WO2016143037A1

WO2016143037A1 - Logistics plan generation method and system

Info

Publication number: WO2016143037A1
Application number: PCT/JP2015/056847
Authority: WO
Inventors: 幸久藤田; 健司大家
Original assignee: 株式会社日立製作所
Priority date: 2015-03-09
Filing date: 2015-03-09
Publication date: 2016-09-15

Abstract

The purpose of the present invention is to form a low-risk, low-cost logistics plan for dealing with both goods and people simultaneously by considering various risks. Provided is a logistics plan generation method that is executed by a computer capable of performing at least data input, data output, and inputted data processing, the method provided with: a reference plan generation unit for generating a logistics reference plan from supply and demand information of goods and human resource information of people; a stable production risk calculation unit for calculating stable production risk; and a risk adjustment unit for adjusting, on the basis of the stable production risk calculated by the stable production risk calculation unit, the logistics reference plan generated by the reference plan generation unit. The logistics plan generation method is characterized by extracting, with respect to the logistics plan generated by the reference plan generation unit, a plan-changeable part on the basis of the stable production risk calculated by the stable production risk calculation unit, and generating a logistics plan by adjusting the plan within the changeable range by the risk adjustment unit.

Description

Logistics plan generation method and system

The present invention relates to a logistics plan generation method and system.

In logistics and other fields, including the Oil & Gas field, logistics necessary to carry materials and people necessary for operation are indispensable. For example, when oil field development is performed on an offshore platform, it is necessary to carry equipment necessary for development, consumables for operating the equipment, and workers who handle them to the offshore platform. Therefore, it is necessary to make a delivery plan for carrying goods and people, that is, a logistics plan. Since the efficiency of the logistics plan directly affects the cost, it is necessary to formulate an efficient plan as much as possible to reduce the cost. On the other hand, if the operation is stopped due to a shortage of goods, etc., it will be seriously damaged, so it is necessary to fully consider the risks. Therefore, it is necessary to develop a logistics plan that carries goods and people while taking risks and costs into consideration.

As an example of such a logistics planning method, for example, Patent Document 1 discloses a technique for assigning a large number of personnel to a predetermined place in consideration of human technical information, vacation acquisition status, and the like. Regarding delivery of goods, Patent Document 2 discloses a technique for assigning transportation equipment from supply and demand information.

JP 2003-157343 A Japanese Patent Laid-Open No. 11-328573

However, conventional techniques related to logistics planning focus only on either goods or people, and it is difficult to formulate an efficient logistics plan considering both. For example, if a logistics plan is created by simply considering supplies and people as objects to be delivered, it will be possible to carry both goods and people at the same time if the person to be delivered is one hour earlier in the work plan. For example, two transport devices are required, and there is a problem that it is not possible to consider a place where the efficiency can be improved.

Note that the technique disclosed in Patent Document 1 only assigns necessary persons to the required work, and does not consider a delivery method to the office. In Patent Document 2, although transportation equipment is allocated, since supply and demand information is fixed, it is not possible to consider a portion that can be made efficient by early departure as described above.

The present invention takes the above-mentioned problems into consideration and aims to generate a logistics plan for goods and people that is safe and low in cost.

A typical example of the invention disclosed in the present application is as follows. The present invention is calculated from a standard plan generation unit that generates a logistics standard plan from supply and demand information of goods and human resource information, a stable production risk calculation unit that calculates stable production risk from the logistics standard plan, and a stable production risk calculation unit And a risk adjustment unit that adjusts the logistics standard plan based on the determined stable production risk. From the stable production risk calculated by the stable production risk calculation unit, the plan changeable part is extracted from the logistics plan generated by the standard plan generation unit, and the risk adjustment unit adjusts the plan within the changeable range and creates the logistics plan. Generate.

Another aspect of the present invention is a logistics plan generation method executed by a computer having a data input unit, a data output unit, and a processing unit that processes input data. The processing unit is based on the stable production risk against the logistics standard plan, the standard plan generation unit that generates the logistics standard plan from the supply and demand information of goods and the human resource information, the stable production risk calculation unit that calculates the stable production risk A risk adjustment unit that extracts a plan changeable part and adjusts the plan within a changeable range to generate a logistics plan.

More specific examples are shown below. The supply and demand information of goods includes information specifying the target goods, information specifying the place where the goods are supplied, and the time to be supplied. The human resource information includes information specifying a human resource (for example, an employee), and information specifying a place where the human resource is arranged and a time zone (working time zone) where the human resource is to be arranged. Logistics plan and logistics standard plan are the plans to transport goods and human resources to the place specified by the time specified by supply and demand information and human resource information. Including.

