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This paper aims to propose a simulation model integrated with an empirical regression analysis to provide a new mathematical formulation for automated storage and retrieval system (AS/RS) travel time estimation under class-based storage and different input/output (I/O) point vertical levels.

A simulation approach is adopted to compute the travel time under different warehouse scenarios. Simulation runs with several I/O point levels and multiple shape factor values.

The proposed model is extremely precise for both single command (SC) and dual command (DC) cycles and very well fitted for a reliable computation of travel times.

The proposed mathematical formulation for estimating the AS/RS travel time advances widely applied methodologies existing in literature. As well as, it provides a practical implication by supporting faster and more accurate travel time computations for both SC and DC cycles. However, the regression analysis is conducted based on simulated data and can be refined by numerical values coming from real warehouses.

This work provides a new simulation model and a refined mathematical equation to estimate AS/RS travel time.

Being the final end of the logistic distribution, attended home delivery (AHD) plays an important role in the distribution network. AHD typically refers to the service provided by the distribution service provider to the recipient's doorstep. Researchers have always identified AHD as a bottleneck for last-mile delivery. This paper addresses a real-life stochastic multi-objective AHD problem in the context of the Indian public distribution system (PDS).

Two multi-objective models are proposed. Initially, the problem is formulated in a deterministic environment, and later on, it is extended to a multi-objective AHD model with stochastic travel and response time. This stochastic AHD model is used to extensively analyze the impact of stochastic travel time and customer response time on the total expected cost and time-window violation. Due to the NP-hard nature of the problem, an ant colony optimization (ACO) algorithm, tuned via response surface methodology (RSM), is proposed to solve the problem.

Experimental results show that a change in travel time and response time does not significantly alter the service level of an AHD problem. However, it is strongly correlated with the planning horizon and an increase in the planning horizon reduces the time-window violation drastically. It is also observed that a relatively longer planning horizon has a lower expected cost per delivery associated.

The paper does not consider the uncertainty of supply from the warehouse. Also, stochastic delivery failure probabilities and randomness in customer behavior have not been taken into consideration in this study.

In this paper, the role of uncertainty in an AHD problem is extensively studied through a case of the Indian PDS. The paper analyzes the role of uncertain travel time and response time over different planning horizons in an AHD system. Further, the impact of the delivery planning horizon, travel time and response time on the overall cost and service level of an AHD system is also investigated.

This paper investigates a unique and practical AHD problem in the context of Indian PDS. In the present context of AHD, this study is highly relevant for real-world applications and can help build a more efficient delivery system. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.

The most challenging part of an AHD problem is the requirement of the presence of customers during the time of delivery, due to which the probability of failed delivery drastically increases if the delivery deviates from the customer's preferred time slot. The paper modelled an AHD system to incorporate uncertainties to attain higher overall performance and explore the role of uncertainty in travel and response time with respect to the planning horizon in an AHD, which has not been considered by any other literature.

This paper aims to address a pollution-routing problem with one general period of congestion (PRP-1GPC), where the start and finish times of this period can be set freely.

In this paper, three sets of decision variables are optimized, namely, travel speeds before and after congestion and departure times on given routes, aiming to minimize total cost including green-house gas emissions, fuel consumption and driver wages. A two-phase algorithm is introduced to solve this problem. First, an adaptive large neighborhood search heuristic is used where new removal and insertion operators are developed. Second, an analysis of optimal speed before congestion is presented, and a tailored speed-and-departure-time optimization algorithm considering congestion is proposed by obtaining the best node to be served first over the congested period.

The results show that the newly developed operator of congested service-time insertion with noise is generally used more than other insertion operators. Besides, compared to the baseline methods, the proposed algorithm equipped with the new operators provides better solutions in a short time both in PRP-1GPC instances and time-dependent pollution-routing problem instances.

This paper considers a more general situation of the pollution-routing problem that allows drivers to depart before the congestion. The PRP-1GPC is better solved by the proposed algorithm, which adds operators specifically designed from the new perspective of the traveling distance, traveling time and service time during the congestion period.

Mobile travel apps (MTA) smart features were identified based on recent travel application (app) trends and a literature review of MTA smart features. Subsequently, the MTA features that could be prioritised to increase user interest in MTA were determined. The MTA smart feature development challenges that should be mitigated were also identified.

The app identification and selection were based on the one-stop solution characteristics containing the common function of travel apps and eight MTA smart features. A total of 193 Apple apps and 250 Google apps were identified, where 36 apps that met the inclusion and exclusion criteria based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart were selected for evaluation.

The high user ratings for apps from both app stores revealed the acceptance of smart technology in the tourism industry. Geolocation tracking services, travel itinerary generators, and real-time personalisation and recommendation were the three major features available in the included MTA. The challenges of MTA with smart features were highlighted from the tourism organisation, app developer and user perspectives.

