Multiple Trips Research Articles

Dynamics and feedback sensitivity of vertical-cavity surface-emitting lasers with arbitrary optical feedback intensity

Vertical cavity surface emitting lasers (VCSELs) are the key laser sources in the high-performance computers (HPCs) and data centers (DCs). The explosive growth of data traffic in the HPCs and DCs requires increased bandwidths of VCSELs. In this paper, we deduce the Time-Delay (T-D) model considering the multiple round trips of light in VCSELs with an optical feedback cavity to enhance the modulation bandwidth. The T-D model is suitable for the VCSEL with an arbitrary optical feedback intensity. The influences of the round trip time m of light in the optical feedback cavity on the intensity-modulation responses with strong and weak optical feedback are studied. The feedback coefficients considering the multiple round trips of light in the optical feedback cavity and the dispersions of the distributed Bragg reflector are deduced, and are suitable for the arbitrary optical feedback intensity. The feedback coefficients are influenced by the value of m and the optical feedback intensity, and decrease as the value of m under arbitrary optical feedback intensity increases. The feedback coefficients under strong optical feedback are larger than that under weak optical feedback with the same value of m. The theories are useful for the high-speed VCSELs with optical feedback.

A multi-trip vehicle routing problem considering time windows and limited duration under a heterogeneous fleet and parking constraints in cold supply chain logistics

ABSTRACT Cold supply chain distribution systems ensure the freshness of temperature-sensitive products during transportation. In this study, we investigated a fresh food company’s cold supply chain distribution. Making fresh food available and achieving quality and safety, requires proper planning of vehicle routing, we addressed a routing problem that simultaneously considers time windows, multiple trips per vehicle, a heterogeneous fleet, parking constraints, unloading time at customer position, and limited duration, minimizing related operational costs. We formulate this problem as a mixed-integer programming model. Since this problem is NP-hard, we also propose a genetic algorithm with two adaptive-parameter mechanisms to solve it within a reasonable computational time. Extensive experiments were conducted to assess the performance of different approaches in a real-world application. The results demonstrate that the algorithms are robust and efficient. The proposed algorithms can reduce operational costs by more than 20% compared to the current practical planning approach.

Optimal delivery route planning for a fleet of heterogeneous drones: A rescheduling-based genetic algorithm approach

The past decade has witnessed the rapid growth of pilot studies and the adoption of drone-assisted delivery by many e-commerce companies. In practice, delivery companies usually assemble fleets of drones consisting of heterogeneous configurations to satisfy various customer package demands, and this calls for in-depth research on how to deliver packages via drones efficiently. In this study, we extend the classic vehicle routing problem (VRP) by optimally scheduling the operations in a package depot center with a fleet of drones with different capacities, speeds, and maximum flight ranges. The drones travel multiple sorties to serve customers nearby. We formulate this problem as a mixed integer programming (MIP) model that minimizes the total delivery time for a set of packages. Due to the NP-hardness of the problem, we first develop a FIFO-based genetic algorithm (FBGA) to solve the problem. In anticipation of multiple trips, we further propose a modified, rescheduling-based genetic algorithm (RBGA) that incorporates a small-scale routing-planning optimization model that “reschedules” drone operations at the end of drone sorties, with the objective of minimizing the completion time. The RBGA features the combination of the knapsack problem, the shortest path problem, and a scheduling problem, from which near-optimal delivery plans are obtained for the fleet of drones. We conduct extensive numerical experiments with different numbers of customers and drones to evaluate the performance of the algorithms. The results suggest that compared with MIP, the proposed algorithms solve the problem efficiently for the testing instances, particularly for large instances that MIP fails to solve in time, indicating the practicality of the proposed methods for real-world implementations.

The multi-trip container drayage problem with synchronization for efficient empty containers re-usage

We study a typical daily drayage problem concerning the last-mile logistics at seaports for inland container supply chains. A set of trucks available at an inland container terminal must fulfil shippers’ requests of transporting containers within time windows and, to do so, can perform multiple daily trips. A request may entail picking up or delivering containers either at the shippers’ premises, the inland terminal or the seaport. Demand for empty containers can be satisfied by either using the available limited stock at the inland terminal, by street-turning or, ultimately, by retrieving them at a local depot for empties resulting in extra mileage. Hence, the minimization of routing costs also entails synchronizing trucks’ trips that retrieve and add empty containers to the inland terminal stock to avoid unnecessary visits to the empty depot. After modelling the problem mathematically, we develop an exact column-and-row generation approach embedded in a branch-and-price framework. To accelerate the solving process of the pricing problem, we propose effective strategies by combining a set of tailored pricing algorithms. These strategies perform well on a set of adapted Solomon’s instances up to 100 nodes and against a standard branch-and-cut solver. Finally, experiments on real-world instances, inspired by a case study of an inland terminal at the Port of Rotterdam region, provide insights into current planning practices.

