Temporary workspace assignment

There are provided a system, a method and a computer program product for assigning a workspace. The system receives one or more reservation request for the workspace, associated with one or more facilities including one or more workspace areas. Each workspace area includes one or more workspaces. The system receives inputs including one or more of: weather condition data, occupancy rates data, and date data, associated with the one or more facilities. The system predicts an energy cost for each workspace area. The system determines a user desirability value for the each workspace area. The system determines a minimum cost for operating the one or more facilities, which satisfies the received reservation request. The system selects one or more workspace area in the one or more facilities according to the determined minimum cost and the received reservation request. The selected workspace area has maximum user desirability values.

BACKGROUND

This disclosure relates generally to managing facilities, and particularly to assigning temporary workspaces.

BACKGROUND OF THE INVENTION

In facilities management, a facility being a business or any structure having space(s) where operations are conducted and/or where people work/interact, e.g., a “workspace”, one goal is analyze and address all types of facility planning. One type of planning addressed in current solutions is to increase facilities utilization, e.g., reduce occupancy and operating costs of those facilities. By using current techniques for modeling and analysis, a facility management entity may currently perform one or more of: (1) project or program management; (2) fund management; (3) cost management; (4) schedule management; (5) resource management; (6) quality management; (7) vendor engagement; (8) procurement, etc.

A current facilities management solution, such as provided by IBM's TRIRIGA®, provides software and hardware solutions that supports mobile workers who work temporarily at different facilities at different times, facility portfolio management and reduction of occupancy costs. By using these solutions, facilities may reduce their operating costs, and energy usage and maintenance costs. By using the solutions, facilities may increase facility utilization, increase employee productivity, and mitigate environmental risks.

Such current facilities management techniques are currently implemented to assign temporary workspaces in one or more facility to maximize a “quality” (e.g., a user satisfaction level, etc.) of each workspace while minimizing a cost of operating the facility. Many originations (e.g., companies, etc.) have facilities that are designed to provide temporary workspace for mobile workers (i.e., workers who work in a plurality of locations). Temporary workspaces benefit the organizations because the temporary workspaces utilize an available space(s). The temporary workspaces benefit the mobile workers because the mobile workers can get workspaces in any geographic area that the workers happen to be working in as long as there is a facility available that supports a temporary workspace assignment.

For facilities whose primary role is to provide temporary workspaces, there are unique opportunities for optimization since the most efficient utilization of workspaces in the facilities can be recalculated each day. If on a given day, a facility is less than fully utilized, then an operating cost of that facility can be reduced by deactivating an unused portion of the facility. Cost savings can be achieved if one or more sections of the facility is deactivated, e.g., provided with no electric power, no water, and no gas. The deactivated sections may be either the most expensive to operate or the least desirable workspaces, .e.g., workspace located at farthest from a restroom, an elevator, etc.

SUMMARY

There are provided a system, a method and a computer program product for assigning a workspace. The system receives one or more reservation request for the workspace, associated with one or more facilities including one or more workspace areas. Each workspace area includes one or more workspaces. The system receives inputs including one or more of: weather condition data, occupancy rates data, and date data, associated with the one or more facilities. The system predicts, based on the received inputs and the received reservation request, an energy cost for each workspace area. The system determines a user desirability value for the each workspace area. The system determines, based on the predicted energy cost and the determined user desirability value, a minimum cost for operating the one or more facilities, which satisfies the received reservation request. The system selects one or more workspace area in the one or more facilities according to the determined minimum cost and the received reservation request. The selected workspace area has maximum user desirability values.

In order to predict the energy cost of each workspace area, the system runs a learning algorithm in order to find a match between the received inputs and the received reservation request and historical data which includes one or more of: historical weather condition data, historical occupancy rates data, historical date data, and historical reservation request. The system derives, based on the found match, the energy cost which corresponds to the matched historical data. The derived energy cost is the predicted energy cost.

DETAILED DESCRIPTION

A computing system (e.g., a computing system shown inFIG. 2) assigns one or more workspaces in a facility (e.g., building or areas therein) or in a group of facilities while minimizing the cost of running the facility and maximizing comfortability of users based on a combination of a prediction of the daily environmental conditions (e.g., weather and cloud coverage), historical operation cost of the facility and user desirability values (i.e., ratings of satisfactions of workspaces that each user used).