In a more preferable specific example, a personnel information update unit for updating personnel information using the adjustment result of the logistics standard plan is provided. The personnel information includes, for example, information on working conditions such as information for identifying employees and information on remuneration. As a specific example of the risk, the operational risk and the delivery risk can be considered as the stable production risk. The operation risk includes, for example, an action risk that is a possibility that an operation error may occur during human work. In addition, the operation risk may include a failure risk that the device does not operate normally during operation due to aging or exceeding the maintenance cycle. Further, the delivery risk includes a shortage risk that may cause a predetermined schedule to not be executed due to, for example, a delivery delay of human resources or goods. Further, the delivery risk may include a demand fluctuation in which the demand for delivery goods to be delivered suddenly fluctuates. For these risks, for example, factors that affect the overall schedule of processes defined in the production management table can be extracted and various definitions can be made. The defined risk is embodied as a model or formula that is processed in the system. For example, behavioral risk can be represented using a human state model that is a function of time difference from the logistics reference plan.

There are various methods for adjusting the logistics standard plan, but in the case of paying attention to the cost, all the people who can be transported are allocated without changing the schedule of transport equipment used for transporting goods. After that, the above-mentioned risk caused by the adjustment is evaluated, and it is determined using a threshold value or the like whether or not it is within an allowable range. The plan adjusted within the allowable risk range is re-evaluated, and if there is a cost reduction effect, the plan is adopted.

According to the present invention, a low-risk and low-cost logistics plan can be made by considering various risks.

It is a block diagram which shows the structural example of the logistics plan production | generation apparatus of the 1st Embodiment of this invention. It is a block diagram explaining the logistics plan production | generation process of the 1st Embodiment of this invention. It is a flowchart which shows the logistics plan production | generation process of the 1st Embodiment of this invention. It is a table | surface figure which shows an example of the personnel information table of the 1st Embodiment of this invention. It is a table | surface figure which shows an example of the production management table of the 1st Embodiment of this invention. It is a table | surface figure which shows an example of the goods demand table of the 1st Embodiment of this invention. It is a table | surface figure which shows an example of the human resource management table of the 1st Embodiment of this invention. It is a table | surface figure which shows an example of the monitoring table of the 1st Embodiment of this invention. It is a flowchart which shows the plan adjustment process of the 1st Embodiment of this invention. It is a timing diagram which shows an example of the adjustable location of the 1st Embodiment of this invention. It is a graph which shows an example of the human condition risk model of the 1st Embodiment of this invention. It is a table | surface figure which shows an example of the calculation method of the stable production risk of the 1st Embodiment of this invention. It is a top view which shows an example of management GUI of the 1st Embodiment of this invention. It is a block diagram which shows the structural example of the human state model automatic generation process of the 2nd Embodiment of this invention. It is a block diagram explaining the human state model automatic generation process of the 2nd Embodiment of this invention. It is a flowchart which shows the human state model automatic generation process of the 2nd Embodiment of this invention.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

Embodiments will be described in detail with reference to the drawings. However, the present invention is not construed as being limited to the description of the embodiments below. Those skilled in the art will readily understand that the specific configuration can be changed without departing from the spirit or the spirit of the present invention.

In the structure of the invention described below, the same portions or portions having similar functions are denoted by the same reference numerals in different drawings, and redundant description may be omitted.

In this specification and the like, notations such as “first”, “second”, and “third” are attached to identify the constituent elements, and do not necessarily limit the number or order. In addition, a number for identifying a component is used for each context, and a number used in one context does not necessarily indicate the same configuration in another context. Further, it does not preclude that a component identified by a certain number also functions as a component identified by another number.

The position, size, shape, range, etc. of each component shown in the drawings and the like may not represent the actual position, size, shape, range, etc. in order to facilitate understanding of the invention. For this reason, the present invention is not necessarily limited to the position, size, shape, range, and the like disclosed in the drawings and the like.

In the following embodiment, various risks are calculated from supply and demand information of goods and human resources information managing human information, and a logistics plan of goods and people that is safe and low cost is generated from those risks and costs.

FIG. 1 is a diagram showing a configuration example of an apparatus according to the first embodiment of the present invention. The data management server 101, the logistics plan generation server 111, and the monitoring server 121 are connected by a network 100. The network 100 is a LAN (Local Area Network) or an Internet line. In FIG. 1, three servers advance the process in cooperation, but this server division method is an example and is not limited to this. Each server may be further divided or integrated.

The data management server 101 includes a CPU (Central Processing Unit) 102, an output device 103, an input device 104, a memory 105, a network interface 106, and an external storage device 107 connected to each other via a bus.

The CPU 102 is an arithmetic processing unit that executes various processes by processing a program stored in the memory 105. The external storage device 107 is a storage device that stores data used by a program main body stored in the memory 105 or a program stored in the memory 105. The memory 105 is a storage device that stores a program processed by the CPU 102 and data used. Programs and data that are not processed by the CPU 102 are stored in the external storage device 107.

The memory 105 stores a program that realizes the function of the demand calculation unit 203. The external storage device 107 stores a personnel information table 201, a production management table 202, a material demand table 204, and a human resource management table 205. In addition, a table used for logistic plan generation, for example, transportation equipment data, production Data of necessary equipment may be stored.

The output device 103 is a display device such as a display. The output device 103 displays information on the personnel information table 201, the production management table 202, the material demand table 204, and the personnel management table 205. Further, other data, an interface for managing these data, and the like may be displayed as necessary.

