Patent Publication Number: US-11397906-B1

Title: Predicting demand for route generation

Description:
BACKGROUND 
     Same day delivery of customer orders requires matching customer order to driver availability. For example, a customer may order a product in the morning and expect that product to be delivered in the afternoon. A delivery planning system tries to match customer orders to driver availability in an efficient manner so that a driver can deliver the most orders within the delivery time window. In one example, a delivery planning system estimates the required labor for performing the deliveries using deliveries per hour and the total orders. 
     However, the current delivery planning systems have limited accuracy. For example, the current systems do not consider the physical routes that a driver may use which impacts the accuracy of the estimated labor needed to deliver the customer orders. Further, current delivery planning systems rely on a single demand scenario to estimate the labor needs. As a result, attempting to accurately identify the total customer orders is difficult. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  illustrates a predictive delivery planning system, according to various embodiments. 
         FIG. 2  is a flowchart for predictively releasing labor blocks for delivering customer orders, according to various embodiments. 
         FIG. 3  illustrates data flow within a predictive delivery planning system, according to various embodiments. 
         FIG. 4  is a flowchart for forecasting simulated orders in multiple demand scenarios, according to various embodiments. 
         FIG. 5  is a plot of historical order data for a specified time period, according to various embodiments. 
         FIG. 6  is a chart illustrating clusters dividing up a geographic region, according to various embodiments. 
         FIG. 7  is a chart illustrating customer orders over time, according to various embodiments. 
         FIG. 8  is a chart illustrating simulated orders and customer orders for a particular demand scenario over time, according to various embodiments. 
         FIG. 9  is a flowchart for releasing labor blocks in response to labor plans corresponding to the demand scenarios, according to various embodiments. 
         FIG. 10  illustrates data flow in a labor releasing system, according to various embodiments. 
         FIG. 11  is a chart illustrating releasing labor blocks using different predefined thresholds, according to various embodiments. 
         FIG. 12  is a flowchart for assigning routes to delivery drivers who selected the released labor blocks, according to various embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments herein describe a predictive delivery planning system that includes a forecaster that predicts simulated orders (e.g., forecasted orders) for multiple different demand scenarios. That is, the planning system may have currently received only ten customer orders, but using a volume distribution for a scenario, the forecaster generates a plurality of different demand scenarios which each predict the planning system will have received a different total number of customer orders by delivery time. The forecaster can use a geographic demand distribution to then select simulated orders to make up the difference between the total number of customer orders predicted by each demand scenario and the current number of received customer orders. 
     Once the simulated orders are selected, a route planner generates routes for delivering the simulated and actual customer orders for each scenario. In contrast to previous solutions, the embodiments herein integrate route generation with the labor planner to predict the amount of customer orders and the amount of labor needed to deliver those orders. For example, in previous solutions, even if routing and labor planning use the same order information, it is possible for the two systems to request a different number of drivers. The embodiments herein improve efficiency by removing the costly decoupling between labor planning and routing. Also, by directly inheriting routing solver modifications (e.g., modified routes), labor planning can add, test, and leverage these changes, which minimizes the amount of compute resources used by the planning system. 
     The planning system converts the routes generated by the route planner into labor plans indicating the amount of time needed by a delivery driver to deliver the orders. The planning system identifies a set of labor blocks (e.g., 0.5 hour or 1 hour chunks of time) from the labor plans and determines whether these blocks satisfy a utilization threshold. Put differently, the planning system uses a releasing policy that releases labor blocks whose expected utilization is higher than a predetermined threshold. The released labor blocks are then displayed to delivery drivers who can select how many of the labor blocks they would like to work. 
     At increments, the planning system can repeat the process above as new customer orders are received. That is, the simulated orders in the demand scenarios are replaced with the newly received customer orders which results in the planning system updating the routes, labor plans, and the labor blocks. Thus, over time, the planning system can release additional labor blocks as those labor blocks satisfy the utilization threshold. In this manner, the planning system can predictively schedule drivers to deliver the customer orders before all (or even most) of the customer orders have been received. Once it is time to deliver the orders (and all the customer orders have been received), the planning system once more runs the customer orders through the route planner and provide routes to the delivery drivers that correspond to their selected labor blocks. 
     The predictive delivery planning system described herein can advantageously execute within predefined increments (e.g., every fifteen minutes) to release additional labor blocks using minimum compute resources. Moreover, the planning system reduces the total labor used to deliver the customer orders while simultaneously avoiding surge charges that can occur when labor blocks are released right before delivery time rather than at intervals well before delivery time. 
       FIG. 1  illustrates a predictive delivery planning system  100 , according to various embodiments. The planning system  100  includes a computing system  110  which receives customer orders  105 . In one embodiment, the planning system  100  schedules the customer orders  105  for same-day delivery, although the embodiments herein are not limited to such. In one embodiment, the planning system  100  establishes multiple time windows throughout the day (e.g., two hour time window) where customer orders  105  can be received and then scheduled for delivery. For example, all customer orders  105  received between 5:30-7:30 AM are scheduled for delivery at 8 AM, all customer orders  105  received between 7:30-9:30 AM are scheduled for delivery at 10 AM, all customer orders  105  received between 9:30-11:30 AM are scheduled for delivery at 12 PM, and so forth. 
