Service Request Routing via Optimization Model
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Solution Overview
Problem
In enterprises and service centers, heterogeneous service requests create difficulties in allocating tasks due to varying processing times and workloads, leading to significant variance in request loads across servers.
Innovation Solution
A method and system that use system parameters to generate an optimization model, solving for mixing weights to distribute service requests across server teams, minimizing variance in workloads by allocating requests based on throughput capabilities of each team.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If service requests are distributed to server teams without optimization, then the distribution process is simple, but the workload variance across teams increases significantly
Solution Approach 1:
The patent transforms the request distribution problem into a mathematical optimization problem by changing parameters (throughput capabilities, request characteristics, mixing weights) and solving for optimal parameter combinations that minimize workload variance while respecting team capacities
Solution Approach 2:
The patent introduces an intermediary optimization model that acts as a mediator between raw service requests and server teams. This model calculates optimal mixing weights and portfolios, transforming the direct distribution into an optimized indirect distribution that reduces variance
2Ease of operation
If heterogeneous requests are allocated without considering throughput, then allocation is straightforward, but processing time increases due to mismatched workloads
Solution Approach 1:
The patent incorporates throughput parameters (nij) into the optimization model, transforming the allocation process from simple to optimized. The model uses these parameters to calculate mixing weights that match request types with teams having appropriate processing speeds, reducing overall processing time
Solution Approach 2:
The patent performs preliminary optimization calculations before actual request distribution. By pre-calculating optimal portfolios and mixing weights based on throughput capabilities, the system prepares the best allocation strategy in advance, minimizing processing time during actual execution
3Ease of manufacture
If requests are distributed uniformly across teams, then distribution is easy to implement, but throughput utilization becomes inefficient
Solution Approach 1:
The patent applies local quality by allowing different teams to receive different proportions of request types based on their local throughput capabilities. Instead of uniform distribution, each team gets a customized mix optimized for its specific processing strengths, improving overall throughput utilization
Data Source
AI summary
A method and system are disclosed for creating portfolios of requests so as to reduce or minimize the variance in the workloads generated by those requests. The invention also takes into account the throughput of the servers that are servicing the requests. In a preferred embodiment, the method comprises the steps of establishing a set of system parameters; using said parameters to generate a model, said model including a defined optimization problem; and solving said optimization problem to output a set of mixing weights. The set of parameters and said mixing weights are used to generate a task; and the service requests are distributed to different service teams according to the generated task. Also, for example, the distributing may be done by distributing requests of type i to team j with weight proportional to wij.