带参量的非合作装箱博弈

带参量的非合作装箱博弈是指:每个物品的尺寸都介于0和参量x(0相似文献   

Convergence Time to Nash Equilibrium in Selfish Bin Packing

We consider a game-theoretic bin packing problem and we study the convergence time to a Nash equilibrium. We show that, if the best-response strategy is used, then the number of steps needed to reach Nash equilibrium is $O(m{w}_{\max }^2+n{w}_{\max })$ and $O(nk{w}_{\max })$, where n, m, k and $w_{\max }$ denotes, resp., the number of items, the number of bins, the number of distinct item sizes, and the size of a largest item.     

Packing Rectangles into the Unit Square

Every sequence of rectangles of side lengths not greater than 1, whose total area is smaller than or equal to can be packed into the unit square.     

Packing up to 50 Equal Circles in a Square

The problem of maximizing the radius of n equal circles that can be packed into a given square is a well-known geometrical problem. An equivalent problem is to find the largest distance d, such that n points can be placed into the square with all mutual distances at least d. Recently, all optimal packings of at most 20 circles in a square were exactly determined. In this paper, computational methods to find good packings of more than 20 circles are discussed. The best packings found with up to 50 circles are displayed. A new packing of 49 circles settles the proof that when n is a square number, the best packing is the square lattice exactly when n≤ 36. Received April 24, 1995, and in revised form June 14, 1995.     

Valid constraints for the Point Packing in a Square problem

We consider the problem of placing n$n$ points in the unit square in such a way as to maximize their minimum pairwise distance m$m$. Starting from two properties of the optimal solution presented by Locatelli and Raber in [Discrete Applied Mathematics 122 (1–3) (2002) 139–166], and using the known theoretical lower and upper bounds, we derive some constraints for tightening the original formulation of the problem.     

Selfish routing in public services

It is well observed that individual behaviour can have an effect on the efficiency of queueing systems. The impact of this behaviour on the economic efficiency of public services is considered in this paper where we present results concerning the congestion related implications of decisions made by individuals when choosing between facilities. The work presented has important managerial implications at a public policy level when considering the effect of allowing individuals to choose between providers. We show that in general the introduction of choice in an already inefficient system will not have a negative effect. Introducing choice in a system that copes with demand will have a negative effect.     

Packing Arrays and Packing Designs

A packing array is a b × k array, A with entriesa _i,j from a g-ary alphabet such that given any two columns,i and j, and for all ordered pairs of elements from a g-ary alphabet,(g ₁, g ₂), there is at most one row, r, such thata _r,i = g ₁ anda _r,j = g ₂. Further, there is a set of at leastn rows that pairwise differ in each column: they are disjoint. A central question is to determine, forgiven g, k and n, the maximum possible b. We examine the implications whenn is close to g. We give a brief analysis of the case n = g and showthat 2g rows is always achievable whenever more than g exist. We give an upper bound derivedfrom design packing numbers when n = g – 1. When g + 1 k then this bound is always at least as good as the modified Plotkin bound of [12]. When theassociated packing has as many points as blocks and has reasonably uniform replication numbers, we show thatthis bound is tight. In particular, finite geometries imply the existence of a family of optimal or near optimalpacking arrays. When no projective plane exists we present similarly strong results. This article completelydetermines the packing numbers, D(v, k, 1), when .     

一类包含刻板用户的Wardrop路由博弈

在平行边网络的背景下,考虑包含刻板用户的Wardrop路由博弈.针对线性函数下最大费用边模型和M/M/1型函数下用户和函数模型,分别给出了调和率(price of anarchy)的上界.     

Selfish load balancing for jobs with favorite machines

This paper studies the load balancing game for the favorite machine model, where each job has a certain set of favorite machines with the shortest processing time for the job. We obtain tight bounds on the Strong Price of Anarchy (strong PoA) for the general favorite machine model and a special case of the model. Our results generalize the well-known bounds on the strong PoA for the unrelated machine and identical machine models.     

Some Relations Between Packing Premeasure and Packing Measure

Let K be a compact subset of Rⁿ, 0 s n. Let , P^s denote s-dimensional packing premeasure andmeasure, respectively. We discuss in this paper the relationbetween and P^s. We prove:if , then ; and if , then for any > 0, there exists a compact subset F of K such that and P^s(F) P^s(K) – .1991 Mathematics Subject Classification 28A80, 28A78.     

