Patent Document

RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Application No. 60/163,304 filed Nov. 3, 1999, the entire teachings of which are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     The present invention relates to scheduling workshifts for employees based on work requirements. Scheduling is typically done in incremental time periods, such as 15-minute periods. 
     For example, a supermarket may need four baggers and five cashiers between 9:15 AM and 1:15 PM, and only two baggers and three cashiers from 1:15 PM until 5:30 PM, at which time the need may again be elevated. Of course, scheduling must be satisfied from the existing pool of employees. At the same time, many rules or constraints must also be satisfied. These rules include minimum and maximum hours for a given employee, rules related to employment of minors, rules relating to break times, rules dictated by union contracts, etc. 
     In the not-too-distant past, of course, scheduling was performed manually by a manager who sat down at a table with pencil, paper, or more recently with a spreadsheet or other simple computer program, and who filled in the schedule with the names of employees. After filling in the schedule, the manager might check that no rules have been broken. 
     While this method may still be performed in many small shops, it is wasteful of the manager&#39;s time, and does not lead to efficient scheduling. In particular, such a method is highly impractical for a medium-to-large-scale operation comprising tens to thousands of employees. 
     More recently, computer methods have been developed to determine near-optimal schedules from the myriad of possible schedules. In particular, Gary M. Thompson,  A Simulated-Annealing Heuristic For Shift Scheduling Using Non - Continuously Available Employees , Computers Ops. Res. Vol. 23, No.3, pp 275-288 (1996), incorporated herein by reference, describes a method of scheduling workshifts using a “simulated annealing” process which heuristically develops a trial schedule from an “incumbent” schedule, and compares the trial schedule both with the incumbent schedule and the best schedule found so far. Comparisons are made between values of an “objective” function, calculated for each schedule. 
     Generally, according to Thompson, a trial schedule which is “better” than the “best” schedule determined thus far, replaces both the best and incumbent (or current) schedules, while a trial schedule which is better than just the incumbent replaces only the incumbent schedule. However, a trial schedule which is not better than the incumbent may still replace the incumbent, depending on a randomized annealing function. The advantage of simulated annealing is that it generates new solutions iteratively, exploring areas that other algorithms fail to examine. 
     SUMMARY OF THE INVENTION 
     Thompson&#39;s objective function relies on several factors, in particular, cost, underscheduling and overscheduling. However, Thompson does not take into account employee preferences, such as preferred hours, preferred jobs, etc. Furthermore, Thompson assumes homogeneously-skilled employees. 
     It is an objective of the present invention to develop a cost-effective workforce schedule based on workforce requirements, using a simulated annealing function while considering all of the above factors including employee preferences and job skills. Jobs requiring different skills or activities can be listed in preferential order for each employee. While not essential, in most cases, this is based on the employee&#39;s skill level. Furthermore, the schedule must comply with certain constraints such as hours rules, minor rules, break rules, etc. 
     The weights assigned to employee preferences can be based on any data maintained, for example, in a database. For example, weights could be based wholly or in part on seniority, residential distance from the employer, zip code, age, and so on. 
     Accordingly, a method of dynamically scheduling a workforce comprises obtaining workforce requirements, attributes and preferences, determining a workforce schedule based on the workforce requirements and attributes, determining a schedule value, or cost, based on workforce requirements and preferences, and iteratively modifying the workforce schedule, determining a schedule value based on workforce requirements and preferences for the modified workforce schedule, and comparing schedule values to determine a best workforce schedule. 
     Determining a workforce schedule includes determining shifts and assigning employees to the shifts. 
     Specific steps performed by a particular embodiment include determining workforce requirements for a given time period. Then, responsive to the workforce requirements, an initial workforce schedule is determined for the given time period by determining shifts and assigning employees to the shifts, designating the initial workforce schedule as a trial workforce schedule and a best workforce schedule, and determining a value associated with the best workforce schedule responsive to the best workforce schedule, the workforce requirements and employee preferences. Next, the trial workforce schedule is modified. A value associated with the trial workforce schedule is determined based on the trial workforce schedule, the workforce requirements and employee preferences. If the value associated with the trial workforce schedule indicates a better match to the workforce requirements than a value associated with the best workforce schedule, the trial workforce schedule is designated as the best workforce schedule. This process is repeated for a predetermined number of iterations, after which the best workforce schedule is selected. 
     The value associated with the trial workforce schedule is further responsive to overscheduling and underscheduling of employees in each period. Furthermore, overscheduling, underscheduling and employee preferences can be weighted with respect to each other. In a particular embodiment, each job is associated with a weighting factor, which is applied where the associated job is scheduled. Similarly, in a particular embodiment, each period is associated with a weighting factor which is applied to the associated period. 
     In a particular embodiment, weights are set by a user. 
     Employee preferences can comprise, for example, preferred availability, preferred jobs, preferred days off, and/or a preferred total number of hours assigned for some duration, such as a week. 
     Furthermore, in a particular embodiment, employee preferences are weighted per employee. Such weighting can be, for example, according to seniority, or work status. 
     In a particular embodiment, the trial workforce schedule is modified by removing shifts, e.g., costly shifts, from the trial workforce schedule, adding shifts to the trial workforce schedule, and replacing shifts in the trial workforce schedule. Costly shifts are removed, in one embodiment, by repeatedly selecting a first predetermined number of most costly shifts and randomly removing one of the selected most costly shifts until some predetermined number of costly shifts have been removed. 
