A Discrete-time Dynamical Model for Optimal Dispatching and Rebalancing of Autonomous Mobility-on-Demand Systems

Autonomous vehicles are rapidly evolving and will soon enable the application of large-scale mobility-on-demand (MoD) systems. Managing the fleets of available vehicles, commonly known as "rebalancing," is crucial to ensure that vehicles are distributed properly to meet customer demands. This paper presents an optimal control approach to optimize vehicle scheduling and rebalancing in an autonomous mobility-on-demand (AMoD) system. We use graph theory to model a city partitioned into virtual zones. Zones represent small areas of the city where vehicles can stop and pick up/drop off customers, whereas links denote corridors of the city along which autonomous vehicles can move. They are considered vertices and edges in the graph. Vehicles employed in the AMoD scheme are autonomous, and rebalancing can be executed by dispatching available empty vehicles to areas undersupplied. Rebalancing is performed on the graph's vertices, i.e., between city areas. We propose a linear, discrete-time model of an AMoD system using a transformed network. After acquiring the model, the desired number of rebalancing vehicles for the AMoD model is derived through an optimization problem. Moreover, the well-posedness of the model is illustrated. To leverage the proposed model, we implemented the model predictive control (MPC) framework to find the optimal rebalancing and scheduling policy. We show the MPC's effectiveness and how the MPC framework can be implemented in real-time for a real-world case study. The numerical results show that the MPC with a linear cost function and linear reference, which it tracks, is effective, outperforming other MPC-based and state-of-the-art algorithms across all evaluation criteria

Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems

Reinforcement learning (RL) is a class of artificial intelligence algorithms being used to design adaptive optimal controllers through online learning. This paper presents a model-free, real-time, data-efficient Q-learning-based algorithm to solve the H$_{\infty}$ control of linear discrete-time systems. The computational complexity is shown to reduce from $\mathcal{O}(\underline{q}^3)$ in the literature to $\mathcal{O}(\underline{q}^2)$ in the proposed algorithm, where $\underline{q}$ is quadratic in the sum of the size of state variables, control inputs, and disturbance. An adaptive optimal controller is designed and the parameters of the action and critic networks are learned online without the knowledge of the system dynamics, making the proposed algorithm completely model-free. Also, a sufficient probing noise is only needed in the first iteration and does not affect the proposed algorithm. With no need for an initial stabilizing policy, the algorithm converges to the closed-form solution obtained by solving the Riccati equation. A simulation study is performed by applying the proposed algorithm to real-time control of an autonomous mobility-on-demand (AMoD) system for a real-world case study to evaluate the effectiveness of the proposed algorithm

Planning of integrated mobility-on-demand and urban transit networks

We envision a multimodal transportation system where Mobility-on-Demand (MoD) service is used to serve the first mile and last mile of transit trips. For this purpose, the current research formulates an optimization model for designing an integrated MoD and urban transit system. The proposed model is a mixed-integer non-linear programming model that captures the strategic behavior of passengers in a multimodal network through a passenger assignment model. It determines which transit routes to operate, the frequency of the operating routes, the fleet size of vehicles required in each transportation analysis zone to serve the demand, and the passenger flow on both road and transit networks. A Benders decomposition approach with several enhancements is proposed to solve the given optimization program. Computational experiments are presented for the Sioux Falls multimodal network. The results show a significant improvement in the congestion in the city center with the introduction and optimization of an integrated transportation system. The proposed design allocates more vehicles to the outskirt zones in the network (to serve the first mile and last mile of transit trips) and more frequency to the transit routes in the city center. The integrated system significantly improves the share of transit passengers and their level of service in comparison to the base optimized transit system. The sensitivity analysis of the bus and vehicle fleet shows that increasing the number of buses has more impact on improving the level of service of passengers compared to increasing the number of MoD vehicles. Finally, we provide managerial insights for deploying such multimodal service.Comment: 39 pages, 6 figure

