JP7588554B2 - Joint transportation planning system - Google Patents

Description

The present invention relates to a collaborative transportation plan creation system that creates transportation plans for transporting users to various facilities using vehicles.

Traditionally, in the logistics industry, mathematical search methods have been proposed that use logistics costs and other factors as objective functions to realize optimal delivery plans under constraints such as logistics costs and delivery dates (for example, Patent Document 1).

On the other hand, in recent years, due to the problem of labor shortages in the nursing care industry, nursing care providers themselves are increasingly taking on the task of transporting users to and from the facility, making it difficult for them to handle everything on their own. Furthermore, with the number of users expected to increase in the future, there is a demand for more efficient transportation services.

Japanese Patent Application No. 7-175504

In the meantime, it is considered that the transportation work in the above-mentioned nursing care facility is effective in terms of efficiency, particularly when multiple vehicles are used to transport multiple users from multiple nursing care facilities scattered in the neighboring area. Therefore, the transportation work in the nursing care facility has more complex constraints on the transportation plan compared to the collection and delivery plan in logistics. Specifically, the transportation work requires consideration, for example, to prepare seats for wheelchairs and to ensure that the user does not spend too much time in the vehicle. However, when the invention in Patent Document 1 is used to create a plan for the transportation work, there is a problem that an appropriate solution specialized for the transportation work, such as consideration for the user, cannot be obtained. In addition, when the invention in Patent Document 1 is used to create a plan for the transportation work, there is a problem that the processing time until the plan is created becomes too long due to the increased constraints.

Therefore, the present invention aims to provide a joint transportation plan creation system that can streamline joint transportation operations in nursing care facilities and efficiently create joint transportation plans even under complex constraints.

(1) A joint transportation plan creation system provided to solve the above-mentioned problems is a joint transportation plan creation system that creates a joint transportation plan for transporting multiple users to multiple facilities using multiple vehicles, and includes a reservation information acquisition unit that acquires reservation information including the user's transportation location and desired transportation time, a user information acquisition unit that acquires user information including the user's attributes, a facility information acquisition unit that acquires facility information including information related to the facility locations of the multiple facilities, a vehicle information acquisition unit that acquires vehicle information including the available number of vehicles and vehicle attributes, a travel time information acquisition unit that acquires travel time information for the vehicles, and a joint transportation plan creation unit that creates a joint transportation plan, and The pick-up plan creation unit defines pick-up and drop-off locations related to the pick-up and drop-off locations and the boarding and disembarking times corresponding to each of the pick-up and drop-off locations as pick-up and drop-off units, and groups the pick-up and drop-off units to form multiple pick-up and drop-off groups. A plurality of objective functions including the total travel time of the plurality of vehicles are set on the condition that a predetermined constraint condition prepared in advance is satisfied, and a predetermined weight is assigned to each of the plurality of objective functions in response to the deviation from a predetermined target value. An optimal solution is obtained by performing a search process using a mathematical optimization method that minimizes the sum of the objective functions, and a joint pick-up and drop-off plan is created based on the optimal solution.

In the above-mentioned joint transportation plan creation system, the joint transportation plan creation unit sets the pick-up and drop-off locations related to the pick-up and drop-off locations and the boarding and disembarking times corresponding to each of the pick-up and drop-off locations as a transportation unit. In addition, in the above-mentioned joint transportation plan creation system, the joint transportation plan creation unit configures a plurality of transportation groups by grouping the predetermined transportation units. Therefore, information (data) regarding the pick-up and drop-off locations and the boarding and disembarking locations, the boarding times and the disembarking times (also referred to as boarding and disembarking times) can be handled in units of transportation groups. In other words, information regarding "drop-off" and "pick-up" such as from the user's home to the facility and from the facility to the user's home can be generalized and handled as data. This allows information regarding "drop-off" and "pick-up" of each user in one vehicle to be handled centrally as the same logic.

In addition, the above-mentioned joint transportation planning system sets multiple objective functions including the total travel time of the multiple vehicles, on the condition that the prepared predetermined constraint conditions are satisfied. In addition, the multiple objective functions are each assigned a predetermined weighting based on the deviation from a predetermined target value.

The constraints here are conditions that are set for various matters that must necessarily be met, such as completing transportation of all users, not traveling at a speed faster than the expected travel time between points (sections), taking into consideration boarding and disembarking times at users' homes, not using more than the number of vehicles available, not having more passengers on board than the vehicle's number of standard seats or wheelchair seats, and using vehicles that can run near users' homes (taking into consideration the size of the vehicle).

The objective function (also called the cost function) is a function that is set for various factors that affect efficiency and costs such as labor costs, such as total travel time, each user's boarding time, the user's desired pick-up/drop-off time, pick-up/drop-off arrival time at the facility, and seating arrangement. Therefore, the above-mentioned joint transportation plan creation system can create a joint transportation plan that takes into account factors that affect efficiency and costs while satisfying the constraints.

In addition, a predetermined weight is assigned to each of the above-mentioned multiple objective functions according to the deviation from a predetermined target value. Here, the weight is assigned to the objective functions so that, for example, a large weight is assigned to an item that has a large effect on efficiency or cost. In other words, the larger the sum of the objective functions, the lower the efficiency and the higher the cost. Therefore, the above-mentioned joint transportation plan creation system obtains an optimal solution by performing a search process using a mathematical optimization method that minimizes the sum of the objective functions, and creates a joint transportation plan based on the optimal solution. This makes it possible to create a joint transportation plan that is efficient and can reduce costs. Various mathematical optimization methods can be preferably used for the search process here. In addition, the search process may be performed by appropriately limiting the number of samples to a predetermined number. This is expected to have the effect of suppressing the processing time from becoming longer than necessary.