     The process of generating a schedule, e.g., adding shifts to a schedule, is aided in a particular embodiment by maintaining a scheduled employee list, a shift lookup table, and period totals. 
     Shifts can be added to the schedule by generating a list of candidate shifts that can be scheduled for a selected job, start time and stop time, and for each candidate shift. A most cost-reducing employee in a shift region who can fill the candidate shift is determined, and, if overall cost of adding this shift is less than costs corresponding to a predetermined number of lowest cost shifts thus far determined, added to a temporary list of lowest cost shifts. Finally, one of the candidate shifts in the temporary list is randomly selected, and added to the trial workforce schedule. 
     Shifts are replaced in a particular embodiment by initially stretching, shrinking or moving breaks around within a shift responsive to marginal cost data and marginal dissatisfaction costs. A replacement cost is calculated for every shift whose cost can be reduced. Finally, a lowest cost replacement is repeatedly determined, replacing the shift replaced with the replacement and the replacement cost of other shifts recalculated. 
     In a particular embodiment, the value of the objective function is a weighted sum of overscheduling and underscheduling over all periods and all jobs for a given duration, and employee dissatisfaction over all employees. Employee dissatisfaction is weighted responsive to any or all of, but is not limited to, seniority, work status, age, or commuting distance. 
     In particular, in at least one embodiment, employee dissatisfaction is a weighted sum of the number of hours that an employee spends working outside the employee&#39;s preferred availability, the number of hours that the employee spends working outside the employee&#39;s preferred jobs, the number of hours that the employee spends working on the employee&#39;s preferred day off, and the difference between a number of scheduled hours and a number of preferred hours. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. 
     FIG. 1 is a block diagram of a scheduler embodiment. 
     FIG. 2 is a graph of an exemplary workforce requirement for a particular job. 
     FIG. 3 is a graph of an exemplary workforce schedule produced by the present invention. 
     FIG. 4A is a schematic diagram of a particular embodiment of the present invention. 
     FIG. 4B is a flowchart of the process employed by an embodiment of the present invention. 
     FIG. 5 is a schematic diagram illustrating the calculation of the objective function f trial  as performed by the objective function calculator of FIG.  4 A. 
     FIG. 6 is a schematic diagram illustrating the derivation of individual employee dissatisfaction, as used in FIG.  5 . 
     FIG. 7 is a schematic diagram of the trial schedule generator of FIG.  4 A. 
     FIG. 8 is a flowchart of the Remove Shifts step of FIG.  7 . 
     FIG. 9 is a block diagram illustrating the ScheduleEmployeeList and a ScheduleEmployee object of the present invention. 
     FIG. 10 is a table illustrating an exemplary assignment of shift identifiers to particular shifts. 
     FIG. 11 is a schematic diagram illustrating bitmaps which match employees to shifts for which the employees are available. 
     FIG. 12 is a flowchart illustrating the creation of a Shift Lookup Table. 
     FIG. 13 is a schematic diagram of a Shift Lookup Table. 
     FIG. 14 a flowchart of the Add Shifts step of FIG.  7 . 
     FIG. 15 is a detailed flowchart of the step of FIG. 14 showing how areas needing the most coverage are determined. 
     FIG. 16 is a detailed flowchart of the step of FIG. 14 showing how the most cost-reducing shift in an area is determined. 
     FIG. 17 is a detailed flowchart of the step FIG. 16 showing how a shift candidate list is generated. 
     FIGS. 18A and 18B are a flowchart of the Replace Shifts Algorithm of FIG.  7 . 
     FIG. 19 is a detailed flowchart of the FindCostReducingReplacementShift procedure of FIG.  18 A. 
     FIG. 20 is a detailed flowchart of the FindBestReplacementShift procedure of FIG.  19 . 
     FIG. 21 is a flowchart of the SwapShifts procedure of FIG.  18 B. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     A preferred embodiment of the present invention schedules a group of employees for a number of jobs over some period, e.g., one week, in order to satisfy a set of workforce requirements for those jobs. The scheduler must not break any constraints involving employee availability, skills, weekly hours, daily hours, breaks and minor rules. In addition the scheduler must try to satisfy employee preferences as closely as possible in seniority order as defined by the user. 
     The dynamic scheduling algorithm of an embodiment of the present invention uses a heuristic algorithm known as “simulated annealing” to create a schedule that satisfies both labor requirements and employee preferences. This approach deals with the problem as a typical optimization problem, where an attempt is made to minimize some “objective function,” subject to a set of constraints. 
     The solution is not necessarily the optimal solution, but it is usually very close to optimal. Finding the solution is an iterative process where it is never known how close the solution is to the true optimum. 
     One embodiment always gives the same solution under the same exact circumstances every time, by using a fixed random sequence, and a fixed number of iterations, specified by the user, and by controlling roundoff error at every step. 
     FIG. 1 provides an overview of an embodiment of the present invention. Given a set of workforce requirements  4 , produced, for example, by a forecasting program, a set of constraints  6 , and employee preferences  7 , a scheduler  2  produces a workforce schedule  8 . 
     FIG. 2 illustrates a portion of an exemplary workforce requirement  4  for a single job, say a cashier, although it will be understood that each of a plurality of jobs has its own requirements. As seen in FIG. 2, on the particular day shown, no cashiers are needed before 7:45 am (Period  10 ). From 7:45 am until 8:45 am (Period  12 ), one cashier is needed. From 8:45 am until 9:30 am (Period  14 ), two cashiers are needed, and from 9:30 am until some later time (Period  16 ), four cashiers are needed. 