The input device 104 is an input device such as a keyboard or a mouse. Using the input device 104, it is possible to add, change, and delete information in the personnel information table 201, production management table 202, material demand table 204, and human resource management table 205.

The network interface 106 is an interface device for connecting and communicating with the logistics plan generation server 111, the monitoring server 121, and other external computers and monitoring devices.

The logistics plan generation server 111 includes a CPU 112, an output device 113, an input device 114, a memory 115, a network interface 116, and an external storage device 117 connected to each other by a bus. The external storage device 117 stores a human state model table 207. The memory 115 stores programs for realizing the functions of the logistics plan generation unit 211 and the logistics plan setting unit 212.

The monitoring server 121 includes a CPU 122, a memory 123, an input device 124, an output device 125, a network interface 126, and an external storage device 127 that are connected to each other via a bus. The external storage device 127 stores a monitoring data table 206.

FIG. 2 is a software block diagram for realizing a logistics plan generation process performed in the system of FIG. As described above, processing can be arbitrarily shared by the three servers. The overall flow of the logistics plan generation process will be described with reference to FIG. When it is necessary to generate a logistics plan, the demand calculation unit 203 supplies the material based on the future production plan stored in the production management table 202 and the individual worker information stored in the personnel information table 201. And demand for both people.

When a logistics plan needs to be generated, for example, when the situation has changed significantly since the previous generation, when the user instructs the system, when the period covered by the generated plan ends, etc. It is.

For example, in the Oil & Gas field, the necessary materials and people are different between the stage of oil well development for oil extraction and the stage of extracting crude oil from the developed oil well. Therefore, the demand for goods and people is determined according to the future production plan and the nature of the well to be developed.
In addition, a known method is used to determine a person to be actually assigned to a necessary person. In general, in a technique called nurse scheduling, a person in charge of work is determined based on a combination of individual abilities and skills, whether or not a vacation is acquired, and the like. The nurse scheduling technique may be used to determine demand information for human resources, or other techniques may be used.

Then, the calculated demand for supplies is stored in the supply demand table 204, and the demand for people is stored in the personnel management table 205.

The logistics plan generation unit 211 generates a logistics plan using the calculated demand, various types of monitoring information stored in the monitoring table 206, and the human state model stored in the human state model table 207.

The plurality of generated logistics plans are presented to the user in the logistics plan setting unit 212. Then, the logistics plan selected from them is executed, and the data of the personnel information table 201 is updated according to the execution result.

In this embodiment, it is assumed that a plurality of logistics plans are generated using indices such as cost, risk, and lead time and presented to the user side. A single logistics plan may be generated.

After selecting the logistics plan, the information in the personnel information table 201 is updated. The update is performed in order to feed back the contents of the execution result to the person who worked. For example, to reduce the logistics cost, raise the assessment for those who have worked overtime.

The logistics plan generation unit 211 includes a reference plan generation unit 221, a stable production risk calculation unit 222, a risk adjustment unit 223, and a plan evaluation unit 224. The reference plan generation 221 further includes a material distribution plan generation unit 231 and a human resource distribution plan unit 232. The stable production risk calculation unit 222 includes an operation risk calculation unit 233 and a delivery risk calculation unit 234. Further, the operation risk calculation unit 233 includes a human behavior risk calculation unit 235 and a failure risk calculation unit 236, and the delivery risk calculation unit 234 includes a shortage risk calculation unit 237 and a demand fluctuation risk calculation unit 238.

FIG. 3 is a diagram showing a flow for generating a logistics plan using these components. First, in step 301, a period for which a logistics plan is to be input is input. For example, from the following month to 3 months later, or for the next year. This step may be omitted by holding a set value in advance.

Next, in step 302, the demand for goods and people required in the planning period determined in step 301 is calculated. This corresponds to the processing of the demand calculation unit 203. Steps 303 to 308 correspond to processing of the logistics plan generation unit 211, and step 309 corresponds to processing of the logistics plan setting unit 212.

First, in step 303, a material delivery plan is generated from data obtained from the material demand table 204 and the monitoring table 206. This is equivalent to the conventional delivery plan generation method, and is also disclosed in Patent Document 2. The calculation may be performed in consideration of external factors such as weather data included in the monitoring table 206, that is, weather, waves, and wind speed. This processing corresponds to the processing of the material delivery plan generation unit 231.

Next, in step 304, a person to be transported is assigned to the delivery plan using the data of the personnel management table 205 and the monitoring table 206. This allocation indicates that people will be allocated as much as they can be transported without changing the schedule of transport equipment used for goods transportation. For those who cannot deliver only with the schedule of goods transportation, new transportation equipment is newly allocated. Transport equipment is set for helicopters and transport ships in consideration of cost and lead time. This corresponds to the processing of the personnel delivery plan generation unit 232.

In step 305, the stable production risk is calculated and the plan is adjusted. This corresponds to the processing of the stable production risk calculation unit 222 and the risk adjustment unit 223. In the adjusted plan, the cost change due to the adjustment is calculated in step 306 and evaluated in step 307. If it is determined in step 307 that the total cost has been reduced from before the adjustment, step 309 is executed, otherwise step 308 is executed. Evaluation criteria may be stable production risk and lead time, not total cost.

