Intelligent scheduling method

Abstract

The invention provides an intelligent scheduling method, which comprises the following steps of: (1) collecting all business data of an enterprise, and constructing a user business database; step (2), collecting all rule information, and constructing structured data; step (3), constructing a workload evaluation model by taking the basic business guarantee plan as a main line; step (4), constructing an algorithm model, performing global overall planning on existing human resources, searching for optimal matching, quantifying distribution results, performing evaluation analysis, performing multi-round iterative optimization according to feedback conditions, and formulating shift plans; and step (5), constructing a scheduling set by planning a rest rotation strategy, and further formulating a scheduling plan. All problems of enterprise operation are changed into mathematical problems, informatization, digitization and intelligentization of enterprises can be truly achieved through data and algorithms, whole-process refinement and visual management of the enterprises are achieved through the data, and the enterprises are helped to improve quality and efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of resource scheduling algorithms, and in particular to an intelligent scheduling method. Background Art

[0002] Intelligent scheduling is based on the department's planned workload, combined with the attendance rules formulated by national laws and airport management systems, and with the help of scheduling algorithms, generates pre-scheduling results. By analyzing the pre-scheduling results, the attendance rules are optimized until the most reasonable scheduling plan for human resource allocation is calculated and published to form the final employee schedule. The system mainly solves the problem of efficient allocation of ground service personnel and resources to maximize the efficiency of human resource utilization. Summary of the invention

[0003] The technical problem solved by the present invention is to provide an intelligent scheduling method to solve the problems raised in the above background technology.

[0004] The technical problem solved by the present invention is achieved by adopting the following technical solution: an intelligent scheduling method, comprising the following steps:

[0005] Step (1). Collect all the business data of the enterprise, build a user business database, and realize the digitization of all the resources of the enterprise;

[0006] Step (2). Collect all rule information, build structured data, and realize the digitization of scheduling rules;

[0007] Step (3). Build a workload assessment model based on the basic business assurance plan;

[0008] Step (4). Construct an algorithm model, use the workload assessment results to coordinate the existing human resources globally, find the best match, quantify the allocation results, conduct evaluation and analysis, perform multiple rounds of iterative optimization based on the feedback, and formulate a shift plan;

[0009] Step (5). Construct a shift set by planning a shift rotation strategy and further develop a shift plan.

[0010] The shift planning is to design a set of shifts with different start times and end times, which can meet the workload requirements within the planned scope on the one hand, and require the least total number of human resources on the other hand.

[0011] The shift planning is divided into two stages for the entire shift planning, namely, the shift rotation strategy and the employee shift planning, and finally all the shift sets are uniformly generated.

[0012] The workload forecast in step (3) is to estimate the number of resources required at different times in the future according to the flight plan; the workload scale can be subdivided into the number of required resources calculated every 5 minutes;

[0013] Define the "Flight Support Plan Workload" collection:

[0014] P={p1,p2,...,p i},

[0015]

[0016] f = flight number,

[0017] t task = Type of support mission,

[0018] t begin = Guarantee start time,

[0019] t end = Guarantee end time,

[0020] q = the amount of resources required;

[0021] Define the Workload Forecast collection:

[0022] W={w1,w2,...,w i},

[0023] t slice =Guarantee time;

[0024] After performing aggregation operations based on the guarantee task type and guarantee time, the elements of set P are mapped to set W, f:P→W.

[0025] In the step (4), a shift set is constructed, and a working period set is constructed according to the three parameters of "start time range", "working time range" and "time interval size" in the shift rule definition.