The findings can guide tourism organisations and app developers on the smart features that MTA should offer for user engagement. Technological organisations could optimise their technology stack by considering the identified smart features. The findings are valuable for scholars in terms of MTA aesthetics and usability to gain acceptability. The development challenges included significant investment in technology, location accuracy and privacy concerns when implementing MTA smart features.

The previous literature mainly focused on evaluating app quality, assessing app functionality, and user ratings using the Mobile Application Rating Scale, and scoping reviews of MTA articles. Contrastingly, this study is among the first in which MTA smart features were examined from a developer-centric perspective. Moreover, it is suggested that MTA includes integrated smart features for better tourism services and market penetration in the tourism industry.

The rapid development of urban areas in Sleman District, Indonesia, has created new challenges for firefighting response services. One of the primary challenges is to identify the optimal locations for new fire stations, to improve service quality and maximize service coverage within the specified time.

This paper proposes a method for precisely calculating travel time that integrates delay time caused by traffic lights, intersections and congestion. The study highlights the importance of precise calculation of travel time in order to provide a more accurate understanding of the service area covered by the fire stations. The proposed method utilizes network analysis in ArcGIS, the analytical hierarchy process (AHP) and simple additive weighting (SAW) to accurately calculate travel time and to identify the best locations for new fire stations. The identification of new site was based on service safety, service quality, service costs and demographic factors and applied to the Sleman district in Indonesia.

The results showed that the total area covered by old and new fire stations decreased from 61% to 31.8% of the study area when the adjusted default speed scenario was implemented.

The results indicated that the default speed scenario could provide misleading information about the service area, while the adjusted default speed scenario improved service quality and maximized service coverage.

The proposed method provides decision-makers with an effective tool to make informed decisions on optimal locations for new fire stations and thus enhance emergency response and public safety.

The purpose of this study is to address the extended travel time caused by dwelling time at stations for passengers on traditional rail transit lines. To mitigate this issue, the authors propose the “Non-stop” design, which involves trains comprised of modular vehicles that can couple and uncouple from each other during operation, thereby eliminating dwelling time at stations..

The main contributions of this paper are threefold: first, to introduce the concept of non-stop rail transit lines, which, to the best of the authors’ knowledge, has not been researched in the literature; second, to develop a framework for the operation schedule of such a line; and third, the author evaluate the potential of its implementation in terms of total passenger travel time.

The total travel time was reduced by 6% to 32.91%. The results show that the savings were more significant for long commutes and low train occupancy rates.

The non-stop system can improve existing lines without the need for the construction of additional facilities, but it requires technological advances for rolling stock.

To eliminate dwelling time at stations, the authors present the “Non-stop” design, which is based on trains composed of locomotives that couple and uncouple from each other during operation, which to the best of the authors’ knowledge has not been researched in the literature.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

Women's travel pattern is different from those of men. Women who have both paid employment and unpaid care work have more complex travel patterns. However, land-use policies and urban mobility strategies in the Egyptian context do not consider these differences. This paper analyzes and discusses the travel patterns of the Egyptian working women with children. It examines the difference between men's and women's travel behavior in different income levels. The paper aims at determining the main factors that affect working women's travel patterns within the care economy framework and suggesting recommendations for enhancing women's travel patterns in Greater Cairo Region (GCR).

The methodology relies on conducting a quantitative and qualitative analysis using questionnaires and interviews with working women and men from different social/economic levels in two different workplaces in GCR.

The results demonstrate that income level, workplace locations, schools locations, and schools typologies greatly affect working women's travel patterns in GCR.

The study findings will help urban planners and decision-makers to improve working women's mobility to make their daily trips shorter and more accessible to achieve equitable cities through understanding the conducted affecting factors and considering the suggested recommendations.

This paper examines the post-COVID-19 travel intentions to Kenya among Hong Kong outbound travelers using the theory of planned behavior (TPB) over three different time horizons of 1, 5, and 10 years.

An extension was made by including two new constructs of perceived destination image and travel constraints. A cross-sectional sample of Hongkongers was surveyed. Data were collected using a self-administrated bilingual (English and Chinese) online survey. Exploratory factor analysis, linear regression and mediation analysis were conducted to test the research model.

The findings from 216 Hongkongers reveal that different combinations of the four constructs, namely, perceived behavioral control, attitude, subjective norms, and destination image, share a positive effect on individuals' travel intention to Kenya over the three different time horizons. Travel constraints act as a significant negative mediator on the four constructs in predicting travel intention to Kenya among Hongkongers.

The results provide useful insight to Kenya's destination marketing organization (DMO) and Hong Kong outbound travel agencies to integrate prominent elements into marketing strategies to arouse travel intention and expand their business prospects, which will also accelerate tourism recovery in the post-pandemic era.

By integrating two extended variables into the TPB model, this study makes a contribution by overcoming the deficiency of the original theory.

Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.