An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem
| Autor: | Jingling Zhang, Chunmiao Zhang, Yanwei Zhao, Longlong Leng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Computer science Heuristic (computer science) Health Toxicology and Mutagenesis 0211 other engineering and technologies Evolutionary algorithm lcsh:Medicine 02 engineering and technology Multi-objective optimization Article Set (abstract data type) fuel consumption 0202 electrical engineering electronic engineering information engineering Humans multiobjective optimization Integer programming Carbon Footprint Air Pollutants Models Statistical 021103 operations research lcsh:R Public Health Environmental and Occupational Health Pareto principle Environmental Exposure Sustainable Development carbon emission 020201 artificial intelligence & image processing Performance indicator regional low-carbon location-routing problem Heuristics Algorithms multiobjective hyperheuristics |
| Zdroj: | International Journal of Environmental Research and Public Health Volume 16 Issue 11 International Journal of Environmental Research and Public Health, Vol 16, Iss 11, p 2064 (2019) |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph16112064 |
| Popis: | In this paper, we consider a variant of the location-routing problem (LRP), namely the the multiobjective regional low-carbon LRP (MORLCLRP). The MORLCLRP seeks to minimize service duration, client waiting time, and total costs, which includes carbon emission costs and total depot, vehicle, and travelling costs with respect to fuel consumption, and considers three practical constraints: simultaneous pickup and delivery, heterogeneous fleet, and hard time windows. We formulated a multiobjective mixed integer programming formulations for the problem under study. Due to the complexity of the proposed problem, a general framework, named the multiobjective hyper-heuristic approach (MOHH), was applied for obtaining Pareto-optimal solutions. Aiming at improving the performance of the proposed approach, four selection strategies and three acceptance criteria were developed as the high-level heuristic (HLH), and three multiobjective evolutionary algorithms (MOEAs) were designed as the low-level heuristics (LLHs). The performance of the proposed approach was tested for a set of different instances and comparative analyses were also conducted against eight domain-tailored MOEAs. The results showed that the proposed algorithm produced a high-quality Pareto set for most instances. Additionally, extensive analyses were also carried out to empirically assess the effects of domain-specific parameters (i.e., fleet composition, client and depot distribution, and zones area) on key performance indicators (i.e., hypervolume, inverted generated distance, and ratio of nondominated individuals). Several management insights are provided by analyzing the Pareto solutions. |
| Databáze: | OpenAIRE |
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