[0026] The construction of the shift set includes:

[0027] Define the "shift start time" set A, and construct the set A by the parameters "start time range" and "time interval size", A={a1,a2,...,a n}, each member represents a different starting time point;

[0028] Define the "working time" set B, by setting the parameters "working time" and "time interval size"

[0029] To construct a set B, B = {b1, b2, ..., b m}, each member represents a different working time;

[0030] Define a set C that arranges and combines the members of sets A and B;

[0031] C=A×B={(a1,b1),(a1,b2),...,(a1,b m ),(a2,b1),(a2,b2),...,(a2,b m ),...(a n ,b m )};

[0032] After grouping the set C, it is split into small sets. The grouping strategy is to group members with the same starting time point into one group, C = C1∪C2∪...∪C n ,

[0033] Where: C1={(a1,b1),(a1,b2),...,(a1,b m )}

[0034] C2={(a2,b1),(a2,b2),...,(a2,b m )}

[0035] C n ={(a n ,b1),(a n ,b2),...,(a n ,b m )}

[0036] Define a shift set D, and arrange and combine the members of the grouping sets C1, C2, ..., Cn. Each member of D represents a different shift.

[0037]

[0038] In the step (4), the optimal shift set E is selected based on the shift set D and the workload prediction set W. According to the workload coverage relationship between D and W, the optimal shift set E in a continuous time period is found. The optimal standard is that the smallest shift set covers the largest workload set.

[0039] The selecting of the optimal shift schedule includes:

[0040] Define the set E of “optimal shift schedules”,

[0041] E={e1,e2,...,e i}; 1≤i≤day; where day represents the maximum number of consecutive days;

[0042] represents the optimal set of shifts for each day;

[0043]

[0044] q i Represents the number of resources required on a certain day; according to the optimal shift schedule, the number of resources required each day is allocated to different shifts;

[0045] Solve the "optimal shift schedule" set E:

[0046] FOR iin(1..day): traverse continuous time (day unit)

[0047] w t ;Store the workload that matches the time

[0048] FOR w in W: traverse the workload set W

[0049] Finding the right amount of work to do on your schedule

[0050] FOR d in D: Traverse the shift set

[0051] Minimum working time + required resources

[0052]

[0053] In step (5), a rotation strategy is planned. According to different attendance rules, rest days are inserted within the time range of the shift schedule, and all working days and rest days are finally determined to ensure that the inserted working days and rest days are balanced and that there are corresponding staff members present every day. When planning rotation, a circular displacement strategy is used for planning.

[0054] The planning rotation strategy includes:

[0055] Define an attendance rule set R, R = {"work one day and rest one day", "work two days and rest one day", "work two days and rest two days", ...}; "work one day and rest one day" means work one day and rest one day, and the cycle repeats;

[0056] Define attendance rule set R s , each attendance rule in the set R is expressed as a binary string, with "1" representing working days and "0" representing rest days. After the mapping transformation, R s ={"10","110","1100",...};

[0057] Construct a shift schedule S with a time range content according to the attendance rules.

[0058] REPEAT: string copy function

[0059] R s i : A binary string of an attendance rule

[0060] day: the number of days for the shift schedule

[0061] Calculate the length of the binary string for the attendance rule

[0062] ceil(day / n): Calculate the number of replications and round up

[0063] According to the shift schedule, ensure the balance of daily attendance and construct the shift schedule set S p ,

[0064] S p ={s1,s2,...,s i}; i∈(1,n);

[0065] s i =substr(S,i,day);i∈(1,n);

[0066] substr(S,i,day): string truncation function. Parameter 1 indicates the string to be truncated, parameter 2 indicates the starting position of the truncation, and parameter 3 indicates the truncation length.

[0067] In step (5), a shift schedule set is constructed by using the optimal shift schedule set E = {e1, e2, ..., e i}; Combined with the shift plan set S p ={s1,s2,...,s i} is used to construct the shift set Q;

[0068]

[0069] Compared with the existing technology, the beneficial effects of the present invention are: the present invention transforms all problems of enterprise operation into mathematical problems, uses data and algorithms to enable enterprises to truly realize informatization, digitization and intelligence, and realizes refined and visualized management of the entire process of the enterprise through data, helping enterprises to improve quality and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0071] In order to make the technical means for realizing the present invention, the creative features, the objectives and effects to be achieved easy to understand, the present invention is further explained below in conjunction with specific diagrams. In the description of the present invention, it should be noted that, unless otherwise clearly stipulated and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense. For example, it can be a fixed connection or a detachable connection, or an integral connection can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements.

