JP2003132500A - Device and method for preparing airplane parking schedule - Google Patents

Abstract

PROBLEM TO BE SOLVED: To prepare an airplane parking schedule high in spot operation efficiency at a practical speed. SOLUTION: This device is provided with a first storage means 0111 for storing an airplane parking request, second storage means 0112 for storing parking spot attribute information, third storage means 0113 for storing a spot allocation cost, fixed spot allocating means 0101 for allocating the parking request to a fixed spot at a maximum by optimizing arithmetic while using the parking request, the spot attribute information and the spot allocation cost, fourth storage means 0114 for storing a non-allocated parking request, which is not allocated to the fixed spot as a result after the parking request is allocated to the fixed spot at a maximum, open spot allocating means 0102 for allocating the non-allocated parking request to an open spot by using the spot attribute information and the spot allocation cost, and fifth storage means 0115 for storing the parking schedule obtained by the fixed spot allocating means and the open spot allocating means.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus and method for assisting a computer in preparing a parking plan for allocating an appropriate parking lot in an airport to a parking request of an aircraft, and particularly to a spot. The present invention relates to a parking plan creation device that creates a parking plan with high operation efficiency by using optimization calculation.

[0002]

2. Description of the Related Art An aircraft parking place at an airport is called a spot. In order to smoothly carry out work such as getting on and off passengers and refueling, it is necessary to determine in advance an appropriate parking spot for the parking request of each aircraft. Therefore, conventionally, a parking plan is created using a parking plan creation device that uses a computer.

FIG. 7 shows a conventional parking plan creating apparatus. This is the parking plan creation unit 0701, parking request storage unit
0711, spot attribute information storage unit 0712, spot allocation cost storage unit 0713, parking plan storage unit 0714. The parking request storage unit, the spot attribute information storage unit, the spot allocation cost storage unit, and the parking plan storage unit are configured by a rewritable storage medium such as a disk device. The parking request includes information such as a request number, a scheduled parking start time, the same ending time, an aircraft type, and a flight number. The spot attribute information includes information such as a spot number and size, a parking type and a type.

The spot allocation cost is a numerical value representing the desirability of allocation to each spot for each parking request. The desirability may be restated as the priority order of allocation. The definition of desirability depends on how the planning objectives are set. For example, if a plan is made to keep the parking fee as low as possible, the allocation cost will be a value proportional to the parking fee for each spot. Further, if it is desired to reduce the travel distance of the passenger as much as possible, a higher cost is set for a spot located farther away. When there are a plurality of purposes, the costs corresponding to each purpose are weighted according to the degree of importance and combined.

The parking plan creation unit 0701 has a storage medium (not shown) in which a parking plan creation program is stored. The parking plan creation unit performs processing for creating a parking plan for a plurality of parking requests according to the parking plan creation program. The created parking plan includes information such as a request number, parking start time, same end time, aircraft type, flight number, and spot number. The parking plan storage unit is composed of a rewritable storage medium such as a disk device, and stores a plurality of parking plans created by the parking plan creation unit.

The processing contents of the parking plan creation unit 0701 will be described with reference to the processing flow chart of FIG. First, in step 0801, all the spot attribute information is read from the spot attribute information storage unit 0712. In step 0802, all the spot allocation costs are read from the spot allocation cost storage unit 07013. At step 0803, the parking request is sent from the parking request storage unit 0711 to 1
Read the case. In step 0804, spot allocation is performed for the previously read parking request. The spot with the smallest allocation cost is selected from the spots that can be allocated without overlapping with other parking requests that have already been allocated spots. In step 0805, the end of processing is determined. It is checked whether or not there is a spot unallocated parking request, and if there is no unallocated parking request, the process ends. If there is an unallocated parking request, the process returns to step 0803 to perform the allocation process again.