(2) In the joint transportation plan creation system of the present invention described above, the search process may include an initial solution acquisition step of acquiring an initial solution by assigning available vehicles to all of the transportation groups based on the reservation information, the user information, the facility information, the vehicle information, and the constraint conditions and determining the order of movement within the transportation groups, and a user replacement step of randomly replacing one or more of the users in the transportation group assigned to the vehicles by the initial solution from one vehicle to another vehicle, or from one vehicle to multiple other vehicles.

The above-mentioned joint transportation planning system can obtain an initial solution by allocating available vehicles to all transportation groups and determining the order of movement within the transportation groups based on the above-mentioned various information and constraint conditions in the initial solution acquisition process. This makes it possible to obtain a certain degree of solution (initial solution) that satisfies the constraint conditions, etc. Therefore, a search process can be performed to find an optimal solution based on the initial solution.

The above-mentioned joint transportation schedule creation system also includes a user replacement process for randomly replacing one or more users of a transportation group assigned to a vehicle based on the obtained initial solution from one vehicle to another vehicle, or from one vehicle to multiple other vehicles. This allows a search process to be performed to find an optimal solution while randomly replacing users for multiple vehicles, with the transportation group based on the initial solution as one unit. Therefore, the search process can be simplified without performing complex processes.

(3) In the joint transportation planning system of the present invention described above, the objective function may be calculated by converting at least a portion of the deviation amount to an exponential power and aggregating it.

With this configuration, the deviation from the target value in the objective function can be exaggerated and tallied by converting it to an exponential power. This makes it possible to exaggerate factors that affect efficiency and costs, allowing the optimal solution to be calculated efficiently.

(4) In the joint transportation planning system of the present invention described above, the search process may be performed by any one or a combination of random search, simulated annealing, and genetic algorithms.

According to this configuration, the search process can be performed efficiently, and the optimal solution can be calculated efficiently. Therefore, the search process time can be expected to be shortened. Here, the random search randomly creates various solutions, performs cost calculations, and finds a solution with low cost. Simulated annealing adopts solutions with a certain probability even if the cost is poor in the early stages of optimization, and reduces the range as optimization progresses, so that it is possible to overcome small peaks where the cost becomes high. Genetic algorithms randomly create various solutions, perform cost calculations, and select multiple solutions with low cost as elite (superior solutions). Genetic algorithms are also considered to be algorithms that search for solutions close to the optimal solution by creating the next solution by imitating good solutions or creating solutions that have undergone mutation.

(5) The joint transportation plan creation system of the present invention described above may satisfy all of the constraints, that is, transportation of all of the users, not exceeding the capacity of the vehicle, and not exceeding the number of available vehicles.

This configuration makes it possible to prevent the creation of joint transportation plans that are physically impossible or that do not satisfy the needs of users. The constraints can include the user's desired transportation time and arrival time at the facility.

(6) In the joint transportation plan creation system of the present invention described above, the objective function may be set based on the deviation amount for at least one of the desired transportation time of the user, the arrival time at the facility, the total travel time, and the riding time of each of the users.

With this configuration, it is possible to set an objective function that takes the user into consideration, thereby reducing the burden on the user. In addition to being the above constraints, the user's desired pick-up and drop-off time and arrival time at the facility can also be set as an objective function, making it possible to obtain a solution that allows for slight deviations. Here, for example, if the user's boarding time is long, the burden on the user is considered to be large. Therefore, if the boarding time of each user is set as the objective function, the burden on the user can be reduced. In addition, the above-mentioned desired pick-up and drop-off time, arrival time at the facility, and each boarding time of each user can be any one or a combination of multiple values according to the usage situation.

(7) The joint transportation plan creation system of the present invention described above has a usage time acquisition unit capable of acquiring the usage time for using the facility, and the usage time may include the time spent at the facility.

With this configuration, even if a user stops by, for example, a hospital or shopping during a transfer, a joint transfer plan can be created that takes this into account. In addition, by including the time spent in a care facility, etc., it is possible to create a joint transfer plan that takes into account both "drop-off" and "pick-up."

The present invention provides a joint transportation plan creation system that can streamline joint transportation operations in nursing care facilities and efficiently create joint transportation plans that take into account the convenience and burden on users, even under complex constraints.

1 is an overall configuration diagram illustrating an embodiment of a joint transportation planning system according to the present invention. 4 is a data table showing an embodiment of acquired information in the joint transportation planning system according to the present invention. FIG. 2 is a flowchart showing an outline of the overall process in the joint transportation schedule creation system. FIG. 1 is a schematic flow diagram of optimization in collaborative transportation planning. FIG. 13 is a flow diagram of creating an initial solution in joint transportation planning. FIG. 13A is an explanatory diagram of creating a transportation group in creating an initial solution for joint transportation schedule creation, and FIG. 13B is an explanatory diagram of adding a facility where a user gets off in creating the initial solution. FIG. 13 is an explanatory diagram of schedule creation in creating an initial solution for joint transportation planning. FIG. 13A is an explanatory diagram showing the movement of users in the optimization of a joint transportation plan, and FIG. 13B is an explanatory diagram showing the replacement of users in the optimization of a joint transportation plan. FIG. 13A is an explanatory diagram showing a specific example of transportation items related to the creation of a joint transportation plan, and FIG. 13B is an explanatory diagram showing travel times in a matrix. FIG. 13 is an explanatory diagram showing a specific example of optimization of a joint transportation plan. FIG. 13 is an explanatory diagram showing a specific example of optimization of a joint transportation plan. FIG. 12 is an explanatory diagram showing vehicle attributes in FIG. 11 . FIG. 13 is an explanatory diagram showing point-to-point travel times in optimizing a joint transportation plan. FIG. 13 is an explanatory diagram showing overlap times between locations in optimizing a joint transportation plan. FIG. 11 is an explanatory diagram showing a state in which departure times and arrival times are set in the optimization of a joint transportation plan. FIG. 11 is a flow diagram showing the flow of optimization processing in joint transportation plan creation. FIG. 17 is a flow chart showing details of step S50 in FIG. 16. 1 is a schematic overall layout diagram showing an example of use of a joint transportation planning system according to the present invention.

