Storage assignment optimization for fishbone robotic mobile fulfillment systems

Robotic Mobile Fulfillment System (RMFS) affects the traditional scheduling problems heavily while operating a warehouse. This paper focuses on storage assignment optimization for Fishbone Robotic Mobile Fulfilment Systems (FRMFS). Based on analyzing operation characteristics of FRMFS, a storage assignment optimization model is proposed with the objectives of maximizing operation efficiency and balancing aisle workload. Adaptive Genetic Algorithm (AGA) is designed to solve the proposed model. To validate the effectiveness of AGA in terms of iteration and optimization rate, this paper designs a variety of scenarios with different task sizes and storage cells. AGA outperforms other four algorithm in terms of fitness value and convergence and has better convergence rate and stability. The experimental results also show the advancement of AGA in large size FRMFS. In conclusion, this paper proposes a storage assignment model for FRMFS to reduce goods movement and travel distance and improve the order picking efficiency.

On the Reliability Assessment of Artificial Neural Networks Running on AI-Oriented MPSoCs

Nowadays, the usage of electronic devices running artificial neural networks (ANNs)-based applications is spreading in our everyday life. Due to their outstanding computational capabilities, ANNs have become appealing solutions for safety-critical systems as well. Frequently, they are considered intrinsically robust and fault tolerant for being brain-inspired and redundant computing models. However, when ANNs are deployed on resource-constrained hardware devices, single physical faults may compromise the activity of multiple neurons. Therefore, it is crucial to assess the reliability of the entire neural computing system, including both the software and the hardware components. This article systematically addresses reliability concerns for ANNs running on multiprocessor system-on-a-chips (MPSoCs). It presents a methodology to assign resilience scores to individual neurons and, based on that, schedule the workload of an ANN on the target MPSoC so that critical neurons are neatly distributed among the available processing elements. This reliability-oriented methodology exploits an integer linear programming solver to find the optimal solution. Experimental results are given for three different convolutional neural networks trained on MNIST, SVHN, and CIFAR-10. We carried out a comprehensive assessment on an open-source artificial intelligence-based RISC-V MPSoC. The results show the reliability improvements of the proposed methodology against the traditional scheduling.

TRUST BASE JOB SCHEDULING IN CLOUD COMPUTING

Cloud computing is a new technology recently being developed seriously. Scheduling is an essential issue in the area of cloud computing. There is an extensive literature concerning scheduling in the area of distributed systems. Some of them are applicable for cloud computing. Traditional scheduling methods are unable to provide scheduling in cloud environments. According to a simple classification, scheduling algorithms in the cloud environment are divided into two main groups: batch mode and online heuristics scheduling. This paper focuses on the trust of cloud-based scheduling algorithms. According to the literature, the existing algorithm examinee latest algorithm is related to an algorithm trying to optimize scheduling using the Trust method. The existing algorithm has some drawbacks, including the additional overhead and inaccessibility to the past transaction data. This paper is an improvement of the trust-based algorithm to reduce the drawbacks of the existing algorithms. Experimental results indicate that the proposed method can execute better than the previous method. The efficiency of this method depends on the number of nods and tasks. The more trust in the number of nods and tasks, the more the performance improves when the time cost increases

A Solution Method for the Maritime Pilot Scheduling Problem with Working Hour Regulations

Motivated by real needs of the industry, this paper studies a maritime pilot scheduling problem with working hour regulations. The existing traditional manual scheduling method of the targeted pilot station is actually a greedy-based approach which may lead to an extremely high operating cost solution and also some dissatisfaction of pilots. A mixed-integer programming (MIP)-based formulation is established and a variable neighborhood search (VNS) approach is proposed to solve the problem efficiently. Compared to the traditional scheduling method, the VNS approach significantly reduces the operating costs in different scales of realistic instances. At the same time, working time and working timespan of pilots are also prominently reduced, which hence directly reduce the workloads of pilots and improve their job satisfaction.

