A mathematical model for open pit mine production scheduling with Grade Engineering® and stockpiling

This paper presents the development and implementation of an innovative mixed integer programming based mathematical model for an open pit mining operation with Grade Engineering framework. Grade Engineering comprises a range of coarse-separation based pre-processing techniques that separate the desirable (i.e. high-grade) and undesirable (i.e. low-grade or uneconomic) materials and ensure the delivery of only selected quantity of high quality (or high-grade) material to energy, water, and cost-intensive processing plant. The model maximizes the net present value under a range of operational and processing constraints. Given that the proposed model is computationally complex, the authors employ a data pre-processing procedure and then evaluate the performance of the model at several practical instances using computation time, optimality gap, and the net present value as valid measures. In addition, a comparison of the proposed and traditional (without Grade Engineering) models reflects that the proposed model outperforms the traditional formulation.     

A Survey of Various Schedulers Used for Fair Bandwidth Allocation in WiMAX: IEEE 802.16

ABSTRACT

Wireless Interoperability for Multiple Access (WiMAX) is one of the emerging fields of high-speed wireless communication that has enormous capabilities due to its range and the connection speed. Like wireless local area networks (LANs), WiMAX networks implement multiple quality of service (QoS) frameworks at the Media Access Control (MAC) level for assured data, voice, and video services. The question of ensuring QoS is basically how to distribute available resources to users in order to satisfy QoS parameters such as latency, jitter and throughput requirements. IEEE 802.16 standard does not have any particular guidelines on scheduling of incoming and outgoing data. This has caught the attention of researchers working on WiMAX. This article discusses the various issues in WiMAX along with a classification of various scheduling approaches based upon the type of scheduler for the sake of better understanding the scheduling problem and analyzing various available theories.     

Multi-resource scheduling for FPGA systems

In modern cloud data centers, reconfigurable devices (FPGAs) are used as an alternative to Graphics Processing Units to accelerate data-intensive computations (e.g., machine learning, image and signal processing). Currently, FPGAs are configured to execute fixed workloads, repeatedly over long periods of time. This conflicts with the needs, proper to cloud computing, to flexibly allocate different workloads and to offer the use of physical devices to multiple users. This raises the need for novel, efficient FPGA scheduling algorithms that can decide execution orders close to the optimum in a short time. In this context, we propose a novel scheduling heuristic where groups of tasks that execute together are interposed by hardware reconfigurations. Our contribution is based on gathering tasks around a high-latency task that hides the latency of tasks, within the same group, that run in parallel and have shorter latencies. We evaluated our solution on a benchmark of 37500 random workloads, synthesized from realistic designs (i.e., topology, resource occupancy). For this testbench, on average, our heuristic produces optimum makespan solutions in 47.4% of the cases. It produces acceptable solutions for moderately constrained systems (i.e., the deadline falls within 10% of the optimum makespan) in 90.1% of the cases.     

Cokriging-Based Sequential Design Strategies Using Fast Cross-Validation Techniques for Multi-Fidelity Computer Codes

Cokriging-based surrogate models have become popular in recent decades to approximate a computer code output from a few simulations using both coarse and more complex versions of the code. In practical applications, it is common to sequentially add new simulations to obtain more accurate approximations. We propose a method of cokriging-based sequential design, which combines both the error evaluation provided by the cokriging model and the observed errors of a leave-one-out cross-validation procedure. This method is proposed in two versions, the first one selects points one at a time. The second one allows us to parallelize the simulations and to add several design points at a time. The main advantage of the suggested strategies is that at a new design point they choose which code versions should be simulated (i.e., the complex code or one of its fast approximations). A multifidelity application is used to illustrate the efficiency of the proposed approaches. In this example, the accurate code is a two-dimensional finite element model and the less accurate one is a one-dimensional approximation of the system. This article has supplementary material online.     

A nature-inspired firefly algorithm based approach for nanoscale leakage optimal RTL structure

Optimization of leakage power is essential for nanoscale CMOS (nano-CMOS) technology based integrated circuits for numerous reasons, including improving battery life of the system in which they are used as well as enhancing reliability. Leakage optimization at an early stage of the design cycle such as the register-transfer level (RTL) or architectural level provides more degrees of freedom to design engineers and ensures that the design is optimized at higher levels before proceeding to the next and more detailed phases of the design cycle. In this paper, an RTL optimization approach is presented that targets leakage-power optimization while performing simultaneous scheduling, allocation and binding. The optimization approach uses a nature-inspired firefly algorithm so that large digital integrated circuits can be effectively handled without convergence issues. The firefly algorithm optimizes the cost of leakage delay product (LDP) under various resource constraints. As a specific example, gate-oxide leakage is optimized using a 45 nm CMOS dual-oxide based pre-characterized datapath library. Experimental results over various architectural level benchmark integrated circuits show that average leakage optimization of 90% can be obtained. For a comparative perspective, an integer linear programming (ILP) based algorithm is also presented and it is observed that the firefly algorithm is as accurate as ILP while converging much faster. To the best of the authors׳ knowledge, this is the first ever paper that applies firefly based algorithms for RTL optimization.     