One embodiment of the joint transportation planning system 1 according to the present invention will be described in detail with reference to Figures 1 to 18.

The joint transportation plan creation system 1 creates a joint transportation plan for transporting multiple users M (e.g., people requiring care or elderly people) to multiple facilities F (e.g., nursing care facilities) using multiple vehicles C. As shown in FIG. 1, the joint transportation plan creation system 1 has a reservation information acquisition unit 10, a user information acquisition unit 20, a facility information acquisition unit 30, a vehicle information acquisition unit 40, a travel time information acquisition unit 55, and a joint transportation plan creation unit 60. In addition to the above, the joint transportation plan creation system 1 also has a usage time acquisition unit 50, a memory unit 70, etc. The joint transportation plan creation system 1 can be provided as a program that can be used on, for example, a terminal device (not shown) or a server (not shown).

The reservation information acquisition unit 10 can acquire reservation information including the location of the facility F to which user M wishes to be dropped off, the location of user M's home or other location (also referred to as the pick-up/drop-off location), and the desired pick-up/drop-off time. Specifically, as the pick-up/drop-off location, information regarding the latitude and longitude of the pick-up/drop-off location is acquired. As the desired pick-up/drop-off time, information (data) regarding the desired time to be dropped off at facility F and the desired time to be picked up at home is acquired. The acquired reservation information is stored, for example, in the memory unit 70, which will be described later.

The user information acquisition unit 20 can acquire user information including the attributes of user M. Specifically, the user information acquisition unit 20 can acquire various types of information about the user, such as the user M's name, gender, age, address (pick-up and drop-off location), level of care (whether a wheelchair is required, etc.), name of facility to be used, desired size of car, etc., as the attributes of user M. The acquired user information is stored in the memory unit 70, for example.

Here, each user M is managed by a user ID assigned to him/her. The user ID is associated with personal information such as the name, sex, age, and address described above. In this embodiment, as shown in FIG. 2, the user information acquisition unit 20 acquires the pick-up/drop-off location of user M, boarding time and disembarking time (also referred to as boarding/disembarking time), whether or not a wheelchair is used, and the vehicle size in association with the user ID. In addition to the above information, the user information acquisition unit 20 can acquire pick-up/drop-off usage information such as whether or not the user uses the service for each day of the week and pick-up/drop-off category, the facility used, and the desired time for drop-off or pick-up. Note that the pick-up/drop-off usage information may be acquired by a separate pick-up/drop-off usage information acquisition unit (not shown) that is provided separately.

The facility information acquisition unit 30 can acquire facility information including information related to the facility locations of multiple facilities F. Specifically, the facility information acquisition unit 30 can acquire various information related to facility F, such as the facility name and the latitude and longitude related to the facility location, as facility information. The acquired facility information is stored in the memory unit 70, for example. Here, the facilities F are managed by the facility ID assigned to each of them. As a result, a pick-up and drop-off location is associated with each of the multiple users M and the multiple facilities F. In addition, a desired time is associated with each of the multiple users M.

The vehicle information acquisition unit 40 can acquire vehicle information including the number of available vehicles C and vehicle attributes. Specifically, the number of available vehicles is the number of vehicles owned by each facility that can be used for transportation on the relevant day of the week. In addition, vehicle attributes such as vehicle size (e.g., large vehicles such as microbuses, ordinary vehicles such as wagons, light vehicles, etc.) and the number of seats in the vehicle (e.g., number of regular seats, number of wheelchair seats, etc.) are acquired. The acquired vehicle information is stored in the memory unit 70, for example.

The usage time acquisition unit 50 can acquire the usage time for using the facility F. The usage time can include, for example, the time spent at the facility F. Here, the facility F for which usage time is acquired can be set to, for example, a hospital or shopping destination (store) visited by the user M. The acquired usage time is stored in, for example, the memory unit 70. Note that in this embodiment, the user information acquisition unit 20 acquires the boarding and alighting time of the user M, but the usage time acquisition unit 50 may acquire the boarding and alighting time.

As described above, by providing the usage time acquisition unit 50, even if the user M stops by, for example, a hospital or shopping during the transfer, a joint transfer plan can be created that takes this into consideration. In addition, by including the time spent in a care facility or the like, it is possible to create a joint transfer plan that takes into consideration both "drop-off" and "pick-up." Note that the usage time acquisition unit 50 may be provided as needed, and the usage time acquisition unit 50 may also be omitted.

The travel time information acquisition unit 55 can acquire travel time information when the vehicle C travels between the home of the user M and the facility F. The travel time information is acquired based on, for example, the travel distance, the average speed of the vehicle C, or a time calculated based on traffic congestion information. The travel time information may be updated while providing feedback based on actual travel information such as travel frequency and time of day.

The joint transportation plan creation unit 60 can create and prepare a joint transportation plan using a calculation device such as a CPU. The joint transportation plan creation unit 60 sets pick-up and drop-off locations related to pick-up and drop-off locations and facility locations, and the pick-up and drop-off times corresponding to each of the pick-up and drop-off locations as a transportation unit. The joint transportation plan creation unit 60 also groups predetermined pick-up and drop-off units to form a plurality of transportation groups. Details regarding the composition of transportation groups will be described later.

The joint transportation scheduler 60 can also set multiple objective functions including the total travel time of multiple vehicles C. The objective functions are set on the condition that certain constraint conditions prepared in advance are satisfied.

