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Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

This study aims to analyze the importance and performance of customer-facing technologies in luxury hotels. The study also assessed differences between and within the four generations in the importance-performance analysis (IPA).

Data were collected using a Qualtrics panel of recent luxury hotel customers in the USA belonging to all four generations. The cross-generational IPA was conducted using t-tests and (ANAOVA).

The IPA matrix concentrated most technology items in either low importance – low performance or high importance – high performance quadrants. One-way ANOVA revealed significant differences between generations on the importance ratings of all technology items except wireless charging power solutions and on the performance ratings of all technology items. Furthermore, post hoc tests indicated that millennials rated luxury technology most favorably among the four cohorts, followed by generations Z, X and Baby Boomers. In addition, significant differences between the importance and performance of many technology items within each generational cohort were observed. Overall, Wi-Fi was unanimously ranked across generations as the most important technology among luxury guests, but it was the only one that scored lower in performance than importance.

The findings of this study contribute to hospitality scholarship in two primary ways: the importance and performance of technology and generational differences. The results advance the understanding of the impact of generational factors on customer-facing technological adoptions in the luxury hotel sector.

Technologies that are pervasive in the home also become vital offerings for hotels. The more pervasive technology, the more a luxury hotel must work to ensure that it performs at optimal levels. Additionally, which technologies are most important to targeted generations are provided so practitioners may budget for their implementation.

This research is a pivotal step forward in unraveling the intricate interplay between generational factors and technological evaluations, providing a foundation for future research and practical applications in a rapidly evolving technological landscape in the hospitality industry.