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Using Jeppesen’s network optimization tool, Purolator optimized existing baseline routes and improved driver shifts and vehicle schedules.
was optimized. Also, any error in the baseline data would more than likely have meant the solutions PlanOp obtained would have been useless because the vehicle tours could not have been run in real life. The challenging job of accurately representing current operations gave a solid foundation and enabled the team to confidently enter the optimization phase of the project. Purolator optimized their existing base- line routes and obtained new and improved driver shifts and vehicle schedules. The size of the problem space was large with over 700 shipments to be carried and over 500 loca- tions in the model. This, coupled with many other constraints on the network such as vari- able load and unload rules at locations and peak-hour travel time overrides, made the optimization problem very complex. Two of PlanOp’s optimization engines were used on the model. The Vehicle Routing Optimizer was used initially to create new lower-cost vehicle tours using its Metaheuristic opti- mization engine. The Metaheuristic engine proved very effective at eliminating inefficient vehicle tours in the model while minimiz- ing vehicle kilometers at the same time. The Vehicle Crew Schedule Optimizer was then used to create efficient driver shifts from the vehicle routes and minimize the number of vehicles necessary to run all the tours. At the end of the optimization process an efficient set of vehicle and crew schedules were ready to be tested in the real world. Purolator recognized that a strong net- work plan would not be enough to meet its goals and that carefully managed stakeholder engagement and change management needed to occur. The Network Engineering team fre- quently met with their Operations colleagues receiving feedback on the scenarios to model, measure and compare. Operations were initially skeptical of the new plan and concerned about the accuracy of the new vehicle routes, since the Network Engineering team had used PlanOp to do a clean slate route overhaul. Montreal Operations went out on-road and tested almost 80% of the new routes and all of them
passed with 100% accuracy. PlanOp had the detail to answer all questions with facts and allay any concerns. “This is an amazing case of theory meet- ing reality. Our team has earned total buy-in from our Operations colleagues and from other parts of the organization on our ideas to improve efficiency. PlanOp provides an unbiased analytical set of figures as our orga-
continuous shift with the labour force, achieve a steady package flow, and fully leverage the investment in the automated hub, which has fulfilled project goals. Purolator will achieve annual savings in excess of C$800,000/US$733,000 attrib- uted to the inbound network. It was also able to reduce its power equipment needs as PlanOp allowed Purolator to model situations where a tractor can swap trailers. Purolator’s product shipments are either in the form of an actual trailer with a partial or full load on or just the portion of product to be carried in skids or pieces. Through the use of a sophis- ticated cost function it is possible to model both these types of shipments in PlanOp. The Vehicle Routing Optimizer capitalized on this flexible modeling structure and was able to intelligently place trailer exchanges in tours to minimize non-productive time and distance during the trailer swaps. Purolator was able to remove power equipment assets from the oper- ation, redeploying to other operational areas. Jeppesen consultant Graham Prickett explains: “The Purolator Montreal project is an excellent example of the paradigm, ‘model conservatively but optimize aggressively’. Richard Weiner and his team modeled the Montreal network with its implementation in mind and were able to achieve accurate routes that are able to be run in real life”. Richard Weiner concludes, “We are immensely proud of our achievement with the recent optimization and remodeling of the Montreal inbound network. All of our project goals have been achieved or exceeded. Acceptance from our employees was attained, and the operation is running more effec- tively than ever, and we have reduced costs by over $800,000 while maintaining a qual- ity service.” For further information, please contact Richard Weiner at Purolator (rweiner@purolator.com).
nization considers the implications of different scenarios as opposed to intuition and gut feel- ing about what’s better”. Following the vehicle route testing, Operations accepted the new plan. The plan was then reviewed with the employee groups to gain support. Results were then shared with directors and senior management. RESULTS The scenario selected has changed the inward hub sorting profile of the Montreal hub operations. The new plan ensures that enough volume reaches the hub before 8pm for better productivity and capacity utiliza- tion. For many years there was a 30 minute break from 7.30pm–8pm. The break was not conducive to the new automated hub due to start up and shut down times. Filling the 30 minute void has helped Purolator work out a Purolator handles 275 million packages annually, serving 210 countries worldwide.
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