FIG. 9 shows an example of creating a parking plan using a conventional parking plan creation device. 0901 is a spot allocation cost. The table shows the allocation cost of each spot for each of the five parking requests. The circled one is the minimum allocation cost for each parking request. 0902
Indicates an assignable spot for each parking request. Allocatable spots are aircraft types requested for parking, types of flights (domestic flights,
International flights, etc.), spot size, etc. 0903
Is a parking plan created by a conventional parking plan creation device. The state of creation is as follows. Step
In steps 0801 and 0802, after reading the spot attribute information and the spot allocation cost, respectively, step 0803
Then, in step 0804, parking requests are processed one by one.
Here, it is assumed that the numbers are read in the order of parking request. The first to be read is parking request 1. Since there is no assigned parking request, spot 2 with the lowest cost for parking request 1.
Can be selected unconditionally. Regarding the next parking request 2, the spot 2 has the lowest cost. However, if this is selected, since the allocation is duplicated with the parking request 1, the spot 1 having the next lowest cost is selected. In the parking request 3, the spot 1 having the lowest cost and the spot 2 having the next smallest cost are overlapped with the other parking requests. Then, the allocation candidate is the spot 3 with the third lowest cost, but since this is not the allocatable spot, the spot 4 with the next lowest cost is selected. Similarly, the parking request 4 is assigned to the spot 3 and the parking request 5 is assigned to the spot 2.

[0008] With this configuration, it is possible to automatically create a parking plan in accordance with the purpose of creation without duplication.

[0009]

By the way, spots can be divided into two types: fixed spots and open spots. The fixed spot is a spot adjacent to the passenger terminal building. The aircraft parked at the fixed spot,
It is directly connected to the terminal building by PBB (Passenger Boarding Bridge), which allows passengers to board the aircraft directly from the terminal building. On the other hand, the open spot is a spot located away from the terminal building. When parked at an open spot, passengers will move to and from the terminal building by a private bus. FIG. 6 is an example of an airport layout. 0601 is the passenger terminal building, 0602 is a fixed spot,
0603 represents each open spot. In addition, 0604 is a dedicated bus connecting the passenger terminal building and the open spot.

From the viewpoint of the traveling burden of passengers, it is desirable to park the aircraft at a fixed spot as much as possible. Fixed spots require less travel time and less hassle of changing to a bus, etc., and even if the weather is bad such as rain, you can board smoothly without going outdoors. Further, since it takes a lot of time to dispatch vehicles / personnel for work such as refueling for an aircraft parked at an open spot, the use of a fixed spot has a great advantage for an airline company.

However, in the conventional parking plan preparing apparatus, it is not always possible to prepare a parking plan having a high fixed spot utilization efficiency. In the conventional parking plan planning apparatus, the allocation cost can be guided to the fixed spot by setting the allocation cost of the open spot relatively higher than the allocation cost of the fixed spot. For example, in the parking plan creation example of FIG. 9, if spots 1, 2, and 3 are fixed spots and spots 4 and 5 are open spots, as can be seen from the allocation cost table 0901, spot 4
And the allocation cost of Spot 5 is set sufficiently high compared to others. However, the obtained parking plan is a parking request
3 has been assigned to spot 4, which is an open spot. The optimum solution for this input data is as shown in FIG. In the optimal solution, parking spots can be handled only by fixed spots, and open spots are not required. Therefore, it can be seen that the conventional parking plan creation device causes an unnecessary allocation to an open spot, that is, an "overflow" from a fixed spot. Since even such a small amount of data will be flooded, it is necessary to process a conventional parking plan when creating an actual parking plan that requires processing of larger and more complex data. It was difficult to create a plan with high utilization efficiency of fixed spots using the device.

[0012] With the increasing need for air transportation, it is expected that the congestion of aircraft and passengers at airports will increase more and more in the future. Since the expansion of airport facilities requires a huge amount of capital investment, it is important for future airport operations to maintain the service level for passengers / airlines by improving the operational efficiency of existing facilities even under these circumstances. This is considered to be one of the challenges. An object of the present invention, as one of the solutions to this problem, is to make it possible to create a parking plan with high fixed spot utilization efficiency at a practical speed.

[0013]

In order to solve the above-mentioned problems, the present invention first allocates parking requests to fixed spots by optimization calculation to the maximum extent, and as a result, unallocated parking requests overflowing from fixed spots. Is newly assigned to an open spot.