     The customer orders  105  may trickle in throughout the time windows. For example, the customers may use a website or a smartphone application to place the orders at any time during the two-hour time windows. As such, at 5:45 AM, the planning system  100  may have received ten orders for the 5:30-7:30 AM time window. However, by 7:29 AM, the planning system  100  may have received sixty orders. Rather than waiting until 7:30 to attempt to determine how much labor (e.g., manpower) is needed to deliver the customer orders  105 , the predictive delivery planning system  100  uses the elements in the computing system  110  to predict and release labor blocks using incomplete data (e.g., without yet knowing the total number of orders received in the time window as well as the geographic distribution of those orders). 
     The computing system  110  (which can be a single computing system such as a server, a plurality of computing systems, or a data center or cloud based infrastructure) includes a forecaster  115 , a route planner  130 , a labor plan generator  135 , and a block selector  145 . The forecaster  115  generates multiple scenarios  120  which represent different demand scenarios for delivering the packages. That is, each scenario  120  can have different estimates for how many customer orders will be received in the current time window as well as the geographic distribution of those orders within the delivery area. For example, the scenario  120 A may estimate that there will be seventy total customer orders  105  for the time window with 25% of those orders in a first region while the remaining 75% of the orders are in a second region. In contrast, the scenario  120 B may estimate that there will be only fifty total customer orders  105  with 45% of those orders in the first region and the remaining 55% of the orders in the second region. 
     However, since only a portion of the customer orders  105  for the time window have been received, the forecaster  115  selects simulated orders  125  that make up the difference between current received orders and the total order estimated by each scenario  120 . That is, based on already receiving ten customer orders, scenario  120 A may estimate that the planning system  100  will receive a total of seventy total customer order by the time window ends, while the scenario  120 B estimates the system  100  will receive a total of fifty customer orders. As such, the forecaster selects sixty simulated orders  125 A for the scenario  120 A and forty simulated orders  125 B for the scenario  120 B to make up the difference. 
     Once the forecaster  115  has identified the simulated orders  125  for each scenario  120 , the route planner  130  can use the received (or realized) customer orders  105  and the simulated orders  125  to generate an optimized delivery route for each of the scenarios  120 . Because the simulated orders  125  may be different for each scenario  120 , the routes generated by the route planner  130  may also be different. For example, scenario  120 A may have different routes than the scenario  120 B. 
     The labor plan generator  135  uses the routes generated for each scenario  120  to generate a labor plan  140  for each scenario. The labor plan  140 A indicates the estimated amount of time to complete the routes for the scenario  120 A while the labor plan  140 B indicates the estimated amount of time to complete the routes for the scenario  120 B. The labor plans  140  may be expressed in the number of minutes, hours, etc. that are needed to deliver the actual and simulated orders along the routes generated by the route planner  130 . 
     The block selector  145  converts the labor plans  140  for each scenario  120  into labor blocks. In one embodiment, a labor block has a minimum time value (e.g., an hour) that can be increased in a predefined time chunk (e.g., thirty minute increments). For example, the labor plans  140  (or a representative labor need derived from the labor plans  140 ) may be rounded up if they do not satisfy the requirements of the labor block—e.g. a 160 minute labor plan is rounded up to a 3 hours labor block and a 140 minute labor plan is rounded up to a 2.5 hour labor block. 
     The block selector  145  determines whether one or more labor blocks  150  satisfy a utilization threshold. Put differently, the block selector  145  uses a releasing policy that releases labor blocks whose expected utilization (e.g., the likelihood the labor block will be needed to satisfy the total number of customer orders received during the time window) is higher than a predetermined threshold. The released labor blocks  170  can then be displayed on a user device  160  so the blocks can be selected by a delivery driver  155 . 
     As shown in  FIG. 1 , the user device  160  displays three released labor blocks  170 . In this example, the delivery driver  155  can select one of the labor blocks she wishes to work. This provides flexibility to the driver  155  who can determine his or her own desired work schedule. Selecting one of the released labor blocks  170  informs the planning system  100  that the delivery driver  155  is willing to work that corresponding amount of time to deliver the customer orders  105  being received in the current time window. Later, when the time window has expired and all the customer orders  105  have been received, the planning system  100  provides the delivery driver  155  with a route that can be completed within the time frame of the labor block  170  selected by the driver  155 . 
     While the user device  160  displays three released labor blocks  170 , each labor block can represent multiple different blocks. That is, the block selector  145  may have released three 1 hour labor blocks, two 1.5 hour labor blocks, and four 2 hour labor blocks. Thus, multiple drivers  155  can view the released labor blocks  170  and select which block works best for them. As the drivers  155  select the labor blocks and a particular labor block has been exhausted (e.g., four drivers  155  have selected the four available 2 hour labor blocks), the planning system  100  can, in real-time, transmit updates to the user devices  160  to indicate there are no longer any 2 hour labor blocks available. Moreover, in addition to displaying the released labor blocks  170 , the user device  160  can display a pick-up time when the driver  155  should be available to retrieve the customer orders and a payment the driver  155  receives for each of the released labor blocks  170  (which may vary in real-time depending on the availability of drivers, the number of customer orders, and the number of labor blocks). 