Role of resilience training on compromising of infertile couples’ applicant for divorce: A cross-sectional study

Background: Divorce is a social issue, which challenges not only the structure of family but also of a society. Studies have shown that infertility affects the marital boredom. In addition, resilience training and emphasizing on increasing piety (religiousness) can help to decrease this boredom. Objective: This study aimed to evaluate the resilience training effects on the compromising of infertile couples’ applicant for divorce. Materials and Methods: In this cross-sectional study, 100 infertile couples who had requested for divorce and referred to the Center for consolidation of the family foundation were enrolled. Participants were randomly divided in two categories (n= 50/each): the case group received some consultation classes on social services as well as resilience training by a consultant in 5 sessions lasting 2 hr. In total, 10 hr of treatment; while the control group just received the consultation and social services. Canner and Davidson questionnaires were utilized as pre- and posttest in both groups. Groups answered the resilience’s criterion of Canner and Davidson. Results: The resilience training significantly increased the compromises made by couples in the case group compared to the control (p < 0.01). The results showed that 26% of members of the case group relinquished divorce, while 10% of control group members did the same; this difference was statistically significant (p < 0.01). Conclusion: The resilience training leads to increased psychological well-being elements and compromises in infertile couples. Key words: Resilience, Education, Infertility, Divorce

Performance assessment of meta-heuristics for composite layup optimisation

Peer reviewedPostprin

Intermodal Path Algorithm for Time-Dependent Auto Network and Scheduled Transit Service

A simple but efficient algorithm is proposed for finding the optimal path in an intermodal urban transportation network. The network is a general transportation network with multiple modes (auto, bus, rail, walk, etc.) divided into the two major categories of private and public, with proper transfer constraints. The goal was to find the optimal path according to the generalized cost, including private-side travel cost, public-side travel cost, and transfer cost. A detailed network model of transfers between modes was used to improve the accounting of travel times during these transfers. The intermodal path algorithm was a sequential application of specific cases of transit and auto shortest paths and resulted in the optimal intermodal path, with the optimal park-and-ride location for transferring from private to public modes. The computational complexity of the algorithm was shown to be a significant improvement over existing algorithms. The algorithm was applied to a real network within a dynamic traffic and transit assignment procedure and integrated with a sequential activity choice model

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Reliability and validity testing of the persian version of the derriford appearance scale 24 in a sample of individuals with craniofacial irregularity and amputation

BACKGROUND: Despite the recent advancements in the design and manufacture of prostheses for individuals with craniofacial irregularity and amputation, these individuals tend to become self-conscious about their appearance. The aim of this study was to investigate the reliability and validity of Persian version of the Derriford Appearance Scale24 (P-DAS24) for a sample of individuals with craniofacial irregularity and limb loss.METHODOLOGY: Reliability of the P-DAS24 was determined by computing internal consistency and test-retest reliability utilizing Cronbach’s alpha coefficient and Pearson’s correlation coefficient. Discriminant validity was investigated with comparing the total score of the P-DAS24 between disfigured participants and those with no appearance problem. Known-groups validity was evaluated regarding the participants’ gender and their level of involvement.FINDINGS: The sample size comprised of 251 individuals with disfigurement and 101 without disfigurement who were deemed normal in appearance. The P-DAS24 showed satisfactory internal consistency (Cronbach’s alpha = 0.89) and excellent test-retest reliability (r = 0.96). The total score of the P-DAS24 showed a statistically significant difference between individuals deemed disfigured or normal (P=0.01). The total scores P-DAS24 in individuals with different levels of involvement were significantly different (P<0.001). The scores of the DAS2, DAS18, DAS21, and DAS24 were significantly different between men and women (P<0.01, <0.01, 0.03, and 0.01, respectively).CONCLUSION: The P-DAS24 is a valid and reliable tool that may be utilized in clinical practice and researches to assess the outcomes of prosthetic reconstructions in individuals with disfigurement