A bi-level school bus routing problem with bus stops selection and possibility of demand outsourcing

This paper develops a bi-level mathematical model for the school bus routing problem aiming at designing an efficient transportation system considering the possibility of predicting the students’ response. In the real world, the demand for using private cars depends on how well public transportation systems are operating especially in metropolitan cities. An inefficient public transportation will lead to an increase in the demand for using private cars. This issue will result in problems such as increased traffics and urban pollutions. To address this issue, an efficient public transportation system is designed by developing a new bi-level mathematical model. In the proposed model, the designer of the public transportation system, as the upper-level decision-maker, will locate appropriate bus stops and identify bus navigation routes. Subsequently, the decision regarding the allocation of students to transportation systems or outsourcing them will be made at the lower level which is considered as an operational-level decision-making. To solve this problem, two hybrid metaheuristic approaches named GA-EX-TS and SA-EX-TS have been proposed based on location-allocation-routing (LAR) strategy. The performance of these proposed methods is compared with exact solutions achieved from an explicit enumeration approach followed in the small-scale instances. Finally, the proposed approaches are used to solve 50 random instance problems. Comparing the results of the two tuned hybrid algorithms and conducting the sensitivity analysis of the model provide evidence for the good performance of the proposed approach.     

A new algorithm for stable assignments

A new algorithm for solving stable assignment problems is presented. By using a weight table, the overall situation of a problem is considered. The algorithm can deal with not only the one-to-one type assignments but also multi-function assignments for both equal and unequal sets. Some examples are given to illustrate the algorithm.     

Why pay attention to paths in the practice of environmental modelling?

Taking the ‘path perspective’ helps to understand and improve the practice of environmental modelling and decision making. A path is the sequence of steps taken in a modelling project. The problem solving team faces several forks where alternative choices can be made. These choices determine the path, together with the impact of uncertainties and exogenous effects. This paper discusses phenomena that influence the problem solvers' choices at the forks. Situations are described where it can be desirable to re-direct the path or backtrack on it. Phenomena are identified that can cause the modelling project to get stuck on a poor path. The concept of a path draws attention to the interplay of behavioral phenomena and the sequential nature of modelling processes. This helps understand the overall effect of the behavioral phenomena. A path checklist is developed to help practitioners detect forks and reflect on the path of the modelling project.     

A Guass–Newton-like method for inverse eigenvalue problems

In this paper, we propose a Guass–Newton-like method for finding least-square solutions to inverse eigenvalue problems. We show that the proposed method converges under some mild conditions. In particular, if the method converges to the exact solution, the convergence rate is at least quadratic in the root sense. Numerical examples are given to justify the theoretical result.     

On combination of first order Strang's splitting and additive splitting for the solution of multi-dimensional convection diffusion problem

In this article we present a new splitting approach for the numerical solution of the multi-dimensional convection diffusion equations. The method combines additive and multiplicative splitting. In particular the method combines first order Strang's splitting, multiplicative splitting defined for splitting the convection and diffusion equation, and additive splitting defined in accordance with the spatial variables. The method not only reduces the linear (or nonlinear) original problem into a series of one-dimensional and one physical operator linear problems, but also enables us to compute these one-dimensional problems using parallel processors. The accuracy and stability of the new algorithm are investigated through the solution of different multi-dimensional convection diffusion model problems with scalar coefficients.     

An effective hybrid teaching–learning-based optimization algorithm for permutation flow shop scheduling problem

Permutation flow shop scheduling (PFSP) is among the most studied scheduling settings. In this paper, a hybrid Teaching–Learning-Based Optimization algorithm (HTLBO), which combines a novel teaching–learning-based optimization algorithm for solution evolution and a variable neighborhood search (VNS) for fast solution improvement, is proposed for PFSP to determine the job sequence with minimization of makespan criterion and minimization of maximum lateness criterion, respectively. To convert the individual to the job permutation, a largest order value (LOV) rule is utilized. Furthermore, a simulated annealing (SA) is adopted as the local search method of VNS after the shaking procedure. Experimental comparisons over public PFSP test instances with other competitive algorithms show the effectiveness of the proposed algorithm. For the DMU problems, 19 new upper bounds are obtained for the instances with makespan criterion and 88 new upper bounds are obtained for the instances with maximum lateness criterion.     

Lifting solutions of quasilinear convection-dominated problems

In certain cases, quasilinear convection–diffusion–reaction equations range from parabolic to almost hyperbolic, depending on the ratio between convection and diffusion coefficients. From a numerical point of view, two main difficulties can arise related to the existence of layers and/or the non-smoothness of the coefficients of such equations. In this paper we study the steady-state solution of a convection-dominated problem. We present a new numerical method based on the idea of solving an associated modified problem, whose solution corresponds to a lifting of the solution of the initial problem. The method introduced here avoids an a priori knowledge of the layer(s) location and allows an efficient handling of the lack of smoothness of the coefficients. Numerical simulations that show the effectiveness of our approach are included.     

How attitude strength biases information processing and evaluation on the web

In this study we examined the influence of attitude strength on the processing and evaluation of sources and information on the Web. Seventy-nine German university students participated in an eye-tracking study in which they read information from eight pre-selected websites from different sources on the controversial topic of organic foods. Results showed that participants who felt strongly about the topic (i.e., those with strong prior attitude strength) scrutinized website logos of attitude-inconsistent websites shorter and judged the credibility of attitude-inconsistent websites lower. They also included more attitude-consistent information in an essay task than participants with weaker prior attitudes. Participants who felt less strongly about the topic (i.e., those with weaker prior attitudes) fixated longer on text from attitude-inconsistent websites than from attitude-consistent websites. By contrast, for participants with strong prior attitudes the time spent on the texts from attitude-consistent websites and attitude-inconsistent websites did not differ significantly. The results show that prior attitudes can bias evaluation and processing of information in different ways. Even though participants were not fully biased during initial information processing, they were so when evaluating the information and presenting it in an essay task.     

谈公路桥梁施工中的质量控制技术

对公路桥梁施工中施工管理与施工过程中存在的问题进行了分析,并对施工准备阶段、施工阶段、竣工阶段的质量控制技术进行了阐述,以保证项目施工的整体质量,促进公路交通行业的健康发展。     

An efficient online mapping tool for finding the shortest feasible path for alternative-fuel vehicles

Infrastructure for fuel-cell and other alternative-fuel vehicles is lacking not only in the paucity of fuel stations, but also in inadequate web-based support to help drivers complete their trips via the few stations that do exist. In this paper, we present an online mapping tool for finding the shortest feasible path in a road network given the vehicle's driving range and station locations. Users input their origin, destination, type of fuel, and driving range, and the algorithm generates a new reduced feasible network in which the vertices are the origin and destination nodes and reachable fuel stations and the edges represent feasible paths between them. Dijkstra's shortest path algorithm is applied to this reduced network to find the shortest feasible path. Efficiency is substantially improved by preprocessing and storing the shortest-path distances between stations. We present a web-mapping prototype (www.afvrouting.com) for hydrogen and compressed natural gas stations in the United States. Sample results illustrate the need for this kind of globally optimal solution method by showing that the optimal feasible path and refueling stops can vary tremendously as a result of user inputs for driving range, initial tank level, and one-way or round-trip.