A new solution framework for the limited-stop bus service design problem

The impact of using a naïve approach in the limited-stop bus service design problem

Limited-stop bus service is a service configuration implemented by many large transit agencies along high-demand bus corridors. Limited-stop service is advantageous because it produces faster running times along the route or corridor than those of local service and it increases efficiency in terms of the number of trips a vehicle can make along the route or corridor in a given time span. However, for many transit agencies it is not necessarily apparent whether the implementation of a limited-stop service configuration along specific routes and corridors will provide a net benefit to passengers, especially under a resource-neutral implementation strategy in which local bus trips are replaced with limited-stop trips. Although a resource-neutral implementation strategy offers the greatest potential for cost savings, increases in wait, access, and egress time for passengers along a corridor can easily offset the intended benefits of reduced onboard time and cause a reduction in the quality of service to passengers. This study developed a methodology for transit agencies to evaluate the potential for limited-stop bus service along existing local routes. The methodology was designed to identify existing routes where the net benefits of onboard travel time savings outweighed the net burdens of increased wait, access, and egress times should limited-stop service be implemented. A set of six metrics was used for the evaluation: existing service frequency, concentration of passenger demand, distribution of stops with high passenger demand, passenger trip length, roadway geometry, and traffic congestion. The methodology was applied to identify candidates for limited-stop service within the Massachusetts Bay Transportation Authority bus network.

An integrated model for line planning and train scheduling based on the circulation of trains is proposed to reduce passenger dissatisfaction and operation costs for an urban rail transit line. The turnaround operations of trains and their departures from and arrivals at the depot were included in this model. Furthermore, binary variables were introduced to indicate whether train service existed, and a discrete event model was used to determine the order of the train services. In addition, a bi-level optimization approach is proposed to solve the integrated line-planning and train-scheduling problem, in which the number of required train services, the headways between train services, the departure times, and the arrival times were optimized simultaneously on the basis of passenger demand. The performance of the proposed integrated model and bi-level approach is illustrated with a case study of the Beijing Yizhuang line.

This work investigates an unmanned aerial vehicle (UAV) assisted IoT system, where a UAV flies to each foothold to collect data from IoT devices, and then return to its start point. For such a system, we aim to minimize the energy consumption by jointly optimizing the deployment and flight trajectory of UAV. It is a mixed-integer non-convex and NP-hard problem. In order to address it, a bilevel optimization approach is proposed, where an upper-level method aims to optimize the deployment of UAV and a lower-level one aims to plan UAV flight trajectory. Specifically, the former optimizes the number and locations of footholds of UAV. This work proposes an improved dandelion algorithm with a novel encoding strategy, in which each dandelion represents a foothold of UAV and the entire dandelion population is seen as an entire deployment. Then, two mutation strategies are designed to adjust the number and locations of footholds. Based on the footholds of the UAV provided by the former, the latter transforms flight trajectory planning into a traveling salesman problem (TSP). This work proposes an iterated greedy algorithm to solve it efficiently. The effectiveness of the proposed bilevel optimization approach is verified on ten instances, and the experimental results show that it significantly outperforms other benchmark approaches.

Transit agencies implement limited-stop (express) bus services as a mean to provide an attractive and competitive transit service. The Societe de transport de Montreal (STM), the transit agency providing service on the Island of Montreal, Canada, implemented a limited-stop service to run parallel to route 67 Saint-Michel, a heavily used bus route. The design process regarding which stops to include in the new service involved an evaluation of several scenarios and an estimation of running time savings. In this paper, we use archived vehicle location and passenger count data to measure changes in on-time performance and running times, and to compare these changes to previous estimates through a before-and-after study. In addition, an on-site survey is conducted to measure the riders’ perception of time savings. Implementing a limited-stop service yielded to 4.6 minutes savings in running time for the new limited service; meanwhile, the existing regular service experienced an increase of 0.8 minute in average running time, an increase that could be due to a new smart card system introduced in the same period. The running time savings for the limited-stop service fall in the expected estimates made by the research team, which strengthens our trust in the methodology used to select stops and to estimate savings. Finally, the study shows that riders are generally satisfied with the new service. They also tend to overestimate the savings associated with the implementation of the new limited-stop service by 4 to 7 minutes more than the actual savings.