The constraints here are conditions that are set for various matters that must necessarily be met, such as completing transportation of all users M, not traveling at a speed faster than the expected travel time between points (sections), taking into consideration the boarding and disembarking time at user M's home, not using more than the number of available vehicles C, not having a passenger load that exceeds the number of standard seats or wheelchair seats of vehicle C, and using a vehicle C that can run near user M's home (taking into consideration the size of vehicle C).

The above-mentioned constraint conditions can be, for example, to satisfy all of the following: transporting all users M, not exceeding the capacity of vehicle C, and not exceeding the number of available vehicles C. This can prevent the creation of joint transportation plans that are physically impossible or that do not satisfy the needs of user M.

The objective function (also called the cost function) is a function that is set for various factors that affect efficiency and cost, such as the total travel time, the boarding time of each user M, the desired pick-up/drop-off time of user M, the pick-up/drop-off arrival time at facility F, and seating arrangement considerations. Therefore, the joint pick-up/drop-off plan creation unit 60 can create a joint pick-up/drop-off plan that takes into account factors that affect efficiency and cost while satisfying the constraint conditions. Details of the objective function will be described later.

The joint transportation plan creation unit 60 can execute a search process to minimize the sum of the objective functions using a mathematical optimization method. This allows the joint transportation plan creation unit 60 to obtain an optimal solution that minimizes the sum of the objective functions. Therefore, the joint transportation plan creation unit 60 can create a joint transportation plan based on the optimal solution. In other words, a joint transportation plan that is efficient and can reduce costs can be created. Various mathematical optimization methods can be preferably used for the search process here. In addition, the search process may be performed by appropriately limiting the number of samples to a predetermined amount. This is expected to have the effect of preventing the processing time from becoming longer than necessary. The specific processing in the joint transportation plan creation unit 60 will be described later.

The storage unit 70 can store the joint transportation plan created by the joint transportation plan creation unit 60. The storage unit 70 is configured, for example, with a hard disk, memory, etc. The storage unit 70 need not necessarily be provided in the joint transportation plan creation system 1, but may also be stored in an appropriate external server, etc.

The above-mentioned joint transportation planning system 1 can be provided in various terminal devices, servers, etc. Also, the joint transportation planning system 1 can be provided in various forms, such as as a program or as a device.

The above is the configuration of the joint transportation planning system 1 according to the present invention. Next, one embodiment of the processing in the joint transportation planning system 1 will be described with reference to Figures 3 to 5.

Figure 3 is a flow diagram showing an outline of the overall process in the joint transportation plan creation system 1. First, when creating a joint transportation plan in the joint transportation plan creation unit 60, data related to information for creating the joint transportation plan is selected (step S10). In step S10, data such as reservation information, user information, facility information, and vehicle information required for creating the joint transportation plan is selected.

Next, the selected data is read from the storage unit 70 (step S11). Once the required data has been read, a process for creating a joint transportation plan is carried out (step S12). Once the creation of the joint transportation plan is complete, the joint transportation plan is stored in the storage unit 70 (step S13). Once step S13 is complete, the series of processes is either ended, or the processes from step S10 are repeated again. This is the general flow of the overall process in the joint transportation plan creation system 1 according to the present invention.

Next, the flow of creating a joint transportation plan in the joint transportation plan creation process (step S12) will be explained below with reference to FIG. 4.

When the process of creating a joint transportation plan is started, first, the joint transportation plan creation process (step S12) receives the necessary information (data) (step S20). Next, an initial solution is created (step S21). The process of creating the initial solution in step S21 is also referred to as the initial solution creation step. Details will be described later, but in creating the initial solution, a solution that satisfies the constraints to a certain extent (fairly) is created.

When the creation of the initial solution is completed in step S21, a process of optimizing (reducing costs) the initial solution is performed (step S22). The optimization is repeated a predetermined number of times, as will be described in detail later. When step S22 is completed, the created joint transportation plan is returned to the process of step S12, and is stored in the storage unit 70 in step S13. As described above, the joint transportation plan creation system 1 designs and creates a joint transportation plan by executing the following three processing steps, which can be broadly divided. First, in step S21, an initial solution is created, with the condition that the constraint conditions are satisfied. Next, in step S22, the initial solution is optimized. Next, the optimization is repeated a specified number of times to obtain an optimal solution.

Next, an overview of how the initial solution is created will be explained based on Figure 5. The initial solution is created in three stages, for example, levels A1 to A3.

First, at level A1 (step S30), grouping (formation of a pick-up/drop-off group) is performed based on pick-up/drop-off items (also called pick-up/drop-off units). At level A1, picking up user M and delivering him/her to facility F and delivering user M from facility F to his/her home are treated as separate pick-up/drop-off units, and it is determined how one item should be grouped with the other items.

Here, a pick-up/drop-off group is formed as a pick-up/drop-off location and a boarding/drop-off location related to a facility location (both latitude and longitude information), and a boarding time and alighting time corresponding to each of the boarding/drop-off locations, as a pick-up/drop-off unit. Pick-up/drop-off groups are formed for multiple users M and multiple facilities F respectively. Therefore, information (data) related to the pick-up/drop-off locations and boarding/drop-off locations and boarding/drop-off times can be handled on a pick-up/drop-off group basis. In other words, information related to "drop-off" and "pick-up" such as from the user M's home to the facility and from the facility to the user M's home can be generalized and handled as data. This allows information related to "drop-off" and "pick-up" of each user on board a vehicle C to be handled centrally as the same logic. Note that location information such as boarding/drop-off locations may not only be based on latitude and longitude, but may also be handled as the address itself.

