A Hybrid Heuristic Method for the Compressed Natural Gas (CNG) Truck Routing Problem with Fueling Stations. I-NUF 2015, Long Beach

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Kristina Ruth Floyd
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1 A Hybrid Heuristic Method for the Compressed Natural Gas (CNG) Truck Routing Problem with Fueling Stations Author: Advisor: Yihuan (Ethan) Shao Maged Dessouky

2 Outline Introduction Literature review Problem Statement and Model Computational Results Conclusion 1

3 Introduction Total U.S. Greenhouse Gas Emissions by Economic Sector in 2013 Agriculture 9% Commercial & Residential 12% Electricity 31% Industry 21% Transportation 27% Data from Inventory of U.S. Greenhouse Gas Emissions and Sinks:

4 Introduction (cont.) Alternative Fuels Biodiesel Hydrogen Fuel Compressed Natural Gas Liquefied Natural Gas Pros Low emissions Cheap fuel price Cons High fixed cost Small tank capacity Sparse fueling stations 3

5 Literature review Papers on the vehicle routing problems (VRP) usually assume fuel is adequate for covering the whole tour. However, for alternative fuel vehicles this assumption may not hold. Few papers add the fueling stations into their models. Erdogan and Miller-Hooks (2012) Two construction heuristics and a local search improvement approach are presented to solve the VRP with fueling stations. Schneider, Stenger and Goeke (2014) The recharging problem for electric vehicles is solved by a Variables Neighborhood Search approach with Tabu Search. Hiermann and Puchinger (2014) Based on an Adaptive Learge Neighborhood Search and labeling procedure, a hybrid heuristic is proposed to solve the problem. 4

fleet Gas tank capacity limitation Daily travel distance limitation

6 Problem Statement and Model C4 C1 D C2 C2 Model Assumption Tour1 Minimize fleet size All customers must be served Tour2 Homogenous fleet Gas tank capacity limitation Daily travel distance limitation Load capacity constraints D -> C1 -> C2 -> D D -> C3 -> C5 -> CNG -> C4 -> D C5 5

7 Problem Statement and Model Step 1. Construct an initial solution Step 2. Improve the initial solution Adaptive Large Neighborhood Search Fleet size minimization Fueling station optimization 6

8 Step 1. Construct an initial solution Check if current customer can be inserted into the fleet Yes/No Yes No Insert current customer into the truck with smallest additional traveling distance Check if the truck route violates the gas tank capacity Yes/No No Yes Check if all of the customers are inserted into the fleet Insert an empty truck Insert a fueling station into previous node No Yes/No Yes Output an initial solution 7

9 Step 2. Improve current solution Adaptive Large Neighborhood Search Various kinds of local searches Destroy and repair operators Score function for adaption 8

10 Step 2. Improve current solution (cont.) Fleet size minimization Get current fleet size k to see if k is bigger than minimal fleet size m Yes/No Yes Pick up (k - m) trucks with smallest numbers of served customers, move their customers to the other trucks if feasible and delete those trucks without customers No Stop, the fleet size is minimized 9

11 Step 2. Improve current solution (cont.) Fueling station optimization Given a feasible route, we use a mixed-integer programming problem to find the optimal fueling plan. x i binary decision variable indicates if the truck goes to a fueling station after visiting customer i f i variable calculates the cumulative traveling distance after visiting previous fueling station α i distance between two adjacent customer nodes β i distance from previous fueling station to the next customer node γ i direct detour for visiting the fueling station c tank capacity 10

12 Problem Statement and Model Step 1. Construct an initial solution Step 2. Improve the initial solution Adaptive Large Neighborhood Search Fleet size minimization Fueling station optimization 11

13 Computational Results Benchmark cases Name Opt Sol Best Sol Gap A-n32-k % A-n33-k % A-n33-k % A-n34-k % A-n36-k % A-n37-k % A-n37-k % A-n38-k % A-n39-k % A-n44-k % 12

14 Computational Results (cont.) Test on different number of fueling stations # Stations Avg. Distance 3 3, , , , ,623.0 Benchmark 3,305.0 # Stations Avg. Detour Avg. Total Avg. Traveling Detour Distance 3, , , , , ,

15 Computational Results (cont.) Test on different tank capacity Tank Cap. Avg. Distance 150 3, , , , ,429.4 Benchmark 3,305.0 Tank Cap. Avg. Detour 3, , , , Tank Cap. Avg. Fueling Times Avg. Total Avg. Traveling Detour Distance , ,

16 Conclusion The benchmark cases show our algorithm can efficiently solve the classical VRP. Tank capacity is a very important factor in the CNG-VRP. Especially when the fueling stations are sparsely distributed. Our hybrid heuristic method can effectively solve this new VRP via minimizing the impact of the tank capacity constraint. 15

17 Thank you! Q&A

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