Time-dependent fuzzy random location-scheduling programming for hazardous materials transportation

• We focus on location-scheduling programming for hazardous materials transportation.
• Fuzzy-randomness is incorporated into the expected value location-scheduling models.
• Practical factors such as time-dependent risks and road conditions are considered.
• Greedy method based adaptive hybrid particle swarm optimization algorithm is used.
• Numerical cases show the feasibility and optimality of the models and algorithms.

The tremendous use of hazardous materials has promoted the economic development, which also brings about a growing risk causing a widespread concern. In this work, we consider a location-scheduling problem on hazardous materials transportation under the assumption that transportation risks are time-dependent fuzzy random variables. First, we formulate a scheduling optimization model and design a fuzzy random simulation based genetic algorithm to optimize the departure time and dwell times for each depot–customer pair. Then we establish an expected value model and design a modified particle swarm optimization algorithm to minimize the en route risks and site risks. Finally, numerical examples are given to illustrate the effectiveness of the proposed models and algorithms.