Demonstrating the interplay of machine learning and optimization methods for operational planning decision

Achamu, Gedefaye; Berhan, Eshetie; Geremaw, Sisay

doi:10.1007/s42488-021-00060-4

Cited by 1 publication

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For future work, we plan to integrate aspects such as inventory or location into MDVRP modelling, for hazmat transportation. We will consider more realistic factors, e.g., evacuations (Yang, Liu and Yang 2020) during the process of hazmat transportation, more efficient algorithm (Vidal et al 2012;Zhang, Liu, and Lim 2015;Achamu, Berhan and Geremaw 2021), and analyse hazmat transportation risk using flexible mathematical programming models such as credibilistic programming (Li 2013;Tian and Guo 2021) or stochastic programming (Long, Szeto, and Ding 2019;Ma et al 2021). As the number of attributes that require addressing increases, it would be interesting to investigate how attribute evaluation techniques (Li et al 2018) may be utilised to prioritise the sequence in which different attributes are considered, to raise the efficiency of the overall modelling and solution process.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Multi-depot vehicle routing programming for hazmat transportation with weight variation risk

et al. 2023

Transportmetrica B: Transport Dynamics

View full text Add to dashboard Cite

Reasonable transportation risk models are conducive to achieving the green reform of hazardous material logistics industry. However, existing multi-depot vehicle routing programming models for hazardous material transportation may result in overemphasis on either global risk or local risk. To overcome such shortcomings, we develop two novel two-stage programming models that consider weight variety in risk measures. The ordered weighted averaging risk-based model effectively reduces both the global risk and the maximum local risk with respect to the weight distribution in the aggregation process of local risks, and the state variable weight risk-based model helps reduce the global risk and the maximum local risk based on the variable weights associated with local risk values. Furthermore, we design a constraint reduction mechanism and a variable neighborhood search-based hybrid parallel genetic algorithm to handle the proposed models, such that they could rapidly reach the near-optimal solution using multiplication processors. Experimental investigations demonstrate that the proposed models achieve a good balance between overall risk and local risk, and proposed algorithm can obtain a satisfactory approximate solution within an acceptable time frame. KEYWORDSMulti-depot vehicle routing; hazardous materials transportation; hybrid parallel genetic algorithm; ordered weighted averaging; state variable weight.

show abstract

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Multi-depot vehicle routing programming for hazmat transportation with weight variation risk

et al. 2023

Transportmetrica B: Transport Dynamics

View full text Add to dashboard Cite

show abstract

Demonstrating the interplay of machine learning and optimization methods for operational planning decision

Cited by 1 publication

References 30 publications

Multi-depot vehicle routing programming for hazmat transportation with weight variation risk

Multi-depot vehicle routing programming for hazmat transportation with weight variation risk

Contact Info

Product

Resources

About