Dynamic Route Optimization and Automation of Industrial Routes at WM
Hemachandra Pillutla et al.
What the paper says
WM, the leading provider of environmental and sustainability solutions, faced significant challenges in optimizing its industrial waste collection routes following the coronavirus disease 2019 pandemic. Traditionally, these routes were planned manually, a process that was time consuming, labor intensive, and suboptimal with multiple factors that needed to be considered. WM embarked on a journey to develop and implement a dynamic route optimization program tailored to the unique requirements of its industrial waste collection operations. The industrial waste collection sector presents a complex routing problem because of the dynamic nature of customer service demand and several factors, such as customer-specific service requirements, different container types and sizes, and the disposal of waste materials. The dynamic route optimization system leverages data analytics, forecasting future service demand for effective capacity planning, and advanced algorithms using metaheuristics to automate the generation of routes that improve efficiency while ensuring safety and fulfilling customer service commitments. By effectively combining advanced analytics, optimization, and technology-led automation for the industrial line of business, WM realized best-ever efficiency gains and operating margins, and it set the foundations for driving increased value in the future.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.