In step 308, parameters relating to delivery plan generation and assignment of people are changed, and step 303 is executed again. This means that the parameters used in step 303 and step 304 are changed, and the plan that becomes the reference before adjustment is regenerated. Step 306, step 307, and step 308 correspond to the processing of the plan evaluation unit 224.

In step 309, the personnel information is updated. This corresponds to the processing of the logistics plan setting unit 212. The logistics plan executed by the user is selected, and the data stored in the personnel information table 201 is updated using the selection result. The update may not be after the selection but after the logistics plan is actually executed.

FIG. 4 is a diagram showing an example of the personnel information table 201. The personnel information table 201 stores records indicating workers and individual employees. A typical example is employee data managed by the personnel department of a company, and includes information for identifying the employee, information such as qualification, salary, various benefits, and treatment. The Person ID (Person IDentifier) 401 is an identifier for uniquely identifying the record, and any expression may be used as long as it can be uniquely identified.

Job 402 is an identifier for identifying an individual's job type, and may be a character string or some identifier that refers to data stored in another table. Base Salary 403 is an employee's monthly salary amount, and Assessment Value 404 is a value used for salary assessment, and here indicates an amount that is specially given at the next bonus. These values are values for performing personnel evaluation, and may be in other forms other than the amount.

Good Compatibility 405 and Bad Compatibility 406 indicate compatibility between individuals, Good Compatibility 405 indicates good compatibility, and Bad Compatibility 406 indicates poor compatibility. These values are used in the above-described processing of the demand calculation unit 203 to determine whether the compatibility is good or not when determining the team formation. Therefore, another format may be used as long as compatibility can be determined.

Taking an individual record with Person ID 401 of P1, for example, the individual is a Crane Operator who operates a crane, and the salary is $ 4,000 per month. Also, it is determined that $ 400 will be added at the next bonus. The individual has a good compatibility with the person whose Person ID 401 is P2, and has a bad compatibility with the person whose Person is P4.

FIG. 4 shows two points of salary and compatibility, but other information such as ownership qualification, age, educational history, etc. may be stored.

FIG. 5 is a diagram illustrating an example of the production management table 202. Each record of the production management table 202 stores each phase for each operation site and their start and end dates and associated items.

A Phase ID (Phase IDentifier) 501 is an identifier for uniquely identifying each record, and may be in any format as long as it can be uniquely identified. The target 502 is an identifier indicating what each record is targeted for, and may be a character string or an identifier stored in another table. Phase 503 is an identifier indicating the phase of the activity, and may be in any format as long as the phase can be expressed.

Bdate 504 and Edit 505 represent the start date / time and end date / time of the phase, respectively. In the figure, it is described in the format of “year / month / day-hour / minute / second”, but other expression formats may be used, or only the date / year or the time may be used.

Remarks 506 describes information associated with the phase. A format other than a character string may be used as long as the contents of information can be expressed. Moreover, it may be expressed by a plurality of columns.

For example, paying attention to Platform1, it can be seen that two phases Ph1 and Ph2 are scheduled, and the contents are Well Testing and Well Development, respectively. Further, it is expressed that Ph1 starts at 19:00 on November 13, 2014 and ends at 19:00 on December 13, 2014. It is shown that Ph2 starts at 19:00 on December 13, 2014 and ends at 19:00 on January 30, 2015.

In this embodiment, the material demand table and the human resource management table, which will be described later, are created in accordance with the constraints of the overall schedule defined by the production management table 202. For example, since the schedule of Ph1 in FIG. 5 is scheduled to start at 19:00 on November 13, 2014 and end at 19:00 on December 13, 2014, supplies and human resources are supplied in accordance with this schedule. It will be.

FIG. 6 is a diagram showing an example of the material demand table 204. In the material demand table 204, information on where and what from what to carry to each operation site is stored.

Demand ID (IDentifier) 601 is an identifier for uniquely identifying each record, and may be in any format as long as it can be uniquely identified. Name 602 represents the name of the goods, and may be in a form other than a character string as long as similar contents can be represented.

Qty (Quantity) 603 indicates a necessary number or amount of the material, and is expressed in a format corresponding to the material, such as an amount when the content of the material is a medicine. Source 604 and Destination 605 represent the start and end points when the goods are transported. The Delivery date 606 expresses the deadline of the delivery, and other formats may be used as long as the same contents can be expressed.

For example, a record whose Demand ID 601 is D1 indicates that it is necessary to carry one pipe from Port1 to Platform1. Moreover, it expresses that the deadline is 21:00 on December 13, 2014.

The material demand table 204 shown in FIG. 6 stores only minimum information related to the demand and delivery of goods, but other information such as delivery cost and price, precautions regarding delivery, and the like may also be stored.

FIG. 7 is a diagram showing an example of the human resource management table 205. In the human resource management table 205, a person's work schedule is stored in each record. The work schedule information is, for example, information indicating who is at what time and where. A Shift ID (Shift IDentifier) 701 is an identifier for uniquely identifying each record, and may be in any format as long as it can be uniquely identified.