In the allocation to fixed spots, the temporary spot for temporarily allocating the parking requests overflowing from the fixed spot is set as the buffer spot, and the allocation cost of the buffer spot is set to be larger than that of any fixed spot. Under the first constraint condition that the parking request is assigned to either the buffer spot or the fixed spot and the second constraint condition that duplicate parking requests are not assigned to the fixed spot, the allocation cost is the minimum. A fixed spot allocation optimization model for finding combinations of different spot allocations is constructed, and its best solution is calculated by an optimization calculation.

In the calculation of the best solution, the Lagrangian mitigation problem is constructed by relaxing the second constraint condition in which fixed parking spots are not redundantly allocated, and the Lagrange mitigation problem and the fixed spot allocation optimal The Lagrangian relaxation method is applied to the generalized model to calculate the best solution.

In the application of the Lagrangian relaxation method, a value obtained by adding the Lagrange multiplier to the spot allocation cost is used as the adjustment allocation cost, and the parking request is selected in ascending order of the start time, and the allocation is possible spots that do not become the overlapping allocation. Furthermore, the spot with the lowest adjustment allocation cost is allocated to generate one feasible solution of the fixed spot allocation optimization model.

Similarly, in the application of the Lagrangian relaxation method, the parking requests are selected one by one in an arbitrary order, and the spots having the smallest adjustment allocation cost are allocated while allowing duplication to generate the optimal solution of the Lagrangian relaxation problem. .

[0018]

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, a parking plan creating apparatus according to an embodiment of the present invention will be described with reference to FIGS. 1 to 5.

FIG. 1 shows a functional block diagram of an embodiment of the present invention. In FIG. 1, a parking request storage unit 0111, a spot attribute information storage unit 0112, a spot allocation cost storage unit 0113, and a parking plan storage unit 0115 are the same as the components of the same name in the conventional parking plan creation apparatus described above. Since the configuration and the operation are performed, detailed description will be omitted. The fixed spot allocation unit 0101 has a storage medium (not shown) in which a fixed spot allocation program is stored. The fixed spot allocation unit performs an optimizing operation for maximally allocating a plurality of parking requests to fixed spots according to the fixed spot allocation program.
As a result, with respect to the parking request to which the fixed spot is allocated, the allocation information is stored in the parking plan storage unit 0115. On the other hand, all the parking requests overflowing from the fixed spot are stored in the unallocated parking request storage unit 0114. Unallocated parking request storage
[0114] For example, the rewritable storage medium such as a disk device is configured. The stored data format is parking request storage 01
The same as 11, but among parking requests included in the parking request storage unit 0111, only those overflowing from the fixed spots are stored as a result of the processing in the fixed spot allocation optimizing unit 0101. The open spot allocation unit 0102 has a storage medium (not shown) in which the open spot allocation program is stored. The open spot allocation unit performs processing of allocating unallocated parking lot requests overflowing from the fixed spots to the open spots according to the open spot allocation program. The allocation result is stored in the parking plan storage unit 0115.

The processing contents of the fixed spot allocation unit 0101 will be described below with reference to FIGS. 2 to 5. First, the overall processing flow of the fixed spot allocation unit 0101 will be described with reference to FIG. First, in step 0201, all the spot attribute data are read. Next, at step 0202, all the parking requests for plan creation are read. In step 0203, the fixed spot allocation cost is read. Spot allocation cost is the same as the conventional parking plan creation device
It is a value that is set appropriately according to (parking fees, passenger travel distance, etc.). However, no cost is set to differentiate between fixed spots and open spots in order to guide allocation to fixed spots. In short, read the cost according to other purposes here. In step 0204, one buffer spot is added as a spot to be allocated in addition to the fixed spot. The buffer spot is a temporary spot for temporarily allocating the parking request overflowing from the fixed spot in the subsequent allocation processing. The buffer spot allocation cost is set in the next step 0205. In order to guide the parking request to the fixed spot, the allocation cost of the buffer spot is set to a value sufficiently larger than the allocation cost of any fixed spot. That is, if the allocation cost of an arbitrary fixed spot j to the parking request i is c _ij and the allocation cost of the buffer spot is c _io , c _io is set so that c _ij << c _io . Next, in step 0206, a fixed spot allocation optimization model is constructed. The fixed spot allocation optimization model is a mathematical programming model whose main purpose is to allocate parking requests to fixed spots to the maximum extent. The details will be described later. In step 0207, the fixed spot allocation optimization model is solved by an optimization calculation to determine the allocation spot of each parking request. As the solution of the model, any optimization method such as mathematical programming, expert system, meta-solution method such as genetic algorithm, etc. may be used. In this embodiment, a method based on the Lagrangian relaxation method, which is a type of mathematical programming, is used. This will be described later. In step 0208, the parking request allocated to the buffer spot by the optimization calculation is determined to be an unallocated parking request, and these are stored in the unallocated parking request storage unit 0114. Finally, in step 0209, the parking plan data of the parking request whose allocation to the fixed spot has been confirmed is created, and these are output to the parking plan storage unit 0115, and the entire processing is ended.