Packing T-joins

A consequence of Seymour's characterization of binary clutters with the Max Flow Min Cut property is that the minimum cardinality of a T-cut is equal to the largest number of edge-disjoint T-joins in every graph that cannot be T-contracted to an odd K_2,3. We give a simple “graphic” proof of this fact. © 1996 John Wiley & Sons, Inc.     

Packing seagulls

A seagull in a graph is an induced three-vertex path. When does a graph G have k pairwise vertex-disjoint seagulls? This is NP-complete in general, but for graphs with no stable set of size three we give a complete solution. This case is of special interest because of a connection with Hadwiger’s conjecture which was the motivation for this research; and we deduce a unification and strengthening of two theorems of Blasiak [2] concerned with Hadwiger’s conjecture. Our main result is that a graph G (different from the five-wheel) with no three-vertex stable set contains k disjoint seagulls if and only if

1. |V (G)|≥3k
2. G is k-connected
3. for every clique C of G, if D denotes the set of vertices in V (G)\C that have both a neighbour and a non-neighbour in C then |D|+|V (G)\C|≥2k, and
4. the complement graph of G has a matching with k edges.

We also address the analogous fractional and half-integral packing questions, and give a polynomial time algorithm to test whether there are k disjoint seagulls.     

Packing list-colorings

List coloring is an influential and classic topic in graph theory. We initiate the study of a natural strengthening of this problem, where instead of one list-coloring, we seek many in parallel. Our explorations have uncovered a potentially rich seam of interesting problems spanning chromatic graph theory. Given a $k$-list-assignment $L$ of a graph $G$, which is the assignment of a list $L(v)$ of $k$ colors to each vertex $v\in V(G)$, we study the existence of $k$ pairwise-disjoint proper colorings of $G$ using colors from these lists. We may refer to this as a list-packing. Using a mix of combinatorial and probabilistic methods, we set out some basic upper bounds on the smallest $k$ for which such a list-packing is always guaranteed, in terms of the number of vertices, the degeneracy, the maximum degree, or the (list) chromatic number of $G$. (The reader might already find it interesting that such a minimal $k$ is well defined.) We also pursue a more focused study of the case when $G$ is a bipartite graph. Our results do not yet rule out the tantalising prospect that the minimal $k$ above is not too much larger than the list chromatic number. Our study has taken inspiration from study of the strong chromatic number, and we also explore generalizations of the problem above in the same spirit.     

Multiple Container Packing: A Case Study of Pipe Packing

While the problem of packing single containers and pallets has been thoroughly investigated very little attention has been given to the efficient packing of multiple container loads. Normally in practice a multiple container load is packed by a single container algorithm used in a greedy fashion. This paper introduces the issues involved in multiple container loading. It lays out three different strategies for solving the problem: sequential packing using a single container heuristic, pre-allocating items to the containers and choosing container loads using simultaneous packing models. The principal simultaneous models are pattern selection IP models. We present an application of packing pipes in shipping containers using two pattern selection IP models, a pattern selection heuristic, a sequential greedy algorithm and a pre-allocation method. The experimental results use randomly generated data sets. We discuss several useful insights into the methods and show that for this application the pattern selection methods perform best.     

Packing three trees

We present some results concerning edge-disjoint placement of two or three copies of a tree, as well as a theorem about the packing of three trees into the complete graph K_n.     

Packing random rectangles

A random rectangle is the product of two independent random intervals, each being the interval between two random points drawn independently and uniformly from [0,1]. We prove that te number C _n of items in a maximum cardinality disjoint subset of n random rectangles satisfies

Packing random intervals

Summary Letn random intervalsI ₁, ...,I _n be chosen by selecting endpoints independently from the uniform distribution on [0.1]. Apacking is a pairwise disjoint subset of the intervals; itswasted space is the Lebesgue measure of the points of [0,1] not covered by the packing. In any set of intervals the packing with least wasted space is computationally easy to find; but its expected wasted space in the random case is not obvious. We show that with high probability for largen, this best packing has wasted space . It turns out that if the endpoints 0 and 1 are identified, so that the problem is now one of packing random arcs in a unit-circumference circle, then optimal wasted space is reduced toO(1/n). Interestingly, there is a striking difference between thesizes of the best packings: about logn intervals in the unit interval case, but usually only one or two arcs in the circle case.