Work begin 702 and work end 703 respectively indicate the work start date and time and the work end date and time of each individual. Further, Location 704 indicates a work place.

For example, the shift of P1 starts at 17:00 on December 13, 2014 and is scheduled to end at 17:00 on December 26, 2014. Further, it is indicated that the work place is Platform1.

The human resource management table 205 shown in FIG. 7 presents only the minimum data that should be provided as a work schedule, but other data such as necessary equipment and a detailed schedule may be presented.

FIG. 8 is a diagram illustrating an example of the monitoring table 206. Each record indicates the status of the monitoring target, and indicates, for example, an individual, transportation equipment, weather, or the like. A Monitor ID (IDentifier) 801 is an identifier for uniquely identifying each record, and may be in any format as long as it can be uniquely identified.

Update Time 802 is the date and time when the target information was last acquired. Target 803 indicates a monitoring target, and Location 804 indicates the location of the monitoring target. Remarks 805 indicates information other than those described above, and stores, for example, labor status and sickness status for individuals, weather for weather, and fatigue status and operating cost of devices for equipment. These pieces of information may be stored in a plurality of columns or another table, instead of a single column. Each record also holds all past monitoring results, but only the latest results may be held.

FIG. 9 is a flowchart showing the plan adjustment process, which corresponds to step 305. In step 901, a location that can be adjusted to a low-cost transport device is detected by changing the time of departure and arrival times of the transport device and changing the start and end times of the person. In Step 902, Steps 903 to 905 are repeated for all the adjustable portions detected in Step 901. An example of the place where the plan can be adjusted is described with reference to FIG.

In step 903, a delivery risk is calculated, and in step 904, an operation risk is calculated. In step 905, a stable production risk is calculated from the calculated delivery risk and operation risk. An example of these risk calculation methods will be described with reference to FIGS.

In step 906, the type and time of the delivery device and the working hours of the employee are changed for the portion where the stable production risk after the change is equal to or less than the stability threshold. In step 906, when there are a plurality of locations to be adjusted, the locations that can be adjusted may be searched by a desired index such as the maximum number of locations that can be adjusted or the lowest total cost value by computer simulation or the like.

FIG. 10 is a diagram showing an example of a place where the plan can be adjusted. Here, for the sake of illustration, a logistics plan between Port1 and Platform1 and a work plan in Platform1 are targeted. The situation 1001 shows the reference plan before the change, that is, the adjustment plan created in Step 303. An example of the situation 1001 represents information in the material information table 204 and the human resource information table 205. As a specific example, Port 1 is a mainland port, and Platform 1 is an offshore platform. That is, the exemplary schedule is not a railroad or a bus, but a plan having uncertain elements such as a ship or an airplane.

In the situation 1001, a delivery plan using the helicopter H1 and the transport ship V1 is set up from Port1. That is, the plan is to carry the person P1 who is the substitute for the person P4 by the helicopter H1, and the plan to carry the materials necessary for the operation of the Platform 1 by the transport ship V1.

Helicopter H1 departs from Port 1 at 15:00 on December 13, 2014 and arrives at Platform 1 at 16:00 on December 13, 2014. The helicopter H1 carries a person P1 who is a substitute for the person P4 who finishes working at 17:00 on December 13, 2014. And after work shift, the person P4 is carried to Port1. At this time, the helicopter H1 departs from Platform 1 at 18:00 on December 13, 2014, and arrives at Port 1 at 19:00 on December 13, 2014.

The transport ship V1 is supposed to carry a pipe whose Demand ID 601 is D1, and needs to arrive by Platform 1 on December 13, 2014 at 21:00. Therefore, Port 1 departs at 17:00 on December 13, 2014, and arrives at Platform 1 at 20:00 on December 13, 2014. After that, loading and unloading of goods and receiving of unnecessary goods are performed over 5 hours, and the platform 1 is departed at 1 o'clock on December 14, 2014. And it returns to Port1 at 6:00 on December 14, 2014.

In the case of the example of the situation 1001, the change time of the person P4 and the person P1 is different from the scheduled delivery time of the pipe D1, and cannot be carried by a single transport device. In addition, if the working hours are simply shifted backward, the risk that the employee will make operational mistakes due to fatigue or the like increases. Moreover, when delivery is brought forward, the arrangement location is not secured or the subsequent consumption plan is affected. Therefore, simple time adjustment is difficult because it contains various risks.

The result of the planned adjustment in this example is shown in the situation 1002. The planned adjustment is a result of considering various risks shown in FIGS. 11 and 12. As a result of the plan adjustment, the plan is adjusted so that the pipe D1 and the person P1 are simultaneously carried by the transport ship V1. Thereby, it becomes possible to reduce the cost of using the helicopter H1. On the other hand, the working hours of person P4 will be until 20:00 on December 13, 2014, and overtime will be added for 3 hours. A risk such as an operation error during overtime is calculated as a human behavior risk using the human state model shown in FIG.

