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Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports
M Schilde1, K F Doerner, R F Hartl
1University of Vienna, Department of Business Administration, Bruenner Strasse 72, 1210 Vienna, Austria.
Computers & Operations Research
|April 2, 2013
Summary
This study on patient transportation routing for the Austrian Red Cross found that incorporating stochastic information on return trips significantly improves route efficiency. Utilizing this data led to an average 15% improvement in solution quality for dynamic dial-a-ride problems.
Area of Science:
- Operations Research
- Transportation Logistics
- Healthcare Management
Background:
- Patient and elderly transportation is often modeled as a Dial-a-Ride Problem (DARP).
- The Austrian Red Cross faces challenges in optimizing routes for partially dynamic and stochastic patient transport requests.
- Existing DARP models often do not fully account for the uncertainty of return trips.
Purpose of the Study:
- To investigate the impact of using stochastic information on return transports in route design for the Austrian Red Cross.
- To model and solve a dynamic stochastic dial-a-ride problem with expected return transports.
- To evaluate the effectiveness of modified metaheuristic approaches for this specific problem.
Main Methods:
- Developed a dynamic stochastic dial-a-ride problem model incorporating probabilistic return transports.
- Proposed four modified metaheuristic solution approaches: dynamic Variable Neighborhood Search (VNS), stochastic VNS (S-VNS), Multiple Plan Approach (MPA), and Multiple Scenario Approach (MSA).
- Tested the approaches using real road network data and demand scenarios based on historical data.
Main Results:
- Incorporating stochastic information on return transports yielded average improvements of approximately 15% in solution quality.
- Specific test instances demonstrated significant improvements, reaching up to 41%.
- The proposed metaheuristic modifications effectively addressed the dynamic and stochastic nature of the transport requests.
Conclusions:
- Utilizing historical data to predict stochastic return transports is crucial for optimizing patient transportation logistics.
- The developed dynamic stochastic DARP model and metaheuristic solutions offer practical benefits for organizations like the Austrian Red Cross.
- This research highlights the potential for substantial efficiency gains in healthcare transportation through advanced routing strategies.
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