[0072] like Figure 1 As shown, the intelligent scheduling method includes the following steps:

[0073] Step (1). Collect all the business data of the enterprise, build a user business database, and realize the digitization of all the resources of the enterprise;

[0074] Step (2). Collect all rule information, build structured data, and realize the digitization of scheduling rules;

[0075] Step (3). Build a workload assessment model based on the basic business assurance plan;

[0076] Step (4). Construct an algorithm model, use the workload assessment results to coordinate the existing human resources globally, find the best match, quantify the allocation results, conduct evaluation and analysis, perform multiple rounds of iterative optimization based on the feedback, and formulate a shift plan;

[0077] Step (5). Construct a shift set by planning a shift rotation strategy and further develop a shift plan.

[0078] The workload forecast in step (3) is to estimate the number of resources (personnel) required at different times in the future according to the flight plan. The workload scale can be subdivided into the number of required resources calculated every 5 minutes.

[0079] (1) Define the “flight support plan workload” set:

[0080] P={p1,p2,...,p i},

[0081]

[0082] f = flight number,

[0083] t task = Type of support mission,

[0084] t begin = Guarantee start time,

[0085] t end = Guarantee end time,

[0086] q = the amount of resources required.

[0087] (2) Define the “workload forecast” set:

[0088] W={w1,w2,...,w i}

[0089] t slice =Guaranteed time

[0090] (3) After performing aggregation operations based on the guarantee task type and guarantee time, the elements of set P are mapped to set W. f:P→W

[0091] Example description:

[0092] 1) Construct the “flight support plan workload” set P data

[0093] P = {("MU1222","Come on","2023-5-10 12:00","2023-5-10 12:20",2),("MU1222","Cleaning","2023-5-10 12:10","2023-5-10 12:30",2),("CA1105","Come on","2023-5-10 12:10","2023-5-10 12:30",1),("CA1105","Cleaning","2023-5-10 12:20","2023-5-10 12:40",2)}

[0094] 2) After the scale slice is performed, the aggregation operation is performed to map to the set W

[0095] W = {("Come on", "2023-5-10 12:00", 2), ("Come on", "2023-5-10 12:05", 2), ("Come on", "2023-5-10 12:10", 3), ("Come on", "2023-5-10 12:15", 3), ("Come on", "2023-5-10 12:20", 1), ("Come on", "2023-5-10 12:25", 1), ("Cleaning", "2023-5-10 12:10", 2), ("Cleaning", "2023-5-10 12:15", 2), ("Cleaning", "2023-5-10 12:20",4),("Cleaning","2023-5-10 12:25",4),("Cleaning","2023-5-10 12:30",2),("Cleaning","2023-5-10 12:35",2)}

[0096] Constructing a set of shift schedules

[0097] The working period set is constructed according to the three parameters of "start time range", "working time range" and "time interval size" in the shift rule definition.

[0098] (1) Define the set A of “shift start time” and construct the set A by using the parameters “start time range” and “time interval size”. A={a1,a2,...,a n}, each member represents a different starting time point.

[0099] (2) Define the “working time” set B. Construct the set B by using the parameters “working time” and “time interval size”. B = {b1, b2, ..., b m}, each member represents a different working time.

[0100] (3) Define a set C that arranges and combines the members of sets A and B.

[0101] C=A×B={(a1,b1),(a1,b2),...,(a1,b m ),(a2,b1),(a2,b2),...,(a2,b m ),...(a n ,b m )}

[0102] (4) Group the set C into small sets. The grouping strategy is to group members with the same starting time point into one group. C = C1∪C2∪...∪C n .

[0103] Where: C1={(a1,b1),(a1,b2),...,(a1,b m )}

[0104] C2={(a2,b1),(a2,b2),...,(a2,b m )}

[0105] C n ={(a n ,b1),(a n ,b2),...,(a n ,b m )}

[0106] (5) Define a set of shifts D, and arrange and combine the members of the grouping sets C1, C2, ..., Cn. Each member of D represents a different shift.