In this embodiment, this adjustment is performed within the range of the constraint conditions defined by the production management table 202. Whether adjustment is physically possible is determined by referring to the monitoring table 206. Adjustments shall be made within the scope of various laws and regulations to be followed. For example, if there is a law that regulates the working hours of workers or a law that regulates the operating speed of a transport ship, adjustments are made within a range that satisfies the conditions.

FIG. 11 is a diagram showing an example of a human state model. In FIG. 11, it is written in the form of a graph, but it may be a mathematical expression or a model obtained by machine learning. A model 1101 and a model 1102 respectively represent human condition risk models of Crane Operator and Ballast Control Operator. Such a model can be created on the basis of statistical data for each type of business and type of error. Alternatively, it may be generated for each employee using data of past work results and prepared for each individual. The definition of business content and error content is arbitrary and may be subdivided or may be defined to some extent. Here, a human state model is defined for each occupation, but a known human error model or the like may be used as long as it is possible to calculate the risk of occurrence of human operation errors such as individual or age.

FIG. 12 shows an example of a stable production risk calculation method using various risks.

The delivery risk calculation formula 1201 is an example of a method for calculating a delivery risk, that is, a risk of delivery stagnation and a missing item from a factor due to weather and a time difference that is a difference from the initial schedule, for simplicity. The shortage risk is a possibility that the entire schedule defined by the production management table 202 cannot be executed due to, for example, a delivery delay of human resources or supplies. Here, as an example, this risk is defined by the delivery risk calculation formula 1201 and assumes that the shortage risk becomes unacceptable when the value reaches a predetermined value. The weather factor coefficient varies depending on the weather. For example, the value is 0.5 depending on the weather, 2 if raining, and the like. The delivery adjustment coefficient is a constant for adjusting the range.

The operation risk calculation formula 1202 is a formula for calculating a human behavior risk that causes an operation mistake, and is calculated from the human state model shown in FIG. The operational risk is, for example, the possibility of an operational error occurring during human work. Here, an example in which various types of information are introduced as coefficients using the model 1101 is shown. The injury and illness state coefficient, the immediately preceding state coefficient, and the activity history coefficient are coefficients that are determined by an individual's injury and illness state, the latest activity such as sleep and rest, and the time required for the latest work. The behavior adjustment coefficient is a constant for adjusting the area of operational risk. As described with reference to FIG. 11, the model is determined depending on the work content, the operator, and the type of operation error.

The stable production risk calculation formula 1203 is calculated by treating the delivery risk calculation formula 1201 and the operation risk 1202 as probability values. Here, the calculation is based on the example shown in FIG. 10, the weather factor coefficient is 2, the delivery adjustment coefficient is 4, the sickness condition coefficient is 0.5, the immediately preceding condition coefficient is 1, the activity history coefficient is 1, The behavior adjustment coefficient is calculated as 3.

When changing from the situation 1001 to the situation 1002, it is possible to adjust the delivery by bringing the delivery time one hour ahead and extending the working hours of the employee P4 by three hours. The time differences at that time are 1 and 3, respectively. When these values are used to calculate the stable production risk, it is about 0.280. If the value is equal to or less than a preset stability threshold, it is determined that the adjustment is possible.

In this case, the shortage risk is considered as the delivery risk, and the human behavior risk related to the behavior such as the operation mistake is considered as the operation risk, but other various risks may be considered. For example, demand fluctuation risk that demand of delivery goods suddenly fluctuates due to demand fluctuation (for example, possibility that supplies can not be procured as planned or suddenly necessary supplies), deterioration due to aging or maintenance cycle exceeded Various risks may be taken into consideration, such as a failure risk that a replacement part is required due to damage to the equipment, and a human relationship risk that the combined team does not perform as planned. Moreover, you may use a known method for these risk calculation.

One of the methods for calculating stable production risk using these risks is the same as the method shown in FIG. 12, in which various risks are calculated as probability values, and the probability that an accident will occur due to one of the risks is calculated. However, other methods may be used.

For example, when using human behavior risk and failure risk, shortage risk and demand fluctuation risk, the probability that a failure or operation error will occur when operating the equipment is determined from the human behavior risk and failure risk. The probability that the necessary goods are not in the required time and place from the demand fluctuation risk is defined as the delivery risk, and the probability that either the operation risk or the delivery risk occurs is defined as the stable production risk. Demand fluctuation risk is calculated using economics, and failure risk is calculated using known techniques such as predictive diagnosis.

* As a result of this adjustment, working hours may be extended or shortened. The processing performed in step 309 adds such information to the personnel information in order to reward such extension or shortening with a subsequent bonus or special reward. For example, as a result of the adjustment shown in FIG. 10, the working time of the person P4 is extended. In such a case, the Asset Value 404 in the personnel information table 201 is increased in order to be reflected in overtime pay and later rewards.

Such personnel information may be updated by simply acquiring a substitute holiday or lengthening a later holiday in addition to simply increasing the amount.

FIG. 13 shows a GUI (Graphical User Interface) used in the logistics plan setting unit 212. A window 1301 inputs parameters necessary for the logistics plan generation unit 211 and displays the generated plan. The planning target period 1302 is an input field for designating a period for generating a logistics plan. The stability threshold 1303 is set to a value used in step 906. These values may be input by the user, or the automatically set values may be displayed.

