Fujitsu Corporation

Helion Concepts, Inc.

Indradeep Ghosh

Mukul Ranjan Prasad

Sreeranga P Rajan

Guodong Li

Paparao Palacharla

Xi Wang

Oksana Tkachuk

Hayato Ushijima-Mwesigwa

Liang Zhang

Hiroaki Yoshida

Praveen Murthy

Koichiro Takayama

Ripon K Saha

Maarten H Wiggers

Pouya Rezazadeh Kalehbasti

Avradip Mandal

Arnab Roy

Farzan Fallah

Zhexuan Song

Fan Yu

Sudarshan Krishnamoorthy

Hayato Ushijima

Osman Asif Malik

Cuong Nguyen

Qianqian Wang

Daryl R Shannon

Pouya Shati

Hanjing Xu

Prasad Mukul Ranjan Ranjan

In an embodiment, a set of parameters associated with a vehicle routing problem is received. A set of decision variables and a set of constraints associated with an optimization problem are received. An optimization problem is constructed. The optimization problem is divided into a set of sub-problems. Each of the set of sub-problems corresponds to a subset of warehouses of a set of warehouses. An intermediate solution is determined for each of the set of sub-problems to determine a set of routes associated with the corresponding sub-problem of the set of sub-problems. The intermediate solution associated with each of the set of sub-problems is combined to determine a final solution of the optimization problem based on the received set of constraints. The determined final solution is indicative of the set of optimal routes to be assigned to the set of vehicles. The final solution is rendered on a display device.

Vehicle routing problem (VRP) solution using annealer-based solvers

According to an aspect of an embodiment, operations include receiving data associated with a vehicle routing problem, the data comprising first information about a plurality of vehicles in a geographical region and second information about a set of locations that the plurality of vehicles is required to serve. The operations further include determining a formulation of a multi-objective clustering problem based on the data and converting the formulation into a QUBO formulation. The operations further include generating a binary solution by solving the QUBO formulation on an optimization solver machine. The operations further include partitioning the set of locations into location clusters based on the binary solution and generating a set of candidate routes for the plurality of vehicles based on the location clusters. The operations further include controlling a device to render at least one route recommendation for the plurality of vehicles based on the set of candidate routes.

Route generation for vehicle routing problem and variants thereof

A method may include assigning each node of a network to a single first node cluster and selecting nodes of the network as a first set of nodes. The method may further include solving an optimization problem by reassigning one or more of the nodes of the first set of nodes to a second node cluster while maintaining the nodes that are not part of the first set of nodes in the first node cluster. The method may also include after solving the optimization problem, selecting other nodes of the network as another set of nodes and resolving the optimization problem by reassigning one or more of the nodes of the other set of nodes to a third node cluster while maintaining the node cluster assignment of the nodes that are not part of the other set of nodes.

Network node clustering

A method may include obtaining a first matrix that represents data in a data set and obtaining a number of clusters into which the data is to be grouped. The method may further include constructing a second matrix using the first matrix and the number of clusters. The second matrix may represent a formulation of a first optimization problem in a framework of a second optimization problem. The method may further include solving the second optimization problem using the second matrix to generate a solution of the second optimization problem and mapping the solution of the second optimization problem into a first solution matrix that represents a solution of the first optimization problem. The method may further include grouping the data into multiple data clusters using the first solution matrix. A number of the multiple data clusters may be equal to the number of clusters.

Data clustering

A method may include assigning each node of a network to a different node cluster such that a number of nodes equals a number of node clusters and selecting multiple of the nodes of the network as a set of nodes. The method may further include solving a first optimization problem by reassigning one or more of the nodes of the set of nodes to a different node cluster while maintaining assigned node clusters of the nodes that are not part of the set of nodes and after solving the first optimization problem, selecting multiple of the node clusters as a set of node clusters. The method may also include solving a second optimization problem by merging two or more of the node clusters of the set of node clusters while maintaining the node clusters that are not part of the set of node clusters.

An electrical energy storage system that can store both grid-based electrical power when electricity prices are low or renewable power generated on-site. It can release the stored electricity for consumer applications when necessary based on a software program and configuration. The system may be networked. The system comprises a port for receiving a central processing unit (CPU), and facilitates the use of different CPU products for different users and uses.

Hardware/software reconfigurable, intelligent and versatile electrical energy provisioning system for on-grid and off-grid applications

A method may include obtaining multiple lines of programming code of a program, and obtaining multiple test cases for testing the program, where each of the test cases includes an assertion upon which a result of a respective test case is based. The method may also include executing the program for each of the test cases, and identifying affected lines of programming code that influence the assertions. The method may additionally include calculating a risk score for at least one of the lines of programming code based on the affected lines of programming code and the assertion, the risk score indicative of a likelihood that the at least one of the lines of programming code includes a fault.