[0107] D=C1×C2×...×C n =

[0108] {((a1,b1),(a2,b1),...,(a n ,b1)),

[0109] ((a1,b2),(a2,b1),...,(a n ,b1)),

[0110] ((a1,b n ),(a2,b1),...,(a n ,b1)),

[0111] ...}

[0112] Example description:

[0113] 1) Input parameters

[0114] start_time_scope: 6:00-8:00

[0115] work_duration_scope:16-18hours

[0116] work_interval_size:1hours

[0117] 2) Parameter decomposition, construct "shift start time" and "working time" set A = {6:00, 7:00, 8:00} according to "interval size"

[0118] B={16,17,18}

[0119] 3) Cartesian (product) combination

[0120] C={(6:00,16),(6:00,17),{6:00,18),

[0121] (7:00,16),(7:00,17),{7:00,18),

[0122] (8:00,16),(8:00,17),{8:00,18)}

[0123] 4) Split set C into groups (same start time period)

[0124] C1={(6:00,16),(6:00,17),{6:00,18)}

[0125] C2={(7:00,16),(7:00,17),{7:00,18)}

[0126] C3={(8:00,16),(8:00,17),{8:00,18)}

[0127] 5) Construct a set of shifts

[0128] D=C1×C2×C3=

[0129] {((6:00,16),(7:00,16),(8:00,16)),

[0130] ((6:00,16),(7:00,16),(8:00,17)),

[0131] ((6:00,16),(7:00,16),(8:00,18)),

[0132] ((6:00,16),(7:00,17),(8:00,16)),

[0133] ((6:00,16),(7:00,17),(8:00,17)),

[0134] ((6:00,16),(7:00,17),(8:00,18)),

[0135] ((6:00,16),(7:00,18),(8:00,16)),

[0136] ((6:00,16),(7:00,18),(8:00,17)),

[0137] ((6:00,16),(7:00,18),(8:00,18)),

[0138] ((6:00,17),(7:00,16),(8:00,16)),

[0139] ((6:00,17),(7:00,16),(8:00,17)),

[0140] ((6:00,17),(7:00,16),(8:00,18)),

[0141] ((6:00,17),(7:00,17),(8:00,16)),

[0142] ((6:00,17),(7:00,17),(8:00,17)),

[0143] ((6:00,17),(7:00,17),(8:00,18)),

[0144] ((6:00,17),(7:00,18),(8:00,16)),

[0145] ((6:00,17),(7:00,18),(8:00,17)),

[0146] ((6:00,17),(7:00,18),(8:00,18)),

[0147] ((6:00,18),(7:00,16),(8:00,16)),

[0148] ((6:00,18),(7:00,16),(8:00,17)),

[0149] ((6:00,18),(7:00,16),(8:00,18)),

[0150] ((6:00,18),(7:00,17),(8:00,16)),

[0151] ((6:00,18),(7:00,17),(8:00,17)),

[0152] ((6:00,18),(7:00,17),(8:00,18)),

[0153] ((6:00,18),(7:00,18),(8:00,16)),

[0154] ((6:00,18),(7:00,18),(8:00,17)),

[0155] ((6:00,18),(7:00,18),(8:00,18))}

[0156] Choose the best shift

[0157] According to the shift set D, combined with the workload forecast set W, according to the workload coverage relationship between D and W, find the optimal shift set E in a continuous time period. The optimal standard is that the smallest shift set covers the largest workload set.

[0158] (1) Define the set E of “optimal shift schedules”,

[0159] E={e1,e2,...,e i}; 1≤i≤day; where day represents the number of consecutive days;

[0160] represents the optimal set of daily shifts;

[0161]

[0162] q i Represents the number of resources required for a certain day; according to the optimal shift schedule, the number of resources required each day is allocated to different shifts.