       FIG. 2  is a flowchart of a method  200  for predictively releasing labor blocks for delivering customer orders, according to various embodiments. For clarity, the blocks of the method  200  are discussed in tandem with  FIG. 3  which illustrates data flow within a predictive delivery planning system, according to various embodiments. 
     At block  205 , the predictive delivery planning system  300  receives the customer orders  105 . As discussed above, in one embodiment, the planning system  300  uses predefined time windows to collect and then deliver customer orders received during the window. Rather than waiting until all the orders have been received, the method  200  predictively determines labor blocks which can be released to delivery drivers who can sign up in advance (i.e., before the time window has ended) to help with delivering the customer orders for same-day delivery. 
     At block  210 , the forecaster  115  predicts simulated orders for multiple scenarios using the current time and previous order history. As shown in  FIG. 3 , the forecaster includes a total volume distribution  305  which, in one embodiment, may have a Normal distribution where the mean value for the distribution  305  comes from the forecast, that is currently in the production system. That is, the mean value may vary depending on the current time of day and how that maps to historical data. For example, if the current time window is at a time which historical data suggests there are a lot of customer orders, the mean value may be higher than if the current time window is at a time which historical data suggests there are fewer customer orders. Further, the mean can be adjusted based on the current number of customer numbers already received. The forecaster can take a random sample or draw from the volume distribution  305  to estimate the total number of customer orders expected for the current time window. As such, the scenarios may have different total numbers of orders which are centered around the mean value of the distribution  305 . 
     In addition to different volume distributions  305 , the forecaster  115  includes a geographic distribution  310  for each scenario. In one embodiment, the geographic distribution  310  uses a hierarchical cluster decomposition approach to determine which geographic regions to select the simulated orders  125 . For example, the forecaster  115  can use the volume distributions  305  to determine the number of simulated orders  125  it should select and then use the geographic distributions  310  to determine a geographic location of the simulated order  125 . Additional detail of using the volume distributions  305  and the geographic distributions  310  to select the simulated orders  125  is provided in  FIG. 4 . 
     In one embodiment, the simulated orders  125  are previous customer orders. For example, the planning system  300  may track the order histories for the last several months and then use the volume distribution  305  and the geographic distribution  310  to select one of the past customer orders to use as a simulated order  125 . In this manner, the simulated orders  125  can contain actual street addresses from previous orders. 
     At block  215 , the route planner  130  generates routes  315  for each of the scenarios. That is, the route planner  130  uses the addresses for the simulated orders  125  and the already received customer orders  105  to generate optimized routes  315  for delivering the orders. As discussed below, these routes  315  are not delivered to the delivery drivers but are instead used to estimate a labor plan  140  for each of the scenarios (e.g., an estimate of the amount of time it takes a delivery driver or drivers to deliver packages for the simulated and actual customer orders). 
     The embodiments herein are not limited to any particular router planning technique. In one embodiment, the route planner  130  can use a last mile delivery problem (LMDP) solver. The route planner  130  may consider various inputs to generate the routes such as package delivery time window where instead of time windows for customers, the router planner  130  considers time windows for delivered packages. The planner  130  may also consider the type of vehicles in the delivery fleet, driver&#39;s time window in which the drivers are available to make deliveries, service times for picking up and dropping off packages, time-dependent travel times which is the time it takes to travel between addresses (and can depend on the time of day), multi-depot support, penalties for deliveries scheduled during the last five minutes of the end of a delivery time window, and using total time as an objective function that is the sum of all route times plus the penalties for the last five minutes deliveries, overflow drivers and other soft constraints (where everything can be measured in seconds). The route planner  130  tries to find a feasible solution where the total time is a minimum. 
     At intervals, the scenarios are updated such that the simulated orders are removed and replaced with newly received actual customer orders  105 . As such, the previous routes should be updated in response to some of the orders being removed by the scenario (e.g., a portion of the simulated orders  125 ) which are replaced by the new customer orders  105 . Since the set of scenarios can potentially be large, the route planner can use a fast approach for updating routes. In one embodiment, the first step for routing plan updating is to recover a feasible solution. This is performed for two reasons: 1) the new customer orders must be incorporated in the routes (it is mandatory to visit all customers) and 2) the simulated orders may have been removed (or added in some cases). 
     One technique for recovering feasibility is the following. First, the route planner  130  identifies all the simulated orders that are no longer part of the scenario and removes them from the routes. This can be done by skipping these orders. Then, the route planner  130  adds all new customer orders  105 . For each customer order that is not on a current route, the route planner  130  ties to add them to some route. If it cannot, the route planner  130  creates a new route that only visits that customer. For each route in which the route planner  130  can insert that customer, the planner  130  finds the position where the total route cost is the cheapest. If there is more than one route where the route planner  130  can insert the customer, it picks the cheapest one. 
