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A Method to Optimize the Staffing Levels in a Ticketing Service System with Various Complexities of Service using Simulation

IP.com Disclosure Number: IPCOM000237870D
Publication Date: 2014-Jul-17
Document File: 5 page(s) / 100K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a simple and rapid method for optimizing staffing levels in a ticketing service system using simulation. The core idea is to develop lower bounds for the optimal solution.

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A Method to Optimize the Staffing Levels in a Ticketing Service System with Various Complexities of Service using Simulation

This article addresses search methods for optimal staffing solutions in a complex ticketing system. A faster and more efficient method is needed to the find the optimal number of resources to allocate to the ticketing system. The optimization criterion is the achievability of the Service Level Agreements (SLA) set by the client accounts.

There are no known solutions in the existing literature for the ticketing system.

The search method implemented in the currently used Simulation model goes through all resource allocation numbers and proceeds in increments of one (1) until all SLAs are met.

In contrast, the novel method presented here:

1. Starts the search with a stable solution based on a calculated lower bound 2. Uses system state information with multiple steps to quickly find an optimal solution

Similar problems discussed in the existing literature use other optimization methods.

The core idea of the novel method is to develop lower bounds for the optimal solution. Knowing that the optimal solution is at least as large as the lower bound reduces the search space and thus improves the performance of the search algorithm.

Disclosed is a simple and rapid method for optimizing staffing levels in a ticketing service system using simulation. A ticketing service system may be considered in which incoming tickets are classified based on service complexities. For each service complexity, a pool of resources with the proper skills to serve the tickets may be used. A resource from the pool of resources with skills in a given complexity class of service may also serve other classes of service with lower complexity. A queueing model may be used for allocating a queue for each service complexity class. The queueing model parameters correspond to that of a ticket arrival process; a ticket service time process and pool operations wherein the pool operations consist of dispatching policies and swinging resources between the various queues, shift schedules, and official breaks for the resources. If an arriving ticket of lower complexity finds no available resources having the skill to serve it and if there is an idle resource of higher complexity, then that resource serves the ticket.

An optimization mechanism may be used to determine the minimal number of pool resources required for each skill level and per shift schedule. Two types of tickets are considered: those that are associated with resolution time SLAs and those that are not. The minimum number of pool resources is determined to meet the offered load demand of all incoming tickets and achieve the resolution time SLAs target attainments associated with the former (i.e. associated with resolution time SLAs) type of tickets. Simulation may be used to obtain accurate estimates.

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Two pool operation mechanisms do not exist in previous literature:
 Reso...