Cost weight 1304 and risk weight 1305 are parameters used when presenting the adjustment result to the user. The table starting with Plan No. 1306 presents a part of a plurality of plans generated by the logistics plan generation unit 211. Plan No. 1306 is an identifier for uniquely identifying each record, and may be in any format as long as it can be uniquely identified. The cost 1307 indicates the cost required for the generated plan, and the average stable production risk 1308 indicates a value obtained by averaging the stable production risks at all locations adjusted by the risk adjustment unit 223.

The performance 1309 is a value calculated from the cost weight 1304, the cost 1307, the risk weight 1305, and the average stable production risk 1308, and is used for the user to select a plan to be executed from a plurality of plans. For example, in the record whose Plan No. 1306 is 1, calculation is performed by dividing the value obtained by multiplying the average stable production risk 1308 by the risk weight 1305 and the cost 1307 by the cost weight 1304.

The cost 130 can be calculated by, for example, predetermining the unit price per hour for each means of transportation and work based on the logistics plan shown in FIG.

The graph display 1310 is a graph for clearly showing the trade-off relationship of each generated plan. In the graph display 1310, the values of the cost 1307, the average stable production risk 1308, and the performance 1309 are successively shown, and the positions of the plans are illustrated. As a result, it is possible to easily grasp whether the plan generated by the user is cost advantage or risk advantage.

In this example, the cost and average stable production risk are shown, but the lead time and the number of adjustment points may be displayed as selection indicators.

The plan details 1311 indicate a part of the contents of the selected plan and a part of personnel information changed by execution of the plan. All or a part of the personnel information can be hidden by the viewing authority determined by the ID of the accessing user. Vehicle 1312 indicates the ID of the transport device, and Target 1313 indicates the ID of the transport target. Begin 1314 and End 1315 indicate the transportation start date and time and the transportation end date and time, respectively. Source 1316 and Destination 1317 indicate a transportation source and a transportation destination.

For example, in order to transport the employee P4 and the pipe D1, the transport device V1 departs Port1 at 16:00 on December 13, 2014 and arrives at Platform1 at 19:00 on the same day. Thereafter, the employee P4 and the pipe D1 are lowered, and the employee P1 is put on. Then, it is shown that Platform1 departs at 0 o'clock on December 14, 2014 and arrives at Port 1 at 5 o'clock on the same day.

The post-update personnel information table 1318 is a part of the personnel information table 201, and shows a record updated when the selected plan is executed and its contents.

With the above GUI, it is possible to generate a logistics plan for a specified period and allow the user to select a plan to be executed from the viewpoint of cost and risk. In addition, the user may have a function of modifying the details of the selected plan. Furthermore, if the above functions are realized, a different interface may be used or another input / output may be added.

According to the first embodiment of the present invention described above, it is possible to make a low-risk and low-cost logistics plan that considers both people and things in consideration of various risks.

In the above-described first embodiment, it is necessary to create a human state model in advance. In the second embodiment, a form in which a human state model is automatically generated from monitoring data will be described.

FIG. 14 is a diagram showing a configuration example of the second embodiment of the present invention. In the following description, the same components as those in FIG.

The second embodiment includes a human state model automatic generation unit 1401 in addition to the components of the first embodiment (see FIG. 1) described above. The human state model automatic generation unit 1401 automatically generates a human state model using data in the monitoring data table 206.

FIG. 15 is a block diagram illustrating human state model automatic generation according to the second embodiment of this invention. The overall flow of the human state model automatic generation will be described with reference to FIG. In the following description, the same components as those in FIG.

The automatic generation process of the human state model is executed with the update of the personnel information table 201, the user input, and the update of other tables as a trigger. After the trigger is generated, the human state model automatic generation unit 1401 extracts monitoring data to be learned. Then, among the data, data related to work behavior is acquired from the monitoring data table 206, a human state model is generated, and stored in the human state model table 207.

FIG. 16 is a flowchart of the human state model automatic generation process. First, in step 1601, data that is personal data to be processed and whose Work is included in the action of Remarks 805 is extracted from the monitoring table 206.

In step 1602, classification is performed for each status of Remarks 805 in the extracted data. In step 1603, step 1604 and step 1605 are repeated for each extracted status.

In step 1604, the time required for the target work is set from the work start time to Update time 802, and work that is more than twice the standard deviation from the average of the time required fails, and otherwise, the work success is counted as failure. In step 1605, a sigmoid function is estimated with failure as 1 and success as 0 and the required time as input, and the estimated result function is stored in the human state table 207.

According to the second embodiment of the present invention described above, in addition to the advantages of the first embodiment, it is possible to automatically generate a human state model of an individual and reduce the labor of creating a human state model. .

Each embodiment of the present invention has been described above. However, each of the above embodiments shows one application example of the present invention, and the technical scope of the present invention is limited to the specific configuration of each of the above embodiments. It is not the purpose. The present invention is not limited to the embodiments described above, and includes various modifications. For example, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, it is possible to add, delete, and replace the configurations of other embodiments with respect to a part of the configurations of the embodiments.