     In one embodiment, after recovering feasibility, the route planner  130  can use an improving heuristic. For example, a CROSS exchange can be used which is a generalization of classical improving heuristics such as 2-opt or Or-opt. 
     At block  220 , the block selector  145  converts the routes  315  into labor plans  140 . That is, the block selector  145  estimates the amount of time or manpower needed to deliver packages along the routes  315 . For example, the block selector  145  may add up the total time for each route in the scenario. While  FIG. 3  illustrates the block selector  145  determining the labor plans  140 , in another embodiment the route planner  130  may use the routes to determine the labor plans  140 . 
     At block  225 , the block selector  145  identifies a set of labor blocks from the labor plans  140 . In one embodiment, after estimating the labor in each scenario, the block selector  145  computes the required labor configuration. The configuration tells what fraction of the total labor corresponds to different type of labor needs. For instance, 50% of the required labor may correspond to two hour labor needs, and 50% correspond to 2.5 hour labor needs. Using the labor needs created, the block selector  145  creates blocks trying to maximize its total utilization where one block has one or more labor needs. 
     The labor blocks may have to meet some requirements such as each block is at least one hour long and they have thirty minute increments, i.e., the blocks can be of 1 hour, 1.5 hours, 2 hours, 2.5 hours, etc. 
     At block  230 , the block selector  145  determines whether at least one of the labor blocks satisfies a utilization threshold. In one embodiment, the block selector  145  uses a releasing policy that releases labor blocks whose expected utilization is higher than a predetermined threshold. The details for determining the utilization for the labor blocks is discussed in detail in  FIG. 9 . 
     If at least one of the labor blocks satisfies the utilization threshold, the method  200  proceeds to block  235  wherein the planning system  300  releases the labor block for selection by the delivery drivers. For example, the delivery drives can use personal electronic devices to select one of the released labor blocks  170  (e.g., a time period the driver wants to work). 
     However, if the labor blocks do not satisfy the utilization threshold, or after the labor blocks have been released, the method  200  proceeds to block  240  where the planning system determines whether the time window for accepting new customer orders has expired. For example, the deadline for accepting orders for shipping delivers at 10:00 AM may end at 9:30 AM to give time for the associates in the warehouse to gather the ordered products, package the products, and for the delivery drivers to pick up the packages and begin the routes. 
     If the time window has expired, the method  200  proceeds to  FIG. 11 . However, assuming the time window has not expired, the method  200  proceeds to block  245  where the planning system  300  receives additional customer orders and then returns to block  210  to repeat the method  200 . In one embodiment, the method  200  may repeat at a set time interval —e.g., every fifteen minutes. That is, every fifteen minutes, the planning system updates the scenarios, generates new routes and labor plans, and determines whether the resulting labor blocks satisfy the utilization threshold. If so, those blocks are released to the drivers for selection. However, at some intervals, the planning system  300  may not release any blocks. Over time, the likelihood the blocks will be utilized increases (and satisfy the utilization threshold) which may trigger the planning system to release additional blocks. 
       FIG. 4  is a flowchart of a method  400  for forecasting simulated orders in multiple demand scenarios, according to various embodiments. In one embodiment, the method  400  provides additional detail for performing block  210  of the method  200 . Moreover, the method  400  illustrates how the forecaster can replace the simulated orders in the scenarios with new customer orders as the method  400  (and the method  200 ) repeats. 
     At block  405 , the forecaster identifies a number of simulated orders needed for each scenario based on the total volume distribution for each scenario. As mentioned above, the total volume distribution may be a normal distribution. The mean value and the variance (or error) in the distribution can vary based on the historical data. For example, if the current time period is 7:30-9:30 AM on a Tuesday, historical data for that same time period for the last several weeks or months may vary widely, in which case, the forecaster would increase the variance of the total volume distribution. Moreover, the mean value of the normal distribution can be adjusted to reflect the average total customer numbers for that same time period over the last several weeks or months. Further, the mean can be adjusted in response to the number of customer orders already received. For example, historical data may indicate that for the same time period, the planning system receives 60 orders but if at the beginning of the period the planning system has already received 30 orders, the mean value may be set above 60 orders. 
     The number of scenarios can be a user-defined parameter and be balanced with the amount of compute resources used to execute the predictive delivery planning system. At the beginning of the time period, the forecaster may use a random draw or sample to estimate the total number of customer orders for the time period for each scenario. Although the total number of customer orders is random, it is based on the total volume distribution, which if it is a normal distribution, results in the total number of customer orders being clustered around the mean of the distribution. For example, if the mean of the total volume distribution is 60 orders when the time window first starts, Scenario A may have 60 orders, Scenario B may have 55 orders, Scenario C may have 62 orders, and so forth. How spread out the total customer orders are from the mean varies according the amount of variance in the total volume distribution. 
     The number of simulated orders the forecaster selects is determined by subtracting the current number of received customer orders from the total customer orders for each scenario. Continuing the example above, if the planning system has received 10 orders, the forecaster selects 50 simulated orders for Scenario A, 45 orders for Scenario B, and 52 orders for Scenario C. 
