An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem

Area of Science:

• Operations Research
• Logistics Management
• Environmental Sustainability

Background:

• The location-routing problem (LRP) is a complex combinatorial optimization challenge.
• Minimizing environmental impact and operational costs in logistics is increasingly critical.
• Existing LRP models often do not sufficiently address multiobjective, low-carbon, and real-world constraints.

Purpose of the Study:

• To address the multiobjective regional low-carbon LRP (MORLCLRP), balancing service duration, client waiting time, and total costs including carbon emissions.
• To develop and evaluate a novel multiobjective hyper-heuristic approach (MOHH) for solving the MORLCLRP.
• To analyze the impact of various domain-specific parameters on the performance of the proposed solution.

Main Methods:

• Formulation of a multiobjective mixed integer programming model for the MORLCLRP.
• Development of a general framework, the multiobjective hyper-heuristic approach (MOHH), integrating high-level heuristics (selection strategies, acceptance criteria) and low-level heuristics (multiobjective evolutionary algorithms).
• Extensive computational testing and comparative analysis against eight domain-tailored MOEAs.

Main Results:

• The proposed MOHH approach generated high-quality Pareto sets for most tested instances.
• Comparative analyses demonstrated the effectiveness of the MOHH against specialized algorithms.
• Empirical assessments revealed the influence of fleet composition, client/depot distribution, and zone area on key performance indicators.

Conclusions:

• The MOHH provides an effective framework for solving the complex MORLCLRP.
• The study offers valuable management insights into optimizing low-carbon logistics operations.
• The research highlights the importance of considering multiple objectives and practical constraints in sustainable routing problems.