In one embodiment, a workspace area cost table600is shown inFIG. 5. The computing system creates the workspace area cost table600by receiving data of historical daily conditions (weather and cloud coverage), the historical operation cost of the facility, and the user desirability values. The computing system runs a learning algorithm (e.g., a supervised learning algorithm and/or a decision tree algorithm, etc.) over the workspace area cost table600. The run learning algorithm outputs a workspace area operation cost (e.g., area operating costs670-675, etc.) corresponding to specific daily conditions. For example, the workspace area cost table600includes historical data which includes, but is not limited to: outside temperature ranges605as shown in a table500inFIG. 4A, amounts of cloud coverage610, hours of sunlight615, heights of sun in a sky620, perceptions as shown in a table505inFIG. 4B, a percent of occupancy625, a day of week630, an advanced reservation for workspaces in the each work space area635, a total number of workspaces needed640, a maximum number of conference rooms needed645, 10% excess capacity to cover inaccuracy of the advanced reservation or to cover walk-in650, a percentage of cancellations655, an energy cost (or operating cost)660when a corresponding workspace area is dormant670(i.e., when that corresponding workspace area is occupied by no user; a pre-configured minimum level of energy usage), an energy cost (or operating cost) when that corresponding workspace area is active675(i.e., when that corresponding workspace area is occupied by at least one user), and a user desirability value. Based on this workspace area cost table600, upon meeting same or similar daily conditions (e.g., similar weather conditions, similar advance reservation conditions, and similar percentage of cancellations, etc.), the computing system can determine a corresponding energy cost (or a corresponding operating cost) of each workspace area of each workspace area. The determined energy cost is historical energy cost corresponding to same or similar historical daily conditions.

The following describes example characteristics of example workspace areas. A workspace area11A has a front lobby, administrate offices, 100 cubicles for workspace assignments, three conference rooms, and one conference area. Each of workspace areas11B,21A,21B, and31A has 400 open cubicles, seven conference rooms and one conference area. Each of workspace areas12A,22A, and32A has 300 cubicles and nine conference rooms. Each of areas13A,13B,23A,23B, and33A include various services including raised floor data centers and are always active, and are not used for workspace assignments. An area35A has the cafeteria, a conference area, and 75 cubicles for workspace assignments.

The following further describes example historical data which may be represented in the workspace area cost table600inFIG. 5. Each of workspace areas11A,11B,21A,21B, and31A has south facing windows. On clear summer days (i.e., summer days with no cloud in the sky), these workspaces are expensive to cool (i.e., requires cooling cost higher than a threshold value). On the clear summer days, the 3rdfloor of workspace area31A receives the lowest user desirability value due to complaints of hot room temperatures (e.g., hotter than 75° F.). On the clear summer days, in these workspaces, lighting receives a minimal help from sunlight because the sun angle is higher than a threshold value.

The workspace area cost table600correlates the historical data605-650of each workspace area with a dormant energy cost675of the each workspace area. The workspace area cost table600correlates the historical data605-650of each workspace area with an active energy cost670of the each workspace area. Given a set of specific empirical conditions (e.g., specific weather conditions and specific reservation conditions, etc.), based on the workspace area cost table600, the computing system can determine a dormant or active energy cost of each workspace area.

In one embodiment, there is provided a user desirability value table700as shown inFIG. 6. The user desirability table700illustrates a user desirability value680of each workspace in each workspace area. After using a workspace, a corresponding user receives a survey that includes, but is not limited to: a rating of comfortability, a rating of an access to amenities, and a rating of satisfaction of that workspace, etc. If one or more occupancy sensors (i.e., a control device that detects an occupancy within an area) are capable of determining utilization of a corresponding workspace area, then a workspace area usage and/or the number of assigned workspaces in the workspace area can be factors to determine a user desirability value of the corresponding workspace area.

In another embodiment, the computing system uses any energy modeling tool (e.g., an energy modeling tool from Apogee Wausau Group, Inc., etc.) that is used in a facility design to augment the learning algorithm, especially when there exists no or few historical data to run the learning algorithm.