     At block  410 , the forecaster identifies locations (or addresses) for the simulated orders based on a geographic distribution. In one embodiment, the locations of the simulated orders are selected at random. In this example, the geographic distribution may be a uniform distribution indicating that orders are equally likely to be in any region in the delivery area. For example, the forecaster may select random latitude and longitude values for each simulated orders from the set of latitude and longitude values in the delivery area. As such, the simulated orders for each scenario would be equally distributed across the delivery region. 
     In another embodiment, instead of assuming a uniform distribution, historical data can be used to generate clusters in the delivery region to assign locations to the simulated orders which may better predict where future customer orders received in the time window will originate. 
       FIG. 5  is a plot  500  of historical order data for a specified time period, according to various embodiments. For example, the plot  500  may indicate the locations of the customer orders received at the same time period as the current time period but on a different day. For example, if the current time period is 7:30-7:45 AM on a Tuesday, the plot  500  may illustrate the locations of the customer orders received at 7:30-7:45 AM on a Tuesday for the previous week. 
     In another example, the plot  500  may include the locations of customer order for a plurality of previous days at the same time period—e.g., the customer orders received at 7:30-7:45 AM on Tuesday of the previous three weeks. Regardless, using the plot  500 , the forecaster can group the orders into different clusters which sub-divide the delivery area—i.e., the total area of plot  500 . 
       FIG. 6  is a chart  600  illustrating clusters  605  dividing up the geographic region of plot  500  in  FIG. 5 , according to various embodiments. As shown, there are nine clusters  605 A- 605 I which subdivide the delivery area into different regions. The forecaster can use any suitable clustering technique on the historical data to form the clusters  605 —e.g., a k-means cluster decomposition. 
     In one embodiment, the forecaster assumes that the customer orders are uniformly distributed within each cluster. The forecaster can assign a weight to each cluster that can be used to determine how many of the simulated orders should be in each of the clusters. For example, the forecaster may determine that 10% of the simulated orders should be selected within the cluster  605 A, 20% of simulated orders from cluster  605 B, 40% of the simulated orders from cluster  605 C, and 30% of the simulated orders from cluster  605 D, while the remaining clusters  605 E- 605 I do not include any of the simulated orders. However, in other example, each of the clusters  605  may include some percentage of the simulated orders. 
     The forecaster can use the weights to ensure the geographic locations of both the actual customer orders and the simulated orders match the geographic distributions. For example, if the weights indicate that 20% of the total orders are predicted to come from cluster  605 B and the planning system has already received customer orders from the cluster  605 B that are 20% of the total predicted number of customer numbers, the forecaster does not select any simulated orders from that cluster. However, if the already received customer numbers in cluster  605 B do not amount to 20% of the total predicted number of customer orders, the forecaster can select simulated orders from the cluster  605 B until the 20% weight is achieved. 
     Returning to the method  400 , at block  415 , the forecaster selects the simulated orders for each scenario using the clusters. For example, the forecaster can use the weights to select simulated orders from the clusters  605 . In one embodiment, the forecaster chooses simulated orders from the previous customer orders in that cluster. For example, the forecaster may use the plot  500  in  FIG. 5  to identify the previous customer orders in the clusters  605  and then select simulated orders from those subsets. In one embodiment, the forecaster may assume the orders in each cluster are uniformly distributed, and thus, selects any of the previous customer orders at random to be the simulated orders. As mentioned above, the forecaster can select the simulated orders for each scenario so that the distribution of the simulated orders and the actual customer orders match the weights assigned to each cluster  605 . In another embodiment, rather than using previous customer orders as the simulated orders, the forecaster can choose random latitude and longitude values within the clusters  605  to use as addresses for the simulated orders. 
     At block  420 , the planning system determines whether, during a subsequent time interval, additional customer orders have been received. If not, the method  400  can continue to wait until additional orders arrive, for example, in the next time interval. 
     However, if the planning system has received additional customer orders, at block  425 , the forecaster selects which simulated orders to replace with the additional customer orders to maintain consistency with the total volume distribution and the geographic distribution. That is, rather than randomly replacing a portion of the simulated orders in each scenario with the newly received customer orders, the forecaster can replace simulated orders in a manner that maintains the total volume predicted by the total volume distribution and the locations of the orders predicted by the geographic distribution. 
     In one embodiment, the total volume distribution and the geographic distribution can change during each interval in the time window. For example, the total volume distribution corresponding to the time interval between 7:30-7:45 AM may be different than the total volume distribution corresponding to the time interval between 7:45-8:00 AM (i.e., the next time interval in the time window). As a result, the forecaster can change the total forecasted number of customer orders for each of the scenarios. Put differently, the total number of customer orders in the each of the scenarios can change at each interval in the time window. 
       FIG. 7  is a chart  700  illustrating customer orders over time, according to various embodiments. In chart  700 , the line  705  is the mean of the total volume distribution which can change over the time intervals (e.g., fifteen minute intervals) for the time window—i.e., T-180 to T-45. For example, as more customer orders are received, the forecaster can update the mean of the total volume distribution to better predict the total number of customer orders that will be received by the end of the time window. 