[0163] (2) Solve the “optimal shift schedule” set E:

[0164]

[0165]

[0166] Shift Planning

[0167] The entire shift planning is divided into two stages, namely, the shift rotation strategy and the construction of employee shift planning, and finally all the shift sets are generated in a unified manner.

[0168] Plan your shift strategy

[0169] According to different attendance rules, the process of inserting rest days within the time range of the shift schedule will eventually determine all working days and rest days. In order to ensure that the inserted working days and rest days are balanced, and that there are corresponding staff members present every day, a circular displacement strategy is used to plan the rest days.

[0170] (1) Define the attendance rule set R, R = {"work one day and rest one day", "work two days and rest one day", "work two days and rest two days", ...};

[0171] "Work one day and rest one day" means working one day and resting one day, and doing this in a cycle.

[0172] (2) Define the attendance rule set R s , each attendance rule in the set R is expressed as a binary string, with "1" representing working days and "0" representing rest days. After the mapping transformation, R s ={"10","110","1100",...};

[0173] (3) Construct a shift schedule S with a time range based on the attendance rules.

[0174]

[0175] REPEAT: string copy function

[0176] A binary string of an attendance rule

[0177] day: the day of the shift schedule

[0178] Calculate the length of the binary string for the attendance rule

[0179] ceil(day / n): Calculate the number of replications and round up

[0180] (4) According to the shift schedule, ensure the balance of daily attendance and construct the shift schedule set S p ,

[0181] S p ={s1,s2,...,s i}; i∈(1,n);

[0182] s i =substr(S,i,day);i∈(1,n);

[0183] substr(S,i,day): string truncation function. Parameter 1 indicates the string to be truncated, parameter 2 indicates the starting position of the truncation, and parameter 3 indicates the truncation length.

[0184]

[0185]

[0186] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. Intelligent scheduling method, characterized by: The following steps are involved: Step (1). Collect all the business data of the enterprise, build a user business database, and realize the digitization of all the resources of the enterprise; Step (2). Collect all rule information, build structured data, and realize the digitization of scheduling rules; Step (3). Build a workload assessment model based on the basic business assurance plan; Step (4). Construct an algorithm model, use the workload assessment results to coordinate the existing human resources globally, find the best match, quantify the allocation results, conduct evaluation and analysis, perform multiple rounds of iterative optimization based on the feedback, and formulate a shift plan; Step (5). Construct a shift set by planning a shift rotation strategy and further develop a shift plan.

2. The intelligent scheduling method according to claim 1, characterized in that: The shift planning is to design a set of shifts with different start times and end times, which can meet the workload requirements within the planned scope on the one hand, and require the least total number of human resources on the other hand.

3. The intelligent scheduling method according to claim 1, characterized in that: The shift planning is divided into two stages for the entire shift planning, namely, the shift rotation strategy and the employee shift planning, and finally all the shift sets are uniformly generated.

4. The intelligent scheduling method according to claim 1, characterized in that: The workload forecast in step (3) is to estimate the number of resources required at different times in the future according to the flight plan; the workload scale can be subdivided into the number of required resources calculated every 5 minutes; Define the "Flight Support Plan Workload" collection: P={p1,p2,...,p i }, f = flight number, t task = type of support mission, t begin = Guarantee start time, t end = Guarantee end time, q = the amount of resources required; Define the "Workload Forecast" collection: In={in1,in2,...,in i }, t slice =Guaranteed time After performing aggregation operations by guaranteeing task type and guarantee time, the elements of set P are mapped to the set W, f:P→W.

5. The intelligent scheduling method according to claim 1, characterized in that: In step (4), a shift set is constructed, and a working period set is constructed according to the three parameters of "start time range", "working time range" and "time interval size" in the shift rule definition.

