Algorithmic method for scraper load-time optimization

Scrapers have established an important position in the earthmoving field as they are independently capable of accomplishing an earthmoving operation. Given that loading a scraper to its capacity does not entail its maximum production, optimizing the scraper’s loading time is an essential prerequisite for successful operations management. The relevant literature addresses the loading time optimization through a graphical method that is founded on the invalid assumption that the hauling time is independent of the load time. To correct this, a new algorithmic optimization method that incorporates the golden section search and the bisection algorithm is proposed. Comparison of the results derived from the proposed and the existing method demonstrates that the latter entails the systematic needless prolongation of the loading stage thus resulting in reduced hourly production and increased cost. Therefore, the proposed method achieves an improved modeling of scraper earthmoving operations and contributes toward a more efficient cost management.

Power-Capped DVFS and Thread Allocation with ANN Models on Modern NUMA Systems

Abstract—Power capping is an essential function for efficient power budgeting and cost management on modern server systems. Contemporary server processors operate under power caps by using dynamic voltage and frequency scaling (DVFS). However, these processors are often deployed in non-uniform memory
access (NUMA) architectures, where thread allocation between cores may significantly affect performance and power consumption. This paper proposes a method which maximizes performance under power caps on NUMA systems by dynamically optimizing two knobs: DVFS and thread allocation. The method selects the optimal combination of the two knobs with models based on artificial neural network (ANN) that captures the nonlinear effect of thread allocation on performance. We implement
the proposed method as a runtime system and evaluate it with twelve multithreaded benchmarks on a real AMD Opteron based NUMA system. The evaluation results show that our method outperforms a naive technique optimizing only DVFS by up to
67.1%, under a power cap.

The impact of union presence and strategic human resource management on employee voice in multinational enterprises in Australia

In this article, we examine the use and character of employee voice mechanisms of foreign-owned multinational enterprises operating in Australia, as well as the influence of a strategic human resource management approach and union presence. Findings indicate that foreign-owned multinational enterprises are high-level users of the full range of employee voice mechanisms, with the exceptions of use of employee suggestion schemes, trade union recognition and the use of joint consultation committees across all sites. Using logistic regression analysis, findings show that trade union presence, a strategic human resource management approach, greenfield site and country of origin impact the employee voice approach adopted. High trade union presence is associated with an indirect employee voice approach. A low trade union presence is associated with a direct or a minimalist approach to employee voice. Moreover, a strategic human resource management approach is associated with both direct and dualistic approaches to employee voice. Implications are drawn for theory and practice. © Australian Labour and Employment Relations Association (ALERA) SAGE Publications Ltd, Los Angeles, London, New Delhi, Singapore and Washington DC.