     The lines  710  illustrate the variance of the total volume distribution which decrease as the current time reaches the end of the time window—i.e., T-45. Put differently, as the current time gets closer to the end of the time window, the variance (or error) in the mean of the total volume distribution approaches zero. The variance can vary depending on how accurately the current number of customer orders matches the historical data, thereby indicating the accuracy of the mean. Moreover, the variance can change depending on the amount of time before the end of the period since predictions made at the beginning of the time period (when few customers orders have been received) is less accurate than when more customer orders have been received near the end of the time window. 
     The bars represent the total number of customer orders received by the planning system at each of the time intervals. That is, at time T-180, the planning system has received 21 orders, at time T-165, the system has received 24 orders, and so forth. As time progress, the number of customer orders approaches the line  705  representing the mean of the total volume distribution. That is, the line  705  for the mean, the lines  710  for the variance, and the total number of customer orders converge at the end of the time window. 
       FIG. 8  is a chart  800  illustrating simulated orders and customer orders for a particular demand scenario over time, according to various embodiments. In this example, the chart  800  illustrates the simulated orders for the scenario  120 A. The total number of customer orders predicted by the scenario  120 A is the summation of the simulated orders and the actual customer orders. 
     As illustrated in  FIG. 7 , the total volume distribution (e.g., its mean and variance) can change during the time window. As such, the total predicted customer numbers shown in chart  800  also changes. However, at times T-180 through T-135, the total predicted customer orders does not change. Thus, when new customer orders arrive during each interval, the forecaster removes a corresponding number of simulated orders so that the total predicted number of customer orders remains constant. However, during intervals T-120 and T-105, the total number increases, thus, the forecaster may add simulated orders (assuming there were not enough customer orders to account for the total increase in the predicted customer orders). Thus, the forecaster may both remove and add simulated orders during the time window. 
     In addition to changes in the total predicted customer number, the geographic distribution can change. For example, during each interval, the weights assigned to the different clusters may change. Thus, the forecaster may remove simulated orders from the clusters where the weight decreased relative to its weight in the previous time interval. In other words, the forecaster removes or adds the simulated orders according to the new updated weights. In this manner, the forecaster can update the scenarios so that the total number of simulated/actual orders and their locations are consistent with the updated total volume distribution and geographic distribution. 
     At block  430 , the planning system determines whether the time window has expired. If so, the planning system proceeds to the flowchart in  FIG. 12 . If not, the method  400  returns to block  420  where the forecaster waits for additional customer orders, updates the total volume and geographic distributions, and then removes/adds the simulated orders from each scenario to maintain consistency with those distributions. 
       FIG. 9  is a flowchart of a method  900  for releasing labor blocks in response to labor plans corresponding to the demand scenarios, according to various embodiments. In one embodiment, the method  900  provides additional details for performing blocks  225 ,  230 , and  235  of the method  200 . That is, method  900  may be performed after receiving labor plans derived from planning routes for the simulated and actual customer orders for each scenario. 
     For clarity, the blocks of method  900  are discussed in tandem with  FIG. 10  which illustrates data flow in a labor releasing system, according to various embodiments. At block  905 , the block selector  145  ranks the labor plans  140 . In one embodiment, the block selector  145  stacks the labor plans  140  from longest (i.e., the labor plan that requires the most time to complete) to shortest (i.e., the labor plan that requires the least amount of time to complete). 
     At block  910 , the block selector  145  identifies representative labor needs  1010  for the labor plans  140  generated from the scenarios using a risk-adjusted selection algorithm  1005 . Although the selection algorithm  1005  may vary, in one embodiment, the selection algorithm  1005  is a risk adverse algorithm which creates representative labor needs  1010  that cover all labor needs of the scenario. In this example, the representative labor needs  1010  corresponds to the longest labor plan  140 . That is, if the longest labor plan  140  is 200 minutes, than the representative labor needs  1010  is 200 minutes. 
     In another embodiment, the selection algorithm  1005  is an average risk algorithm which creates representative labor needs  1010  taking the average of the labor need of the scenarios. In this example, the block selector  145  identifies the average time of all the labor plans which is then assigned as the representative labor needs  1010 . 
     Regardless of the technique for selecting the representative labor needs  1010 , the block selector  145  converts the representative labor needs into one or more labor blocks (i.e., a set of labor blocks). As mentioned above, the labor blocks may have minimum requirements—e.g., at least a 1 hour minimum and have 30 minutes increments—which may better match the drivers&#39; preferences. For example, if the representative labor needs  1010  is 110 minutes, the block selector  145  may round up to form a 2 hour labor block or two 1 hour labor blocks. 
     At block  920 , the block selector determines the utilization of each labor block formed at block  915 . In one embodiment, the block selector  145  assumes that all labor blocks, when released, are accepted. The block selector  145  releases a block if the expected utilization of the constructed block is above a predetermined threshold. Equation 1 provides one technique for calculating the utilization of the set of labor blocks derived from the representative labor needs: 
     