An angle of sunlight entering a facility may also be a factor to determine a user desirability value and an energy cost of that facility. The computing system may calculate this angle of sunlight based on a current date and latitude of that facility. The lower the sun in the sky, the smaller the angle of sunlight, and consequently more solar heating, and the more issues with glares.

The computing system may improve quality of workspaces, e.g., by adjusting factors under a direct or indirect (through work orders) control of a control system which controls and maintains a facility which includes the workspaces. The most efficient or most comfortable occupancy of a workspace area may be less than the maximum occupancy of that workspace area.

By assigning a workspace by running the learning algorithm over the historical data in the workspace area cost table600, the computing system arranges workspaces so that each user has an access to services provided by the facility and is provided with a maximum comfortability (i.e., maximum user desirability values). At the same time, the computing system minimizes resource usages and energy cost (or operating cost) of the facility, e.g., by operating only workspace areas which satisfies a daily demand and requires minimum energy costs.

The computing system selects workspace areas to be operated based on static empirical data and/or transient factors, which include, but is not limited to: historical data605-680shown in the table600inFIG. 5. For example, the historical data in the table600may indicate that in a facility with south facing windows, workspaces near the windows may overheat on clear summer days and be cold and drafty on cloudy winter day, but can be considered desirable (e.g., corresponding user desirability value is higher than a threshold) on cloudy summer days and sunny winter days. The table600may further indicate that the energy cost of the workspace near the windows for heating and cooling as well as lighting varies with different weather conditions. Then, based on the energy cost and the user desirability values, the computing system may select the workspace near the south facing windows to be operated on cloudy summer days and sunny winter days. For example, the computing system selects one or more workspaces that can satisfy the reservation request. The selected workspaces may need a minimum operating cost according to the table600inFIG. 5.

A facility or a group of facilities may be wholly or partially used for workspaces. A facility may include a plurality of workspace areas as shown inFIG. 3.FIG. 3illustrates example workspace areas, e.g., a workspace area represented by zone15A (400), a workspace area represented by zone13A (405), a workspace area represented by zone14A (410), etc. The computing system assigns workspaces on a daily basis, e.g., by running a method shown inFIG. 1.FIG. 1is described in detail below. More than one HVAC (Heating, Ventilation, and Air Conditioning) and lighting are used for in workspace areas. There may be provided sensors in each workspace area which measure an actual cost of the HVAC and the lighting on a daily basis. The computing system may deliver a different level of services to each workspace area based on occupancy of each workspace area. For example, the higher occupancy, the more energy costs to spend on the HVAC and lighting. There may be further provided in each workspace area with an occupancy sensor that determines occupancy of a corresponding workspace area.

During operations of workspace areas, the computing system monitors, e.g., by using one or more sensors, etc., the following on a daily basis: (1) an outside temperature (these temperatures may also be available in daily weather forecast information) of each workspace area; (2) an amount of sunlight penetrated to each workspace area (or an amount of cloud coverage in a sky) (these amounts may also be available in daily weather forecast information); (3) the number of advanced reservations of workspaces needed; (4) a percent of advanced reservations that are cancelled; (5) the number of walk-in reservations for workspaces; (6) a percent utilization of each workspace area; and/or (7) an energy used by each workspace area, etc.

The system may provide an Internet based portal for workspace reservation. Each facility may also include a kiosk for walk-in workspace requests. Each day before a facility opens, but after weather forecast data is available, the computing system runs a method shown inFIG. 1in order manage one or more facilities which include one or more workspaces. At110inFIG. 1, the computing system receives one or more reservation request for the workspace, associated with one or more facilities including one or more workspace areas. Each workspace area includes one or more workspaces. The computing system further receives one or more types of input data, which include but are not limited to: weather condition data, occupancy rates data, and date data, associated with the one or more facilities. In one embodiment, in order to receive the input data and the reservation request, the computing system retrieves, from one or more database, the weather condition data, the occupancy rates data, the date data, and the reservation request.

