to the new operation arrangement plan, so that the logistics park operation system is ensured to be carried out smoothly and orderly.
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