     FIG. 3 illustrates an exemplary schedule  8  for employees A, B, C and D, as produced by the scheduler  2  of FIG.  1 . Employee B is scheduled to work as a cashier from 7:30 am until 9:45 am, and as a bagger from 9:45 am until some later time. A 15-minute break  7  has also been scheduled for Employee B starting at 9:15 am. Similarly, Employees A, C and D have been scheduled to work as cashiers starting at 9:30 am, 9:00 am and 10:00 am respectively. 
     FIG. 4A is a more detailed view of the embodiment of FIG. 1, while FIG. 4B is a flowchart of the corresponding process. Simulated annealing is a general trial and error approach used to find a solution to an optimization problem. During the process, three solutions to the problem are maintained: a trial solution S trial,    51 , a current or incumbent solution S current    52 , and the best solution obtained so far S best    53 . 
     First, the current solution S current    52  and S best    53  are set to some initial solution (Step  201 ), for example, an empty schedule. Then, a function f, called the “objective” function, and described in much greater detail below, is calculated for this initial solution and the value saved as f current    62  (Step  203 ) and as f best    63 . 
     At Step  205 , a trial schedule generator  101  generates a trial solution S trial    51  from S current    52  by modifying S current    52  using a heuristic which performs local optimizations At Step  207 , an objective function calculator  103  calculates a value f trial    61  for the trial solution S trial    51  based in part on employee preferences  7 . 
     At Step  209 , a comparator  105  compares f trial    61  with f best    63 . If f trial    61  is less than f best    63 , that is, if S trail    51  is a better solution than S best    53 , then at Steps  210  and  214 , the comparator output  121  causes S trial    51  to be copied into both S best    53  and S current    52 , as indicated by dashed lines  123 . Similarly, the corresponding objective function value f trial    61  is copied into f current    62  and f best    63 , as indicated by dashed lines  125 . 
     If, at Step  209 , it is determined that f trial    61  is not less than f trial    63 , then at Step  211 , comparator  105  compares f trial    61  with f current    62 . If f trial    61  is less than f current    62 , that is, S trial    51  is better than S current    52  but not as good as S best    53 , then Step  214  is executed, and the comparator output  121  causes S trial    51  to be copied only to S current    52 . Similarly, the corresponding objective function value f trial    61  is copied into f current    62 . 
     Otherwise if f trail    61  is greater than f current    62 , that is, S trial    51  is worse than S current    52 , a decision is made at Step  213  by the annealing temperature comparator  107 , which applies an “annealing function” to the current and trial values f current    62  and f trial    61  respectively, to determine whether to keep S trial    51 . The reason for keeping S trial    51  is that ultimately, it could lead to a better solution. 
     The annealing function of a particular embodiment is exp((f current −f trial )/T a ) where T a  is the annealing temperature. This number starts out high and “cools down.” At Step  213 , the value of the annealing function is compared to a randomly generated number ranging from 0.0 to 1.0. If the annealing function is higher than this number, then S trail    51  is not discarded and is copied to S current    53  (Step  214 ), as controlled by the output  129  of the annealing temperature comparator  107 . 
     At Step  217 , the number of iterations executed thus far is compared with some predetermined threshold I max . If I max  iterations have executed, the process is complete, and the best solution so far determined, saved in S current    53  becomes the final schedule  8 . If, on the other hand, I max  iterations have not been executed, the annealing temperature T a  is updated by the annealing temperature updater  109 , and the process repeats from step  205 . 
     A defining aspect of the simulated annealing approach is that it explores solutions that would be rejected by other algorithms and it does not get trapped in a local minimum. The annealing function prevents the process from spending too much time looking at bad solutions, especially towards the end of the process as the annealing temperature cools down. 
     Generating an initial solution (part of Step  201 ) presents a problem if the initial solution must be a strictly “feasible” solution, that is, a solution that satisfies all constraints. For example, the simplest initial solution is to generate an empty schedule in which no employees are schedule to work. However, this solution is infeasible because it breaks a Minimum-Hours-Per-Week constraint. 
     Fortunately, this problem can be ignored in Step  201  because Step  205  ensures that any solution is a feasible solution. Since no evaluations are performed until Step  207 , a feasible solution is not required in Step  201 . In order to handle this situation correctly however, an artificial cost is added to the objective function f for breaking the Minimum-Hours-Per-Week constraint, as discussed below. 
     The Objective Function 
     The objective function calculator  103  of FIG. 4A calculates, for a given set of workforce requirements and a trial schedule S trial    51 , the value of an objective function. 
     FIG. 5 illustrates a preferred objective function, which expresses the goals of satisfying both labor requirements and employee preferences: 
     
       
           f=W   ro   ΣΣo   ij   +W   ru   ΣΣu   ij   +W   d   ΣW   k   d   k   (Eq. 1) 
       
     
     Here, f is the cost, or objective, function to be minimized, and is the output of the objective function calculator  103 , corresponding to f trial    61  (FIG.  4 A). 
     Assuming for the sake of discussion that 15-minute intervals are used, ΣΣo ij  is the total number of overscheduled minutes  501  for all 15-minute periods i and all jobs j, calculated by the summation  503 , that is, for i=1 to 672 (where there are 672 15-minute intervals in a week), and for j=1 to N jobs , where N jobs  is the total number of jobs. Of course, periods other than one week and intervals other than 15-minute intervals can also be used. W ro    505  is a weighting factor or penalty factor associated with overscheduling any job during any period. 