The weather condition data represents conditions external to the one or more facilities. These represented conditions includes, but is not limited to: an outside temperature range and an amount of cloud coverage in a sky. The date data includes, but is not limited to: an amount of a shade made by one or more building nearby the one or more facilities, an angle of sunlight relative to a horizon, and a duration of sunlight. The reservation request includes, but is not limited to: an advanced reservation for workspaces, an expected number of walk-in reservations on a corresponding day of a week, the number of particular types of workspaces needed, and a total number of workspaces needed, etc. In one embodiment, the received one or more types of input data and the reservation request reflect past, current and/or future daily conditions (e.g., weather conditions, reservation conditions, etc.) associated with the facilities.

The computing system predicts, based on the received one or more types of input data and the received reservation request, all the conditions that may exist in the facilities for a corresponding day. The conditions to be predicted include, but are not limited to: a dormant and an active energy cost for each workspace area, the number of cancellations to be made for the corresponding day, etc. In order to predict those conditions, the computing system runs the learning algorithm with the received one or more types of input data and the received reservation request, to determine historical data which corresponds to the received input data and the received reservation request. In one embodiment, the computing system runs a supervised learning algorithm or a decision tree algorithm in order to find a match between the received input data and the received reservation request and historical data which includes one or more of: historical weather condition data (e.g., historical weather condition data605-610shown in the table600inFIG. 5), historical occupancy rates data (e.g., historical occupancy rate data625shown in the table600inFIG. 5), historical date data (e.g., historical date data615-620shown in the table600inFIG. 5), and historical reservation request (e.g., historical reservation request635-640shown in the table600inFIG. 5). The computing system derives, based on the found match, those conditions which corresponds to the matched historical data. In one embodiment, a historical energy cost, which corresponds to the matched historical data, is the predicted energy cost that corresponds to the received data and the received reservation request. In one embodiment, the historical number of cancellations (or the historical percentage of cancellations), which corresponds to the matched historical data, is the predicted number of cancellations for the corresponding day.

In one embodiment, the computing system optimizes the prediction of all the conditions to be existed in the facilities, e.g., by using a heuristic to identify which historical data have a higher correlation to the received input data and the reservation request than other historical data. For example, the computing system determines the correlation, e.g., by calculating correlation coefficients between values of one or more historical data and values of the received input data and the received reservation request. Less value of a coefficient, less correlation between corresponding historical data and corresponding received input data and/or corresponding received reservation request.

FIG. 7illustrates a flowchart that describes method steps for optimizing a prediction of conditions to be existed in the facilities for a corresponding day in one embodiment. At800, the computing system identify historical data whose values are most similar to values of the received input data and the received reservation request in order to predict conditions of that corresponding day, e.g., by comparing values of historical data605-650in each column of the table600to values of the received input data and the reservation request. At810, the computing system determines actual values of daily conditions (e.g., daily weather condition for the corresponding day, daily cancellation condition for the corresponding day, daily reservation condition for the corresponding day, daily energy cost for the corresponding day, etc.), e.g., by using temperature and/or occupancy sensors associated with the facilities.

At820, computing system identify historical data whose values are negatively correlated to the values of the actual daily conditions of the corresponding day. A negative correlation may represent that values of corresponding historical data change against (e.g., have a revere relationship with) the values of the actual daily conditions. For example, the computing system may calculate a correlation coefficient between an average of values of each row in the table600against each corresponding value of the actual daily conditions of the corresponding day in order to identify the negatively correlated historical data. If a value of a correlation coefficient is less than zero, corresponding historical data and the corresponding actual daily condition are negatively correlated. At830, the computing gives a higher weight (e.g., a higher priority, etc.) to the negatively correlated historical data when the computing system runs the prediction of daily conditions (e.g., daily energy cost, daily number of reservation cancellations etc.) for another day. For example, if the hours of sunlight615in the table600is identified as a negatively correlated historical data, the computing system may first compare each value in the hours of sunlight615row in the table600to a “hours of sunlight” value in the received weather condition data during a prediction of the daily conditions for the another day. The computing system may select three or four columns in the table600whose hours of sunlight values are most similar to a value of the hours of sunlight in the received weather condition data of the another day. At the end of the prediction for the another day, the computing system may eventually choose, among the selected columns, one column whose values of other historical data are most similar to the received input data and received reservation request. Values of the historical data in the chosen column in the table600may represent predicted values of the daily conditions of the another day.