Partnering to build surgical capacity in low-resource settings: a qualitative study of Canadian global surgeons

ObjectivesThis qualitative study aimed to explore the perspectives of Canadian global surgeons with experience developing surgical education partnerships with low- and middle-income countries (LMICs) for the purpose of identifying factors...

Optimal Vehicle Routing Problem (VRP) For The Distribution Of Medical Devices By Applying The Clarke-Wright Algorithm

In the current era of modernization, the development of the need for medical devices is increasing,g so that the companies need goods distribution system with criteria that have speed, safety, and convenience to distribute various kinds of products according to optimal distribution routes. The aim of this study is to overcome the problem of the distribution route for medical devices from PT Alkes Medan because the number of demand for goods varies for each consumers, vehicle capacites, delivery time limits, average speed that can be reached on certain routes and times and the existence of the different conditions of multiple trips at the customer's location. A heuristic algorithm named Clarke-Wright Algorithm is applied in this problem. This algorithm will be later provide an optimal solution to route problem in the distribution of medical devices and provide effectiveness in finding the shortest optimal route of medical device goods distributed to consumers. The optimal route will be described in the results section of this study

Proactive in-house part-feeding for mixed-model assembly systems with dynamics

The rich data collected from smart shop floors necessitate proactive decision-making to relieve system nervousness in a dynamic environment. This proactivity takes real-time actions based on disruption-driven prediction but not on disruption-created negative effects. This paper focuses on investigating a proactive in-house part-feeding (PIP) approach by integrating real-time data collected from shop floors with the physical properties of a mixed-model assembly system. An event-driven proactive prediction for replenishments is first implemented after coupling such data as disruptions, job rescheduling, part re-releasing, and real-time lineside inventories with a widely used reorder-point-based replenishment policy. This prediction can provide sufficient prior information about replenishments and, in turn, trigger tow-train rerouting to simultaneously minimize the costs of distribution and lineside inventories. In terms of limited tow train capacity and the just-in-time part-feeding goal, this rerouting problem should address various constraints on multiple trips, time windows, and the real-time working status of tow trains. An adaptive large neighbourhood search (ALNS) is developed to obtain the best solution for rerouting by designing customized removal and insertion operators, as well as local search heuristics. A case study of a real-life car seat assembly system is presented to verify the efficiency of the PIP. Several rerouting benchmark instances are obtained from the case study to evaluate the performance of ALNS, and the computational results indicate that the proposed ALNS has a satisfactory performance in solving the rerouting instances compared with some of the formerly proposed and published methods. The results of the practical case study illustrate that the PIP may increase the lineside inventory cost slightly but can significantly reduce the distribution cost compared with prevailing reactive approaches

Bi-level optimization for customized bus routing serving passengers with multiple-trips based on state–space–time network

The emerging customized bus (CB) can serve personalized trip requests in a more flexible and convenient way, especially for passengers who have multiple-trip requests in a short time period. Considering the CB service for passengers with multiple trips (CSPMT), a loading-state-oriented state–space–time network-based bi-level programming model is proposed to optimize the routing of CBs, with the objectives of maximizing the operational profit and minimizing the travel cost, while considering the characteristics of multiple trips, time windows, capacity and mixed loads. Besides, a nested algorithm combining the genetic algorithm (GA) and the augmented Lagrangian relaxation-based dynamic programming algorithm is proposed. Then, the proposed model and algorithm are verified and analyzed through a Sioux Falls network and a Beijing sketch network. It can be found from the results that the method can optimize a CB routing plan for passengers with multiple-trip requests and for different network scales. The proposed bi-level model and corresponding algorithm can better adapt to passengers’ personalized trip requests, and promote a higher level of public transport service which will attract more residents from private cars to public transportation, ultimately reducing energy consumption and exhaust emissions, and promoting the sustainable development of modern metropolises.

