Adaptive Snow Plow Routing Using Genetic Algorithm Artificial Intelligence
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1 Adaptive Snow Plow Routing Using Genetic Algorithm Artificial Intelligence James Zollweg, Ph.D. Associate Professor, The College at Brockport Survivor of countless snowy winters in the vicinity of the three snowiest major cities in the USA
2 Learning Outcomes Broaden understanding of complexities and considerations involved in routing snowplows Recognize the potential of advanced computing technologies to reduce costs and improve service Consider the possibility of routings that vary from event-to-event depending on conditions (i.e. adaptive)
3 Snowplow Routing: the obvious Snow removal is a $3 Billion yearly expense in the USA, and is among the largest expenditures for many municipalities at a time when budgets are strained more than ever. Calculating optimum (or even near optimum) routes for a fleet of snow removal vehicles is extraordinarily difficult
4 Snowplow Routing: complications Unlike other vehicle routing problems, every street must be covered, in both directions Street segments have varying priorities cul-de-sac, local, collector, arterial Snowplows are especially difficult vehicles to operate U-turns and left turns are disadvantageous Differences in intensity and duration of snowfall create vastly differing needs for snow removal Road networks are complicated and illogical
5 Snowplow Routing: characteristics of great solutions Minimizes total cost (directly related to miles driven) Reduce deadheading Finishes job in acceptable length of time Balance workload among operators Prioritize more important roads Time of day? Minimizes number of trucks used Tradeoff between efficiency and completion time Avoid U-turns and left turns Closely linked to a GIS for effective I/O
6 Solutions to snowplow routing? The vehicle routing problem (VRP) is a long-studied topic with many variations and approaches Traveling salesman problem (Rural) Chinese postman problem Capacitated VRP (road salt problem) VRP with Pickup and Delivery Much more difficult is the multiple vehicle routing problem (mvrp)
7 mvrp requires allocation Need to divide the entire municipality into service areas, each handled by one plow How to do this? Can the workload be balanced among operators? Since the process of allocation reduces the solution space, does it limit the efficiency of the final solution?
8 mvrp requires allocation Need to divide the entire municipality into service areas, each handled by one plow How to do this? Can the workload be balanced among operators? Since the process of allocation reduces the solution space, does it limit the efficiency of the final solution?!
9 What to do? Develop a method by which allocation and routing progress (evolve) simultaneously A Genetic Algorithm
10 Genetic Algorithm Possible solutions are encoded as if they were chromosomes
11 Crossover, Mutation
12 Genetic Algorithms applied An initial population of candidate solutions is created In case of snow plow routing, plows are allowed random travel around town until all roads are plowed Don t need to be good, just complete A fitness test is applied to each solution The best solutions are bred to generate the next generation of solutions REPEAT
13 Empirical evidence Numerous tiny-towns were divided up into service areas in a variety of ways then each service area was routed Also, the genetic algorithm solution was applied to each tiny-town In each case, the genetic algorithm solution produced a better result
14 Results - Town of Parma
15 Feedstock - OpenStreetMap
16 V. of Brockport GIS roads data set
17 Plow Route #4
18 SHOW ANIMATION
19 How this might be used Run the GA SPR at the start of every event to optimize the number of plows and their routing Snowfall rate Expected storm duration Time of day Generate turn-by-turn GPS instructions for operators
20 Learning Outcomes Broaden understanding of complexities and considerations involved in routing snowplows Recognize the potential of advanced computing technologies to reduce costs and improve service Consider the possibility of routings that vary from event-to-event depending on conditions (i.e. adaptive)
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