The fixed spot allocation optimization model of step 0206 will be described below.

[0022]

[Equation 1]

Under this definition, the fixed spot allocation optimization model is formulated as follows.

[0024]

[Equation 2]

[0025]

[Equation 3]

[0026]

[Equation 4]

[0027]

[Equation 5]

[0028]

[Equation 6]

Expression (1) is an objective function representing the sum of allocation costs. Equations (2) to (5) represent constraint conditions, and among them, equations (2) and (3) are important constraint conditions that characterize the model. Expression (2) means that the parking request must be assigned to any one of the allocatable spots (including buffer spots). Expression (3) means that the parking spots cannot be assigned to the same fixed spot at the same time as other parking requests that overlap. The buffer spot allows allocation even if there is competition, and does not impose this constraint. Since this constraint defines the relationship between parking requests, it will be referred to as a link constraint below. Equation (4) means fixing the decision variable for non-allocatable spots to zero. Expression (5) indicates that the decision variable is a 0-1 integer variable that takes 0 or 1.

The purpose of this model is to find the combination of spots that minimizes the objective function (1) under constraints (2)-(5). As described above, the allocation cost of the buffer spots is set to be sufficiently higher than the allocation costs of the other fixed spots, so that a solution for maximally allocating the parking request to the fixed spots can be obtained by optimization.

Next, the solution processing of the fixed spot allocation optimization model in step 0207 will be described. In this embodiment, the Lagrangian relaxation method, which is a type of mathematical programming, is applied. The processing content will be described with reference to the processing flow chart of FIG. First, in step 0301, a Lagrangian mitigation problem is constructed from a fixed spot allocation optimization model.
In the following, the fixed spot allocation optimization model will be called the main problem.

The Lagrangian relaxation problem is a problem in which the constraint is partially removed by incorporating a part of the constraint equation of the main problem into the objective function using a Lagrange multiplier. It is called a relaxation problem because the restrictions imposed on the solution space are relaxed as much as the constraints are removed. Which constraint is relaxed depends on the main problem and other components of the Lagrangian relaxation method. In the present invention, the link constraint of equation (3) is relaxed to form the following Lagrangian relaxation problem.

[0033]

[Equation 7]

[0034]

[Equation 8]

[0035]

[Equation 9]

[0036]

[Equation 10]

The constraint expressions (7), (8) and (9) are the same as the constraint expressions (2), (4) and (5) of the main problem, respectively. Equation (6) is the objective function.
This is an extension of the objective function (1) of the main problem, and the term consisting of the constraint equation (3) and the nonnegative Lagrange multiplier λ is added. This term can be interpreted as adding the penalty λ to the total cost according to the number of violations of the constraint equation (3).
Therefore, the value of the objective function increases if the obtained solution has a constraint violation. If the solution is a feasible solution without constraint violation, this term is 0, and the objective function value matches the main problem.