In one embodiment, in order to predict the active energy cost of each workspace area, the computing system determines, based on the received input data, the received reservation request, and the found matched historical data, the energy cost of the each workspace area when the each workspace area is occupied by at least one user. For example, the historical energy cost, which corresponds to the found matched historical data, is the energy cost of the each workspace area when the each workspace area is occupied by at least one user. In order to predict the dormant energy cost of each workspace area, the computing system determines, based on the received input data, the received reservation request, and the found matched historical data, the energy cost of the each workspace area when the each workspace area is not occupied by any user. For example, the historical energy cost, which corresponds to the found matched historical data, is the energy cost of the each workspace area when the each workspace area is not occupied by any user.

The computing system determines a user desirability value for the each workspace area. In order to determine the user desirability value of each workspace area, the computing system receives, from each user, a survey that includes, but is not limited to: questions asking a user to rate his/her comfortability, rate the workspace's access to amenities, and rate the user's level of satisfaction of a workspace that the user used. Each question may include numerical ratings one of which is chosen by the user. The system sends and collects the survey, e.g., by using an email, a webpage, etc.

An example of the survey may indicate as follows: on a cloudy summer day (i.e., a summer day with a cloud in the sky), these workspace areas11A,11B,21A,21B, and31A require comparable cooling cost per sq ft to other workspace areas. On the cloudy summer day, these workspace areas receive user desirability values higher than a threshold value. On a sunny winter day (i.e., a winter day with no cloud in the sky), in these workspace areas, there exist good cost savings on heating due to heating provided from sunlight. On the sunny winter day, in these workspace areas, there exists a reduced lighting cost due to a sunlight angle lower than a threshold. On the sunny winter day, in these workspace areas, window cubicles receive user desirability values lower than a threshold due to sun glare and when these window cubicles are assigned, shades are made and reduces cost savings in the lighting cost. On a cloudy winter day (i.e., a winter day with a cloud in the sky), these workspace areas require energy cost per sq ft which is similar to other workspace areas. On the cloudy winter day, in these workspace areas, there exist complaints about window cubicles being cold.

An another example of the survey may indicate as follows: workspace areas12A,22A, and32A have west facing windows. On clear summer days, these workspaces receive sunlight in the Morning and are cooler than a threshold during a daytime and thus energy costs are less than a threshold. On a cloudy summer day, in these workspace areas, there exist cooling costs per sq ft similar to cooking cost of other workspace areas. On the cloudy summer day, these workspace areas receive user desirability values higher than a threshold. On a sunny winter day, in these workspace areas, there exist cost savings on heating due to sunlight penetrated to these workspace areas. On the sunny winter day, these workspace areas require reduced lighting costs due to a sunlight angle lower than a threshold. On the sunny winter day, in these workspace areas, window cubicles receive user desirability values lower than a threshold due to sun glare and when these window cubicles are assigned, shades are made and reduces cost savings in the lighting and heating costs. On a cloudy winter day, in these workspace areas, there exist energy costs per sq ft similar to energy costs of other workspace areas. On the cloudy winter day, in these workspace areas, there exist complaints about window cubicles being cold.

The computing system determines, based on the predicted energy cost and the determined user desirability value, a minimum cost, for operating the one or more facilities, which satisfies the received reservation request. In one embodiment, in order to determine the minimum cost, the computing system may assign each different weight to each of the received one or more types of input data (e.g., the received weather condition data, the received occupancy rates data, the received date data) and the received reservation data, etc. By running the energy modeling tool with these weighted data and request, the computing system can determine the dormant and active energy cost of each workspace area. In one embodiment, the dormant energy cost of a workspace area represents a minimum cost for operating that workspace area. A dormant workspace area represents a workspace area which is not occupied by any user. An active workspace area represents a workspace area which is occupied by at least one user.