     ΣΣu ij  is the total number of underscheduled minutes  507  for all 15 minute periods i and all jobs j, that is for i=1 to 672 and for j=1 to N jobs , and is calculated by the summation  509 . W ru    511  is a weighting factor or penalty factor associated with underscheduling any job during any period. 
     Σw k  d k  is the sum of dissatisfaction d k    513  of each employee k weighted by w k    515  for k=1 to N employees  where w k  reflects seniority, work status, etc., and is calculated by the summation  517 . W d    519  is an overall weighting of total employee dissatisfaction. 
     Summation  521  adds the weighted overs, unders and dissatisfaction to produce the resulting f trial    61 . 
     FIG. 6 shows a preferred embodiment in which four factors contribute to a determination of employee dissatisfaction d k : 
     
       
           d   k   =w   ak   d   ak   +w   jk   d   jk   +w   ok   d   ok   +w   hk   d   hk   (Eq. 2) 
       
     
     where: 
     d ak  is the number of hours that employee k spends working outside his preferred availability; 
     d jk  is the number of hours that employee k spends working outside his preferred jobs; 
     d ok  is the number of hours that employee k spends working on his preferred day off; and 
     d hk  is the difference between an employee&#39;s preferred number of hours and the number of hours actually scheduled for the employee, that is, d hk =abs(scheduled−preferred) hours for the week for employee k. 
     w ak ,w jk ,w ok  and w hk  are weighting factors  527  for preferred availability, preferred job, preferred days off, and preferred hours per week for employee k, respectively. Summation  529  adds the weighted dissatisfaction costs to produce a total dissatisfaction cost d k  for employee k. 
     Note that weighting factors for the labor requirements could be assigned for each individual job or for each period of the day. For example, different jobs could have different associated weighting factors for overs and unders. Finally, certain periods of the week could be favored over others by varying the distribution of the weights, that is, by assigning different weighting factors to different periods. 
     Looking again at FIG. 5, in order to deal with the three main weighting factors, i.e., W ro    505 , W ru    511  and W d    519 , assume for discussion that overscheduled intervals (“overs”) are just as undesirable as underscheduled intervals (“unders”), i.e., W ro =W ru . Looking at the objective function of Eq. (1), it is obvious that all three factors can be arbitrarily scaled by the same number without affecting the solution. Since dissatisfaction is preferably in terms of hours, while overs and unders are in minutes, let W ro =W ru ={fraction (1/60)}. This normalizes Eq. (1) so that W d  now represents a ratio R d  of the relative importance of employee satisfaction to satisfying work requirements. 
     This ratio can be assigned by the user. Thus, W d =R d  is a user-configurable ratio from 0.0 to 1.0, where R d =0.0 means employee satisfaction has no importance, and R d =1.0 means employee satisfaction is roughly as important as workforce requirements. 
     The dissatisfaction weighting factors w ak ,w jk ,w ok ,w hk    527  can be assigned by a user in several ways. 
     These weighting factors still do not take into account preference by seniority scheduling. To accomplish this, the user can configure the seniority order and an additional weighting factor is applied to each employee that reflects his position in the order. In one embodiment, this weighting factor is simply 1/k for the k&#39;th employee. 
     In other words, for employee k, 
     
       
         w ak =w a /k 
       
     
     
       
         w jk =w j /k 
       
     
     
       
         w ok =w o /k 
       
     
     
       
         w hk =w h /k 
       
     
     The 1/k factor strongly favors employee satisfaction at the high end. Alternate schemes can be used that are not so top heavy. However, the user can always turn off this progression completely for one or more work statuses. This forces all employees in a work status to have the same dissatisfaction weighting factors. 
     The 1/k factor also tends to weaken the overall employee satisfaction component of the objective function. To account for this, W d  can be further scaled by the following factor:          f   d     =     n   /     (       ∑     i   =   1     n                     1   /   i       )                              
     where n is the number of employees, so that now, W d =f d *R d  where R d  is the user configured ratio ranging from 0.0 to 1.0. 
     Preferably, there is one additional consideration in calculating the dissatisfaction for non-preferred jobs w jk *d jk . That is, d jk  is not exactly the number of minutes worked outside of the employees preferred job. This is because jobs are not simply configured as preferred or non-preferred. Instead, jobs are ranked or scaled by a factor which ranges from 0 to 100, 0 for a job that is completely dissatisfying and 100 for a job that is completely satisfying. Thus, if an employee ranks a job as 50 and works 10 hours in that job and spends the rest of the week working in jobs that are rated 100 then the d jk =10*50%=5 hours. 
     Other factors may also be used to adjust the weight, such as seniority, work status, age and/or commuting distance. 
     Constraints 
     There are several different types of constraints that a solution must satisfy. For example, availability constraints, i.e. times during the week that an employee can work, include all availability periods as specified in the base cycle, base override, personal cycle and personal override configurations for each employee. Every employee that is to be scheduled for the week can be thought of as having a set of availability periods for that week. 
     Skill constraints comprise, for example, the set of jobs that an employee can work, or is capable of working. 
     Hours rules constraints include maximum and minimum shift length, preferably not including breaks; maximum hours and maximum overtime (OT) hours per day; minimum time between shifts, etc. 
     Minor rule constraints, that is, rules pertaining to minors, are, generally, overrides to availability and hours rulesets. 
     Other constraints include meal/break rules for each employee, Shift Transition Time which are short periods of time that may occur at the beginning or end of a shift to allow an employee to prepare for the beginning or end of a shift, and Rounding rules which allow a user to specify values for rounding a resulting schedule, for example, shifts limited to multiples of 1 hour, 30 minute or 15 minute periods, or paid and unpaid breaks limited to multiples of 1 hour, 30 minute, 20 minute, 15 minute, 10 minute or 5 minute periods. 
