A simple proof of Liang's lower bound for on-line bin packing and the extension to the parametric case

In this note we present a simplified proof of a lower bound for on-line bin packing. This proof also covers the well-known result given by Liang in Inform. Process Lett. 10 (1980) 76–79.

Approximating the Randomized Hitting Time Distribution of a Non-stationary Gamma Process

The non-stationary gamma process is a non-decreasing stochastic process with independent increments. By this monotonic behavior this stochastic process serves as a natural candidate for modelling time-dependent phenomena such as degradation. In condition-based maintenance the first time such a process exceeds a random threshold is used as a model for the lifetime of a device or for the random time between two successive imperfect maintenance actions. Therefore there is a need to investigate in detail the cumulative distribution function (cdf) of this so-called randomized hitting time. We first relate the cdf of the (randomized) hitting time of a non-stationary gamma process to the cdf of a related hitting time of a stationary gamma process. Even for a stationary gamma process this cdf has in general no elementary formula and its evaluation is time-consuming. Hence two approximations are proposed in this paper and both have a clear probabilistic interpretation. Numerical experiments show that these approximations are easy to evaluate and their accuracy depends on the scale parameter of the non-stationary gamma process. Finally, we also consider some special cases of randomized hitting times for which it is possible to give an elementary formula for its cdf.Approximation;Condition based maintenance;First hitting time;Non-stationary gamma process;Random threshold

Approximating the randomized hitting time distribution of a non-stationary gamma process

The non-stationary gamma process is a non-decreasing stochasticprocess with independent increments. By this monotonic behavior thisstochastic process serves as a natural candidate for modellingtime-dependent phenomena such as degradation. In condition-basedmaintenance the first time such a process exceeds a random thresholdis used as a model for the lifetime of a device or for the randomtime between two successive imperfect maintenance actions. Thereforethere is a need to investigate in detail the cumulative distributionfunction (cdf) of this so-called randomized hitting time. We firstrelate the cdf of the (randomized) hitting time of a non-stationarygamma process to the cdf of a related hitting time of a stationarygamma process. Even for a stationary gamma process this cdf has ingeneral no elementary formula and its evaluation is time-consuming.Hence two approximations are proposed in this paper and both have aclear probabilistic interpretation. Numerical experiments show thatthese approximations are easy to evaluate and their accuracy dependson the scale parameter of the non-stationary gamma process. Finally,we also consider some special cases of randomized hitting times forwhich it is possible to give an elementary formula for its cdf.approximation;condition based maintencance;first hitting time;non-stationary gamma process;random threshold

On noncooperative games, minimax theorems and equilibrium problems

In this chapter we give an overview on the theory of noncooperative games. In the first part we consider in detail for zero-sum (and constant-sum) noncooperative games under which necessary and sufficient conditions on the payoff function and different (extended) strategy sets for both players an equilibrium saddlepoint exists. This is done by using the most elementary proofs. One proof uses the separation result for disjoint convex sets, while the other proof uses linear programming duality and some elementary properties of compact sets. Also, for the most famous saddlepoint result given by Sion's minmax theorem an elementary proof using only the definition of connectedness is given. In the final part we consider n-person nonzero-sum noncooperative games and show by a simple application of the KKM lemma that a so-called Nash equilibrium point exists for compact strategy sets and concavity conditions on the payoff functions.

On Purchase Timing Models in Marketing

In this paper we consider stochastic purchase timing models used in marketing for low-involvement products and show that important characteristics of those models are easy to compute. As such these calculations are based on an elementary probabilistic argument and cover not only the well-known condensed negative binomial model but also stochastic purchase timing models with other interarrival and mixing distributions.marketing;purchase timing model

On Noncooperative Games, Minimax Theorems and Equilibrium Problems

In this chapter we give an overview on the theory of noncooperative games. In the first part we consider in detail for zero-sum (and constant-sum) noncooperative games under which necessary and sufficient conditions on the payoff function and different (extended) strategy sets for both players an equilibrium saddlepoint exists. This is done by using the most elementary proofs. One proof uses the separation result for disjoint convex sets, while the other proof uses linear programming duality and some elementary properties of compact sets. Also, for the most famous saddlepoint result given by Sion's minmax theorem an elementary proof using only the definition of connectedness is given. In the final part we consider n-person nonzero-sum noncooperative games and show by a simple application of the KKM lemma that a so-called Nash equilibrium point exists for compact strategy sets and concavity conditions on the payoff functions.KKM Lemma;Equilibrium Problems;Minimax Theorems;Nash Equilibrium Point;Non-Cooperative Game Theory

Hybrid next-fit algorithm for the two-dimensional rectangle bin-packing problem

We present a new approximation algorithm for the two-dimensional bin-packing problem. The algorithm is based on two one-dimensional bin-packing algorithms. Since the algorithm is of next-fit type it can also be used for those cases where the output is required to be on-line (e. g. if we open an new bin we have no possibility to pack elements into the earlier opened bins). We give a tight bound for its worst-case and show that this bound is a parameter of the maximal sizes of the items to be packed. Moreover, we also present a probabilistic analysis of this algorithm.worst-case analysis;probabilistic analysis;bin-packing;heuristic algorithm;on-line algorithm;two-dimensional packing

The rate of convergence to optimality of the LPT rule

The LPT rule is a heuristic method to distribute jobs among identical machines so as to minimize the makespan of the resulting schedule. If the processing times of the jobs are assumed to be independent identically distributed random variables, then (under a mild condition on the distribution) the absolute error of this heuristic is known to converge to 0 almost surely. In this note we analyse the asymptotic behaviour of the absolute error and its first and higher moments to show that under quite general assumptions the speed of convergence is proportional to appropriate powers of (log log n)/n and 1/n. Thus, we simplify, strengthen and extend earlier results obtained for the uniform and exponential distribution.

On a pairing heuristic in binpacking

For the analysis of a paairng heuristic in binpacking an important result is used without proof in [1] and [2]. In this note we discuss this result and give a detailed proof of it

Renewal theory for random variables with a heavy tailed distribution and finite variance

Let X-1, X-2,... X-n be independent and identically distributed (i.i.d.) non-negative random variables with a common distribution function (d.f.) F with unbounded support and EX12 < infinity. We show that for a large class of heavy tailed random variables with a finite variance the renewal function U satisfies U(x) - x/mu - mu(2)/2 mu(2) similar to -1/mu x integral(infinity)(x) integral(infinity)(s) (1 - F(u))duds as x -> infinity