       
         
           
             
               
                 
                   
                     E 
                     ⁡ 
                     
                       ( 
                       
                         u 
                         b 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       n 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           s 
                           ∈ 
                           S 
                         
                       
                       ⁢ 
                       
                         
                           
                             
                               L 
                               ^ 
                             
                             b 
                             s 
                           
                           
                             L 
                             b 
                           
                         
                         ⁢ 
                         
                           ∀ 
                           
                             b 
                             ∈ 
                             B 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     Where, u b  is the utilization of block b, B is the set of all blocks created from the representative labor needs, S is the set of all scenarios, n is the number of scenarios, {circumflex over (L)} b   S  is the length of block s, b is the length of labor need from scenario s that will be covered by labor block u b  (if labor block b does not cover any labor need of scenario s, then, {circumflex over (L)} b   S  is 0), and each scenario is equally likely to happen. The utilization metric defined by Equation 1 aids the block selector  145  to identify blocks that maximize the block utilization as well as the expected utilization provides insight on how many of the scenarios are covering each block (the lower the number of scenarios covered, the lower the expected utilization and vice versa). That is, the labor block is assigned a higher utilization score if the labor block includes sufficient time for performing the labor plans of multiple scenarios. Also, Equation 1 changes in response to how well the length determined for each block matches the labor need used to create each block. 
     In one embodiment, the utilization is related to cost per delivery (e.g., the cost of delivering each package to the customer). A higher utilization of the labor blocks can improve efficiency, in terms of using the available labor time. Therefore, when maximizing the block utilization of the solution that minimizes the total delivery time, then the block selector  145  indirectly, minimizes the cost per delivery. 
     While Equation 1 derives the expected utilization assuming that all the released blocks are accepted, i.e., the fill-rate is 100%, in practice this may not happen. For a given block, the fill rate should be higher if the block selector  145  releases the blocks earlier. If f t  is the fill rate at time t, then it is expected that f t  decreases with the time. Given an estimation of f t , the expected utilization of block b if it is released at time t (referred to as u b   t ) can be expressed as Equation 2: 
     
       
         
           
             
               
                 
                   
                     E 
                     ⁡ 
                     
                       ( 
                       
                         u 
                         b 
                         t 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       n 
                     
                     ⁢ 
                     
                       
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                           s 
                           ∈ 
                           S 
                         
                       
                       ⁢ 
                       
                         
                           f 
                           t 
                         
                         ⁢ 
                         
                           
                             
