Two-stage open shop scheduling with a bottleneck machine

Abstract not available

Two-machine open shop scheduling with special transportation times

Abstract not available

Assembly scheduling with batching

Abstract not available

Flow shop scheduling problems under machine-dependent precedence constraints

Abstract not available

Multilevel mesh partitioning for aspect ratio

Multilevel algorithms are a successful class of optimisation techniques which address the mesh partitioning problem. They usually combine a graph contraction algorithm together with a local optimisation method which refines the partition at each graph level. To date these algorithms have been used almost exclusively to minimise the cut-edge weight, however it has been shown that for certain classes of solution algorithm, the convergence of the solver is strongly influenced by the subdomain aspect ratio. In this paper therefore, we modify the multilevel algorithms in order to optimise a cost function based on aspect ratio. Several variants of the algorithms are tested and shown to provide excellent results.

Aspect ratio for mesh partitioning

This paper deals with the measure of Aspect Ratio for mesh partitioning and gives hints why, for certain solvers, the Aspect Ratio of partitions plays an important role. We define and rate different kinds of Aspect Ratio, present a new center-based partitioning method which optimizes this measure implicitly and rate several existing partitioning methods and tools under the criterion of Aspect Ratio.

Advances in Deontic Logic [Review]

Abstract not available

A multilevel approach to the travelling salesman problem

Abstract not available

Artificial intelligence and criminal evidence: A few topics

Abstract not available

High frequency components of the AE signal as useful features for bond/fibre break discriminators

Abstract not available

Earliness penalties on a single machine subject to precedence constraints: SLK due date assignment

The paper considers the single machine due date assignment and scheduling problems with n jobs in which the due dates are to be obtained from the processing times by adding a positive slack q. A schedule is feasible if there are no tardy jobs and the job sequence respects given precedence constraints. The value of q is chosen so as to minimize a function ϕ(F,q) which is non-decreasing in each of its arguments, where F is a certain non-decreasing earliness penalty function. Once q is chosen or fixed, the corresponding scheduling problem is to find a feasible schedule with the minimum value of function F. In the case of arbitrary precedence constraints the problems under consideration are shown to be NP-hard in the strong sense even for F being total earliness. If the precedence constraints are defined by a series-parallel graph, both scheduling and due date assignment problems are proved solvable in time, provided that F is either the sum of linear functions or the sum of exponential functions. The running time of the algorithms can be reduced to if the jobs are independent. Scope and purpose We consider the single machine due date assignment and scheduling problems and design fast algorithms for their solution under a wide range of assumptions. The problems under consideration arise in production planning when the management is faced with a problem of setting the realistic due dates for a number of orders. The due dates of the orders are determined by increasing the time needed for their fulfillment by a common positive slack. If the slack is set to be large enough, the due dates can be easily maintained, thereby producing a good image of the firm. This, however, may result in the substantial holding cost of the finished products before they are brought to the customer. The objective is to explore the trade-off between the size of the slack and the arising holding costs for the early orders.

Research and perspectives on processing instruction

This volume tracks the impact processing instruction has made since its conception. It provides an overview of new research trends on measuring the relative effects of processing instruction. Firstly, the authors explain processing instruction, both its main theoretical underpinnings as well as the guidelines for developing structured input practices. Secondly, they review the empirical research conducted, to date, so that readers have an overview of new research carried out on the effects of processing instruction. The authors finally reflect on the generalizability and limits of the research on processing instruction and offer future directions for processing instruction research.

Delivering processing instruction in classroom and virtual contexts

Processing Instruction (PI) is an approach to grammar instruction for second language learning. It derives its name from the fact that the instruction (both the explicit explanation as well as the practices) attempt to influence, alter, and/or improve the way learners process input. PI contrasts with traditional grammar instruction in many ways, most principally in its focus on input whereas traditional grammar instruction focuses on learners' output. The greatest contribution of PI to both theory and practice is the concept of "structured input", a form of comprehensible input that has been manipulated to maximize learners' benefit of exposure to input. This volume focuses on a new issue for PI, the role of technology in language learning. It examines empirically the differential effects of delivering PI in classrooms with an instructor and students interacting (with each other and with the instructor) versus on computers to students working individually. It also contributes to the growing body of research on the effects of PI on different languages as well as different linguistic items: preterite/imperfect aspectual contrast and negative informal commands in Spanish, the subjunctive of doubt and opinion in Italian, and the subjunctive of doubt in French. Further research contributions are made by comparing PI with other types of instruction, specifically, with meaning-oriented output instruction.