Service network design with consistent multiple trips

We introduce and study a challenging service network design problem arising within a city logistics company based in Beijing, China. To provide cost-effective same-day delivery services, the company divides the whole urban area into regions. Within each region, a fleet of small-capacity vehicles picks up and delivers packages. A fleet of line-haul vehicles (large-capacity vehicles) executes consistent multiple trips over the regions to transport consolidated packages, during which transshipment is allowed. In this study, we focus on designing the trips of line-haul vehicles, i.e., their region visiting sequences, as the performance of the logistics system depends significantly on the coordination of line-haul vehicles. We model the trip design problem as a service network design problem with consistent multiple trips. We develop a continuous-time MIP model to formulate the problem and optimally solve small-size instances. In addition, we propose an adaptive large neighborhood search heuristic to solve larger instances. Numerical experiments on randomly generated instances as well as real-world operational data demonstrate the effectiveness and efficiency of our solution approach.

Joint Optimization of Facility Layout and Spatially Differential Parking Pricing for Parking Lots

The parking cruising problem can be attributed to the unbalanced spatial parking space occupancy in the parking lot with multiple trip purposes and high traffic volumes, and it has already become an obstacle to improving travel efficiency. This study aims to investigate the reasons for the unbalanced occupancy of parking spaces and develop strategies to achieve spatial equilibrium by reducing parking cruising time. A field survey is first conducted to analyze the factors influencing parking space choice behavior, such as searching distances, walking distances, parking price, and parkers’ attributes. Based on the survey, a mixed logit model is calibrated to reveal parking space choice patterns of different parkers. Afterwards, the parking lot is divided into three zones based on searching distances and walking distances, using the K-means algorithm. According to the results, a simulation-based optimization model is then established, for minimizing the variance of average occupancy. An optimal pricing strategy is obtained at last. A case study in Nanjing, China, is conducted to verify the feasibility and rationality of the proposed method. The difference between weekdays and weekends is explored. The results show that, with the optimal strategies, the standard deviation of average occupancy in each zone decreases by 39.52% during weekends and 42.29% during weekdays. Finally, political implications and recommendations on the layout design of facilities and the adoption of differential parking pricing strategies are provided.

The Day-to-Day Departure Time Choice of Heterogeneous Commuters Under an Anonymous Toll Charge for System Optimum

We consider a bottleneck system in a state of disequilibrium, in which commuters are heterogeneous in the values of travel time and schedule delays. Three representative dynamical systems are used to formulate the day-to-day departure time choice of commuters, and a Walrasian toll charge scheme is utilized to control the evolutionary process of traffic flows. The toll charge scheme is anonymous; that is, toll charges are identical for all user classes, and the scheme’s implementation does not need to resort to information on the values of travel time and schedule delays. We find that the system optimum (SO) flow pattern can be achieved at the stationary state under the implementation of the toll charge scheme. The equivalence between the stationary state and the SO, the existence of the toll charge for achieving the SO, and the convergence of day-to-day traffic flows to the SO under the implementation of the toll charge scheme are theoretically proved. The distributions of traffic flows and toll charges at the SO state during morning rush hour are analytically shown. The application of the toll charge scheme to a network with multiple trip origins is presented. The effectiveness of the toll charge scheme for achieving the SO is also examined by numerical analyses. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72171007, 71890972/71890970, and 72021001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1191 .

Bidding and Charging Scheduling Optimization for the Urban Electric Bus Operator

With the development of electric buses (EBs) in urban areas, how to effectively utilize their charging demand flexibility and the considerable energy stored in the on-board batteries becomes an urgent issue for the electric bus operator (EBO) to consider. This paper focuses on the bidding decision in energy and reserve markets for the urban EBO owning multiple electric bus stations with energy storage systems (ESSs), and the charging scheduling of all EBs. A trip-chain based electric vehicle boundary model is proposed firstly to concisely describe the flexibility region for an EB with multiple sequential trips, which can be further aggregated via a state-based approach to adapt to large-scale problems. The uncertain boundaries are also derived taking the uncertainty of trip energy consumption into account. In order to deal with the issue that the boundary aggregation will possibly influence the feasibility of decomposing the total power to each individual EB, the stochastic bidding and EB scheduling optimization problem in a hierarchical optimization framework is proposed. Accelerated Benders decomposition is utilized to efficiently solve the problem. Case studies verify that the proposed model can dramatically reduce the computational burden, effectively consider the uncertainty and take advantage of the flexibility of both EBs and ESSs to achieve better economic performance.