                               L 
                               ^ 
                             
                             b 
                             s 
                           
                           
                             L 
                             b 
                           
                         
                         ⁢ 
                         
                           ∀ 
                           
                             b 
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                             B 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     Note that when f t =1 then Equation 2 is the same as Equation 1. 
     At block  925 , the block selector  145  determines whether the expected utilization of each block in the set of labor blocks generated at block  915  satisfy a predefined threshold. 
       FIG. 11  is a chart  1100  illustrating releasing labor blocks using different predefined thresholds, according to various embodiments. The chart  1100  includes releasing blocks for the same set of scenarios but using different thresholds. The higher the threshold, the higher the utilization scores must be in order to release the time to delivery drivers. As a result, at time T-180 when the planning system first performs the method  900 , using the lowest utilization threshold (e.g., 0.7) results in releasing more labor blocks than using the higher utilization thresholds (e.g., 0.8 and 0.9). While selecting a higher threshold can result in releasing labor blocks earlier (which increases the likelihood the blocks will be selected without having to use surge pricing), the labor blocks are more tentative, and thus, the planning system may release blocks that are not needed. 
     Chart  1100  further illustrates that some intervals (e.g., fifteen minute intervals), the planning system may not release any of the labor blocks. That is, when repeating method  900 , the planning system may determine that none of the labor blocks meet the utilization threshold, and thus, none are released. As the time window progresses, the scenarios begin to converge as discussed above which can increase the expected utilization. For example, the labor blocks may begin to satisfy the labor plans in more scenarios which increases the expected utilization calculated by Equations 1 or 2. The larger the threshold, the later the blocks are expected to be released, when the forecast is more accurate. Because the planning system accounts for the geolocation of the already realized orders, the potential geolocation of the forecasted orders, and the travel times for the instance, then it is able to compute more accurately the expected utilization and delivery efficiency, and therefore the expected requirement of labor hours. 
     Returning to the method  900 , if at least one of the expected utilization scores satisfies the predefined utilization threshold, the method  900  proceeds to block  930  to determine whether a confidence score is above a threshold. For example, the confidence score can be derived from block utilization based on simulations, e.g., a route that appears in multiple scenarios can be released with a high confidence earlier in the planning time window. The confidence score can be derived to reflect the risk in the release policy, where the greater the utilization threshold, the reduced risk that the planning system will release more labor blocks than are needed. On the other hand, increasing the utilization threshold can increase the risk. 
     If the confidence score is above a threshold, at block  935 , the block selector  145  releases the labor blocks that satisfy the utilization threshold. However, if either the utilization threshold or the confidence threshold is not satisfied, the method  900  proceeds to block  940  where the planning system waits for updated labor planes. Once additional labor plans are received, the method  900  may repeat. 
     Further, the released labor blocks can be used to set the labor plans. For example, if two 1 hour labor blocks have already been released and an updated labor plan for a scenario indicates it uses 200 minutes, the block selector may subtract the 120 minutes of the two 1 hour labor blocks from the 200 minutes of the labor plan to result in a labor plan of 80 minutes. The method  900  can then repeat to determine whether additional labor blocks can be released. 
       FIG. 12  is a flowchart of a method  1200  for assigning routes to delivery drivers who selected the released labor blocks, according to various embodiments. In one embodiment, the method  1200  is performed after the time window for accepting customer orders is complete. Using  FIG. 11  as an example, the time window for accepting orders is from T-180 to T-45. Once T-45 has expired, the planning system can use the labor blocks selected from the delivery drivers to assign the routes. 
     At block  1205 , the planning system identifies the total number of released labor blocks and which labor blocks were selected by the delivery drivers. For example, some of the labor blocks may not have been selected. If not enough labor blocks were selected, the planning system can use surge pricing (increase the amount of money paid to the delivery driver) to entice a driver to select the remaining released labor blocks. 
     At block  1210 , the planning system identifies the total number of customer orders. Now that the time window has closed, the planning system may remove all the remaining simulated orders so that only the actual customer orders remain. 
     At block  1215 , the planning system generates routes for delivering the customer orders within the labor blocks. That is, the selected labor blocks and the actual customer orders are inputs into the route planner which once again identifies optimal routes for delivering the customer orders. In one embodiment, the route planning identifies the optimized routes for delivering the customer goods within the time allotted by the labor blocks. Moreover, the route planner may also use any of the inputs discussed above when preparing the simulated routes (which included both simulated and actual customer orders) to prepare the actual routes used by the delivery drivers. 
     At block  1220 , the planning system provides the routes to the delivery drivers who selected the labor blocks. With their routes now assigned, the delivery drivers can retrieve the packages at a predefined location (e.g., a warehouse) and begin their routes. Ideally, the route or routes provided to the delivery drivers are performed within the amount of time of the labor blocks selected by the delivery driver. 
     Once the planning system has finished accepting orders for a first time window, the planning system may begin to accept orders for the next time window. Thus, while the planning system performs the method  1200  for the previous time window, in parallel the planning system can begin collecting customer orders for the next time window and performing the method  200  in  FIG. 2 . 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
     In the preceding, reference is made to embodiments presented in this disclosure. However, the scope of the present disclosure is not limited to specific described embodiments. Instead, any combination of the features and elements described herein, whether related to different embodiments or not, is contemplated to implement and practice contemplated embodiments. Furthermore, although embodiments disclosed herein may achieve advantages over other possible solutions or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the scope of the present disclosure. Thus, the aspects, features, embodiments and advantages described herein are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). Likewise, reference to “the invention” shall not be construed as a generalization of any inventive subject matter disclosed herein and shall not be considered to be an element or limitation of the appended claims except where explicitly recited in a claim(s). 
     Aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the FIGS. illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the FIGS. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.