When grouping by shuttle unit in step S30 is completed, vehicles C are assigned to each shuttle group at level A2 (step S31). Level A2 is a level for optimizing the assignment of shuttle groups and vehicles C. At level A2, the optimal vehicle C is determined based on the seat type, vehicle size, number of people, etc. of users M in the shuttle group.

When step S31 is completed, in level A3 (step S32), the order in which to visit each pick-up/drop-off location and each boarding/disembarking location (also called points) is calculated. In level A3, once the pick-up/drop-off group and vehicle C have been determined, the order in which to visit the points is determined to be most efficient. This is the flow of the initial solution creation process. Next, the details of creating the initial solution will be explained.

Figure 6(a) shows an example of creating shuttle groups when creating an initial solution. In the figure, users M are individually numbered M1 to Mn (n is arbitrary), facilities F are individually numbered F1 to Fn (n is arbitrary), and vehicles C are individually numbered C1 to Cn (n is arbitrary).

The initial solution is created in accordance with each step of levels A1 to A3 described above. Specifically, at level A1, vehicles that are close on six axes, namely the latitude and longitude of the pick-up/drop-off location of user M, the desired pick-up/drop-off time, the latitude and longitude of facility F, and the departure and arrival times at facility F, are roughly grouped. The number of pick-up/drop-off groups is limited to the number of available vehicles (in this embodiment, three vehicles C1 to C3). In addition, when grouping, vehicle C is assigned to the pick-up/drop-off group on the condition that constraints (for example, sending everyone to facility F and the type of seat in vehicle C) are satisfied. Note that at this stage, the number of occupants in vehicle C is not taken into consideration.

Figure 6(b) shows the state of the process for determining the order in which to visit the points in creating the initial solution, and shows an example of how to visit the points. Here, if user M8 is closer to facility F2 than user M2, it may be possible to save time by switching the order of users M2 and M8. Also, due to the number of wheelchair seats, the vehicle goes to facility F1 after picking up users M3 and M5, but the constraint on the number of seats can also be met by picking up user M3, going to facility F1, and then picking up users M5 and M10. This may also save time. Therefore, calculations are performed for multiple combinations, a schedule is created as described below, and then the route to the points with the lowest cost is adopted.

Figure 7(c) shows a list of the order in which the locations should be visited in the creation of the initial solution, with the time to visit each location added. The arrival and departure times should be determined after taking into consideration the boarding and disembarking times and travel times, in accordance with the desired arrival and departure times or the designated time range of the location. When creating a schedule for "delivery" rather than a mixed pick-up and drop-off, the arrival time should be determined by calculating backwards from the last facility F, taking into consideration the boarding and disembarking times. In the case of "pick-up", the time should be determined from the back of the time axis. In addition, if user M has a desired pick-up and drop-off time, the departure time or the entire arrival time should be shifted to match user M. In the schedule creation stage of creating the initial solution, the times should be adjusted to the extent possible (even if they are not within the desired pick-up and drop-off time range or there is a waiting time on the way). In this way, the initial solution is created.

Once the schedule creation is completed, a cost estimate is performed for the created initial solution. The cost estimate is performed, for example, for an objective function consisting of the following items. The items that make up the objective function include, for example, (1) total travel time (objective function), (2) deviation from user M's desired pick-up time, (3) the time 30 minutes before the arrival time at facility F when it is 30 minutes or earlier before the desired time, and (4) the riding time of each user M. The objective function is set based on these items, and the cost is calculated by the sum of the respective objective functions. Note that the riding time of each user M in (4) above may be, for example, the time 60 minutes or more when it is 60 minutes or more.

In this embodiment, a predetermined weighting is applied to the deviation from the predetermined target value for each of the above items (2) to (4). As a result, weighting is applied to the objective function so that, for example, items that have a large impact on efficiency and cost are weighted heavily. In addition, by exponentially raising the deviation (for example, squaring it), the larger the deviation, the higher the cost. Therefore, the larger the sum of the objective functions, the lower the efficiency and the higher the cost. Note that weighting can be applied not only to the square of the deviation, but also to the cube or higher of the deviation. In addition, weighting can be applied using various coefficients other than the exponential power.

As described above, the objective function may be set based on the deviation from the target value for at least one of the desired pick-up/drop-off time of user M, the arrival time at facility F, and each boarding time of user M. This allows an objective function that takes user M into consideration to be set, thereby reducing the burden on user M. Furthermore, by setting the deviation for each boarding time of user M in the objective function, it is possible to prevent user M's boarding time from becoming longer, thereby reducing the burden on user M. Note that the desired pick-up/drop-off time, arrival time at facility, and each boarding time of user M may be any one or a combination of multiple values according to the usage situation.

As described above, in the joint transportation schedule creation system 1 according to the present invention, in the initial solution acquisition step, an initial solution can be obtained by allocating available vehicles to all transportation groups based on the various information and constraints described above. This makes it possible to obtain a solution (initial solution) that satisfies the constraints, etc. to a certain extent. Therefore, the initial solution can be used as a reference for a search process to find a more optimal solution.

Next, the details of the optimization process in step S22 in FIG. 4 will be described below. The optimization process of the initial solution is performed based on a search process using a mathematical optimization method. The mathematical optimization method used will be described later. The optimization process is performed in three stages, levels B1 to B3.

First, at level B1, the best combination is searched for while gradually changing the groups of the transportation items. Specifically, one transportation item is removed from one transportation group and incorporated into another transportation group, or one transportation item from one transportation group is exchanged with one transportation item from another transportation group.

