The coalitional manipulation problem is one of the central problems in computational social choice. In this paper we focus on solving the problem under the important family of positional scoring rules, in an approximate sense that was advocated by Zuckerman et al. [SODA 2008]. Our main result is a polynomial-time algorithm with (roughly speaking) the following theoretical guarantee: given a manipulable instance with m alternatives the algorithm finds a successful manipulation with at most m − 2 additional manipulators. Our technique is based on a reduction to the scheduling problem known as Q|pmtn|Cmax, along with a novel rounding procedure. We demonstrate that our analysis is tight by establishing a new type of integrality gap. We also resolve a known open question in computational social choice by showing that the coalitional manipulation problem remains (strongly) NP-complete for positional scoring rules even when votes are unweighted. Finally, we discuss the implications of our results with respect to the question: “Is there a prominent voting rule that is usually hard to manipulate?”

Recent work has demonstrated that treating resource reasoning separately from causal reasoning can lead to improved planning performance and rational resource management where increase in resources does not degrade planning performance. However, the resources were scheduled procedurally and limited to cases that could be solved backtrackfree. Terming the decoupled framework as RealPlan, in this work, I extend it with a general approach to convert the resource allocation problem as a declaratively specified dynamic constraint satisfaction problem (DCSP), compile it into CSP and solve it with a CSP solver. By doing so, the resource scheduling problem can be handled in its full complexity and can provide a computational characterization of the different scheduling classes. The CSP formulation also facilitates planner-scheduler interaction by helping the scheduler interpret the resource allocation policies proposed by the planner in terms of constraints on values of schedul...

Data-intensive applications executing over a computational grid demand large data transfers. These are costly operations. Therefore, taking them into account is mandatory to achieve efficient scheduling of data-intensive applications on grids. Further, within a heterogeneous and ever changing environment such as a grid, better schedules are typically attained by heuristics that use dynamic information about the grid and the applications. However, these information are often difficult to be accurately obtained. On the other hand, although there are schedulers that attain good performance without requiring dynamic information, they were not designed to take data transfer into account. This paper presents Storage Affinity, a novel scheduling heuristic for bag-of-tasks data-intensive applications running on grid environments. Storage Affinity...

Over the last decade many metaheuristics have been applied to the flowshop scheduling problem, ranging from Simulated Annealing or Tabu Search to complex hybrid techniques. Some of these methods provide excellent effectiveness and efficiency at the expense of being utterly complicated. In fact, several published methods require substantial implemen-tation efforts, exploit problem specific speed-up techniques that cannot be applied to slight variations of the original problem, and often re-implementations of these methods by other researchers produce results that are quite different from the original ones. In this work we present a new iterated greedy algorithm that applies two phases iteratively, named destruc-tion, were some jobs are eliminated from the incumbent solution, and construction, where the eliminated jobs are reinserted into the sequence using the well known NEH construction £Corresponding author 1 heuristic. Optionally, a local search can be applied after the construction phase. Our iterated greedy algorithm is both very simple to implement and, as shown by experimental results, highly effective when compared to state-of-the-art methods.

Scheduling is concerned with allocating limited resources to tasks to optimize certain objective functions. Due to the popularity of the Total Quality Management concept, ontime delivery of jobs has become one of the crucial factors for customer satisfaction. Scheduling plays an important role in achieving this goal. Recent developments in scheduling theory have focused on extending the models to include more practical constraints. Furthermore, due to the complexity studies conducted during the last two decades, it is now widely understood that most practical problems are NP-hard. This is one of the reasons why local search methods have been studied so extensively during the last decade. In this paper, we review briefly some of the recent extensions of scheduling theory, the recent developments in local search techniques and the new developments of scheduling in practice. Particularly, we survey two recent extensions of theory: scheduling with a 1-job-on-r-machine pattern and machine scheduling with availability constraints. We also review several local search techniques, including simulated annealing, tabu search, genetic algorithms and constraint guided heuristic search. Finally, we study the robotic cell scheduling problem, the automated guided vehicles scheduling problem, and the hoist scheduling problem.