     Trial Schedule Generation 
     The purpose of the heuristic used by the trial schedule generator  101  is to generate a new trial solution S trial  that is better than the current solution S current . As FIG. 7 illustrates, this is done in a three-step process, performed by the trial schedule generator  101  of FIG.  4 A and corresponding to Step  205  of FIG. 4B 
     First, at Step  240 , the highest cost shifts, i.e., shifts that increase the overall value of the objective function, are removed. These are replaced by adding, at Step  242 , low cost shifts, i.e. shifts that reduce the overall value of the objective function the most or increase it the least. Finally, at Step  244 , the algorithm attempts to find replacement shifts for each and every shift in the trial schedule. 
     The Remove Shifts step  240  attempts to remove some of the most costly shifts. It removes these one at a time in a loop that first searches for the topmost cost-reducing shifts and then picks one of these at random for removal. The randomness introduced as part of the simulated annealing approach prevents the algorithm from getting stuck in a local optimum. After some fraction of the shifts are removed, this procedure stops. If it were allowed to continue indefinitely, removal would no longer be cost reducing. 
     FIG. 8 is a flowchart of an embodiment the Remove Shifts step  240  of FIG. 4B. A minimum number (minDrop) of shifts is dropped, after which additional shifts are dropped until either dropping shifts no longer lowers the cost function value or a maximum number (maxDrop) of shifts have been dropped. 
     In Step  253 , minDrop is set to some minimum percentage of nShifts, the number of shifts currently scheduled, and maxDrop is set to some maximum percentage of nShifts, so that minDrop and maxDrop form lower and upper limits respectively to the number of iterations to be executed. The number of shifts dropped or removed so far, numDropped, is initialized to zero. 
     If minDrop iterations have not yet occurred, i.e., numDropped&lt;minDrop (Step  255 ), or if maxDrop iterations have not yet occurred and lastCost&lt;0, i.e., (numDropped&lt;maxDrop) AND (lastCost&lt;0) (Step  257 ), then a shift is removed, beginning with Step  259 . Otherwise the Remove Shifts process  240  is complete. 
     At Step  259 , the n most cost-reducing shifts are found, for some number n, for example, by selecting each shift one at a time and recalculating the objective function as if that shift were removed. At Step  261 , one of the n cost-reducing shifts found in Step  259  is randomly selected and removed. The cost of dropping the removed shift, that is, the difference between the cost function values with and without the shift, is determined as the value lastCost. Finally, at Step  263 , numDropped is incremented to indicate the completion of another iteration. 
     Note that it may be necessary to break the Minimum Hours Per Week constraint in order to remove the maxPercentage of the shifts. For this reason, a new component is added to the dissatisfaction cost, discussed below. This cost is proportional to the number of hours below the Minimum Hours Per Week that the employee is scheduled. This is usually a strong enough factor to naturally drive the solution back to a feasible state during the Add Shifts step  242 . However if the solution is still not feasible after the Add Shifts phase, it is forced to be feasible by adding additional shifts in the Replace Phase. In any event, at the end of Step  205  of FIG. 4B, a feasible solution exists that can be evaluated. 
     The Add Shifts step  242  (FIG. 7) is naturally the most complex aspect of the algorithm. It is, after all, creating the schedule. It is responsible for generating low cost shifts that satisfy all constraints. Before a discussion of this step, however, it is important to understand three important objects that Add Shifts depends on. These are a Scheduled Employee List, a Shift Lookup Table, and Period Totals. 
     FIG. 9 illustrates a Schedule Employee List  70 , which is a list of Schedule Employee objects. For example, row  71  of the Schedule Employee List  70  references a Schedule Employee object  72  for Employee k. 
     Each Schedule Employee object  72  maintains all of the constraints and preferences  73 , e.g., availability, minimum and maximum hours, shift lengths and dissatisfaction weighting factors, etc. for an individual employee. From the scheduler&#39;s point of view, each employee has his own individual constraints and preferences, even though they may be derived from a common ruleset or base schedule. 
     Each Schedule Employee object  72  maintains a copy of the current schedule  74 , the trial schedule  76  and the best schedule  78  for each employee. 
     FIG. 10 illustrates the first step in creating a shift lookup table. The shift lookup table allows the scheduler to express availability, skill set and shift length constraints very efficiently so that only shifts that satisfy these constraints are created. It also provides an efficient way to quickly determine which employees can work a specific shift. 
     First, a list  80  of all possible shifts that could be scheduled is generated. This list can be generated, for example, by determining every possible unique combination of start time, duration and job. Each combination can be mapped, for example, to a unique 32-bit integer shift identifier  81 . The result is a set of unique shift IDs from 0 to S−1 (for S shifts) that describe every possible shift that can be worked, and that are used to reference the shift lookup table. 
     A shift is characterized by its start time  85 , length  87  and job  89 . If a shift can start on any 15-minute period within one week, then there are 672 possible start times. Of course, other periods such as ½ hour or 1 hour can be used, in which case the number of possible start times is only 336 or 168 respectively. 
     The number of possible shift lengths is much more limited. For a 2 hr minimum and 8 hour maximum, using 15-minute intervals, there are only 25 possibilities. The number of possible jobs is nearly always less than 100 and more likely less than 20. 
