Optimos.
One primary goal of Business Process Management (BPM) is to improve organizational performance to achieve objectives such as reducing costs and execution times of business proc1e]s.sFeosr[ this purpose, what-if analysis can help to identify optimal interventions to maximize or minimize performance measures under specific constraint2s][. For example, an organization can use what-if analysis to explore diferent stafing schedules within limited working hours, ensuring that resource utilization remains below 80% and employees do not exceed their maximum allowed working hours, balancing labor costs and delivery times.
Business Process (BP) simulation further enhances this capability by providing a powerful technique for assessing what-if scenario3s][. Using simulation models that act as digital twins, BP simulation captures the organizations’ behaviors, ofering a detailed representation of real-world processes. These models enable the prediction of the process performance under current configurations (as-is model) or hypothetical changes (to-be model). This capability is essential for optimizing business processes, as it supports process updates that may improve key performance measures, such as resource cost and cycle times [2].
This paper presents Optimos, an optimization tool designed to help process analysts to automatically improve business processes by minimizing cycle times and execution costs through what-if analysis and simulation. Optimos achieves this by adjusting the allocation of resources to activities and modifying resource availability calendars. Additionally, the tool allows analysts to define organizational constraints that restrict the optimization of their resource allocations.
Achieving an ideal resource configuration that minimizes costs and cycle times is challenging. Typically, no single configuration can improve both objectives simultaneously. For instance, increasing the number of resources reduces activity waiting times, shortening the overall cycle time. Similarly, if resources are less busy due to higher availability or extended working hours, waiting times decrease, making processes faster. However, having more resources and increased working hours also increases labor costs. On the other hand, reducing the number of resources or limiting their working hours
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ceur-ws.org lowers costs but increases waiting times due to busier remaining resources. This trade-of between cost and time is a key consideration in optimizing business proce3ss]e.s [
Manually balancing the number of resources and their working hours is challenging for analysts because it involves considering multiple combinations of variables and constraints. The technical complexity of exploring a wide range of potential solutions makes finding optimal resource allocations dificult without automated tools. Optimos aids analysts by automating the process, allowing them to optimize their resources without requiring any technical background.
Optimos is a Web-based tool logically structured into three layers, as shown in1F.iTghuereFrontend layer includes several dashboards that enable user interactioPnr.oTjheects Dashboard is the entry point for users to manage diferent optimization projects. Within each project, users navigate to the OptiHub Dashboard, the central hub, displaying the optimization status and current configurations and allowing users to start new optimizations. Fro mOpttihHeub Dashboard, users can access the Constraints Dashboard to define and update optimization constraints orRetshuelts Dashboard to view suggested resource allocations and statistics from ongoing or completed optimizations.
The API layer is the intermediary between Ftrhoentend andBackend layers. TheServer-Side Rendering Server retrieves fully-rendered HTML pa-ges to improve performance.APTIheServer manages business logic, data processing, and secure interactions wiBtahctkheend. This separation allows each server to be scaled and maintained independentlyA.PTIhSeerver also handlesAssets provided by the users, such as process models, simulation parameters, or optimization results. Additionally, it manages authenticatioAunt(h) to ensure secure access to the system, verifying user credentials and maintaining session security. Finally, tAhPIe Server interacts withDaatabase, storing and retrieving essential data such as user information, project details, constraints, and results to ensure data persistence in the system.
The Backend layer is where the core optimization processes occur.MeTshseage Queue manages communication and task distribution betweenAPtIheserver andBackend services, ensuring smooth processing of tasks. ThWeorker Service executes the tasks assigned by tMheessage Queue. The Optimizer Core is the central component responsible for performing the optimization calculations and assembling and analyzing the results delivered by each other component. It works closely with the
Constraints Manager, which manages user-defined constraints, ensuring that optimizations respect these rules. TheModel Transformer adjusts (if possible) models to fix potentially problematic elements, like bottleneck activities with high waiting times or over- and underutilized resources, aiming to find an improved model. TheSimulation Engine performs simulations of each transformed model. It retrieves the results to tOhpetimizer Core, which assesses the impact of diferent resource allocations and schedules and accordingly updates the set of optimal solutions.
To interact with Optimos, users must authenticate to accePssrotjheecirts Dashboard. From there, they can view or create optimization projects. By clicking on a specific project, users are redirected to theOptiHub Dashboard, where they can edit or delete the selected project.
An optimization project consists of one or multiple simulation models, each with a BPMN4m]odel [ representing the process control flow and a JSON file with the simulation parameters accord3i]n.g to [ The simulation models supported by Optimos can be generated by Simod following the instructions from the code repositor1yand uploaded to the project Uvpiaload asset. By clicking the desired simulation model in theOptiHub Dashboard, users can define constraints to restrict the optimization process. As shown in Figure2, constraints are divided inGtloobal, Scenario, andResource Constraints.
