We consider the average case behavior of onedmensional bin paekmg algorithms in the case where bins have unit capacity and item sizes are chosen according to the ' 'dficrete uniform" distribution U~; k), 1 s j < k, where each item size in the set {llk,21k,..., ji k) has probability 1/j of beiig chosen.Note that for fixed j,k the distributions U{?nj;mk]' approach the continuous distribution U(O, jlk] as m A W, where in U(O, jl k] the item sizes are chosen uniformly horn the half-open interval (O,jik].In this paper, we show that average case behavior can differ substantially under the two types of distributions.We show that for all j, k, j < k-1, there exist on-line algorithms that have constant expected waste under U~; k], whereas no on-line algorithm can have less than C2(n1'2) waste under U(O, U] for any u s 1. Conmariwise, although the First Fit Decreasing (off-line) algorithm has constant expected waste under U(O, u] for all u < 1/2,

An on-line problem is one in which an algorithm must handle a sequence of requests, satisfying each request without knowledge of the future requests. Examples of on-line problems include scheduling the motion of elevators, finding routes in networks, allocating cache memory, and maintaining dynamic data structures. A competitive algorithm for an on-line problem has the property that its performance on any sequence of requests is within a constant factor of the performance of any other algorithm on the same sequence. This paper presents several general results concerning competitive algorithms, as well as results on specific on-line problems.

The computational complexity of constructing the imbeddings of a given graph into surfaces of different genus is not well understood. In this paper, topological methods and a reduction to linear matroid parity are used to develop a polynomial-time algorithm to find a maximum-genus cellular imbedding. This seems to be the first imbedding algorithm for which the running time is not exponential in the genus of the imbedding surface.

We study the asymptotic expected behavior of the First Fit and First Fit Decreasing bin packing algorithms applied to items chosen uniformly from the interval (0,u], u ≤ 1. Our results indicate that the algorithms perform even better than previously expected.