Returning to the processing flow chart of FIG. 3, the description will be continued. In step 0302, the Lagrange multiplier λ is initialized and the number of process iterations k is set to 1. The method of initializing λ is not specified. It may be set randomly, a constant (for example, 0) may be uniformly given, or different values may be set for each combination of the parking request and the fixed spot according to some policy. Next, in step 0303, the optimal solution of the Lagrangian relaxation problem is generated given the multiplier λ.
This generation method depends on the main problem and other components of the Lagrangian relaxation method, but in the present invention, the method is as follows. First, the objective function of the Lagrangian relaxation problem
(6) becomes the form shown below by applying an appropriate formula transformation.

[0039]

[Equation 11]

[0040]

[Equation 12]

From the structure of equation (10), the Lagrangian relaxation problem can be separated into independent subproblems for each parking request i as shown below.

[0042]

[Equation 13]

[0043]

[Equation 14]

[0044]

[Equation 15]

[0045]

[Equation 16]

An optimal solution generation process for the relaxation problem based on the above concept is shown in the process flow chart of FIG. This will be described sequentially. First, in step 0401, one arbitrary parking request is selected. Next, in step 0402, a spot (however, allocatable) having the lowest adjustment cost is selected and allocated to the selected parking request. In this allocation, duplicate allocation with other parking requests is not considered. In step 0403, the end of processing is determined. If there is a parking request to which spots have not been allocated, the process returns to step 0401 and is repeated.
If there is no parking request to which spots have not been allocated, the processing ends.

Returning to the processing flow chart of FIG. 3, the description of the optimization model solution processing is continued. In step 0304, a feasible solution of the main problem is generated. The generation method should be properly designed according to the target problem to which the Lagrangian relaxation method is applied. The executable solution generation process of the present invention will be described with reference to the process flow chart of FIG.

First, in step 0501, all parking requests are sorted in ascending order of start time. Next step 05
In 02, select one parking request. Since the parking requests are rearranged in the order of the start time in the previous step, the parking requests are selected in ascending order of the start time by repeatedly executing this step. In step 0503, spots (although allocatable) that have the lowest adjustment cost and are not redundantly allocated to other parking requests are selected and allocated.
In step 0504, the end of processing is determined. If there is a spot unallocated parking request, the process returns to step 0501 and is repeated. If there is no parking request to which spots have not been allocated, the processing ends.

In this processing, allocatable spots are selected so that duplicate allocations do not occur in fixed spots for each parking request. If duplicate allocation occurs regardless of which fixed spot is selected, the buffer spot is unconditionally selected. Therefore, this always gives a feasible solution of the main problem. Also, since parking requests are assigned to spots in the order of arrival, the possibility that a halfway empty time will occur in the middle of assignment is small, so overflow from fixed spots is small compared to processing in other orders. The solution is obtained.

Returning to the processing flow chart of FIG. 3, the description of the solution finding processing of the fixed spot allocation optimization model is continued. Step
At 0305, it is determined whether or not the iterative process has ended. Step 0303
Letting A be the optimal solution of the Lagrangian relaxation problem obtained in, the objective function value of A gives the lower bound of the optimal solution of the main problem due to the nature of the Lagrangian relaxation problem. Therefore, if this is equal to the optimum value, the solution of the relaxation problem gives the optimum solution of the main problem. Therefore, ideally, in step 0305, it is determined whether A is equal to the optimum solution, and if it is equal, the iterative process is terminated and A is output as the final result. However, in reality, there is no guarantee that the difference between the optimum value of the relaxation problem and the optimum value of the main problem can be set to 0 for various reasons. Also, the optimum value of the relaxation problem alone cannot determine the level of the optimum value of the main problem. Therefore, the feasible solution of the main problem obtained in step 0304 is used to provide a highly accurate suboptimal solution even if the optimal solution cannot be reached, and to estimate the gap with the optimal value. B). Specifically, of B obtained in the above iterations, the solution with the smallest objective function value (that is, the closest to the optimal solution) is compared with A, and the gap of the objective function value is more than the preset value. When it becomes smaller, the iterative process ends. Further, there is a possibility that the gap will not become smaller than the set value no matter how many times the iteration is repeated, and therefore the iteration is unconditionally ended when the number of iterations reaches the specified number. After finishing the iteration, proceed to Step 0308,
The best B obtained by the iteration is output as the final result, and the whole process ends.