The computing system selects one or more workspace area in the one or more facilities according to the determined minimum cost and the received reservation request. In one embodiment, the selected workspace area has maximum user desirability values. The selected workspaces have enough workspaces that satisfy the received reservation request and may require minimum operating cost according to the table600shown inFIG. 5. The computing system may further select extra workspace areas in order to meet walk-in workspace reservation requests. The computing system notifies users, who have made workspace reservations, of workspaces in the selected workspace areas, e.g., by using emails, instant messaging, text messaging, etc. Returning toFIG. 1, at120, the computing system first assigns workspaces, in the selected workspace area, whose user desirability values are highest to users who have made a workspace reservation at earliest. Then, the computing system assigns workspaces, in the selected workspace area, whose user desirability values are second highest to remaining users who have made workspace reservations later than the earliest users. The workspaces, in the selected workspace area, whose user desirability values are the lowest may be assigned to users who have made workspace reservations at last or who have made walk-in workspace reservations.

A workspace area that is in use by any user becomes an active state, e.g., setting a room temperature to a specific degree, providing lightings, operating elevators or escalators near that workspace area. Unused workspace areas becomes a dormant state, e.g., providing no utility to that unused workspace area. All workspace areas may become dormant (e.g., having no utility available) at the end of a weekday and during a weekend.

Any system that affects either a user desirability value or an operating cost of a workspace area may be added into a determination of an energy cost of that workspace area. An example of this system includes, but is not limited to: automatic window shutters or blinds; and elevators or escalators which can minimize elevator or escalator contention. For example, these systems may be added, e.g., by computing system, etc., to the table600as factors (e.g., rows in the table600) to determine operating costs. For example, the operating cost of these systems will be added to the active operating costs of corresponding workspace areas (also called corresponding workspace zones). As consistent user desirability values of workspaces build, an assignment of a workspace having a user desirability value higher than a pre-determined threshold value can be used to influence behavior of a user who receives the assignment. For example, the computing system assigns workspaces with user desirability values higher than the pre-determined threshold to users who have made workspace reservations in advance and do not have a history of cancellations of workspace reservations, e.g., by using the table600and the method shown inFIG. 2. This example assignment may encourage advanced workspace reservation and improve a workspace usage prediction. In another example, the computing system uses user desirability values as like rating used in airplane seat assignments, e.g., by charging premium rates to workspaces with user desirability values higher than the pre-determined threshold value.

In one embodiment, the received data and the reservation request data dynamically change everyday. By running methods shown inFIGS. 1 and/or 7, the computing system assigns one or more workspaces in the selected zones in which the workspace assignments reflect the dynamically changed data and request.

The following describes three example usage scenarios each of which employs the method shown inFIG. 1.A first example usage scenario: on a clear summer day, there provided a weather forecast—clear high: 85, low: 65. A day of week is Wednesday. The number of advanced reservations is 1720. Predicted cancellation rate is 5%. The predicted number of walk-in workspace reservations is 535. A maximum of four conferences rooms are required. 10% excess capacity of workspace reservations is necessary to cover inaccuracy in the predicted number of walk-in workspace reservations. The total number of cubicles needed is 2575. In order to satisfy these reservation requests, the computing system runs the method shown inFIG. 1and determines as follows:(1) Reservation exceeds a threshold (i.e., 1500 number of workspace reservations). A cafeteria shall be fully operated—workspace area35A shall be active.(2) Start to assign workspaces in workspaces areas with north facing windows because these workspaces both require the least energy cost and receive the highest user desirability value on sunny summer days.(3) Activate (e.g., provide utility in) a workspace area11A which provides 75 workspaces.(4) Activate a workspace areas15A,25A,25B,35A, all of which provide 900 cubicles and four conference rooms.(5) Activate a workspace area34A with a cafeteria (workspace area34A provides 300 cubicles).(6) On summer days, workspaces with east facing windows require next cheapest energy costs. Energy cost to operate workspaces in a facility A is cheaper than in facility B due to shadowing of the facility A from sunlight in the Morning by the facility B. A workspace area14A has a class room that is not used and a reduced number of cubicles in the workspace area14A increases energy cost per cubicle.(7) Activate a workspace area24A—2ndfloor break room space increase user desirability value of the workspace area24A; the workspace area24A provides 300 cubicles.(8) Activate a workspace area14A whose average user desirability is lower than a threshold but whose energy cost is next cheapest; the workspace area14A provides 250 cubicles.(9) There is an incremental saving when adjacent workspace areas are active due to preventing of an isolation of an active workspace area. Activate a workspace area14A because the workspace area14A is adjacent to other active workspace areas; the workspace area14A provides 400 cubicles.(10) Activate a workspace area32A for the remaining cubicles due to its proximity to the cafeterias; the workspace area32A provides 400 cubicles.(11) One or more of the remaining workspace areas are closed (i.e., become dormant).

