Scheduling projects with linear time-dependent cash flows to maximize the net present value.

In this paper we study the unconstrained project scheduling problem with discounted cash flows where the net cash flows are assumed to be linear dependent on the completion times of the corresponding activities. Each activity of this unconstrained project scheduling problem has a known deterministic net cash flow which is linear and non-increasing in time. Progress payments and cash outflows occur at the completion of activities. The objective is to schedule the activities in order to maximize the net present value (npv) subject to the precedence constraints and a fixed deadline. Despite the growing amount of research concerning the financial aspects in project scheduling, little research has been done on the problem with time-dependent cash flows. Nevertheless, this problem gives an incentive to solve more realistic versions of project scheduling problems with financial objectives. We introduce an extension of an exact recursive algorithm which has been used in solving the max-npv problem with time-independent cash flows and which is embedded in an enumeration procedure. The recursive search algorithm schedules the activities as soon as possible and searches for sets of activities to shift towards the deadline in order to increase the net present value. The enumeration procedure enumerates all sets of activities for which such a shift has not been made but could, eventually, have been advantageous. The procedure has been coded in Visual C++ version 4.0 under Windows NT and has been validated on a randomly generated problem set.Net present value; Scheduling; Cash flow; Discounted cash flow; Studies; Problems;

An exact procedure for the unconstrained weighed earliness-tardiness project scheduling problem.

In this paper we study the unconstrained project scheduling problem with weighted earliness-tardiness penalty costs subject to zero-lag finish-start precedence constraints. Each activity of this unconstrained project scheduling problem has a known deterministic due date, a unit earliness penalty cost and a unit tardiness penalty cost. The objective is to schedule the activities in order to minimize the weighted earliness-tardiness penalty cost of the project, in the absence of constraints on the use of resources. With these features the problem setting become highly attractive in just-in-time environments.We introduce a two-step recursive algorithm. The first step consists of a forward pass procedure which schedules the activities such that they finish at their due date or later. The second step applies a recursive search in which the activities are eventually shifted backwards (topwards time zero) in order to minimize the weighted earliness-tardiness cost of the project. The procedure has been coded in Visual C++, version 4.0 under Windows NT 4.0 and has been validated on a randomly generated data set.Scheduling;

Magnetophotoluminescence of negatively charged excitons in narrow quantum wells

We present the results of photoluminescence experiments on the negatively charged exciton X- in GaAs/AlxGa1-xAs quantum wells (QW) in high magnetic fields (≤50 T). Three different QW widths are used here: 100, 120, and 150 Å. All optically allowed transitions of X- are observed, enabling us to experimentally verify its energy-level diagram. All samples behave consistently with this diagram. We have determined the binding energy Eb of the singlet and triplet state of X- between 23 and 50 T for the 120 and 150 Å QW, while only the triplet Eb is observed for the 100 Å QW. A detailed comparison with recent theoretical calculations shows an agreement for all samples across this entire field range

An exact procedure for the resource-constrained weighted earliness-tardiness project scheduling problem.

In this paper we study the resource-constrained project scheduling problem with weighted earliness-tardiness penalty costs. Project activities are assumed to have a known deterministic due date, a unit earliness as well as a unit tardiness penalty cost and constant renewable resource requirements. The objective is to schedule the activities in order to minimize the total weighted earliness-tardiness penalty cost of the project subject to the finish)start precedence constraints and the constant renewable resource availability constraints. With these features the problem becomes highly attractive in just-in -time environments.We introduce e depth-first branch-and-bound algorithm for the unconstrained weighted earliness-tardiness problem to compute lower bounds. The procedure has been coded in Visual C++, version 4.0 under Windows NT and has been validated on a randomly generated problem set.Studies; Scheduling; Costs; Requirements; Just-in-time;

Magnetic-field dependence of the spin states of the negatively charged exciton in GaAs quantum wells

We present high-field (<50 T) photoluminescence measurements of the binding energy of the singlet and triplet states of the negatively charged exciton in a 200-Angstrom quantum well. Comparing our data with those of other groups and with theoretical predictions we clearly show how the singlet, "bright" and "dark" triplet states may be identified according to the high-field dependence of their binding energies. We demonstrate that a very consistent behavior of the binding energy in a magnetic field has been observed in quantum wells of different widths by different groups and conclude that the triplet state found in this, as well as nearly all other experiments, is undoubtedly the bright triplet. By combining our data with that in the literature we are able to present the generic form of the binding energy of the spin states of the charged exciton in a magnetic field, which reveals the predicted singlet to dark triplet ground state transition at about 20 T

A hybrid scatter search. Electromagnetism meta-heuristic for project scheduling.

In the last few decades, several effective algorithms for solving the resource-constrained project scheduling problem have been proposed. However, the challenging nature of this problem, summarised in its strongly NP-hard status, restricts the effectiveness of exact optimisation to relatively small instances. In this paper, we present a new meta-heuristic for this problem, able to provide near-optimal heuristic solutions. The procedure combines elements from scatter search, a generic population-based evolutionary search method, and a recently introduced heuristic method for the optimisation of unconstrained continuous functions based on an analogy with electromagnetism theory, hereafter referred to as the electromagnetism meta-heuristic. We present computational experiments on standard benchmark datasets, compare the results with current state-ofthe-art heuristics, and show that the procedure is capable of producing consistently good results for challenging instances of the resource-constrained project scheduling problem. We also demonstrate that the algorithm outperforms state-of-the-art existing heuristics.Algorithms; Effectiveness; Electromagnetism; Functions; Heuristic; Project scheduling; Scatter; Scatter search; Scheduling; Theory;

Stability of earned value management: Do project characteristics influence the stability moment of the cost and schedule performance index

Stability of the Cost Performance Index (CPI) and Schedule Performance Index (SPI(t)) refers to the moment in the project life cycle at which the CPI and SPI(t) are accurate and constant. For a project manager a reliable CPI and SPI(t) is essential for taking corrective actions in time to keep the project on budget, planning and scope. The focus of this paper lies on identifying project characteristics which in uence this mo- ment of CPI and SPI(t) in the project life cycle. Both existing theories from earlier academic research and newly identi ed project characteristics are tested by using empirical data from nine projects executed by an engineering and consultancy company in the Nether- lands. It is found that some project characteristics in uence the moment of CPI and SPI(t) in the project lifecycle whereas other do not. The results of this paper contribute to the body of knowledge on EVM and might provide valuable information to project managers who consider to use EVM in their projects. The results of this research also point out new areas to explore the understanding of the stability of CPI and SPI(t)

SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, \textit{Liveness Analysis}, \textit{Unified Tensor Pool}, and \textit{Cost-Aware Recomputation}, all together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in those memory saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has $10^4$ basic network layers on a 12GB K40c.Comment: PPoPP '2018: 23nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programmin

On Matrices, Automata, and Double Counting

Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables M, with the same constraint defined by a finite-state automaton A on each row of M and a global cardinality constraint gcc on each column of M. We give two methods for deriving, by double counting, necessary conditions on the cardinality variables of the gcc constraints from the automaton A. The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We evaluate the impact of our methods on a large set of nurse rostering problem instances