     In a worst case scenario using 15-minute intervals with 100 jobs to schedule, there are 672*25*100=1,680,000 possibilities. This a very large number but not unmanageable. A more realistic case using 30-minute intervals and 20 jobs to schedule leaves only 87,360 possibilities. 
     Each of these shifts can be assigned a unique identifier  81  which references a row of the shift lookup table. The trial shift generator  101  can quickly obtain information about a particular shift, such as the feasibility of a shift or which people are available to work the shift, by using this index to reference the shift lookup table. 
     The shift lookup table is an array of pointers indexed by a shift identifier  81 . Each pointer points to a bitmap region that indicates which employees can work the associated shift. If no one can work the shift, then the pointer is null. In addition to the employee bitmap, each region keeps a total count of how many employees can work the shifts. Using this approach many, of the pointers will point to the same region, thereby conserving memory. 
     FIG. 11 illustrates how in one embodiment, to create a shift lookup table, a temporary bitmap  90  is first created for each employee. Each bitmap  90  represents every possible shift that the corresponding employee can work. This can be done, for example, by scanning each employee&#39;s availability periods and minimum and maximum shift length in the employee&#39;s associated Schedule Employee object  72  (FIG.  9 ), and then setting the corresponding bit in the shift lookup table. 
     For example, Employee  1  is available to work shifts identified by identifers  0 ,  1 ,  2  and  3 . Employee  2  is available to work shifts identified by identifiers  0  and  1 , but not shifts  2  or  3 . Similarly, Employee N is not available to work any of shifts  0 - 3 . 
     For a large number of employees, for example, 1000, this can be an unreasonable and impractical amount of data, consuming on the order of 20 Mb. This large amount of data can be greatly condensed by taking advantage of the fact that a large number employees will always have a significant amount of overlap between the shifts that they can work. 
     Thus, in a particular embodiment, shifts can be grouped into “regions” that are characterized by a common set of employees that can work that shift. Every shift belongs to one and only one region and many shifts will belong to a common region. A region is characterized by a bit map where each employee is represented by one bit. For example, 125 bytes are required to represent 1000 employees. 
     The present invention takes advantage of the fact that many employees can work a common set of shifts. The larger the number of employees, the truer this is. Shifts can be grouped into “regions” that are characterized by a common set of employees who can work those shifts. 
     Every shift belongs to one and only one region. But an employee can belong to many regions. Therefore, the set of shifts can be represented as a simple, single dimensional array from 0 to S−1, where each element points to a region. The region reveals every possible employee that can work that shift. 
     Each region can be represented by a bit map that is N/8 bytes long for N employees. Depending on how much overlap there is, and there is typically quite a bit, not all that many regions are needed. The worst case is N regions, but his would be extremely unlikely in a real life situation with many employees. For example, for a set of 100 very diverse employees, a typical number of regions is between 20 and 30. The ratio improves greatly as N gets larger. For example, for 1,0000 employees, the number of regions will likely be less than 100. 
     FIG. 12 is a flowchart demonstrating how the shift lookup table is created. At Step  275 , a shift which has not yet been mapped is selected. If there are no more unmapped shifts, the process is done. 
     Otherwise, at Step  277 , a bitmap  90  (FIG. 11) is determined for the selected shift, indicating which employees are available to work that shift. The resulting bitmap is compared against existing bitmaps of existing regions in Step  279 . If there is a match, as determined in Step  281 , then in Step  283 , a pointer to the region is associated with the shift, by storing the pointer in the row of the shift lookup table indexed by the shift&#39;s identifier. If no match was found in Step  281 , then at Step  285 , a new region is created and a pointer to the new region is stored, or associated, with the shift. 
     The scheduler generates each shift/employee combination one at a time until the requirements are satisfied, and then repeatedly takes shifts away from employees and reassigns completely new shift/employee combinations until it comes up with the best solution it can find in some predetermined number of iterations. In order to do this, the scheduler must be able to generate candidate lists of shift-employee combinations very quickly. The shift lookup table enables this speedy generation. 
     FIG. 13 illustrates a particular embodiment of a shift lookup table  91 . Each entry  92  in the table corresponds to a particular shift, and is referenced by the identifier for that shift. Furthermore, each entry  92  contains a pointer to a region. For example, shifts  3  and  4  each contain a reference to Region X  93  while shift  2  contains a reference to Region Y  95 . 
     It can be seen that, for example, Employee  2  is available to work any shift belonging to Region X  93 , including shifts  3  and  4 . Similarly, Employees  1  and  4  are available to work any shift belonging to Region Y  95 , including shift  2 . Thus, by using the Shift Lookup Table  91 , it is possible to determine, given a shift identifier, which employees are available to work the associated shift, without the necessity of maintaining a separate bitmap for each shift. 
     For each and every period of the week, regardless of whether periods are measure in 15-minute intervals, 30-minute intervals or some other interval, various key totals are tracked for each job. Thus, for each type of total, there is actually an array of size nJobs×nPeriods, where nPeriods is the number of periods in the week. 
     For example, Required[i, j] is the total workforce requirement, in minutes, for period i and job j. 
     Similarly, BestScheduled[i, j] is the total number of minutes scheduled for the best solution, CurrScheduled[i, j] is the total number of minutes scheduled for the current solution, and TrialScheduled[i, j] is the total number of minutes scheduled for the trial solution. 
     Availability[i, j] is the total number of available minutes, i.e., the number of minutes in the period times the number of available employees. 
     FillDifficulty[i, j] is a measure of the difficulty of filling a particular slot, and is calculated as (Required[i, j]−TrialScheduled[i, j])/Availability[i, j]. 
