Optimal static pricing for a tree network

We study the static pricing problem for a network service provider in a loss system with a tree structure. In the network, multiple classes share a common inbound link and then have dedicated outbound links. The motivation is from a company that sells phone cards and needs to price calls to different destinations. We characterize the optimal static prices in order to maximize the steady-state revenue. We report new structural findings as well as alternative proofs for some known results. We compare the optimal static prices versus prices that are asymptotically optimal, and through a set of illustrative numerical examples we show that in certain cases the loss in revenue can be significant. Finally, we show that static prices obtained using the reduced load approximation of the blocking probabilities can be easily obtained and have near-optimal performance, which makes them more attractive for applications.     

A General Decomposition Algorithm for Parallel Queues with Correlated Arrivals

Queueing with correlated arrivals occurs when customers arrive at a set of queues simultaneously. The difficulty in analyzing systems with correlated arrivals is due to the fact that the individual queueing systems are stochastically dependent. Exact methods for analyzing these systems are computationally intensive and are limited to only a few special cases. In this paper, we consider a system of parallel queues with bulk service and correlated arrivals. We show how the matrix-geometric approach can be used to obtain the performance measures of the system. We also develop an algorithm for large systems that efficiently approximates the performance measures by decomposing it into individual queueing systems. Finally, we describe how the principles of our decomposition algorithm can be extended to analyze a variety of different parallel queueing systems with correlated arrivals. We then evaluate the accuracy of our algorithm through a numerical study.     

Analysis of heuristics for the design of tree networks

We analyze the tour partitioning heuristics for the Capacitated Minimum Spanning Tree problem. Lower bounds for the worst-case performance ratios of these heuristics are obtained by using worst-case examples. We also generalize the heuristics to the multi-center case with the same worst-case bounds.The work of the first author was supported by a Dean Summer Research Grant from Owen Graduate School of Management, Vanderbilt University.Work done in part in the Department of Industrial Engineering and Operations Research at Columbia University.The work of the last two authors was supported in part by ONR contract N00014-90-J-1649, NSF contract DDM-8922712 and the Center for Telecommunications Research under NSF contract CDR 84-21402.     

The asymptotic performance ratio of an on-line algorithm for uniform parallel machine scheduling with release dates

Jobs arriving over time must be non-preemptively processed on one of m parallel machines, each running at its own speed, so as to minimize a weighted sum of the job completion times. In this on-line environment, the processing requirement and weight of a job are not known before the job arrives. The Weighted Shortest Processing Requirement (WSPR) heuristic is a simple extension of the well known WSPT heuristic, which is optimal for the single machine problem without release dates. According to WSPR, whenever a machine completes a job, the next job assigned to it is the one with the least ratio of processing requirement to weight among all jobs available for processing at this point in time. We analyze the performance of this heuristic and prove that its asymptotic competitive ratio is one for all instances with bounded job processing requirements and weights. This implies that the WSPR algorithm generates a solution whose relative error approaches zero as the number of jobs increases. Our proof does not require any probabilistic assumption on the job parameters and relies extensively on properties of optimal solutions to a single machine relaxation of the problem. Research supported in part by ONR Contracts N00014-90-J-1649 and N00014-95-1-0232, NSF Contracts DDM-9322828, DMI-9732795, DMI-0085683 and DMI-0245352, NUS Academic Research Grant R314-000-046-112, and a research grant from the Natural Sciences and Research Council of Canada (NSERC).     

Fully polynomial-time approximation schemes for time–cost tradeoff problems in series–parallel project networks

We consider the deadline problem and budget problem of the nonlinear time–cost tradeoff project scheduling model in a series–parallel activity network. We develop fully polynomial-time approximation schemes for both problems using K-approximation sets and functions, together with series and parallel reductions.     

Asymptotic behavior of the Weber location problem on the plane

The asymptotic behavior of the Weber location problem is investigated. We consider problems wheren demand points are randomly generated in a unit disk by a uniform distribution and all weights are equal to one. The main result of the paper is that the probability that the optimal solution be on a demand point is approximately 1/n. Additional results for a largen: the optimal solution converges almost surely to the center of the disk; the difference between the optimal value of the objective function and the minimal value of the objective function on a demand point converges to 1/2.     

Worst-case analyses, linear programming and the bin-packing problem

In this paper we consider the familiar bin-packing problem and its associated set-partitioning formulation. We show that the optimal solution to the bin-packing problem can be no larger than 4/3 ⌈Z _LP⌉, whereZ _LP is the optimal solution value of the linear programming relaxation of the set-partitioning formulation. An example is provided to show that the bound is tight. A by-product of our analysis is a new worst-case bound on the performance of the well studied First Fit Decreasing and Best Fit Decreasing heuristics. This research was supported in part by ONR Contracts N00014-90-J-1649 and N00014-95-1-0232, and NSF Contracts DDM-8922712 and DDM-9322828.     

A note on the prize collecting traveling salesman problem

We study the version of the prize collecting traveling salesman problem, where the objective is to find a tour that visits a subset of vertices such that the length of the tour plus the sum of penalties associated with vertices not in the tour is as small as possible. We present an approximation algorithm with constant bound. The algorithm is based on Christofides' algorithm for the traveling salesman problem as well as a method to round fractional solutions of a linear programming relaxation to integers, feasible for the original problem.Research supported in part by ONR contract N00014-90-J-1649 and NSF contract DDM-8922712.     

Light traffic heuristic for anM/G/1 queue with limited inventory

Motivated by solving a stylized location problem, we develop a light traffic heuristic for anM/G/1 queue with limited inventory that gives rise to a closed form expression for average delay in terms of basic system parameters. Simulation experiments show that the heuristic works well. The inventory operates as follows: the inventory level drops by one unit after each service completion and whenever it drops to a pre-specified levelu, an order is placed with replenishment time exp(). Upon replenishment the inventory is restocked to a pre-specified levels and any arrivals when there is no inventory are placed in queue. Suggestions are given to cover the more general case of a New Better than Used (NBU) replenishment time distribution. Applications to inventory management problems are also discussed.The research was supported in part by ONR Contract N00014-90-J-1649, NSF Contract DDM-8922712 and the Center for Telecommunications Research under NSF grant CDR 84-21402.     

Uniform distribution of inventory positions in two-echelon periodic review systems with batch-ordering policies and interdependent demands

We consider a two-level inventory system in which there are one supplier and multiple retailers. The retailers face stochastic, interdependent customer demands. Each location employs a periodic-review (R,nQ), or lot-size reorder point, inventory policy. We show that each location's inventory positions are stationary and the stationary distribution is uniform and independent of any other's.