8(a) and 8(b) show specific examples of level B1. FIG. 8(a) shows an example of randomly selecting a user M and moving the selected user M from one shuttle group (group 1) to another shuttle group (group 2). FIG. 8(b) shows an example of exchanging user M5 of group 1 with user M7 of group 2. In the figure, user M24 of vehicle C1 is replaced with user M7 of vehicle C2. In this way, at level B1, a solution that is a slight change from the initial solution is created. In level B1, one or more users M of the shuttle group assigned to vehicle C by the initial solution are randomly swapped from one vehicle to another vehicle C, or from one vehicle C to multiple other vehicles C (also referred to as a user swapping process).

The created solution is then subjected to optimization processing from level B2 onwards, which will be described later. If the cost has decreased, the new solution is adopted, and if the cost has not decreased, the original solution is adopted. Note that if simulated annealing is used in the search process, the new solution may be adopted even if the cost has increased in the early stages of optimization.

Level B2 allocates an appropriate vehicle C to each shuttle group. Specifically, the vehicle C is allocated taking into consideration the seat type and vehicle size of users M in the shuttle group, and cost estimation is performed by invoking optimization at level B3, which will be described later. Furthermore, at level B2, all possible vehicle C allocations are tried, and the combination with the lowest sum of optimization costs at level B3 is determined to be the optimal solution for level B2.

Level B3 optimizes the order and time of visiting locations. Specifically, for multiple designated transportation items, when a designated vehicle C is used to transport a person, the cost is estimated to determine which locations and times are most efficient.

As described above, the joint transportation schedule creation system 1 according to the present invention can perform a search process to find an optimal solution by randomly replacing users M, with each group based on an initial solution as a unit. Therefore, the search process can be simplified without performing complex processing.

Next, a specific example of level B3 will be described below with reference to Figs. 9 to 15. For example, a case will be described in which the three transportation items, user M, transportation location, and transportation time, as shown in Fig. 9(a) are optimized.

In the case shown in Figure 9(a), there are six points to visit, including overlaps, from points a1 to a6, so it is a good idea to find out in what order (travel order) and at what time it is best to visit these points. If efficiency is not a consideration, an example would be to visit points a1, a2, a3, a4, a5, and a6 in that order, as shown in Figure 10.

Next, as shown in FIG. 11, consider swapping points a2 and a3 and visiting points a1, a3, a2, a4, a5, and a6 in that order. By visiting the points in this order, it is possible to reach points a2, a4, and a5 at the same time. However, as shown in FIG. 12, if vehicle C only has one wheelchair seat and two users M1 and M2 use wheelchairs, it is not possible to visit the points in the order described above. Therefore, the optimal way to visit the points is searched for, taking into account the attributes of vehicle C.

Here, once the order of visiting the above-mentioned points is determined, it is basically possible to determine which point is the shortest to go to at what time. Therefore, for example, by calculating the cost of all combinations of the tours, the optimal solution can be derived. The combinations of the tours in the above case are 6 permutations (factorial ways of 6), but combinations in which points a1 and a2 are reversed, or combinations in which the time is reversed, such as from point a5 to point a3, are impossible combinations, so by eliminating these combinations, the time required for cost calculation can be reduced. It is possible to perform cost calculations for all combinations, but if the number of combinations is large, it is also possible to perform calculations for only a specified number of combinations. In this way, by limiting the number of combinations and performing cost calculations, the time required for cost calculations can be reduced.

As mentioned above, when the order of visiting the locations is determined, the time to visit each location is basically determined in a single way. Therefore, for the three pick-up and drop-off items mentioned above, the travel time between each location when visiting the locations a1, a3, a2, a4, a5, and a6 in the order shown in Figure 11 is taken into consideration.

Figure 13 shows the travel time (in minutes) between each point, and Figure 9(b) shows a matrix of the travel time (in minutes) between each point. Note that in reality, the travel time may differ between the outbound and return journeys, but here, to simplify the explanation, the travel time is set to be the same for the outbound and return journeys.

As shown in FIG. 13, points a2, a4, and a5 are the same point and have overlapping time periods, so they can be aggregated into one. The aggregated point information is also referred to as point b2. Therefore, the order of visiting the points after aggregation can be expressed as points a1, a3, b2, and a6. As a result, the time period for point b2 becomes the overlapping time of 12:40 to 13:00, as shown in FIG. 14.

Here, the time is specified for points a3 and b2. Also, the travel time between point a3 (user M2's home) and point b2 (facility F1) is 10 minutes. Therefore, when deciding the time to stop at points a3 and b2, these are taken into consideration, and point a3 is decided to be 12:30, which is the end of the time slot, and point b2 is decided to be 12:40, which is the beginning of the time slot. Since there is no time specification for points a1 and a6, the travel time from the previous and next points is taken into consideration and the shortest time can be set. In this way, as shown in FIG. 15, point a1 is decided to be 12:20, point a3 is decided to be 12:30, point b2 (points a2, a4, a5) is decided to be 12:40, and point a6 is decided to be 12:45. In reality, there is a boarding and alighting time for user M, but for the sake of convenience, the boarding and alighting time is not taken into consideration (in actual cost estimation, the boarding and alighting time is also included in the calculation).

The search process using the mathematical optimization method used to perform the optimization described above is now explained.

In the joint transportation planning system 1 according to the present invention, the search process can be performed by, for example, random search, simulated annealing, or a genetic algorithm, or a combination of two or more of these. Note that the above search process is an example, and other mathematical optimization methods can be used, or these mathematical optimization methods can be used in combination with other mathematical optimization methods.

The random search randomly creates various solutions, performs cost calculations, and finds low-cost solutions. Simulated annealing adopts solutions with a certain probability of being poorly costly in the early stages of optimization, while narrowing the range as optimization progresses, making it possible to overcome small high-cost peaks. Genetic algorithms randomly create various solutions, perform cost calculations, and select multiple low-cost solutions as elite (superior) solutions. Genetic algorithms are algorithms that search for solutions close to the optimal solution by creating the next solution by imitating good solutions or by creating solutions that have undergone mutation. Therefore, by using these mathematical optimization methods, optimization can proceed while overcoming any high-cost peaks. This makes it possible to prevent high-cost peaks from becoming an obstacle and terminating the optimization midway.