The above configuration may be configured by a single computer, or may be configured by another computer in which any part of the input device, output device, processing device, and storage device is connected via a network.

In this embodiment, functions equivalent to those configured by software can also be realized by hardware such as FPGA (Field Programmable Gate Array) and ASIC (Application Specific Integrated Circuit). Such an embodiment is also included in the scope of the present invention.

DESCRIPTION OF SYMBOLS 101 Data management server 111 Logistics plan production | generation server 121 Monitoring server 201 Personnel information table 202 Production management table 203 Demand calculation part 204 Material demand table 205 Human resource management table 206 Monitoring table 207 Human condition model table 211 Logistics plan production | generation part 212 Logistics plan setting part

Claims

A logistic plan generation method executed by a computer having a data input unit, a data output unit, and a processing unit for processing input data,
The processor is
A base plan generation unit for generating a logistics base plan from supply and demand information of goods and human resource information;
A stable production risk calculator that calculates stable production risk;
A risk adjustment unit that extracts a plan changeable portion based on the stable production risk with respect to the logistics standard plan, adjusts the plan within the changeable range, and generates a logistics plan;
A logistics plan generation method characterized by comprising:
A logistics plan generation method according to claim 1,
The calculator is
Using the adjusted logistics plan, the personnel information update unit that updates the personnel information of the person,
A logistics plan generation method characterized by comprising:
A logistics plan generation method according to claim 1,
The stable production risk calculation unit
Calculating the stable production risk from at least one of an operation risk for calculating an operational accident risk and a delivery risk for calculating a material delivery risk;
The operational risk is
Behavioral risk calculated from a human condition model that calculates the probability of a future accident occurrence using the work status of the person as input,
With the risk of failure calculated from aging or maintenance cycle of operating equipment used for the operation,
Calculated from one or both,
A logistics plan generation method characterized by that.
A logistics plan generation method according to claim 3,
The human state model is
As the work status, calculating the future accident occurrence probability by inputting at least one of the work shift of the person, the sickness status, and the time required for the operation,
A logistics plan generation method characterized by that.
A logistics plan generation method according to claim 3,
The delivery risk is
The shortage risk for calculating the delivery delay probability using at least one of the catalog value of the transport equipment, weather forecast, and wave forecast used for execution of the logistics plan,
With demand fluctuation risk calculated from future demand fluctuation,
Calculated from one or both,
A logistics plan generation method characterized by that.
A logistics plan generation method according to claim 3,
The calculator is
Generating display data of contents of the created logistics plan and stable production risk of the plan;
A logistics plan generation method characterized by that.
A logistics plan generation method according to claim 3,
The human state model is
A plurality of models corresponding to individual persons, generated using the past work performance data of the person,
A logistics plan generation method characterized by that.
A logistics plan generation system having a data input unit, a data output unit, and a processing unit for processing input data,
The processor is
A base plan generation unit for generating a logistics base plan from supply and demand information of goods and human resource information;
A stable production risk calculator that calculates stable production risk;
A risk adjustment unit that extracts a plan changeable portion based on the stable production risk with respect to the logistics standard plan, adjusts the plan within the changeable range, and generates a logistics plan;
A logistics plan generation system characterized by comprising:
The logistics plan generation system according to claim 8,
The calculator is
Using the adjusted logistics plan, the personnel information update unit that updates the personnel information of the person,
A logistics plan generation system characterized by comprising:
The logistics plan generation system according to claim 8,
The stable production risk calculation unit
Calculating the stable production risk from at least one of an operation risk for calculating an operational accident risk and a delivery risk for calculating a material delivery risk;
The operational risk is
Behavioral risk calculated from a human condition model that calculates the probability of a future accident occurrence using the work status of the person as input,
With the risk of failure calculated from aging or maintenance cycle of operating equipment used for the operation,
Calculated from one or both,
A logistics plan generation system characterized by that.
A logistics plan generation system according to claim 9,
The human state model is
As the work status, the future accident occurrence probability is calculated by inputting at least one of the work shift of the employee, the sickness status, and the time required for the operation,
A logistics plan generation system characterized by that.
A logistics plan generation system according to claim 10,
The delivery risk is
The shortage risk for calculating the delivery delay probability using at least one of the catalog value of the transport equipment, weather forecast, and wave forecast used for execution of the logistics plan,
With demand fluctuation risk calculated from future demand fluctuation,
Calculated from one or both,
A logistics plan generation system characterized by that.
A logistics plan generation system according to claim 10,
The risk adjustment unit
Search and set adjustment points so that the stable production risk is below the stability threshold by simulation,
A logistics plan generation system characterized by that.
A logistics plan generation system according to claim 10,
The calculator is
Generating display data of contents of the created logistics plan and stable production risk of the plan;
A logistics plan generation system characterized by that.
A logistics plan generation system according to claim 11,
The human state model is
A plurality of models corresponding to individual persons, generated using the past work performance data of the person,
A logistics plan generation system characterized by that.