Identification of software program fault locations

In some examples, a method to interactively repair a software program using one or more automatically generated tests with human-provided test oracles may include identifying a fault location in a software program, generating a potential repair at the fault location based on a repair candidate, automatically generating a first test to test the potential repair, and generating a first query for a first test oracle based on the first test. The method may also include obtaining a response to the first query from a human, generating a first human-provided test oracle based on the first query and the obtained response to the first query, augmenting a test suite to include the first automatically generated test with the first human-provided test oracle, and testing the potential repair using the augmented test suite including the first automatically generated test with the first human-provided test oracle.

Interactive software program repair

A method may include determining sequence-execution constraints that constrain execution orders of a plurality of events of an event-driven software application. The method may also include determining sequence-position constraints that constrain positions of the plurality of events in one or more possible event sequences of the plurality of events. Further, the method may include determining event-relation constraints that each indicates a relationship between an event input and an event output of each of the plurality of events. Moreover, the method may include forming a constraint set that enumerates the one or more possible event sequences and that includes the sequence-execution constraints, the sequence-position constraints, and the event-relation constraints. In addition, the method may include encoding control flow information of the one or more possible event sequences into the constraint set.

Event-driven software testing

Systems and methods are disclosed to determine 'don't care' variables in programming code. The method comprises obtaining an SMT formula having a plurality of SMT variables from programming code; obtaining a simplified SMT formula from the SMT formula, the simplified SMT formula includes a plurality of simplified SMT variables; obtaining an SAT formula from the simplified SMT formula, the SAT formula includes a plurality of SAT variables; determining which of the plurality of the SMT variables are 'don't care' variables from the simplified SMT formula; and determining which of the plurality of the SMT variables are &#x201c;don't care&#x201d; variables from the simplified SAT formula.

Test size reduction using don't care analysis

A method of provisioning a network may include receiving a mapping solution for one or more virtual optical network (VON) requests including virtual nodes and virtual links. The mapping solution may include one mapping pattern for each VON request and each mapping pattern may include an ordered list of physical nodes. The method may also include formulating inequality constraint equations based on physical links that are shared by multiple virtual links. The method may also include formulating range constraint equations for each virtual link based on a physical link for each virtual link with a lowest number of optical transmission slots such that each virtual link occupy a same optical transmission slot across all physical links that are traversed by the virtual link. The inequality and range constraint equations may be solved with satisfiability modulo theories to obtain a slot assignment solution.

Provisioning virtual optical networks

A method of provisioning an optical network may include receiving one or more virtual optical network (VON) requests. Each VON request may include one or more virtual nodes and one or more virtual links. The virtual nodes may have one or more candidate nodes corresponding to a physical optical network. The method may also include identifying mapping patterns for VON requests by iteratively assigning each virtual node to one of their candidate nodes for each combination of candidate nodes and assigning each virtual link to corresponding physical links. The method may also include formulating a first set of constraint equations based on mapping patterns for the VON requests and formulating a second set of constraint equations based on physical optical network constraints. The first and second set of constraint equations may be solved by satisfiability modulo theories to obtain a mapping solution.

According to an aspect of an embodiment, a method may include determining event sequences of an event-driven software application. The method may further include determining, for each event sequence, a distance with respect to each of one or more target conditions of the event-driven software application. The event sequence distance may indicate a degree to which execution of its corresponding event sequence satisfies a corresponding target condition. The method may also include prioritizing execution of the plurality of event sequences based on the event sequence distances. Further, the method may include exploring, according to the prioritization of execution, an event space that includes one or more of the event sequences and a dependent event that corresponds to the one or more target conditions.

Event sequence construction of event-driven software by combinational computations

A method of mapping virtual optical networks (VONs) to a physical network that includes physical links that connect physical nodes. The method includes reading physical network information, VON demands, and technology constraints of the physical network. The method includes determining whether an iteration parameter violates an iterative condition that determines whether to continue processing the VON demands. If not, the method includes determining whether there are unprocessed subgroups of the VON demands. In response to there being unprocessed subgroups, the method includes selecting a subgroup of the VONs. The method includes computing feasible mapping patterns for the subgroup of the VONs. The method includes finding a size of a largest subset of the subgroup that has a feasible mapping and slot assignment solution. The method includes finding a mapping solution of the subset that has a feasible slot assignment and a slot assignment of the mapping solution.

Scalable provisioning of virtual optical network demands

Virtual optical network (VON) provisioning using implicit encoding of mapping constraints may include evaluating mapping choices to exclude certain mapping patterns before evaluating the mapping patterns. For each virtual node in a VON request, candidate physical nodes may be assigned and evaluated for compliance with constraints associated with the VON request. The constraints may be expanded to allow for various selection criteria for the VON request. Multiple VON requests may be simultaneously evaluated to find optimal solutions for the physical network.

Growing community of inventors

Cupertino, CA, United States of America

Indradeep Ghosh