6. The intelligent scheduling method according to claim 5, characterized in that: The construction of the shift set includes: Define the "shift start time" set A, and construct the set A by the parameters "start time range" and "time interval size", A={a1,a2,...,a n }, each member represents a different starting time point; Define the "working time" set B, and construct the set B by the parameters "working time" and "time interval size", B = {b1, b2, ..., b m }, each member represents a different working time; Define a set C that arranges and combines the members of sets A and B; <h2 style=";text-align:left;direction:ltr">C = A × B = {(a1,b1),(a1,b2),...,(a1,b<h2 style=";text-align:left;direction:ltr"> m <h2 style=";text-align:left;direction:ltr"> ),(a2,b1),(a2,b2),...,(a2,b<h2 style=";text-align:left;direction:ltr"> m <h2 style=";text-align:left;direction:ltr"> ),...(a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ,b<h2 style=";text-align:left;direction:ltr"> m <h2 style=";text-align:left;direction:ltr"> )}; After grouping the set C, it is split into small sets. The grouping strategy is to group members with the same starting time point into one group, C = C1∪C2∪...∪C n , Where: C1={(a1,b1),(a1,b2),...,(a1,b m )} <h2 style=";text-align:left;direction:ltr">C2 = {(a2,b1),(a2,b2),...,(a2,b<h2 style=";text-align:left;direction:ltr"> m <h2 style=";text-align:left;direction:ltr"> )} C n ={(a n ,b1),(a n ,b2),...,(a n ,b m )} Define a shift set D, and arrange and combine the members of the grouping sets C1, C2, ..., Cn. Each member of D represents a different shift. <h2 style=";text-align:left;direction:ltr">D = C1 × C2 ×... × C<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> (a1,b1),(a2,b1),...,(a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> (b1)),((a1,b2),(a2,b1),...,(a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ,b1)),((a1,b<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ),(a2,b1),...,(a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ,b1)),...}。 7. The intelligent scheduling method according to claim 1, characterized in that: In the step (4), the optimal shift set E is selected based on the shift set D and the workload prediction set W. According to the workload coverage relationship between D and W, the optimal shift set E in a continuous time period is found. The optimal standard is that the smallest shift set covers the largest workload set.

8. The intelligent scheduling method according to claim 7, characterized in that: The selecting of the optimal shift schedule includes: Define the set E of "optimal shift schedules", E={e1,e2,...,e i }; 1≤i≤day; where day represents the maximum number of consecutive days; represents the optimal set of shifts for each day; q i Represents the number of resources required on a certain day; according to the optimal shift schedule, the number of resources required each day is allocated to different shifts; Solve the "optimal shift schedule" set E:

9. The intelligent scheduling method according to claim 1, characterized in that: In the step (5), a rotation strategy is planned. According to different attendance rules, rest days are inserted within the time range of the shift schedule to ultimately determine all working days and rest days. In order to ensure that the inserted working days and rest days are balanced and that there are corresponding personnel on duty every day, a cyclic displacement strategy is used for planning when planning rotation days.

10. The intelligent scheduling method according to claim 9, characterized in that: The planning rotation strategy includes: Define an attendance rule set R, R = {"work one day and rest one day", "work two days and rest one day", "work two days and rest two days", ...}; "work one day and rest one day" means work one day and rest one day, and the cycle repeats; Define attendance rule set R s , each attendance rule in the set R is expressed as a binary string, with "1" representing working days and "0" representing rest days. After the mapping transformation, R s ={"10","110","1100",...}; construct a shift schedule S with a time range content according to the attendance rule, REPEAT: string copy function A binary string of an attendance rule day: the day of the shift schedule Calculate the length of the binary string for the attendance rule ceil(day / n): Calculate the number of replications and round up According to the shift schedule, ensure the balance of daily attendance and construct the shift schedule set S p , S p ={s1,s2,...,s i };i∈(1,n); si=substr(S,i,day);i∈(1,n); substr(S,i,day): string interception function, parameter 1 indicates the string to be intercepted, parameter 2 indicates the starting position of the interception, and parameter 3 indicates the interception length; Construct a shift schedule set, through the optimal shift set E = {e1, e2, ..., e i } Combined with the shift plan set S p ={s1,s2,...,s i } is used to construct the shift set Q;

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