On the Evaluation of a Drone-Based Delivery System on a Mixed Euclidean-Manhattan Grid

In this work, we investigate the use of drones in a delivery scenario formed by two contiguous areas. In one area the drones can freely fly on straight lines between any two locations (Euclidean metric), while in the other one the drones must follow the open space above the roads (Manhattan metric). We model this delivery scenario as a Euclidean-Manhattan-Grid (EM-grid). Given a set of customers to be served in an EM-grid, the objective is to find the distribution point (DP) for the drone that minimizes the overall traveled distance, considering that the drone has to do multiple round trips to/from the DP. In our view, the DP is optimized with respect to the set of customers and its computation must be light because it needs to be recomputed every time the set of customers varies. Accordingly, we define the Single Distribution Point Problem (SDPP) and devise sub-optimal time-efficient algorithms for solving it. We numerically compare the cost of our sub-optimal solutions with that of an optimal solution computed with a brute-force approach. Finally, using the BlueSky open air simulator, we compare the cost of our best solution with the cost of a solution that serves the costumers from a fixed DP, like the location of a delivery company’s depot. The fixed DP can perform very poorly for some customer instances, while our solution is highly adaptive and reduces the time and the distance covered by the drone.

Herbal medicinal plants concerning scorpion sting envenomation by ethnic groups of Paderu division, Visakhapatnam district, Andhra Pradesh, India

Worldwide, both traditional and modern medicine relies heavily on plants. In this work, we looked at local medicinal herbs and their use as a kind of treatment for scorpion sting envenomation. Information from the local tribal people from the research area for the year 2021 regarding phytotherapy for the treatment of scorpion stings was collected. Personal interviews were used to gather ethnobotanical data throughout multiple field trips. According to the study, the tribal chiefs have been treating scorpion stings with 20 different plant species from 17 different plant families. Most of the time, roots and paste forms were used. Cow urine, garlic, ginger, and pepper were occasionally combined with primitive medicines made from plants.

Strategic Transit Route Recommendation Considering Multi-Trip Feature Desirability Using Logit Model with Optimal Travel Time Analysis

Route recommendation continues to manifest noteworthy contributions to the intelligent transportation system field of research as it evolves through time. Early related studies helped passengers and tourists experience a more convenient travel. At the same time, these helped transport planners analyze people’s trip preferences and its correlation with the region-specific economic status in a more time-relevant data. Majority, however, require historical data and heavy data collection methods. For user quantified metrics such as route cost in terms of travel time and distance, the complexity and sparsity of preferences between travelers are persistent challenges. The strategic transit route recommendation proposed in this study takes into account multiple trip features (both quantitative and qualitative) desirability using logit model and the optimal travel time with respect to a given road traffic condition, headway, and passenger demand. The chosen area of study is the Western Visayas region of the Philippines specific to the public utility bus (PUB) and jeepney (PUJ) transit routes. The results of the research exhibited the feasibility of an optimal and strategic recommendation of public transportation route for passengers considering present time relevant trip conditions rather than relying on the historical data which are difficult to obtain, or worse, non-existent.

Future Development of NBA Teams In Chinese Market

Since David Stern explored the NBA market in China in the 1990s, it has become one of the biggest markets of the league. Since China has the largest economy besides the United States and is also a country with the most vital consumption power, every NBA team tries to achieve success in the Chinese market. However, due to China's special environment, the success in social media and the Chinese market differs from that in other countries. At the same time, due to the significant cultural differences, the market in China is different from that in Europe. Therefore, this article mainly attempts to analyze the business strategy of the NBA in China in the past 30 years. The purpose is to put forward how individual NBA teams should follow in the footsteps of the league and try to achieve success in the Chinese market by reviewing past marketing strategies. It argues multiple ways that the NBA works as a league and individual teams, along with the leadership’s commitments to capture the Chinese market through events chronologically. The NBA has its most significant global market in China, thanks to Chinese fans and David Stern’s arrangements. Multiple trips to China from the NBA teams were responded with large groups of stabilized fanbases and partnerships with Chinese companies. Besides the league and teams, NBA stars get their recognition through shoe deals and visits to China. Such close interactions give Chinese fans more love and support for players on and off the court. Shoe companies benefited from the attention of the fans as they started buying more signature shoes to admire NBA stars.

