关键词: 车辆路径问题, 干扰管理, 同时取送, 感知服务质量, NSGA-Ⅱ

Abstract: Since the onset of the COVID-19 pandemic, fresh e-commerce sector has witnessed exponential growth in order volumes and transaction values, accompanied by a marked increase in customer-initiated cancellations. Accelerated urban lifestyles have further intensified vulnerabilities in cold chain last-mile delivery, where disruptions-particularly time window changes and demand fluctuations-frequently invalidate pre-generated routing plans and may even compromise cold chain integrity. Such disruptions not only provoke customer dissatisfaction due to untimely service (triggering customer attrition), but also exacerbate product spoilage and potentially jeopardize consumer safety. Consequently, rescheduling vehicle routes of fresh products with simultaneous pickup and delivery in the change of time windows-while balancing competing operational constraints-represents a critical research challenge. This entails specifically: (1)accommodating revised time window requests without diminishing service expectations for other customers; (2)dynamically readjusting vehicle routes; and (3)rigorously preserving product freshness throughout the delivery process.
To address these challenges, this study integrates service quality perception theory from marketing with quantitative optimization methodologies from operations research within a disruption management framework. Firstly, an initial mathematical model is formulated by analyzing key distribution costs: spoilage costs, transportation costs, refrigeration costs, and penalty costs. Secondly, the paper quantifies the impacts of disruption events on customers, vehicles, and products, specifically measuring the psychological perception gap between expected and actual service experiences in last-mile delivery. Moreover, building upon both the original distribution objectives and associated deviation costs, it develops a disruption management model for vehicle routing problem of fresh products with simultaneous pickup and delivery in the change of time windows. Grounded in initial routing objectives, this model minimizes perceived service gaps while mitigating the negative effects of time window deviations. Finally, an improved non-dominated sorting genetic algorithm-II (INSGA-II) is demonstrated to solve the model. Diverging from NSGA-II’s stochastic initialization, INSGA-II strategically constructs the initial population via a saving algorithm to accelerate convergence. Furthermore, the incorporation of a queen-preserving order crossover (OX) operator effectively retainshigh-quality genetic traits from elite solutions, enhancing search efficiency. Concurrently, parent selection via population traversal maintains genetic diversity, facilitating robust global exploration.
Experimental results demonstrate that, compared to both executing the original plan and implementing the global rescheduling strategy, the proposed approach achieves comparable total costs while significantly minimizing customers’ perceived cost losses. This reduction in perceived service gaps is likely to positively influence subsequent purchasing decisions. Although the approach entails a marginal increase in routing costs, it substantially reduces spoilage costs and narrows the psychological discrepancy between expected and actual service experiences. Consequently, product freshness is better preserved, enhancing the potential for retaining latent customers. Additionally, in solving the vehicle routing problem of fresh products with simultaneous pickup and delivery and other complex optimization problems, the INSGA-II exhibits superior solution quality and greater Pareto solution diversity compared to the standard NSGA-II.
This study combines the concept of service quality with disruption management theory, translating theoretical concepts into practical methods through quantitative analysis in operations research. It also represents an interdisciplinary convergence of fields such as logistics management and marketing, not only enriching the theory and methodology of disruption management but also enabling creative practices of service quality. The proposed disruption management model for simultaneous pickup and delivery of fresh products under time window variations efficiently improves the proficiency of last-mile logistics distribution to handle the disruptions, thereby helping to improve customer satisfaction. It can provide decision support for cold chain last-mile logistics planning in fresh product e-commerce enterprises.

Key words: vehicle routing problem, disruption management, simultaneous pickup and delivery, perceived service quality, NSGA-II