The Global Constraints (the tab shown in Figur2e) relate to the optimization settings. They
include the maximum number of iterations, the choice of the search algorithm, and the optimization
approach to follow. An iteration refers to one cycle of evaluating potential solutions. Depending on the
selected “Algorithm”, Optimos keeps only optimal solutions at the current iteration and discard the rest
(option HC-STRICT), retains near-optimal solutions for future iterations (HC-FLEX), or considers all
solutions until the iteration limit is reached (TS). The selected “Approach” specifies whether to only
add/remove resources (AR), only update schedules (CA), or combine both. Optimos allows three ways of
combining them: (
Under theScenario Constraints tab, users can set general restrictions for all resources, such as maximum weekly hours, consecutive working hours (shifts), and daily working limits.,e.g., to define a rule that resources can work at most in two shifts of 4 hours on working days, with some rest in between. Then, theResource Constraints tab defines conditions specific to each resource, such as personal capacity limits and maximum shifts they can work daily or weekly. Additionally, it allows users to restrict intervals of a day in which a given resource must always work or, conversely, never work. For example, users can specify that a particular part-time resource can work at most five shifts of 4 hours per week and must always be available from 9 AM to 12 PM from Monday to Friday but 1https://github.com/AutomatedProcessImprovement/Simod never work after 1 PM or on weekends. Finally, thCeonstraint Validation tab displays and allows to correct all the potential errors on the constraint definitions, e.g., if they contradict the current calendars of a given resource in the simulation model. If all the constraints are valid (green), users can start the optimization by clicking the corresponding button.
Users can view the processing queue for ongoing and completed simulations on the right side of theOptiHub Dashboard (see Figure2). By clicking on an entry, they can access detailed optimization reports. These reports show metrics like estimated mean costs, mean times, and average resource utilization achieved at each iteration, helping users compare improvements to the original process.
For example, Figure3 illustrates the general results after optimizing a business process, including a scatter plot, average costs and cycle times, and comparisons to the initial solution. The scatter plot shows the performance achieved by the solutions explored by the optimizer, with the y-axis representing cost and the x-axis representing cycle time. It highlights the initial solution (red dot), optimal solutions (blue dots), and other (sub-optimal) solutions (gray dots). This visual plot aids users in getting general insights into the distribution of costs and cycle times and how they improve across iterations. Also, by clicking any solution in the chart, optimal or not, users can get more detailed information for each resource allocation, such as assigned tasks, total working times, hourly rates, and timetable changes.
Next, Figure4 displays the information regarding a specific solution, i.e., corresponding to the 75th allocation assessed by the optimizer. While the values in the general chart (3F)iaguggrreegate all the optimal allocations, the values shown in Figu4rfeocus only on the costs and cycle times achieved with the current allocation. It also shows average resource utilization and improvements over the original allocation. Specifically, in this allocation, the optimizer detected that two clerks are unused, suggesting they are unnecessary, and their exclusion leads to improvements. Indeed, the unused clerks’ dynamic information, like utilization, cost, and available time, is missing as they did not participate in the simulated execution. In the example used for this demo, four clerks were underutilized initially, while only one credit oficer was overutilized, causing increased waiting times and a bottleneck. Therefore, although not shown in the figure, the optimizer also identified that adding more credit oficers or extending their schedules could lead to an optimal allocation. Users can also click on any resource (e.g., Clerk-2) to see allocated tasks and their adjusted timetables after optimization.
In this way, analysts can understand how each iteration adjusts the resource allocations and timetables to enhance performance. Users can review these results and choose a resource allocation from the solutions produced at each iteration based on their preferences for balancing time and cost. Optimos allows users to stop the optimization process at any point if they are satisfied with the results.
Optimos is accessible as a cloud service frohmttps://pix-w1.cloud.ut.e.eD/ocumentation of Optimos and the source code of thBeackend can be found athttps://github.com/AutomatedProcessImprovement/ roptimus-prim.eThis GitHub repository contains links to separate repositories for the frontend and backend (API) layers. A demonstration video of Optimos is availabhltetapts://youtu.be/jBtBEwO-w T.4 The example file containing the simulation model and constraints used in the video can be found in the Optimos repositoryh:ttps://github.com/AutomatedProcessImprovement/roptimus-prime/raw/main/ test_assets/demo/Demo%20Files.z.ip
Optimos implements the optimization approaches presente5d,3i]n. [The first paper [5] describes a basic model with resources grouped in pools. The optimizer may add or remove resources to reduce cycle times and costs. The second pape3r] p[roposes a more sophisticated model with diferentiated resources, where each resource has its own availability and performance. The optimizer may add or remove resources or change the timetable of a resource while ensuring that the optimized timetables fulfill a given set of constraints (e.g., no more than 40 hours per week). Optimos relies on Pros6i,m3o]s [ as a simulation engine to estimate the impact after transforming the model in each iteration.
Work funded by European Research Council (PIX project) and the Estonian Centre of Excellence in Artificial Intelligence funded by the Estonian Ministry of Education and Research.