Maximizing throughput in queueing networks with limited flexibility

We study a queueing network where customers go through several stages of processing, with the class of a customer used to indicate the stage of processing. The customers are serviced by a set of flexible servers, i.e., a server is capable of serving more than one class of customers and the sets of classes that the servers are capable of serving may overlap. We would like to choose an assignment of servers that achieves the maximal capacity of the given queueing network, where the maximal capacity is λ^∗ if the network can be stabilized for all arrival rates λ < λ^∗ and cannot possibly be stabilized for all λ > λ^∗. We examine the situation where there is a restriction on the number of servers that are able to serve a class, and reduce the maximal capacity objective to a maximum throughput allocation problem of independent interest: the total discrete capacity constrained problem (TDCCP). We prove that solving TDCCP is in general NP-complete, but we also give exact or approximation algorithms for several important special cases and discuss the implications for building limited flexibility into a system.     

Admission control and pricing in a queue with batch arrivals

We investigate a problem of admission control in a queue with batch arrivals. We consider a single server with exponential service times and a compound Poisson arrival process. Each arriving batch computes its expected benefit and decides whether or not to enter the system. The controller’s problem is to set state dependent prices for arriving batches. Once prices have been set we formulate the admission control problem, derive properties of the value function, and obtain the optimal admission policy.     

Admission control of a service station when the arrivals are internal

All studies in the admission control of a service station make decisions at arrival epochs. When arrivals are internal and are rejected from a queue, the rejected jobs have to be routed to other stations in the system. However the system will not know whether a job will be admitted to a queue or not until its arrival epoch to that queue. Thus, the system has to react dynamically and agilely to the decisions made at a specific queue and may try several queues before finding a queue that admits the job. This paper remedies these difficulties by changing the decision epochs of the admission control from arrival epochs to departure epochs with the actions of switching (keeping) the arrival stream on or off. Thus upstream stations will have information on the admission status of their downstream stations all the time. It is proved that the optimal policy for this revised admission control system is of control limit type for an M/G/1 queue. Comparisons of the optimal values and optimal policies for the admission controls made at arrival epochs and at departure epochs are included in the paper.     

Parallel machine scheduling with nested job assignment restrictions

We derive a polynomial time approximation scheme for a special case of makespan minimization on unrelated machines.     

Improved analysis of an algorithm for the coupled task problem with UET jobs

The coupled task problem is to schedule n jobs, each one consisting of two subtasks with exact delay times between them, on a single machine. We derive a new lower bound for the problem variant with unit execution times and correct a previously published analysis.     

Optimizing bandwidth allocation in elastic optical networks with application to scheduling

We study a problem of optimal bandwidth allocation in the elastic optical networks technology, where usable frequency intervals are of variable width. In this setting, each lightpath has a lower and upper bound on the width of its frequency interval, as well as an associated profit, and we seek a bandwidth assignment that maximizes the total profit. This problem is known to be NP-complete. We strengthen this result by showing that, in fact, the problem is inapproximable within any constant ratio even on a path network. We further derive NP-hardness results and present approximation algorithms for several special cases of the path and ring networks, which are of practical interest. Finally, while in general our problem is hard to approximate, we show that an optimal solution can be obtained by allowing resource augmentation. Some of our results resolve open problems posed by Shalom et al. (2013) [28]. Our study has applications also in real-time scheduling.     

On the machine scheduling problem with job delivery coordination

This paper examines two scheduling problems with job delivery coordination, in which each job demands different amount of storage space during transportation. For the first problem, in which jobs are processed on a single machine and delivered by one vehicle to a customer, we present a best possible approximation algorithm with a worst-case ratio arbitrarily close to 3/2. For the second problem, which differs from the first problem in that jobs are processed by two parallel machines, we give an improved algorithm with a worst-case ratio 5/3.     

Approximation algorithms for scheduling problems with a modified total weighted tardiness objective

We study the approximability of minimum total weighted tardiness with a modified objective which includes an additive constant. This ensures the existence of a positive lower bound for the minimum value. Moreover the new objective has a natural interpretation in just-in-time production systems.     