As described above, the joint transportation plan creation system 1 according to the present invention uses the above-mentioned mathematical optimization method when optimizing the joint transportation plan, so that the search process can be performed efficiently and the optimal solution can be calculated efficiently. Therefore, the search process time can be shortened.

The above is a detailed explanation of the optimization process for creating a joint transportation plan. Next, we will explain the process flow for the optimization process using the flow charts in Figures 16 and 17.

As shown in FIG. 16, when the optimization process starts, step S40 is where a predetermined number of repetitive processes (loop 1, corresponding to level B1) are executed between steps S40 and S43. In loop 1, first, the items in the transportation group of the transportation items are changed (step S50).

When the process in step S50 starts, as shown in FIG. 17, step S51 is a step in which steps S51 to S60 are repeated a predetermined number of times (loop 2, corresponding to level B2). In loop 2, first, the allocation of vehicle C to the shuttle group is changed (step S52).

When step S52 is completed, step S53 is where steps S53 to S57 are repeated a predetermined number of times (loop 3, corresponding to level B3 described above). In loop 3, first the order of visiting the locations is changed (step S54). When step S54 is completed, cost calculation (trial calculation) is performed (step S55). In step S55, once the cost has been calculated, a comparison is made between the solution before the change and the solution after the change, and the optimal solution is selected (step S56). When step S56 is completed, the process from step S53 to step S56 is repeated a predetermined number of times (loop 3, corresponding to level B3 described above).

When the processing in loop 3 has been performed a predetermined number of times, cost calculation in loop 2 is performed again (step S58). When the cost is calculated in step S58, the solution before the change is compared with the solution after the change, and the optimal solution is selected (step S59). When step S59 is completed, the processing in loop 2 is repeated a predetermined number of times. When the processing in loop 2 has been performed a predetermined number of times, cost calculation in loop 1 is performed (step S41). When the cost is calculated in step S41, the solution before the change is compared with the solution after the change, and the optimal solution is selected (step S42). When step S42 is completed, the processing in loop 1 is repeated a predetermined number of times (step S43). When the processing in loop 1 is completed, optimization is terminated. As a result, a joint transportation plan is created, and the transportation plan save process is executed in step S13 of FIG. 3.

In this embodiment, each of the above-mentioned loops 1 to 3 is repeated a predetermined number of times, but the number of times each process is repeated can be changed as appropriate depending on the number of combinations in the search process, the accuracy of creating the joint transportation plan, and the creation time. It is also possible to repeat the process for all combinations in the search process.

The above is the flow of the optimization process of the joint transportation schedule creation system 1 according to the present invention. Next, one mode of use of the joint transportation schedule creation system 1 will be explained below with reference to FIG. 18.

As shown in the figure, the joint transportation plan creation system 1 can be connected to various networks. In this embodiment, the joint transportation plan creation system 1 is connected to a user web 100, a transportation group web 101, application APIs 103 and 106, an inquiry API 104, and a facility web 105. The joint transportation plan creation system 1 also has a memory unit 70, and data such as the created joint transportation plan is stored in the memory unit 70.

The user web 100 can be accessed from the terminal of user M. Incidentally, access to the joint transportation plan creation system 1, including the user web 100, is restricted by appropriate passwords or the like. The app API 103 is an API (application programming interface) provided to enable access to the joint transportation plan creation system 1 through a user app 109 provided for a terminal such as user M's smartphone.

The shuttle group Web 101 is connected to the vehicle dispatch algorithm 102 and can be accessed by the shuttle group 108. The vehicle dispatch algorithm 102 provides optimal dispatch information for vehicle C to the shuttle group Web 101 based on the joint shuttle plan. This allows the shuttle group to obtain optimal dispatch information based on the shuttle group Web 101.

The app API 106 is an API provided to enable access to the joint transportation plan creation system 1 through the driver app 107. The driver app 107 is an application provided to the terminal of the driver who drives the vehicle C. This allows the driver to view the joint transportation plan.

The inquiry API 104 is an API provided to enable user M to access the joint transportation plan creation system 1 via SNS 110 or the like (also referred to as Twilio (registered trademark)). In this way, the joint transportation plan creation system 1 provides various network connection means for user M to view and create joint transportation plans.

The facility Web 105 is accessible from a facility F, such as a nursing facility. This makes it possible to view and create joint transportation plans from facility F.

The joint transportation plan creation system 1 is also connected to an external system 111 such as a server. For example, if the external system 111 is a server, calculations can be performed using the external system 111 for part or all of the joint transportation plan creation. This can reduce the load on the joint transportation plan creation system 1 and speed up calculations. Note that the above-mentioned usage form can be modified in various ways. For example, the joint transportation plan creation system 1 can be placed on the external system 111.

The above is one embodiment of the joint transportation planning system 1 according to the present invention, but the present invention is not limited to the above-described embodiment and various modifications can be made.

In this embodiment, a joint transportation plan is created to transport multiple users M to multiple facilities F using multiple vehicles C, but it can also be used when there is only one user M, vehicle C, or facility F. In addition, the information acquired by the reservation information acquisition unit 10, user information acquisition unit 20, facility information acquisition unit 30, and vehicle information acquisition unit 40 is not limited to that in the embodiment, and can be changed as appropriate.

The units of transportation set by the joint transportation plan creation unit 60 are not limited to pick-up and drop-off locations related to pick-up and drop-off locations and facility locations, and the boarding times and disembarking times corresponding to the pick-up and drop-off locations, respectively, and can be changed as appropriate according to the plan to be created. The units of transportation to be grouped can also be changed as appropriate for transportation groups. The objective function can also be one that does not include the total travel time of vehicle C. In such cases, an objective function that includes other transportation items that should be prioritized can be set.

