A multi-objective scheduling advisor for generating a multi-stop visitation schedule includes generating, by a computer, a road network map corresponding to a predetermined area including a plurality of tasks locations. A task to be performed is assigned to each of the plurality of task locations. The computer calculates a business value for each task location using at least one of a calculation, a selected business rule applied to a delay duration, and an input value received from a client. A duration of a respective task is calculated using historical data on task durations associated with a staff of operators over different predetermined areas to determine an average task duration for every operator in the staff. Finally, using a metaheuristic binary optimization algorithm, the computer chooses different candidate tasks for the multi-stop visitation schedule to visit multiple assets in a single trip within the predetermined area.

A processing system including at least one processor may identify a demand for a plurality of types of items in an area that is assigned to a delivery vehicle, determine a first route to traverse the area to maximize a fulfillment of the demand, identify items from the plurality of types of items to load onto the delivery vehicle, and dispatch the delivery vehicle on a first trip over the first route, the delivery vehicle carrying the items that are identified.

Disclosed here are systems and methods that allow users, upon detecting errors within a running workflow, to either 1) pause the workflow and directly correct its design before resuming the workflow, or 2) pause the workflow, correct the erred action within the workflow, resume running the workflow, and afterwards apply the corrections to the design of the workflow. The disclosure comprises functionality that pauses a single workflow and other relevant workflows as soon as the error is detected and while it is corrected. The disclosed systems and methods improve communication technology between the networks and servers of separate parties relevant and/or dependent on successful execution of other workflows.

A computer-implemented method can receive a new plan deviation alert having a deviation level that quantifies a mismatch between expected supply chain parameters specified by a supply chain plan and observed supply chain parameters. Responsive to the new plan deviation alert, the method can perform a rule-based search to find a plurality of potential remediation solutions to correct the mismatch. The method can simulate implementation of the potential remediation solutions and evaluate expended resources associated with them. Based on the evaluated expended resources, the method can generate a ranked list of candidate remediation solutions and display the ranked list of candidate remediation solutions in a user interface. The method can receive a selected remediation solution from the ranked list of candidate remediation solutions for initiation. Machine learning can be used on an expert user's selection to adapt to the expert's preferences and provide more relevant remediation solutions.

A system and method for a restaurant-as-a-service system, comprising one or more database(s), a cluster manager, and a platform which provides restaurants automated, multiple-domain spanning, intelligent business predictions and optimizations leveraging modular, highly integrable microservices which perform various domain-specific functions and tasks to enhance restaurant operations and patron experience. The cluster manager may intercept data requests and algorithmically forward requests to the appropriate microservice operating on an available cluster of computing devices.

Methods and systems that include collection, by a processor, of allotments having an available date before a need date, generation, by the processor, of: one or more supply available events, one or more supply pending events, one or more demand need events, and one or more target change events, processing, by the processor, the one or more supply available events, the one or more supply pending events, the one or more demand need events, and the one or more target change events sequentially until a last event of a day is reached, and processing, by the processor, the last event of the day.

An operation server operates a plurality of MSTs, and includes a reception unit that receives a charging request that specifies at least a charging date and time and a charging place, a planning unit that generates deployment plan information for the MSTs based on the charging request received by the reception unit, and a deployment unit that executes a deployment process for the MSTs based on the deployment plan information. When there are not two or more of the MSTs that are available at the charging date and time specified by the charging request, the reception unit rejects the charging request. When the MST that has been deployed or is scheduled to be deployed fails, the deployment unit executes the deployment process such that the MST that is available at the charging date and time assigned to the failed MST is deployed instead of the failed MST, among the MSTs.

In some embodiments, environment solutions are received from local solution control centers for respective companies. The environment solutions generated from environmental data received from multiple environments being operated by a respective company. The environment solutions for the environment solutions are converted to anonymous environment solutions and the environmental data to anonymous environmental data. The method applies first machine learning logic to the anonymous environment solutions for the environment solutions to generate first set of solutions and applies second machine learning logic to the anonymous environmental data to generate second set of solutions. The first set of solutions and the second set of solutions are tested for comparable solutions. One of the first set of solutions and the second set of solutions is then selected based on the testing.

Improvement of work is instructed based on an analysis result obtained by combining data having different timescales. A productivity improvement support system includes a time scale setting unit configured to, when 4M data having different time scales acquired from a target device contains data that satisfy a condition for detecting a state fluctuation, switch time scale of the 4M data to time scales according to a state fluctuation, a loss analysis calculation unit configured to analyze a production loss factor by using analysis model data in which the production loss factor of the target device when the condition is satisfied is determined, and a recommended work selection unit configured to select a recommended work when the production loss factor occurs from one or a plurality of recommended works by using recommended work data stored in association with the production loss factor.

A plan evaluation apparatus, which evaluates a schedule planned by combining a plurality of plans, includes: a feature conversion unit that divides the schedule into plan components based on a predetermined conversion rule, and convert the divided plan components into features; a model learning unit that uses the features as an input and creates a machine learning model having a key performance indicator (KPI) of the schedule as an objective variable; a contribution rate calculation unit that calculates a contribution rate of each of the features with respect to the machine learning model; and an influence degree calculation unit that calculates an influence degree of influence, on the KPI of the schedule, of the plan component which is a conversion source of the feature, based on the contribution rate of the feature.