If the end determination in step 0305 is No, the process proceeds to step 0306. Here, the Lagrange multiplier λ is updated. Since the solution (A) of the Lagrangian relaxation problem gives the lower bound of the optimal solution as described above, λ may be changed here so that A further approaches the optimal solution. This updating method is closely related to the field of non-differentiable optimization, and various methods have been proposed in the past. The present invention does not particularly define which method is used. For example, in this embodiment, the subgradient method is used. Step 03 after updating λ
Proceed to 07 and increment the value of iteration count k by 1. After that, the process returns to step 0303 and the process is repeated.

By the above optimization processing, parking requests can be allocated to fixed spots to the maximum extent. By the way,
An example of a conventional parking plan creation device causing overflow (Fig. 9)
When this method is applied to, the overflow-free allocation result shown in FIG. 10 is obtained. In this case, in particular, there is no overflow from the fixed spot, and the optimum allocation result is obtained in the sense of the minimum allocation cost. There are various allocation costs such as parking fees, passenger travel distance, airline's wishes, etc. as described above, but whatever it is,
Using this method, overflow can be minimized and other objectives can be optimized.

Returning to the functional block diagram of FIG. 1, the description will be continued. As a result of the processing in the fixed spot allocating unit 0101, the open spot allocating unit 0102 allocates an open spot to a parking request overflowing from the fixed spot. In the present invention, the specific processing contents are not specified. The conventional processing method (FIG. 8) of the parking plan creating apparatus may be used for the open spot, or another method may be used. Also, the above-described optimization method in the fixed spot allocation unit can be applied to open spots (however, in that case, since it is not necessary to consider overflow, buffer spots have no meaning).

Since the overflow of parking requests is minimized by the optimization processing in the fixed spot allocation unit 0101, the allocation of open spots can be easily performed regardless of which method is used. In particular, if a simple process like the conventional method is used for this rather than an optimization process that requires a calculation cost, the overall processing time can be shortened without degrading the quality of the plan. Therefore, in this embodiment, the conventional method shown in FIG. 8 is limited to open spots.

[0055]

Since the present invention is constructed as described above, it has the following effects.

By constructing a fixed spot allocation optimization model for the purpose of allocating a parking request to a fixed spot at the minimum cost, by solving the model, the parking request is first allocated to the fixed spot to the maximum extent, and then fixed. Since unallocated parking requests overflowing from spots are newly allocated to open spots, it is possible to automatically create a parking plan with high utilization efficiency of fixed spots.

Further, with this configuration, it is possible to allocate an open spot with high accuracy without using a method with high calculation cost. Therefore, if a simple process is used for this, a parking plan can be created in a short time without degrading the quality of the plan.

The fixed spot allocation optimization model is
A temporary spot (buffer spot) for temporarily allocating parking spot requests overflowing from fixed spots is added to the allocation candidate for parking spot requests, and the allocation cost of the buffer spot is set to be higher than the allocation cost of any fixed spot. ,
Fixed spot allocation costs include parking fees, passenger travel distance,
If properly set according to the airline's wishes, the overflow of fixed spots can be minimized, and at the same time, other purposes can be optimized.

When the above model is solved by the Lagrangian relaxation method, it is possible to obtain a good quality solution that is very close to the optimum solution, that is, there is almost no unnecessary overflow from the fixed spot. In addition, by applying the Lagrangian relaxation method, information about the gap between the lower bound of the optimal solution and the objective function value of the optimal solution can be obtained, so that we can objectively grasp how close the obtained solution is to the optimal solution. be able to.

[Brief description of drawings]

FIG. 1 is a functional configuration diagram of a parking plan creating apparatus according to the present invention.

FIG. 2 is a process flow diagram of a fixed spot allocation unit 0101 in FIG.

FIG. 3 is an optimization model solution process in FIG. 2 (step 0
207) is a processing flowchart.

FIG. 4 is a processing flow chart of optimal solution generation processing (step 0303) of the relaxation problem in FIG.