We describe a simple on-line heuristic for scheduling job-shops. We assume there is a fixed set of routes for the jobs, and many jobs, say N , on each route. The heuristic uses safety stocks and keeps the bottleneck machine busy at almost all times, while the other machines are paced by the bottleneck machine. We perform a probabilistic analysis of the heuristic, under some assumptions on the distributions of the processing times. We show that our heuristic produces makespan which exceeds the optimal makespan by no more than c log N with a probability which exceeds 1 - 1/N for all N # 1, where c is some constant independent of N . 1 The Job-Shop Scheduling Problem with Fixed Routes A job-shop consists of machines i = 1, . . . , I, and routes r = 1, . . . , R. Route r consists of steps (r, k) where k = 1, . . . , K r indicate the steps along route r, in their required order of execution, and step (r, k) is carried out by machine #(r, k). We let C i denote the set of steps performed on machine i. In the standard job-shop formulation [23] there is one job on each route, and the objective is to schedule all the jobs so as to minimize the makespan, the earliest time by which all the jobs are completed. In our formulation of the job shop problem we assume that there are many jobs on each of the routes. In practice, in particular in factories, routes may correspond to various production processes, or to various types of products manufactured in the factory. In that case the jobs may correspond to parts or lots and there will indeed be many such jobs for each route. # School of Industrial and Systems Engineering and School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332-0205, USA; Research supported in part by NSF grants DMI-9457336 and DMI-9813345, US-...

Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In this study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangean-based decomposition. Exploring some of these properties, we characterize combinatorial auction using auction protocols and payment functions. This study is a #rst step toward developing a distributed scheduling framework that maintains system-wide performance while accommodating local preferences and objectives. We provide some insights to this framework by demonstrating four di#erent versions of the auction mechanism using job shop scheduling proble...

Many current and potential AI applications are intended to operate in dynamic environments, including those with multiple agents. As a result, standard AI plan-generation technology must be augmented with mechanisms for managing changing information, for focusing attention when multiple events occur, and for coordinating with other planning processes. The DIPART testbed (Distributed, Interactive Planner's Assistant for Real-time Transportation planning) was developed to serve as an experimental platform for analyzing a variety of such mechanisms. In this paper, we present an overview both of the DIPART system and of some of the methods for planning in dynamic environments that we have been investigating using DIPART. Many of these methods derive from theoretical work in real-time AI and in related fields, such as real-time operating systems. Introduction Many current and potential AI applications are intended to operate in dynamic environments, including those with multiple agents. An...

In the flow shop weighted completion time problem, a set of jobs has to be processed on m machines. Every machine has to process each one of the jobs, and every job has the same routing through the machines. The objective is to determine a sequence of the jobs on the machines so as to minimize the sum of the weighted completion times of all jobs on the final machine. In this paper, we present a characterization of the asymptotic optimal solution value for general distributions of the job processing times and weights. In particular, we show that the optimal objective value of this problem is asymptotically equivalent to certain single and parallel machine scheduling problems. This characterization leads to a better understanding of the effectiveness of the celebrated weighted shortest processing time algorithm, as well as to the development of an effective algorithm closely related to the profile fitting heuristic, which was previously utilized for flow shop makespan problems. Computational results show the effectiveness of WSPT and this modified profile fitting heuristic on a set of random test problems. In the m-machine flow shop problem, a set of jobs, each consisting of m operations, must be sequentially processed on m machines. Each machine can handle at most one job at a time, and a job can only be processed on one machine at a time. The jobs have to be processed on each of the machines without preemption, and every machine

Scheduling is a key factor for manufacturing productivity. Effective scheduling can improve on-time delivery, reduce inventory, cut lead times, and improve the utilization of bottleneck resources. Because of the combinatorial nature of scheduling problems, it is often difficult to find optimal schedules, especially within a limited amount of computation time. Production schedules therefore are usually generated by using heuristics in practice. However, it is very difficult to evaluate the quality of these schedules, and the consistency of performance may also be an issue. In this paper