Solving the multi‐objective bike routing problem by meta‐heuristic algorithms

Nunes, Pedro; Moura, Ana; Santos, José

doi:10.1111/itor.13114

Cited by 7 publications

(4 citation statements)

References 40 publications

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“…Considering the multi‐objective optimization characteristic of the addressed parallel line balancing problem with mixed disassembly and assembly tasks, a multi‐objective optimization processing method based on the idea of Pareto optimal solution (Hacardiaux et al., 2022, Nunes et al., 2023) is introduced in the proposed multi‐objective HESA. Here, the concept of Pareto optimal solution is first illustrated by taking a multi‐objective minimization problem with m optimization objectives as an example.…”

Section: Multi‐objective Hesamentioning

confidence: 99%

A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

Zhang

Li-xia

Chen

et al. 2022

Int Trans Operational Res

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This paper proposes a multi‐objective hybrid evolutionary search algorithm to simultaneously optimize the number of workstations, the idle index and the quantity of the production equipment required for the parallel production line balancing problem including disassembly and assembly tasks. The proposed algorithm uses the crossover operation and the mutation operation to generate neighborhood individuals of the current evolutionary population. Based on the mixed population composed of the current evolutionary population and the newly generated neighborhood population, a novel population update method is developed. The individuals of the new generation population are composed of Pareto dominant individuals filtered from the mixed population and several individuals randomly selected from the dominated individuals. Then the local search strategy based on simulated annealing operation is applied to the new generation of population individuals to overcome the defect of premature convergence of the proposed algorithm. By solving test examples of different scales and comparing them with a variety of classical algorithms, the advantages in the aspects of the diversity, the convergence, and the distribution of the solution results of the proposed algorithm are proved. Finally, the established mathematical model and the developed algorithm are applied to the parallel production line designed for the bogie remanufacturing production line of a railway freight car maintenance company. The advantages of the parallel production line in improving the production efficiency and reducing the production cost are identified.

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Section: Multi‐objective Hesamentioning

confidence: 99%

A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

Zhang

Li-xia

Chen

et al. 2022

Int Trans Operational Res

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“…There are three solution methods to solve multiobjective problems: a priori, interactive, and a posteriori. In the a priori approach, the decision maker provides preferences for objective function; in interactive approaches, the preferences are made during the search process; while in a posteriori approaches, a set of potential nondominated solutions is generated, and the decision makers have the flexibility to select the most desired solution (Nunes et al, 2023). Evolutionary multiobjective optimization has been shown to be an efficient a posteriori method to preserve a balance between convergence and diversity in the provided solutions (Zhang and Li, 2007).…”

Section: Moea/d Algorithmmentioning

confidence: 99%

Learn to decompose multiobjective optimization models for large‐scale networks

Aslani

Mohebbi

2022

Int Trans Operational Res

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Infrastructures can be modeled as large‐scale networks consisting of nodes and arcs, making network optimization a popular modeling option for arising problems. In specific, providing timely restoration plans for interdependent infrastructures facing disruptions has been a challenge for decision makers. In this study, we focus on geospatial (co‐location) and functional interdependencies to capture the impact of cascading failures on infrastructure systems. The dynamics of real networks are more complicated to be captured by one objective function. Therefore, we define three objective functions in three pillars of sustainability: (a) economic, (b) social, and (c) environmental. To solve the multiobjective optimization model, we develop a learn‐to‐decompose framework, consisting of a multiobjective evolutionary algorithm based on decomposition module and a Gaussian process regression (GPR) module to periodically learn from the obtained Pareto front and guide the search direction. We also included a heuristic module to address two significant challenges in restoring interdependent infrastructures: the island scenario and co‐location interdependencies. We applied the proposed framework to benchmark problems and interdependent water and transportation networks in the City of Tampa, FL. We carried out sensitivity analyses to monitor the performance of the GPR by different kernel functions. We also provided insights for decision makers by finding the trade‐off between fortification (proactive) and restoration (reactive) costs. The result demonstrates the proposed framework is feasible and applicable for large‐scale networks.

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“…Many applications in real life often contain multiple objectives to be optimized simultaneously such as in the fields of intelligent manufacture systems, environment energy systems, financial and management science, and so forth, which are termed multiobjective optimization problems (MOPs) (Liu et al, 2022; Ma et al, 2023; Morteza et al, 2023; Nunes et al, 2023; Yazdani et al, 2023). For example, the portfolio optimization problem is to minimize the risk and maximize the return (Morteza et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…For example, the portfolio optimization problem is to minimize the risk and maximize the return (Morteza et al, 2023). In the routing planning problem, along with the goal of shortest route distance, it is also vital to consider cost, safety, and risk (Nunes et al, 2023). Frequently, multiple objectives in MOPs are contradictory to each other.…”

Section: Introductionmentioning

confidence: 99%

An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems

Wang

Liu

et al. 2023

Expert Systems

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The information transfer mechanism within the population is an essential factor for population‐based multiobjective optimization algorithms. An efficient leader selection strategy can effectively help the population to approach the true Pareto front. However, traditional population‐based multiobjective optimization algorithms are restricted to a single global leader and cannot transfer information efficiently. To overcome those limitations, in this paper, a multiobjective bacterial colony optimization with dynamic multi‐leader co‐evolution (MBCO/DML) is proposed, and a novel information transfer mechanism is developed within the group for adaptive evolution. Specifically, to enhance convergence and diversity, a multi‐leaders learning mechanism is designed based on a dynamically evolving elite archive via direction‐based hierarchical clustering. Finally, adaptive bacterial elimination is proposed to enable bacteria to escape from the local Pareto front according to convergence status. The results of numerical experiments show the superiority of the proposed algorithm in comparison with related population‐based multiobjective optimization algorithms on 24 frequently used benchmarks. This paper demonstrates the effectiveness of our dynamic leader selection in information transfer for improving both convergence and diversity to solve multiobjective optimization problems, which plays a significant role in information transfer of population evolution. Furthermore, we confirm the validity of the co‐evolution framework to the bacterial‐based optimization algorithm, greatly enhancing the searching capability for bacterial colony.

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Solving the multi‐objective bike routing problem by meta‐heuristic algorithms

Cited by 7 publications

References 40 publications

A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

Learn to decompose multiobjective optimization models for large‐scale networks

An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems

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