Earliness–tardiness minimization on scheduling a batch processing machine with non-identical job sizes

This paper considers the scheduling problem of minimizing earliness–tardiness (E/T) on a single batch processing machine with a common due date. The problem is extended to the environment of non-identical job sizes. First, a mathematical model is formulated, which is tested effectively under IBM ILOG CPLEX using the constraint programming solver. Then several optimal properties are given to schedule batches effectively, and by introducing the concept of ARB (Attribute Ratio of Batch), it is proven that the ARB of each batch should be made as small as possible in order to minimize the objective, designed as the heuristic information for assigning jobs into batches. Based on these properties, a heuristic algorithm MARB (Minimum Attribute Ratio of Batch) for batch forming is proposed, and a hybrid genetic algorithm is developed for the problem under study by combining GA (genetic algorithm) with MARB. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in the literature, both for small and large problem instances.     

Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

We study a two-agent scheduling problem in a two-machine permutation flowshop with learning effects. The objective is to minimize the total completion time of the jobs from one agent, given that the maximum tardiness of the jobs from the other agent cannot exceed a bound. We provide a branch-and-bound algorithm for the problem. In addition, we present several genetic algorithms to obtain near-optimal solutions. Computational results indicate that the algorithms perform well in either solving the problem or efficiently generating near-optimal solutions.     

More than bin packing: Dynamic resource allocation strategies in cloud data centers

Resource allocation strategies in virtualized data centers have received considerable attention recently as they can have substantial impact on the energy efficiency of a data center. This led to new decision and control strategies with significant managerial impact for IT service providers. We focus on dynamic environments where virtual machines need to be allocated and deallocated to servers over time. Simple bin packing heuristics have been analyzed and used to place virtual machines upon arrival. However, these placement heuristics can lead to suboptimal server utilization, because they cannot consider virtual machines, which arrive in the future. We ran extensive lab experiments and simulations with different controllers and different workloads to understand which control strategies achieve high levels of energy efficiency in different workload environments. We found that combinations of placement controllers and periodic reallocations achieve the highest energy efficiency subject to predefined service levels. While the type of placement heuristic had little impact on the average server demand, the type of virtual machine resource demand estimator used for the placement decisions had a significant impact on the overall energy efficiency.     

Efficient meta-heuristics based on various dominance criteria for a single-machine bi-criteria scheduling problem with rejection

In this paper, we try to fill in the gap between theory and practice in production scheduling by defining a new term as “rejection” and treating the corresponding scheduling problem with multi-objective optimization approach. We study a bi-objective single machine scheduling problem with rejection. At the beginning of scheduling time horizon, scheduler needs to decide which job shall be rejected due to the resource constraints regarding two objective functions: minimization of total weighted completion time of accepted jobs and total rejection penalty of rejected jobs. We develop different algorithms to find the best estimation of Pareto-optimal front for this problem. In order to improve the quality of the solutions, on the one hand, and facilitate the process of selecting best solution for the final decision maker, on the other hand, we integrate various dominance criteria into our proposed algorithms. Finally we compare the performance of those methods by testing on a large set of instances and highlight the advantages and weak points of each one.     

vMES: Virtualization aware manufacturing execution system

The large scale emergence in the last decade of various cloud solutions, ranging from software-as-a-service (SaaS) based solutions for business process management and implementation to very sophisticated private cloud solutions capable of high performance computing (HPC) and efficient virtualization, constitute the building blocks for engineering the next generation of flexible enterprise systems that can respond with great agility to changes in their environment. These new technologies are adopted at a certain level by manufacturing enterprises in order to advance in a new era of mass customization where flexibility, scalability and agility are the differentiating factors. In this context, this paper introduces the virtualized manufacturing execution system (vMES), an intermediate layer in the manufacturing stack, and discusses the advantages and limitations offered by this approach for manufacturing enterprises. A classification of MES workloads based on the ISA-95 function model is presented, focusing on the virtualization techniques suitable for each workload, considering the algorithms and technologies used and the virtualization overhead. A pilot vMES implementation using a parallel process for smart resource provisioning and automatic scaling is also presented. The pilot implementation using six Adept robots and one IBM CloudBurst 2.1 private cloud and an ISA-95 based MES is described; the virtualization sequence is analyzed in several scenarios of resource workload collocation on physical cloud blades with and without perturbations.