Self-Pulsations in Terahertz Quantum Cascade Lasers under Strong Optical Feedback: The Effect of Multiple Reflections in the External Cavity.

We have recently reported the self-pulsation phenomenon under strong optical feedback in terahertz (THz) quantum cascade lasers (QCLs). One important issue, however, we left open: the effect of multiple round trips in the external cavity on the laser response to feedback. Our current analysis also casts additional light on the phenomenon of self-pulsations. Using only one external cavity round trip (ECRT) in the model has been the common approach following the seminal paper by Lang-Kobayashi in 1980. However, the conditions under which the Lang-Kobayashi model, in its original single-ECRT formulation, is applicable has been rarely explored. In this work, we investigate the self-pulsation phenomenon under multiple ECRTs. We found that the self-pulsation waveform changes when considering more than one ECRT. This we attribute to the combined effect of the extended external cavity length and the frequency modulation of the pulsation frequency by the optical feedback. Our findings add to the understanding of the optical feedback dynamics under multiple ECRTs and provide a pathway for selecting the appropriate numerical model to study the optical feedback dynamics in THz QCLs and semiconductor lasers in general.

Branch and Price Algorithm for Multi-Trip Vehicle Routing with a Variable Number of Wagons and Time Windows

Motivated by the transportation needs of modern-day retailers, we consider a variant of the vehicle routing problem with time windows in which each truck has a variable capacity. In our model, each vehicle can bring one or more wagons. The clients are visited within specified time windows, and the vehicles can also make multiple trips. We give a mathematical programming formulation for the problem, and a branch and price algorithm is developed to solve the model. In each iteration of branch and price, column generation is used. Different subproblems are created based on the different capacities to find the best column. We use CPLEX to solve the problem computationally and extend Solomon’s instances to evaluate our approach. To our knowledge, ours is the first such study in this field.

Emergency logistics network optimization with time window assignment

During natural disasters or accidents, an emergency logistics network aims to ensure the distribution of relief supplies to victims in time and efficiently. When the coronavirus disease 2019 (COVID-19) emerged, the government closed the outbreak areas to control the risk of transmission. The closed areas were divided into high-risk and middle-/low-risk areas, and travel restrictions were enforced in the different risk areas. The distribution of daily essential supplies to residents in the closed areas became a major challenge for the government. This study introduces a new variant of the vehicle routing problem with travel restrictions in closed areas called the two-echelon emergency vehicle routing problem with time window assignment (2E-EVRPTWA). 2E-EVRPTWA involves transporting goods from distribution centers (DCs) to satellites in high-risk areas in the first echelon and delivering goods from DCs or satellites to customers in the second echelon. Vehicle sharing and time window assignment (TWA) strategies are applied to optimize the transportation resource configuration and improve the operational efficiency of the emergency logistics network. A tri-objective mathematical model for 2E-EVRPTWA is also constructed to minimize the total operating cost, total delivery time, and number of vehicles. A multi-objective adaptive large neighborhood search with split algorithm (MOALNS-SA) is proposed to obtain the Pareto optimal solution for 2E-EVRPTWA. The split algorithm (SA) calculates the objective values associated with each solution and assigns multiple trips to shared vehicles. A non-dominated sorting strategy is used to retain the optimal labels obtained with the SA algorithm and evaluate the quality of the multi-objective solution. The TWA strategy embedded in MOALNS-SA assigns appropriate candidate time windows to customers. The proposed MOALNS-SA produces results that are comparable with the CPLEX solver and those of the self-learning non-dominated sorting genetic algorithm-II, multi-objective ant colony algorithm, and multi-objective particle swarm optimization algorithm for 2E-EVRPTWA. A real-world COVID-19 case study from Chongqing City, China, is performed to test the performance of the proposed model and algorithm. This study helps the government and logistics enterprises design an efficient, collaborative, emergency logistics network, and promote the healthy and sustainable development of cities.

TB REACTIVATION FOLLOWING COVID-19 INFECTION

TB REACTIVATION FOLLOWING COVID-19 INFECTION