Traffic assignment problem under tradable credit scheme in a bi-modal stochastic transportation network: A cumulative prospect theory approach

The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertainty, the cumulative prospect theory(CPT) is adopted. Travelers are assumed to choose the paths with the minimum perceived generalized path costs, consisting of time prospect value(PV) and monetary cost. At equilibrium with a given TCS, the endogenous reference points and credit price remain constant, and are consistent with the equilibrium flow pattern and the corresponding travel time distributions of road sub-network. To describe such an equilibrium state, the CPT-based stochastic user equilibrium(SUE) conditions can be formulated under TCS. An equivalent variational inequality(VI) model embedding a parameterized fixed point(FP) model is then established, with its properties analyzed theoretically. A heuristic solution algorithm is developed to solve the model, which contains two-layer iterations. The outer iteration is a bisection-based contraction method to find the equilibrium credit price, and the inner iteration is essentially the method of successive averages(MSA) to determine the corresponding CPT-based SUE network flow pattern. Numerical experiments are provided to validate the model and algorithm.

Challenges and Opportunities in Algorithmic Solutions for Re-Balancing in Bike Sharing Systems

In recent years,the booming of the Bike Sharing System(BSS)has played an important role in offering a convenient means of public transport.The BSS is also viewed as a solution to the first/last mile connection issue in urban cities.The BSS can be classified into dock and dock-less.However,due to imbalance in bike usage over spatial and temporal domains,stations in the BSS may exhibit overflow(full stations)or underflow(empty stations).In this paper,we will take a holistic view of the BSS design by examining the following four components:system design,system prediction,system balancing,and trip advisor.We will focus on system balancing,addressing the issue of overflow/underflow.We will look at two main methods of bike re-balancing:with trucks and with workers.Discussion on the other three components that are related to system balancing will also be given.Specifically,we will study various algorithmic solutions with the availability of data in spacial and temporal domains.Finally,we will discuss several key challenges and opportunities of the BSS design and applications as well as the future of dock and dock-less BSS in a bigger setting of the transportation system.

Coordinate scheduling approach for EDS observation tasks and data transmission jobs

Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements.And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.

Multi-timescale Energy Sharing with Grid-BESS Capacity Rental Considering Uncertainties

The growing number of distributed energy resources(DERs)in distribution networks brings new opportunities for local energy sharing.This paper proposes a multi-timescale en-ergy sharing approach among DER aggregators and distribution system operators(DSOs)considering grid-battery energy storage system(BESS)capacity rental and network operations.An energy sharing coordinator is created to manage the energy sharing with price determination.In an hour-ahead stage,the buying/selling energy and required grid-BESS rental capacity are optimally determined by the aggregators while the network operation is robustly considered by the DSO.In addition to renewable generation and loads,the power exchanges of the aggregators are treated as uncertainties.Then during each hour,15-min-ahead energy transaction and controllable DERs are optimized to track uncertainty realization.The uncertainties in the aggregators and the DSO are addressed by stochastic and robust optimization methods,respectively.To efficiently solve the proposed energy sharing problem,a distributed solution algorithm with step length control and step reduction techniques is developed.The simulation results verify the high efficiency of the proposed energy sharing approach.Index Terms-Battery capacity rental,distributed solution algorithm,energy sharing,optimization,uncertainty.

Bibliometric analysis and system review of vehicle routing optimization for emergency material distribution

Determining the optimal vehicle routing of emergency material distribution(VREMD)is one of the core issues of emergency management,which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency events.To summarize the latest research progress,we collected 511VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer software.Subsequently,we cautiously selected 49 articles from these publications for system review;sorted out the latest research progress in model construction and solution algorithms;and summarized the evolution trend of keywords,research gaps,and future works.The results show that domestic scholars and research organizations held an unqualified advantage regarding the number of published papers.However,these organizations with the most publications performed poorly regarding the number of literature citations.China and the US have contributed the vast majority of the literature,and there are close collaborations between researchers from both countries.The optimization model of VREMD can be divided into single-,multi-,and joint-objective models.The shortest travel time is the most common optimization objective in the single-objective optimization model.Several scholars focus on multiobjective optimization models to consider conflicting objectives simultaneously.In recent literature,scholars have focused on the impact of uncertainty and special events(e.g.,COVID-19)on VREMD.Moreover,some scholars focus on joint optimization models to optimize vehicle routes and central locations(or material allocation)simultaneously.Solution algorithms can be divided into two primary categories,i.e.,mathematical planning methods and intelligent evolutionary algorithms.The branch and bound algorithm is the most dominant mathematical planning algorithm,while genetic algorithms and their enhancements are the most commonly used intelligent evolutionary algorithms.It is shown that the nondominated sorting genetic algorithmⅡ(NSGA-Ⅱ)can effectively solve the multiobjective model of VREMD.To further improve the algorithm’s performance,researchers have proposed improved hybrid intelligent algorithms that combine the advantages of NSGA-Ⅱand certain other algorithms.Scholars have also proposed a series of optimization algorithms for specific scenarios.With the development of new technologies and computation methods,it will be exciting to construct optimization models that consider uncertainty,heterogeneity,and temporality for large-scale real-world issues and develop generalized solution approaches rather than those applicable to specific scenarios.