Special Issue on Exact and Heuristic Scheduling Algorithms

This special issue of Algorithms is a follow-up issue of an earlier one, entitled ‘Algorithms for Scheduling Problems’. In particular, the new issue is devoted to the development of exact and heuristic scheduling algorithms. Submissions were welcome both for traditional scheduling problems as well as for new practical applications. In the Call for Papers, we mentioned topics such as single-criterion and multi-criteria scheduling problems with additional constraints including setup times (costs), precedence constraints, batching (lot sizing), resource constraints as well as scheduling problems arising in emerging applications.

Big Data Analytics for Proactively Optimizing Rolling Stock Maintenance

Rolling stock, particularly of freight railroads, is currently maintained using regular preventative and corrective maintenance schedules. This maintenance approach recommends sets of inspections and maintenance procedures based on the average expected wear and tear across their inventory. In practice, however, this approach to scheduling preventative maintenance is not always effective. When scheduled too soon, it results in a loss of operating revenue, whereas when it is scheduled too late, equipment failure could lead to costly and disastrous derailments. Instead, proactive maintenance scheduling based on Big Data Analytics (BDA) could be utilized to replace traditional scheduling, resulting in optimized maintenance cycles for higher train safety, availability, and reliability. BDA could also be used to discover patterns and relationships that lead to train failures, identify manufacturer reliability concerns, and help validate the effectiveness of operational improvements. In this work, we introduce a train inventory simulation platform that enables the modelling of different train components such as wheels, brakes, axles, and bearings. The simulator accounts for the wear and tear in each component and generates a comprehensive data set suitable for BDA that can be used to evaluate the effectiveness of different BDA approaches in discerning patterns and extracting knowledge from the data. It provides the basis for showing that BDA algorithms such as Random Forest [9] and Linear Regression can be utilized to create models for proactive train maintenance scheduling. We also show the capability of BDA to detect hidden patterns and to predict failure of train components with high accuracy.

Malleable parallelism with minimal effort for maximal throughput and maximal hardware load.

A new parallel high-performance computing setup, which can use every little bit of computing resources left over by traditional scheduling, regardless how small or big it may be. This enables HPC providers to achieve 100 percent load on their machines at all times, and it enables HPC users to get substantial computing time on HPC systems that are "full" with traditional jobs.<br>

Malleable parallelism with minimal effort for maximal throughput and maximal hardware load.

A new parallel high-performance computing setup, which can use every little bit of computing resources left over by traditional scheduling, regardless how small or big it may be. This enables HPC providers to achieve 100 percent load on their machines at all times, and it enables HPC users to get substantial computing time on HPC systems that are "full" with traditional jobs.<br>

Adaptive Hierarchical Scheduling Framework for TiRTOS

The complexity of embedded real-time systems has increased, and most applications have large diversity in execution times of their tasks. Therefore, most traditional scheduling techniques do not satisfy requirements of such applications. This article proposes an adaptive hierarchical scheduling framework for a set of independent concurrent applications composing of soft and hard real time tasks, that run on a single processor. It ensures temporal partitioning between independent applications with budget adaption feature, where CPU time of each application is periodically and dynamically assigned. Implemented in the kernel of TI-RTOS on a resource constrained platform, experiments show that proposed scheme provides good performance for multiple applications with dynamic tasks under overload conditions. Compared with traditional priority scheduler originally implemented in TI-RTOS and EDF scheduler, it achieves low miss ratio with minimal overhead while yielding temporal partitioning.

Modeling and Verification of ARINC 653 Hierarchical Preemptive Scheduling

Avionics Application Standard Software Interface (ARINC 653) is a software specification for space and time partitioning in safety-critical avionics real-time operating systems. Correctly designed task schedulers are crucial for ARINC 653 running systems. This paper proposes a model-checking-based method for analyzing and verifying ARINC 653 scheduling model. Based on priced timed automata theory, an ARINC 653 scheduling system was modeled as a priced timed automata network. The schedulability of the system was described as a set of temporal logic expressions, and was analyzed and verified by a model checker. Our research shows that it is feasible to use model checking to analyze task schedulability in an ARINC 653 hierarchical scheduling system. The method discussed modeled preemptive scheduling by using the stop/watch features of priced timed automata. Unlike traditional scheduling analysis techniques, the proposed approach uses an exhaustive method to automate analysis of the schedulability of a system, resulting in a more precise analysis