5 is a process flow diagram of an executable solution generation process (step 0304) of the main problem in FIG.

FIG. 6 is an explanatory diagram of an airport layout.

FIG. 7 is a functional configuration diagram of a conventional parking plan creation device.

FIG. 8 is a process flow diagram of a parking plan creation unit 0701 in FIG.

FIG. 9 is an example of a parking plan creation by a conventional parking plan creation device.

FIG. 10 is an example of an optimal parking plan.

[Explanation of symbols]

0101 ... Fixed spot allocation unit, 0102 ... Open spot allocation unit, 0111 ... Parking request storage unit, 0112 ...
Spot attribute information storage unit, 0113 ... Spot allocation cost storage unit, 0114 ... Unallocated parking request storage unit, 0115
… Parking plan storage.

   ─────────────────────────────────────────────────── ─── Continued front page    (72) Inventor Toshiro Sasaki             1-280, Higashi Koikekubo, Kokubunji, Tokyo             Hitachi, Ltd. Research & Development Division (72) Inventor Kiki Koki             1099 Ozenji, Aso-ku, Kawasaki City, Kanagawa Prefecture             Ceremony company Hitachi Systems Development Laboratory F-term (reference) 5H180 AA26 KK06 KK07

Claims (6)

[Claims] 1. A first storage means for storing an aircraft parking request, a second storage means for storing parking spot attribute information, and a third storage means for storing a spot allocation cost.
Fixed spot allocation means for maximally allocating parking requests to fixed spots through optimization calculation using the parking requests, spot attribute information and spot allocation costs, and fixed as a result of maximum allocation of the parking requests to fixed spots. Fourth storage means for storing unallocated parking request not allocated to spot, open spot allocation means for allocating unallocated parking request to open spot using spot attribute information and spot allocation cost, and fixed And a fifth storage means for storing the parking plan obtained by the spot allocation means and the open spot allocation means. 2. The fixed spot allocating means according to claim 1, wherein the buffer spot is a temporary spot for tentatively allocating a parking request overflowing from the fixed spot, and the fixed cost for allocating the buffer spot is fixed. Means to set it larger than the allocation cost of the parking lot, the first constraint condition that allocates the parking request to either the buffer spot or the fixed spot, and the second constraint condition that duplicate parking request is not assigned to the fixed spot. And a means for constructing a fixed spot allocation optimization model for obtaining a combination of spot allocations that minimizes the allocation cost, and a best solution calculation means for calculating the best solution of the fixed spot allocation optimization model by an optimization calculation. A means for outputting the parking request allocated to the buffer spot in the best solution to the unallocated parking request storage section; Parked planning apparatus that creates a fixed parked plan parked requests assigned to the spot in the Jokai characterized by comprising a means for outputting to the stationed machine project storage unit. 3. The best solution calculation means according to claim 2, wherein the Lagrange mitigation problem is configured by relaxing the second constraint condition in which fixed parking spots are not duplicately allocated, and the Lagrangian problem is formed. An apparatus for preparing a parking plan, characterized by applying a Lagrangian relaxation method to a relaxation problem and the fixed spot allocation optimization model to calculate a best solution. 4. The application of the Lagrangian relaxation method according to claim 3, wherein the adjusted allocation cost is a value in which the Lagrange multiplier is added to the spot allocation cost, and the parking request is selected in ascending order of start time, and overlapped. An apparatus for creating a parking plan, characterized by allocating spots having a minimum adjusted allocation cost among allocatable spots that are not allocated and generating one feasible solution of the fixed spot allocation optimization model. 5. The application of the Lagrangian mitigation method according to claim 3, wherein the parking requests are selected one by one in an arbitrary order, and the spots having the minimum adjustment allocation cost are allocated by allowing duplication, and the Lagrange is allocated. A parking plan planning device characterized by generating an optimum solution of a relaxation problem. 6. A step of maximally assigning a parking lot request to a fixed spot by reading an aircraft parking lot request, a parking spot attribute information and a spot allocation cost and performing an optimizing operation. As a result of the step, an open spot allocation step of allocating an unallocated parking request that has not been allocated to a fixed spot to an open spot.