A second example usage scenario: on a clear winter day, there are provided with a weather forecast—clear high: 17, low: −12. A day of week is Monday. The number of advanced reservations is 832. The predicted cancellation rate is 15%. The number of predicted walk-ins workspace reservations is 841. A maximum of four conferences rooms are required. 10% excess capacity of workspace reservations is necessary to cover inaccuracy in the predicted number of walk-in workspace reservations. The total number of cubicles needed is 1978. In order to satisfy these reservation requests, the computing system runs the method shown inFIG. 1and determines as follows:(1) Reservation exceeds a threshold (i.e., 1500 number of workspace reservations). A cafeteria shall be fully operated—workspace area35A shall be active.(2) Start to assign workspaces in workspace areas having south facing windows because these workspaces benefit most from sun heating and an angle of sunlight (lower than a threshold) reduces lighting requirements.(3) Activate a workspace area11A which provides 75 cubicles.(4) Activate workspace areas11b,21A,21B,31A, all of which provide 1600 cubicles and four conference rooms.(5) Activate a workspace area34A with a cafeteria (workspace area34A provides 300 cubicles).(6) Assign cubicles near windows at last because user desirability values of these cubicles are the lowest.

A third example usage scenario: on a snowy winter day, there are provided with a weather forecast—clear high: 28, low 15, Morning snow. A day of week is Thursday. The number of advanced reservations is 542. The predicted cancellation rate is 30%. The number of predicted walk-ins workspace reservations is 256. A maximum of two conferences rooms are required. 10% excess capacity of workspace reservations is necessary to cover inaccuracy in the predicted number of walk-in workspace reservations. The total number of cubicles needed is 1978. In order to satisfy these reservation requests, the computing system runs the method shown inFIG. 1and determines as follows:(1) Reservation exceeds a threshold (i.e., 1500 number of workspace reservations). A cafeteria shall be fully operated—workspace area35A shall be active.(2) Start to assign workspaces in workspace areas having south facing windows because these workspaces benefit most from sun heating and an angle of sunlight (lower than a threshold) reduces lighting requirements.(3) Activate a workspace area11A which provides 75 cubicles.(4) Activate workspace areas11b,21A,21B,31A, all of which provide 1600 cubicles and four conference rooms.(5) Activate a workspace area34A with a cafeteria (workspace area34A provides 300 cubicles).(6) Assign cubicles near windows at last because user desirability values of these cubicles are the lowest.

In one embodiment, a computing system may run the method illustrated inFIG. 1.FIG. 2illustrates examples of the computing system. Examples of the computing system may include, but are not limited to: a parallel computing system300including at least one processor355and at least one memory device370, a mainframe computer305including at least one processor356and at least one memory device371, a desktop computer310including at least one processor357and at least one memory device372, a workstation315including at least one processor358and at least one memory device373, a tablet computer320including at least one processor356and at least one memory device374, a netbook computer325including at least one processor360and at least one memory device375, a smartphone330including at least one processor361and at least one memory device376, a laptop computer335including at least one processor362and at least one memory device377, a physical server340including at least one processor361and at least one memory device378, a software server380, e.g., web server, HTTP server, application server, or a wearable computer385, e.g., smartwatch, etc., including at least one processor390and at least one memory device395.

In one embodiment, the methods shown inFIG. 1may be implemented as hardware on a reconfigurable hardware, e.g., FPGA (Field Programmable Gate Array) or CPLD (Complex Programmable Logic Device), by using a hardware description language (Verilog, VHDL, Handel-C, or System C). In another embodiment, the method shown inFIG. 1may be implemented on a semiconductor chip, e.g., ASIC (Application-Specific Integrated Circuit), by using a semi custom design methodology, i.e., designing a semiconductor chip using standard cells and a hardware description language.