Online clustering reduction based on parametric and non-parametric correlation for a many-objective vehicle routing problem with demand responsive transport

Mendes, Renan Santos; Lush, Victoria; Wanner, Elizabeth F.; Martins, Flávio Vinícius Cruzeiro; Sarubbi, Joao F. M.; Deb, Kalyanmoy

doi:10.1016/j.eswa.2020.114467

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…In MO-VRPs, Pareto-dominance is often used in conjunction with an evolutionary algorithm (EA) framework, such as NSGA-II (37), MOEA/D (38), and SPEA2 (39). Most EAs in both research and application areas follow a similar framework, using a search operator based on Pareto domination and an iterative reproduction operator (40).…”

Section: Multi-objective Optimization Methodsmentioning

confidence: 99%

Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

Lin,

Musa,

Yap

2023

Transportation Research Record: Journal of the Transportation R

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This paper addresses the complex issue of managing medical waste transportation using electric vehicles, with the goal of minimizing both energy consumption and the risks associated with hazardous waste. A multi-objective mixed-integer linear programming model is introduced, incorporating practical factors such as time windows, partial recharge policy, load-dependent discharge, infection risk, and trips to waste disposal facilities. Our proposed method, a combination of the multi-objective evolutionary algorithm using decomposition (MOEA/D) with adaptive large neighborhood search (ALNS) and local search (LS) techniques, is referred to as MOEA/D-ALNS. This method demonstrates superior performance compared with the non-dominated sorting genetic algorithm, NSGA-II, modified MOEA/D and MOEA/D-LNS in benchmark instances with realistic assumptions. Our experimental results revealed an inverse correlation between energy consumption and risk objectives. Sensitivity analyses showed that eliminating time-window constraints results in more energy-efficient and safer routes while maintaining a slightly lower battery energy level can strike an ideal balance between energy consumption, risk, and battery health. This research contributes to the understanding of infectious medical waste management with its consideration of electric vehicles and waste disposal. It lays a solid foundation for future studies aiming to improve the sustainability and efficiency of medical waste routing practices.

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Section: Multi-objective Optimization Methodsmentioning

confidence: 99%

Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

Lin,

Musa,

Yap

2023

Transportation Research Record: Journal of the Transportation R

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“…PCC 26,27 is one of the linear correlation coefficients in statistics and can be used to display the degree of linear correlation between two variables. Unlike Spearman's rank coefficient of correlation 28,29 and the Kendall tau rank correlation coefficient, 30,31 PCCs are obtained based on calculating the variance and covariance of the data, which are more sensitive to outliers in the data and better reflect the linear relationship between the variables. In this paper, we use the PCCs to measure the correlation between SH and diversity.…”

Section: Pearson Correlation Coefficient (Pccs)mentioning

confidence: 99%

Investigating the impact of structural holes on the value creation in mobile application service ecosystems: Evidence from computational experiments

Zhang,

Chen,

Xue

et al. 2023

J Software Evolu Process

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Mobile application service ecosystems (MASEs) are highly complex systems that involve multiple factors influencing value creation. However, current research on the impact of structural holes (SHs) as an organizational characteristic on value creation in MASE is still insufficient. To address this research gap, this paper proposes a value creation model for MASE and investigates the impact of SH on the value creation of MASE. Moreover, this paper investigates the correlation between SH and diversity, which can facilitate regulating the value creation process in MASE. Finally, we construct a computational experiment, comparing and analyzing how changes in SH affect the value creation of MASE and the correlation between SH and diversity. The findings of this study can be used to induce the evolution of MASE and promote its value maximization.

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“…Wang and Lin [37] developed a scenario-based heuristic algorithm in which an index of "intimacy degree" is defined for grouping customers into different clusters. Mendes et al [38] used a Pearson's and τ-Kendall hierarchical cluster approach to reduce the dimensionality of the multi-objective vehicle routing problem solved by the MOEA/D evolutionary approach. Oezdamar and Demir [39] described a hierarchical clustering and heuristic routing procedure to coordinate vehicle routing in large-scale disaster distribution planning.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Stage Solution for Meal Delivery Routing Optimization on Time-Sensitive Customer Satisfaction

Wang

Jiang

2022

Journal of Advanced Transportation

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The online-to-offline (O2O) meal delivery mode in which takeout meals are ordered online and delivered offline is recently emerging. The fast delivery of huge meal orders for time-sensitive customers imposes great challenges on O2O meal delivery platforms. This study establishes a two-stage solution for meal delivery routing optimization with the objective of maximizing time-sensitive customer satisfaction. In the first stage, a large number of meal orders are hierarchically classified and merged into delivery bundles based on the nearest pickup location rule by applying the hierarchical agglomerative clustering (HAC) algorithm, to increase fast meal delivery efficiency. In the second stage, a genetic algorithm (GA) is applied to solve the cluster-based delivery routing optimization model to find an optimal delivery route for meal orders in each delivery bundle. The numerical simulation results verify that the two-stage routing optimization solution is effective to schedule timely meal delivery and improve customer time satisfaction. The comparison of the results indicates the superiority of the proposed two-stage solution with HAC and GA on customer satisfaction while ensuring the delivery of all orders within 60 minutes. The sensitivity analysis shows the impact of time-sensitive customer heterogeneity on meal delivery satisfaction. This research has significant managerial insights for fast delivery services of O2O meal delivery platforms.

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Online clustering reduction based on parametric and non-parametric correlation for a many-objective vehicle routing problem with demand responsive transport

Cited by 10 publications

References 26 publications

Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

Investigating the impact of structural holes on the value creation in mobile application service ecosystems: Evidence from computational experiments

Two-Stage Solution for Meal Delivery Routing Optimization on Time-Sensitive Customer Satisfaction

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