Approximating maximum edge 2-coloring in simple graphs

We present a polynomial-time approximation algorithm for legally coloring as many edges of a given simple graph as possible using two colors. It achieves an approximation ratio of roughly 0.842 and runs in O(n³m) time, where n (respectively, m) is the number of vertices (respectively, edges) in the input graph. The previously best ratio achieved by a polynomial-time approximation algorithm was .     

Approximation algorithms for problems in scheduling with set-ups

In this paper, we present new approximation results for the offline problem of single machine scheduling with sequence-independent set-ups and item availability, where the jobs to be scheduled are independent (i.e., have no precedence constraints) and have a common release time.We present polynomial-time approximation algorithms for two versions of this problem. In the first version, the input includes a weight for each job, and the goal is to minimize the total weighted completion time. On any input, our algorithm produces a schedule whose total weighted completion time is within a factor 2 of optimal for that input.In the second version, the input includes a due date for each job, and the goal is to minimize the maximum lateness of any job. On any input, our algorithm produces a schedule with the following performance guarantee: the maximum lateness of a job is at most the maximum lateness of the optimal schedule on a machine that runs at half the speed of our machine.     

Bounded-hops power assignment in ad hoc wireless networks

Motivated by topology control in ad hoc wireless networks, Power Assignment is a family of problems, each defined by a certain connectivity constraint (such as strong connectivity). The input consists of a directed complete weighted digraph G=(V,c) (that is, ). The power of a vertex u in a directed spanning subgraph H is given by , and corresponds to the energy consumption required for node u to transmit to all nodes v with uv∈E(H). The power of H is given by . Power Assignment seeks to minimize p(H) while H satisfies the given connectivity constraint.Min-Power Bounded-Hops Broadcast is a power assignment problem which has as additional input a positive integer d and a r∈V. The output H must be a r-rooted outgoing arborescence of depth at most d. We give an (O(logn),O(logn)) bicriteria approximation algorithm for Min-Power Bounded-Hops Broadcast: that is, our output has depth at most O(dlogn) and power at most O(logn) times the optimum solution.For the Euclidean case, when c(u,v)=c(v,u)=∥u,v∥^κ (here ∥u,v∥ is the Euclidean distance and κ is a constant between 2 and 5), the output of our algorithm can be modified to give a O((logn)^κ) approximation ratio. Previous results for Min-Power Bounded-Hops Broadcast are only exact algorithms based on dynamic programming for the case when the nodes lie on the line and c(u,v)=c(v,u)=∥u,v∥^κ.Our bicriteria results extend to Min-Power Bounded-Hops Strong Connectivity, where H must have a path of at most d edges in between any two nodes. Previous work for Min-Power Bounded-Hops Strong Connectivity consists only of constant or better approximation for special cases of the Euclidean case.     

Approximating the least core value and least core of cooperative games with supermodular costs

We study the approximation of the least core value and the least core of supermodular cost cooperative games. We provide a framework for approximation based on oracles that approximately determine maximally violated constraints. This framework yields a 3-approximation algorithm for computing the least core value of supermodular cost cooperative games, and a polynomial-time algorithm for computing a cost allocation in the 2-approximate least core of these games. This approximation framework extends naturally to submodular profit cooperative games. For scheduling games, a special class of supermodular cost cooperative games, we give a fully polynomial-time approximation scheme for computing the least core value. For matroid profit games, a special class of submodular profit cooperative games, we give exact polynomial-time algorithms for computing the least core value as well as a least core cost allocation.     

Minimizing makespan on a single batching machine with release times and non-identical job sizes

We consider the problem of scheduling jobs with release times and non-identical job sizes on a single batching machine; our objective is to minimize makespan. We present an approximation algorithm with worst-case ratio 2+ε, where ε>0 can be made arbitrarily small.     

