The multi-agent programming contest from 2005–2010

The Multi-Agent Programming Contest is an annual international event on programming multi-agent systems: Teams of agents participate in a simulated cooperative scenario. It started in 2005 and is organised in 2010 for the sixth time. The contest is an attempt to stimulate research in the area of multi-agent system development and programming by (i) identifying key problems in the field and (ii) collecting suitable benchmarks that can serve as milestones for testing multi-agent programming languages, platforms and tools. This article provides a short history of the contest since it started and reports in more detail on the cows and cowboys scenario implemented for the 2008, 2009 and 2010 contest editions. We briefly discuss the underlying technological background and conclude with a critical discussion of the experiences and lessons learned.     

A method for automatic calibration of 3D vision systems by means of high-resolution data

Calibration of the digital projector is a necessary procedure for many 3D scanners. The proposed method for automatic calibration of a projector uses binary color templates and searches for the most stable points for minimization of decoding errors. The camera resolution must significantly exceed the projector’s resolution. The method can be easily adapted for operation with black-and-white templates.     

Margin-setting with hyperellipsoidal surfaces

In a number of prior papers we described and explored a new pattern recognition method called Margin-Setting that accomplishes excellent generalization using very few training samples. The result was a multi-round classifier with each round consisting of a set of hyperspheres such that if a datum fell within a certain hypersphere, it was labeled with one particular class. Margin-Setting achieves concurrent low Vapnik-Chervonenkis (VC) dimension and high accuracy which is a consequence of partitioning the training set into smaller sets that make this possible. This paper extends Margin-Setting from hyperspheres to hyperellipsoids resulting in improved performance.     

A fluid approach to large volume job shop scheduling

We consider large volume job shop scheduling problems, in which there is a fixed number of machines, a bounded number of activities per job, and a large number of jobs. In large volume job shops it makes sense to solve a fluid problem and to schedule the jobs in such a way as to track the fluid solution. There have been several papers which used this idea to propose approximate solutions which are asymptotically optimal as the volume increases. We survey some of these results here. In most of these papers it is assumed that the problem consists of many identical copies of a fixed set of jobs. Our contribution in this paper is to extend the results to the far more general situation in which the many jobs are all different. We propose a very simple heuristic which can schedule such problems. We discuss asymptotic optimality of this heuristic, under a wide range of previously unexplored situations. We provide a software package to explore the performance of our policy, and present extensive computational evidence for its effectiveness.     

An improved constraint satisfaction adaptive neural network for job-shop scheduling

This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.     

Parallel batch scheduling of equal-length jobs with release and due dates

In this paper we study parallel batch scheduling problems with bounded batch capacity and equal-length jobs in a single and parallel machine environment. It is shown that the feasibility problem 1|p-batch,b<n,r _j ,p _j =p,C _j ≤d _j |− can be solved in O(n ²) time and that the problem of minimizing the maximum lateness can be solved in O(n ²log n) time. For the parallel machine problem P|p-batch,b<n,r _j ,p _j =p,C _j ≤d _j |− an O(n ³log n)-time algorithm is provided, which can also be used to solve the problem of minimizing the maximum lateness in O(n ³log ² n) time.     

Throughput optimization in two-machine flowshops with flexible operations

In this study, a two-machine flowshop producing identical parts is considered. Each of the identical parts is assumed to require a number of manufacturing operations, and the machines are assumed to be flexible enough to perform different operations. Due to economical or technological constraints, some specific operations are preassigned to one of the machines. The remaining operations, called flexible operations, can be performed on either one of the machines, so that the same flexible operation can be performed on different machines for different parts. The problem is to determine the assignment of the flexible operations to the machines for each part, with the objective of maximizing the throughput rate. We consider various cases regarding the number of parts to be produced and the capacity of the buffer between the machines. We present solution methods for each variant of the problem.     

Minimizing total tardiness in a stochastic single machine scheduling problem using approximate dynamic programming

This paper addresses the non-preemptive single machine scheduling problem to minimize total tardiness. We are interested in the online version of this problem, where orders arrive at the system at random times. Jobs have to be scheduled without knowledge of what jobs will come afterwards. The processing times and the due dates become known when the order is placed. The order release date occurs only at the beginning of periodic intervals. A customized approximate dynamic programming method is introduced for this problem. The authors also present numerical experiments that assess the reliability of the new approach and show that it performs better than a myopic policy.     

Grammar constraints

With the introduction of the Regular Membership Constraint, a new line of research has opened where constraints are based on formal languages. This paper is taking the next step, namely to investigate constraints based on grammars higher up in the Chomsky hierarchy. We devise a time- and space-efficient incremental arc-consistency algorithm for context-free grammars, investigate when logic combinations of grammar constraints are tractable, show how to exploit non-constant size grammars and reorderings of languages, and study where the boundaries run between regular, context-free, and context-sensitive grammar filtering.