In this embodiment, the search process includes an initial solution acquisition step in which an initial solution is acquired by assigning available vehicles C to all of a plurality of shuttle groups based on reservation information, user information, facility information, vehicle information, and constraint conditions, but the constraint conditions in the initial solution acquisition step can be changed as appropriate. Furthermore, the creation of shuttle groups and the assignment of vehicles C in the initial solution acquisition step do not have to be performed for all shuttle groups or all vehicles C. In such cases, the necessary shuttle groups and vehicles C can be selected.

In this embodiment, the deviation from the target value is converted to an exponential power in the objective function and then tabulated, but the deviation from the target value may be tabulated by a method other than converting to an exponential power. Furthermore, preference may be given to values with a small deviation from the target value. In such a case, the deviation may be tabulated as the dominant value. Furthermore, in this embodiment, weighting is applied to the deviation to the square, but weighting may be applied not only to the square of the deviation, but also to the cube or more of the deviation. Furthermore, weighting may be applied using various coefficients other than the exponential power.

In this embodiment, the search process is performed using any one or a combination of random search, simulated annealing, and genetic algorithms, but the search process is not limited to the above, and the search process can be performed using various mathematical optimization methods. In addition, mathematical optimization methods other than these can be used, and these mathematical optimization methods can be used in combination with other mathematical optimization methods.

In this embodiment, the constraint conditions are set to satisfy all of the following: transporting all users M, not exceeding the capacity of vehicle C, and not exceeding the number of vehicles C available; however, the constraint conditions are not limited to the above conditions and can be set for various items.

In this embodiment, the objective function is set based on the deviation from the target value for at least one of the desired pick-up/drop-off time of user M, the arrival time at facility F, and each boarding time of user M, but the objective function is not limited to the above and can be set for various pick-up/drop-off items. In addition, the target value and the deviation from the target value can be changed appropriately according to the pick-up/drop-off item.

In this embodiment, a usage time acquisition unit 50 capable of acquiring the usage time for using facility F is provided, but the configuration may also be such that the usage time acquisition unit 50 is not provided. Also, instead of the usage time acquisition unit 50, the facility information acquisition unit 30 may acquire the usage time (stay time, etc.) for facility F, etc. In this embodiment, the created joint transportation plan is stored in the storage unit 70, but the storage unit 70 may be omitted. In such a case, a storage means such as the external system 111 may be used.

The above are various embodiments and modifications of the joint transportation planning system 1 according to the present invention, but the present invention is not limited to the above-mentioned embodiments and modifications, and it will be readily apparent to those skilled in the art that other embodiments are possible within the scope of the teachings and spirit of the present invention without departing from the scope of the claims.

The joint transportation planning system of the present invention can be used for joint transportation of users in various facilities such as nursing homes and hospitals.

1: Joint transportation plan creation system 10: Reservation information acquisition unit 20: User information acquisition unit 30: Facility information acquisition unit 40: Vehicle information acquisition unit 50: Usage time acquisition unit 55: Travel time information acquisition unit 60: Joint transportation plan creation unit 70: Storage unit

Claims (4)

A joint transportation plan creation system for creating a joint transportation plan for transporting a plurality of users to a plurality of facilities using a plurality of vehicles,
A reservation information acquisition unit that acquires reservation information including a pick-up/drop-off location and a desired pick-up/drop-off time period of the user;
a user information acquiring unit that acquires user information including attributes of the user, a boarding time required for the user to board the vehicle, and an alighting time required for the user to alight the vehicle ;
a facility information acquisition unit that acquires facility information including information related to facility locations of the plurality of facilities;
a vehicle information acquisition unit that acquires vehicle information including the available number of vehicles and vehicle attributes;
A travel time information acquisition unit that acquires travel time information of the vehicle;
A joint transportation plan creation unit that creates a joint transportation plan,
The joint transportation planning unit
The pick-up and drop-off locations, which are the pick-up and drop-off locations and the boarding times and the disembarking times of the user information corresponding to the pick-up and drop-off locations, are set as pick-up and drop-off units, and a plurality of pick-up and drop-off groups are formed by grouping predetermined pick-up and drop-off units;
A plurality of objective functions are set, the objective functions including a total driving time of the plurality of vehicles , the desired pick-up/drop-off time zone, and a riding time of each of the users calculated based on the travel time information , on the condition that a predetermined constraint condition prepared in advance is satisfied, the condition including not using more than the available number of vehicles of the vehicle information is satisfied, and a predetermined weight is assigned to each of the plurality of objective functions in accordance with an amount of deviation from a predetermined target value;
A joint transportation plan creation system characterized by obtaining an optimal solution by executing a search process using a mathematical optimization method that minimizes the sum of the objective functions, and creating a joint transportation plan based on the optimal solution. The search process includes:
an initial solution acquisition step of acquiring an initial solution by allocating available vehicles to all of the plurality of shuttle groups based on the reservation information, the user information, the facility information, the vehicle information, and the constraint conditions and determining a movement order within the shuttle groups;
The joint shuttle plan creation system of claim 1, further comprising a user replacement process for randomly replacing one or more of the users of the shuttle group assigned to the vehicle by the initial solution from one vehicle to another vehicle, or from one vehicle to multiple other vehicles. The joint transportation planning system according to claim 1 or 2, characterized in that the objective function is calculated by converting at least a portion of the deviation amount to an exponential power and aggregating it. The joint transportation planning system according to any one of claims 1 to 3, characterized in that the search process is performed by random search, simulated annealing, genetic algorithm, or a combination of these.

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