新能源汽车空调系统技术探索

在能源与环境的双重压力下,新能源汽车产业迅速发展。其空调系统的研究也引起了人们重视。对应用在新能源汽车上的热泵式和燃料电池余热利用空调系统进行了调研,并对其进行分析。从压缩机、制冷、供热系统等方面介绍了新能源汽车空调系统与传统汽车空调系统的区别。介绍了用作汽车空调系统的R1234yf和CO2两种新型制冷剂。论述了新能源汽车空调系统发展的关键性技术问题。     

Exponential irreducible neighborhoods for combinatorial optimization problems

This paper deals with irreducible augmentation vectors associated with three combinatorial optimization problems: the TSP, the ATSP, and the SOP. We study families of irreducible vectors of exponential size, derived from alternating cycles, where optimizing a linear function over each of these families can be done in polynomial time. A family of irreducible vectors induces an irreducible neighborhood; an algorithm for optimizing over this family is known as a local search heuristic. Irreducible neighborhoods do not only serve as a tool to improve feasible solutions, but do play an important role in an exact primal algorithm; such families are the primal counterpart of a families of facet inducing inequalities.     

Radial Graphs of Constant Mean Curvature and Doubly Connected Minimal Surfaces with Prescribed Boundary

In this paper we investigate existence and uniqueness of radial graphs ofconstant mean curvature (cmc) with prescribed boundary. Our main resultestablishes the existence of a minimal radial anullus spanning two givenconvex curves in parallel planes of R³; we also obtain a variant ofa well-known result of James Serrin about the existence of radial cmc graphsover convex domains in the sphere.     

Mechanisms for local search

Local search methods for combinatorial optimization make a series of steps, at each stage improving the current solution by moving to a neighbouring solution. This is usually done by considering the neighbouring solutions one at a time and moving to the first one which gives an improvement (a first-improving method). In this paper we consider whether there are circumstances in which some other strategy will have better performance. In exploring this question we begin by giving a theoretical treatment of a simple model with random objective values at each solution point. We carry out some experiments on the Travelling Salesman Problem and the Quadratic Assignment Problem using varying values of a spread parameter, k. The value of k corresponds to the number of improving solutions looked at before making a move. We also make some conjectures relating the overall performance of the local search method to the average number of solutions which are evaluated before a local minimum is reached.     

Control decisions and personal beliefs: their effect on solving mathematical problems

The main purpose of this paper is to discuss how college students enrolled in a college level elementary algebra course exercised control decisions while working on routine and non-routine problems, and how their personal belief systems shaped those control decisions. In order to prepare students for success in mathematics we as educators need to understand the process steps they use to solve homework or examination questions, in other words, understand how they “do” mathematics. The findings in this study suggest that an individual’s belief system impacts how they approach a problem. Lack of confidence and previous lack of success combined to prompt swift decisions to stop working. Further findings indicate that students continue with unsuccessful strategies when working on unfamiliar problems due to a perceived dependence of solution strategies to specific problem types. In this situation, the students persisted in an inappropriate solution strategy, never reaching a correct solution. Control decisions concerning the pursuit of alternative strategies are not an issue if the students are unaware that they might need to make different choices during their solutions. More successful control decisions were made when working with familiar problems.     

NUMERICAL INVERSION OF MULTIDIMENSIONAL LAPLACE TRANSFORMS USING MOMENT METHODSe

This paper develops a numerical method to invert multi-dimensional Laplace transforms. By a variable transform, Laplace transforms are converted to multi-dimensional Hausdorff moment problems so that the numerical solution can be achieved. Stability estimation is also obtained. Numerical simulations show the efficiency and practicality of the method.     

On sharply vertex transitive 2-factorizations of the complete graph

We introduce the concept of a 2-starter in a group G of odd order. We prove that any 2-factorization of the complete graph admitting G as a sharply vertex transitive automorphism group is equivalent to a suitable 2-starter in G. Some classes of 2-starters are studied, with special attention given to those leading to solutions of some Oberwolfach or Hamilton-Waterloo problems.     

Routing relatively few customers per route

This paper presents a modification of the well-known Solomon (1987) sequential insertion heuristic I1 for the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW involves servicing customers within a pre-specified service time window by homogeneously capacitated vehicles from a single depot. By using two new measures for the additional time needed to insert a customer in the route, significantly better solutions are obtained for relatively short routes compared to the original Solomon (1987) sequential insertion heuristic I1. Because high-quality initial heuristics often allow local searches and metaheuristics to achieve better solutions more quickly, the new approach is likely to help generating better solutions to practical routing problems.     

A new ILP-based refinement heuristic for Vehicle Routing Problems

In this paper we address the Distance-Constrained Capacitated Vehicle Routing Problem (DCVRP), where k minimum-cost routes through a central depot have to be constructed so as to cover all customers while satisfying, for each route, both a capacity and a total-distance-travelled limit. Our starting point is the following refinement procedure proposed in 1981 by Sarvanov and Doroshko for the pure Travelling Salesman Problem (TSP): given a starting tour, (a) remove all the nodes in even position, thus leaving an equal number of ``empty holes' in the tour; (b) optimally re-assign the removed nodes to the empty holes through the efficient solution of a min-sum assignment (weighted bipartite matching) problem. We first extend the Sarvanov-Doroshko method to DCVRP, and then generalize it. Our generalization involves a procedure to generate a large number of new sequences through the extracted nodes, as well as a more sophisticated ILP model for the reallocation of some of these sequences. An important feature of our method is that it does not rely on any specialized ILP code, as any general-purpose ILP solver can be used to solve the reallocation model. We report computational results on a large set of capacitated VRP instances from the literature (with symmetric/asymmetric costs and with/without distance constraints), along with an analysis of the performance of the new method and of its features. Interestingly, in 13 cases the new method was able to improve the best-know solution available from the literature. Work supported by M.I.U.R. and by C.N.R., Italy.     

Creating very large scale neighborhoods out of smaller ones by compounding moves

This paper discusses neighborhood search algorithms where the size of the neighborhood is “very large” with respect to the size of the input data. We concentrate on such a very large scale neighborhood (VLSN) search technique based on compounding independent moves (CIM) such as 2-opts, swaps, and insertions. We present a systematic way of creating and searching CIM neighborhoods for routing problems with side constraints. For such problems, the exact search of the CIM neighborhood becomes NP-hard. We introduce a multi-label shortest path algorithm for searching these neighborhoods heuristically. Results of a computational study on the vehicle routing problem with capacity and distance restrictions shows that CIM algorithms are very competitive approaches for solving vehicle routing problems. Overall, the solutions generated by the CIM algorithm have the best performance among the current solution methodologies in terms of percentage deviation from the best-known solutions for large-scale capacitated VRP instances.