     The Add Shifts step  242  (FIG. 7) schedules shifts that reduce the cost of underscheduled periods (“unders”) by finding the “area” that needs the most coverage. An area is defined by a job, a start slot and an end slot. An attempt is then made to find the most cost-reducing shift to schedule in this area. This process is repeated until no more low-coverage areas can be found. If no shift can be found for an area, that area is marked as unfillable and ignored during the search for new areas. The unfillable status is cleared at the end of the Add Shifts routine so that these areas can be explored again later. 
     FIG. 14 is a flowchart of the Add Shifts step  242  of FIG.  7 . At Step  300 , the area having the most unfilled required coverage is determined. This is described in more detail below with respect to FIG.  15 . At Step  302 , if no area is found, the process is complete. If, however, an area is found, then at Step  304 , the most cost-reducing shifts in the area are determined. Step  304  is described in more detail below with respect to FIG.  16 . 
     If, at Step  306 , no cost-reducing shifts are found, the area is marked as unfillable at Step  310 . On the other hand, if cost-reducing shifts are found, then at Step  308 , one of the seven lowest-cost shifts is randomly selected and added to the trial schedule S trail . 
     This process repeats until no more areas are found in Step  302 . 
     FIG. 15 is a flowchart showing the details of Step  300  of FIG. 14, in which the area having the most unfilled required coverage is determined. First, at Step  320 , a variable MaxFillDifficulty is initialized to zero. 
     At Step  322 , a determination is made as whether there are any jobs which have not been processed. If there are more jobs, one is selected and at Step  324 , all continuous sets of slots (or periods) are found, where a continuous set of slots is a set of contiguous slots where each slot&#39;s FillDifficulty is greater or equal to 0, and each slot&#39;s Unfillable status, which is a temporary variable described more fully below, is FALSE. 
     At Step  326 , one of these continuous sets is selected, and at Step  328 , a TotalFillDifficulty is calculated by summing all FillDifficulty&#39;s over all periods of the selected set. 
     If, at Step  330 , this total TotalFillDifficulty is found to be greater than some threshold, MaxFillDifficulty, then at Step  332 , the threshold is updated to the value of TotalFillDifficulty, and the start and end slots of the selected set, and the job, are recorded. 
     If Total Fill Difficulty does not exceed the threshold, Step  332  is skipped. 
     At Step  334 , if another set is available for processing, control returns to Step  326  and a new set is selected. On the other hand, if there are no more sets for the current job, control loops back to Step  322 , and the process is repeated for the next job. If there are no more jobs, the area needing the most coverage has be found. 
     Thus, in Step  302  of FIG. 14, if MaxDifficulty is 0, no areas have been found. 
     FIG. 16 is a flowchart showing the details of Step  304  of FIG. 14, in which the most cost-reducing shift in the area found in FIG. 15 is determined. First, in Step  350 , a Shift Candidate List, that is, a list of shifts which could be scheduled in the area is generated. This is described in greater detail below with respect to FIG.  17 . The Shift Candidate List holds the shift with the best coverage from each region. By generating the list in this manner, the widest possible set of employees to fill each of these shifts is obtained. 
     Next, the loop from Step  352  to Step  358  is executed for each shift in the Shift Candidate List. At Step  354 , the most cost-reducing person in the region to fill that shift is found. At Step  356 , if the overall cost of adding this shift is less than any of the seven lowest cost shifts determined so far, the shift with the highest cost of the seven is removed from the list of seven, and replaced with this new shift (step  357 ). Otherwise, step  357  is skipped. Of course, such a list could retain more or less than seven shifts. 
     Note that some randomness is introduced just as with the Remove Shift routine to keep the solution from getting stuck in a local optimum. 
     FIG. 17 is a flowchart of Step  350  of FIG.  16 . The loop defined by Steps  370  through  376  is executed for every possible shift that could be scheduled in each area. In Step  372 , the average coverage of the shift, AvgCoverage, is calculated, where AvgCoverage=Σ (Required−TrialScheduled)/ShiftLen, over all periods in the selected shift, and where ShiftLen is the length of the shift. 
     At Step  374 , the ShiftLookupTable is used to track the highest AvgCoverage by shift region. 
     FIGS. 18A and 18B together are a flowchart of the Replace Shifts method  244  of FIG.  7 . The Replace Shifts method  244  tries to find a replacement shift for each and every shift in the trial schedule, by first stretching, shrinking or moving breaks around within the shift. It does this efficiently by keeping track of the marginal costs in the Period Totals and the marginal dissatisfaction costs in the Employee Schedule object. Initially, a replacement shift is found and a replacement cost is calculated for every shift whose cost can be reduced. Then, repeatedly, the lowest cost replacement is found, the replacement is performed, and the replacement cost of the other shifts is recalculated if necessary. The cycle stops when no further cost reducing replacement shifts can be found. 
     After the replacements are made, every shift is analyzed to see if the shift can be swapped with that of another employee. In a preferred embodiment, the swapping transaction can be up to three stages deep, for example where employee A swaps with B, B swaps with C and C swaps with D. 
     Next the loop formed by Steps  405 - 411  is performed repeatedly for each scheduled shift. At Step  409 , a cost reducing replacement shift is determined. Step  409  is discussed in greater detail below with respect to FIG.  19 . At Step  411 , the original shift, the replacement shift and cost reduction resulting from the replacement are saved. Note that if the determined replacement shift is null, the old shift is deleted but not replaced. Finally, at Step  413 , the highest Cost Reduction is tracked, that is the Cost Reduction is saved if it is the highest encountered thus far. 