Tighter approximation bounds for LPT scheduling in two special cases

$Q||C_{\max }$ denotes the problem of scheduling n jobs on m machines of different speeds such that the makespan is minimized. In the paper two special cases of $Q||C_{\max }$ are considered: case I, when $m?1$ machine speeds are equal, and there is only one faster machine; and case II, when machine speeds are all powers of 2 (2-divisible machines). Case I has been widely studied in the literature, while case II is significant in an approach to design so called monotone algorithms for the scheduling problem.We deal with the worst case approximation ratio of the classic list scheduling algorithm ‘Largest Processing Time (LPT)’. We provide an analysis of this ratio $\mathit{Lpt}/\mathit{Opt}$ for both special cases: For ‘one fast machine’, a tight bound of $(\sqrt{3}+1)/2\approx 1.3660$ is given. For 2-divisible machines, we show that in the worst case $1.3673<\mathit{Lpt}/\mathit{Opt}<1.4$. Besides, we prove another lower bound of $955/699>(\sqrt{3}+1)/2$ when LPT breaks ties arbitrarily.To our knowledge, the best previous lower and upper bounds were $(4/3,3/2?1/2m]$ in case I [T. Gonzalez, O.H. Ibarra, S. Sahni, Bounds for LPT schedules on uniform processors, SIAM Journal on Computing 6 (1) (1977) 155–166], respectively $[4/3?1/3m,3/2]$ in case II [R.L. Graham, Bounds on multiprocessing timing anomalies, SIAM Journal on Applied Mathematics 17 (1969) 416–429; A. Kovács, Fast monotone 3-approximation algorithm for scheduling related machines, in: Proc. 13th Europ. Symp. on Algs. (ESA), in: LNCS, vol. 3669, Springer, 2005, pp. 616–627]. Moreover, Gonzalez et al. conjectured the lower bound 4/3 to be tight in the ‘one fast machine’ case [T. Gonzalez, O.H. Ibarra, S. Sahni, Bounds for LPT schedules on uniform processors, SIAM Journal on Computing 6 (1) (1977) 155–166].     

Scheduling jobs with time-resource tradeoff via nonlinear programming

We consider a scheduling problem where the processing time of any job is dependent on the usage of a discrete renewable resource, e.g. personnel. An amount of k units of that resource can be allocated to the jobs at any time, and the more of that resource is allocated to a job, the smaller its processing time. The objective is to find a resource allocation and a schedule that minimizes the makespan. We explicitly allow for succinctly encodable time-resource tradeoff functions, which calls for mathematical programming techniques other than those that have been used before. Utilizing a (nonlinear) integer mathematical program, we obtain the first polynomial time approximation algorithm for the scheduling problem, with performance bound (3+ε) for any ε>0. Our approach relies on a fully polynomial time approximation scheme to solve the nonlinear mathematical programming relaxation. We also derive lower bounds for the approximation.     

Local search for the minimum label spanning tree problem with bounded color classes

In the Minimum Label Spanning Tree problem, the input consists of an edge-colored undirected graph, and the goal is to find a spanning tree with the minimum number of different colors. We investigate the special case where every color appears at most r times in the input graph. This special case is polynomially solvable for r=2, and NP- and APX-complete for any fixed r?3.We analyze local search algorithms that are allowed to switch up to k of the colors used in a feasible solution. We show that for k=2 any local optimum yields an (r+1)/2-approximation of the global optimum, and that this bound is tight. For every k?3, there exist instances for which some local optima are a factor of r/2 away from the global optimum.     

Admission control with batch arrivals

We consider the problem of dynamic admission control in a multi-class Markovian loss system receiving random batches, where each admitted class-i job demands an exponential service with rate μ, and brings a reward r_i. We show that the optimal admission policy is a sequential threshold policy with monotone thresholds.     

Online time-constrained scheduling in linear and ring networks

We consider the problem of scheduling a sequence of packets over a linear network, where every packet has a source and a target, as well as a release time and a deadline by which it must arrive at its target. The model we consider is bufferless, where packets are not allowed to be buffered in nodes along their paths other than at their source. This model applies to optical networks where opto-electronic conversion is costly, and packets mostly travel through bufferless hops. The offline version of this problem was previously studied in M. Adler et al. (2002) [3]. In this paper we study the online version of the problem, where we are required to schedule the packets without knowledge of future packet arrivals. We use competitive analysis to evaluate the performance of our algorithms. We present the first online algorithms for several versions of the problem. For the problem of throughput maximization, where all packets have uniform weights, we give an algorithm with a logarithmic competitive ratio, and present some lower bounds. For other weight functions, we show algorithms that achieve optimal competitive ratios.