Inventory control in supply chains: Alternative approaches to a two-stage lot-sizing problem

Distribution expansion planning in the presence of plug-in electric vehicle: A bilevel optimization approach

Risk management model for simultaneous participation of a distribution company in Day-ahead and Real-time markets

PV assisted electric vehicle charging station considering the integration of stationary first- or second-life battery storage

This study designs a limited-stop and local mixed bus service from the perspectives of both transit agencies and passengers based on a proposed optimization model. Close attention was paid to the increase in fares for the limited-stop service in the proposed model to offset the loss in bus fare. The objective function was to minimize the passenger travel time and bus agency costs; the decision variables were the stops for the limited-stop service and the headways of the limited-stop and local services; the constraints included the range of fare increases for the limited-stop service and other factors. The Lingo software and the genetic algorithm were compared in solving the proposed model from the aspects of solving time and results. Because the solutions for the model within the planning time and the influence of the global optimal solution upon parametric analysis were stable, the Lingo software was more suitable for the proposed model. Evaluations of the total costs for the mixed service and local-only service were carried out for different bus corridor lengths. The ratio of the fares for the limited-stop service to the fares for the local service must be within the appropriate range in order to take advantage of the limited-stop service. When the stop spacing and the bus corridor distance were longer, the mixed service was more competitive and noticeable.

This paper proposes a hybrid chaotic biogeography-based optimization (HCBBO) for solving the sequence dependent setup times flowshop scheduling problem with the objective of minimizing the total weighted tardiness. First of all, a largest-order-value rule is employed to transform continuous vectors into discrete job permutations. Second, the chaotic theory and the problem-specific Nawaz-Enscore-Ham heuristic are applied to compose the initial population with the property of intensification and diversification. Third, an improved biogeography-based optimization is introduced to improve the global search ability by designing new migration and mutation schemes. Meanwhile, a further local search is proposed and embedded in HCBBO to enhance the quality of the elite habitats. In addition, an effective perturbation is applied to avoid the solutions getting trapped in the local optima. Computation comparison experiments of seven benchmark algorithms on the Taillard benchmark problems are provided to verify the efficiency of the proposed algorithm. From the experiment results, it can conclude that HCBBO beats other compared algorithms effectively with higher quality and robustness solutions.

We present a method for estimating the parameter sensitivity of a design alternative for use in robust design optimization. The method is non-gradient based: it is applicable even when the objective function of an optimization problem is non-differentiable and/or discontinuous with respect to the parameters. Also, the method does not require a presumed probability distribution for parameters, and is still valid when parameter variations are large. The sensitivity estimate is developed based on the concept that associated with each design alternative there is a region in the parameter variation space whose properties can be used to predict that design’s sensitivity. Our method estimates such a region using a worst-case scenario analysis and uses that estimate in a bi-level robust optimization approach. We present a numerical and an engineering example to demonstrate the applications of our method.

Decentralized Decision System for Closed-Loop Supply Chain: A Bi-Level Multi-Objective Risk-Based Robust Optimization Approach

Digital Twin (DT) and mobile edge computing are two promising solutions for achieving latency-sensitive and computing-intensive applications in Industrial Internet of Things (IIoT). However, existing task offloading schemes with DT empowerment are faced with challenges, such as the spatio-temporal heterogeneity of edge server (ES) resources, resource-constrained ESs, and the explosive growth of data in emerging applications. This paper investigates the issues of task offloading and resource allocation under the assistance of DT and multiple ESs parallel collaboration. One novel scheme, abbreviated as Mes-PCORA, is proposed. With comprehensive information within the digital space, the Mes-PCORA scheme dynamically adjusts task allocation ratios across multiple ESs to achieve collaborative task offloading. The offloading request is formulated as a non-convex problem. To make the non-convex problem solvable in polynomial time, the original problem transformed into a bilevel optimization problem. Then, a bilevel iterative optimization approach is proposed. Specifically, the upper-level optimization problem is formulated as a multi-agent Markov Decision Process, and a deep reinforcement learning-based resource allocation algorithm is designed to solve it. Subsequently, for the lower-level optimization problem, it is solved by the interior point method. Numerical results demonstrate that the proposed scheme reduces the average task completion latency by 27.16%–63.44% and decreases the task offloading failure rate by 35.83%–73.95%, compared to state-of-the-art baselines.

Bus rapid transit (BRT) can offer transit mobility to meet growing travel demands by cost-effectively providing high capacity and quality of service. It is adaptable to a wide range of operating conditions and technological advancements. Stations are elements that typically control BRT line capacity, so it is essential to understand the operation of any potentially critical station to understand and manage the facility. The Transit Capacity and Quality of Service Manual (TCQSM) provides the standard methodology for capacity estimation. However, that model does not account for important operational aspects including the stochastic nature of many parameters beyond dwell time, along with nonstopping buses’ capacity, the degrees of saturation of the stopping and nonstopping bus streams, and the upstream average waiting time and queue length of stopping buses. We adapted the theory developed by Hisham et al. for an onstreet bus stop, to reflect the operational conditions of a BRT station and to account for these aspects. This new reliability-based capacity model tailored to BRT facilities provided superior insight into station bus capacity and quality of service to the TCQSM model.