About Client:

The client is a $71 Million Dollar Company based out of California US. It is pioneer in providing software solutions for many Fortune 500 and Global 2000 clients such as Electrolux, Coca Cola, P & G, Schneider Electric. Monocept was selected as a long-term partner to service the needs of client’s Fortune 500 and Global 2000 customers.

Project Summary:

The client’s on-demand Optimization Engine software adds the field service capabilities that are delivered in a familiar multi-tenant architecture to its customers. It is a leading provider of on-demand field service software across the world. Monocept accomplished the client’s mission:

  1. In delivering a ‘go-to’ application for every field service technicians worldwide.
  2. Through divide and conquer strategy, Monocept has developed a performance optimized application where the job run time has radically reduced from approximately 7.5 hours to less than 5 minutes.


  1. A key challenge of FSM is to optimize service routes to cover all the work orders, with minimum resources (in terms of no of resources, cost of travel etc.)
  2. The route optimization of the Travelling Salesmanship problem (TSP) has multiple time windows and multiple constraints which include:
  3. The real-life scenarios of field service are more complex which include traffic, travelling cost, break-time etc.
  4. The time taken by TSP algorithm to compute the route is complex in nature considering the number of technicians and work orders.
  5. The performance is a bigger challenge for rescheduling appointments for technicians in real time.
  6. To handle huge number of requests sent to Google APIs to arrive at an optimized and cost-effective routing algorithm for technicians.


  1. Implementing multi-tenant architecture that allows serving multiple clients with ease.
  2. Multiple Divide and Conquer strategies were derived which could be dynamically applied. Each strategy divides the larger sets into smaller subsets of work orders/ technicians and executes each subset independently.
  3. Implemented a separate server using Memcache (distributed memory object caching system) which greatly reduced the number of requests made to Google APIs which in turn enhanced the performance of the system.
  4. Implemented a single distribution package which made deployment easy on Amazon EC2 cloud. This cloud deployment allowed on demand load balancing.
  5. Using their existing Java based platforms, did a lot of fine tuning to increase their scalability issues.


  1. Achieved amazing performance to complete the optimization cycle in less than 5 Minutes, which used to take 7.5 hours.
  2. The new caching implementation ensured less than 25,000 requests sent to Google APIs for address resolution and distance matrix calculations.

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