     After all shifts have been processed, the loop formed by Steps  415 - 423  is executed iteratively until the highest Cost Reduction is zero. At Step  417 , the replacement associated with the highest cost reduction is performed. At Step  419 , all other Replacement Cost Reductions affected by this action are updated. At Step  421 , all Marginal costs affected by this action are updated. At Step  423 , the new Highest Cost Reduction is determined. 
     Finally, when the highest cost reduction is zero, the loop terminates and, at Step  425 , shifts are swapped. Step  425  is discussed in greater detail below with respect to FIG.  21 . 
     FIG. 19 is a detailed flowchart of the process of finding a cost-reducing replacement shift, Step  409 . First, at Step  427 , Best Cost Reduction is set to 0, and Best Shift is set to None. At Step  429 , the best breaks, i.e., the best positions for breaks, are found. If, at Step  431 , Best Breaks Cost Reduction&gt;Best Cost Reduction, then Step  433  is executed, in which Best Cost Reduction is set equal to Replace Breaks Cost Reduction, and Best Shift is set equal to Best Breaks Shift. Execution proceeds to Step  435 , regardless of the determination at Step  431 . 
     At Step  435 , the cost reduction of removing the shift is determined. If, at Step  437 , it is determined that Remove Shift Cost Reduction&gt;Best Cost Reduction, then at Step  439 , Best Cost Reduction is set to Remove Shift Cost Reduction and Best Shift is set to null. Otherwise, Step  439  is skipped. 
     At Step  441 , the best replacement shift is found and the cost reduction of adding the replacement shift, ReplacementShiftCostReduction, determined. Step  441  is discussed in greater detail below, with respect to FIG.  20 . 
     If, at Step  443 , it is determined that ReplacementShiftCostReduction&gt;Best Cost Reduction, then at Step  445 , Best Cost Reduction is set to Best Replacement Shift Cost Reduction, and Best Shift is set to Best Replacement Shift. Otherwise, Step  445  is skipped. 
     At Step  447 , the BestShift is returned to the calling routine. 
     FIG. 20 is a detailed flowchart of Step  441 . Beginning, at Step  451 , with the original shift, the lowest over/under cost start time is determined by evaluating different scenarios in which the shift&#39;s start time is moved earlier and later by up to 2 hrs within constraints. 
     Similarly, at Step  453 , the lowest over/under cost end time is determined by evaluating different scenarios in which the shift&#39;s end time is moved earlier and later by up to 2 hrs within constraints. 
     At Step  455 , if lower costs were found by moving both start time (Step  451 ) and end time (Step  453 ), then combinations of these moves, within the constraints, are examined. 
     At Step  457 , the entire shift is moved earlier or later by up to 2 hrs within constraints to determine the lowest over/under cost. 
     At Step  459 , the most cost reducing shift is selected from all of the above. The best breaks for this shift are determined for this new Best Replacement Shift. The over/under cost reduction is recalculated and added to the dissatisfaction cost reductions to yield the Best Cost Reduction. 
     At Step  461 , the Best Replacement Shift and Best Cost Reduction are returned. 
     FIG. 21 is a detailed flowchart of Step  425 , in which employees are swapped between shifts in an effort to improve dissatisfaction costs. Note that this routine only performs swapping and has no effect on the over/under cost. The goal here is only to improve the dissatisfaction cost. 
     At Step  463 , the average dissatisfaction cost of all the scheduled shifts is calculated. At Step  465 , the top dissatisfying shifts are determined. One of these shifts is randomly selected, and an attempt to swap the employee assigned to it with another employee is made as described next. 
     The loop defined by Steps  467 - 479  is repeated until the process has attempted to swap all shifts whose dissatisfaction cost is greater than average. 
     At Step  467 , the dissatisfying shift is removed from the person to whom it is currently assigned. Now the shift is unassigned. To assign the shift, Steps  469 ,  471  and  473  are repeated recursively up to 3 levels deep or until there are no unassigned shifts. 
     At Step  469 , all of the employees who can work this shift are obtained from the shift lookup table. For each employee, an attempt is made to add this shift to the employee&#39;s schedule. The employee with the lowest cost is assigned to the shift and the assignment is complete. 
     If, on the other hand, the shift could not be added, then at Step  471 , an attempt is made to swap it out. The most cost-reducing swap is found, but a swapped shift which can be added back to the original dissatisfied person&#39;s schedule is always favored. 
     After exiting the loop of Steps  469 - 473 , at Step  475 , any shift that was involved in this swap move is marked so that a loop involving the same shifts does not occur. 
     At Step  477 , if the swap move that was found is feasible and cost-reducing, then it is committed, and the top dissatisfying shifts are recalculated. 
     Step  479  causes the loop of Steps  467 - 479  to repeat until the process has attempted to swap all shifts whose dissatisfaction is greater than average. 
     It will be apparent to those of ordinary skill in the art that methods involved in the scheduler may be embodied in a computer program product that includes a computer usable medium and wherein the steps of the method disclosed herein are performed by a computer. For example, such a computer usable medium can include a readable memory device, such as a hard drive device, a CD-ROM, a DVD-ROM, or a computer diskette, having computer readable program code segments stored thereon, The computer readable medium can also include a communications or transmission medium, such as a bus or a communications link, either optical, wired, or wireless having program code segments carried thereon as digital or analog data signals. 
     While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.

Technology Category: g