Job Processing Research Articles

Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

In recent years, integration of production scheduling and machine maintenance has gained increasing attention in order to improve the stability and efficiency of flowshop manufacturing systems. This paper proposes a Q-learning-based aquila optimizer (QL-AO) for solving the integrated optimization problem of blocking flowshop scheduling and preventive maintenance since blocking in the jobs processing requires to be considered in the practice manufacturing environments. In the proposed algorithmic framework, a Q-learning algorithm is designed to adaptively adjust the selection probabilities of four key population update strategies in the classic aquila optimizer. In addition, five local search methods are employed to refine the quality of the individuals according to their fitness level. A series of numerical experiments are carried out according to two groups of flowshop scheduling benchmark. Experimental results show that QL-AO significantly outperforms six peer algorithms and two state-of-the-art hybrid algorithms based on Q-Learning on the investigated integrated scheduling problem. Additionally, the proposed Q-learning and local search strategies are effective in improving its performance.

PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

The IoT and cloud environment renders enormous quantities of geospatial information. Fog and mist computing is the scaling technology that handles geospatial data and sends it to the cloud storage system through fog/mist nodes. Installing a mist–cloud–fog system reduces latency and throughput. This mist–cloud–fog system has processed different types of geospatial web services, i.e., web coverage service (WCS), web processing services (WPS), web feature services (WFS), and web map services (WMS). There is an urgent requirement to increase the number of computer devices tailored to deliver high-priority jobs for processing these geospatial web services. This paper proposes a priority-queueing assisted mist–cloud–fog system for efficient resource allocation for high- and low-priority tasks. In this study, WFS is treated as high-priority service, whereas WMS is treated as low-priority service. This system dynamically allocates mist nodes and is determined by the load on the system. In addition to that, the assignment of tasks is determined by priority. Not only does this classify high-priority tasks and low-priority tasks, which helps reduce the amount of delay experienced by high-priority jobs, but it also dynamically allocates mist devices within the network depending on the computation load, which helps reduce the amount of power that is consumed by the network. The findings indicate that the proposed system can achieve a significantly lower delay for higher-priority jobs for more significant rates of task arrival when compared with other related schemes. In addition to this, it offers a technique that is both mathematical and analytical for investigating and assessing the performance of the proposed system. The QoS requirements for each device demand are factored into calculating the number of mist nodes deployed to satisfy those requirements.

Two-agent single-machine scheduling with a rate-modifying activity

We study single-machine scheduling problems involving a rate-modifying activity and two competing agents with due-date-related functions. Classical scheduling models assume that job processing times remain constant over time; however, in real-world settings, processing times may change due to factors such as technological upgrades or machine maintenance. We complement this with the notion of multiple independent agents competing over the use of a shared resource, each with their own motives. These considerations allow us to model the upcoming trend of the sharing economy, where resources are shared amongst independent competitors in the market. We aim to model these scenarios by considering a variety of scheduling criteria for each agent, including the makespan, the number of late jobs, and the total late work. To account for the change in processing times, we consider an optional rate-modifying activity that once completed, results in a reduction in subsequent job processing times. We show that problems involving the total late work are binary NP-hard and propose efficient pseudo-polynomial dynamic programming algorithms for solving these problems. We also show that the remaining problems are solvable in polynomial time.

Scheduling with Testing of Heterogeneous Jobs

This paper studies a canonical general scheduling model that captures the fundamental trade-off between processing jobs and performing diagnostics (testing). In particular, testing reveals the required processing time and urgency of need-to-schedule jobs to inform future scheduling decisions. The model captures a range of important applications. Prior work focused on special cases (e.g., jobs with independent and identically distributed processing time) to devise optimal policies. In contrast, the current paper studies the most general form of the model and describes two simple heuristics to solve it; adaptive weighted shortest processing time is an adaptive generalization of Smith’s rule that optimally solves several important extensions of previously studied models, whereas index policy optimally solves a closely related stochastic optimization bandit problem. The latter achieves an approximation guarantee that quickly approaches a constant factor that is bounded by two as the number of jobs grows and approaches optimally when the testing time decreases. Extensive numerical experiments suggest that our policies effectively solve the general setting (under 0.1% from optimal on average and under 10% from optimal in rare, worst-case instances). This paper was accepted by Jeannette Song, operations management. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4833 .

GraphTune: An Efficient Dependency-Aware Substrate to Alleviate Irregularity in Concurrent Graph Processing

With the increasing need for graph analysis, massive Concurrent iterative Graph Processing (CGP) jobs are usually performed on the common large-scale real-world graph. Although several solutions have been proposed, these CGP jobs are not coordinated with the consideration of the inherent dependencies in graph data driven by graph topology. As a result, they suffer from redundant and fragmented accesses of the same underlying graph dispersed over distributed platform, because the same graph is typically irregularly traversed by these jobs along different paths at the same time. In this work, we develop GraphTune , which can be integrated into existing distributed graph processing systems, such as D-Galois, Gemini, PowerGraph, and Chaos, to efficiently perform CGP jobs and enhance system throughput. The key component of GraphTune is a dependency-aware synchronous execution engine in conjunction with several optimization strategies based on the constructed cross-iteration dependency graph of chunks. Specifically, GraphTune transparently regularizes the processing behavior of the CGP jobs in a novel synchronous way and assigns the chunks of graph data to be handled by them based on the topological order of the dependency graph so as to maximize the performance. In this way, it can transform the irregular accesses of the chunks into more regular ones so that as many CGP jobs as possible can fully share the data accesses to the common graph. Meanwhile, it also efficiently synchronizes the communications launched by different CGP jobs based on the dependency graph to minimize the communication cost. We integrate it into four cutting-edge distributed graph processing systems and a popular out-of-core graph processing system to demonstrate the efficiency of GraphTune. Experimental results show that GraphTune improves the throughput of CGP jobs by 3.1∼6.2, 3.8∼8.5, 3.5∼10.8, 4.3∼12.4, and 3.8∼6.9 times over D-Galois, Gemini, PowerGraph, Chaos, and GraphChi, respectively.

Algorithms for three-machine flowshop scheduling problem to minimize makespan with uncertain processing times

We address the three-machine flowshop scheduling problem to minimize makespan where processing times are uncertain within some lower and upper bounds. We propose sixteen algorithms based on Johnson’s algorithm, which is known to yield the optimal solution for the three-machine flowshop problem under certain cases. The proposed algorithms are computationally evaluated based on randomly generated data. Computational experiments indicate that one of the proposed algorithms, algorithm AL-7, significantly performs better than the rest. Tests of hypotheses were performed to statistically confirm the results. In algorithm AL-7, more weight is given to the processing times of jobs on the first and the third machines compared to those of the second machine. Moreover, both the lower and upper bounds of job processing times on all three machines are utilized. Furthermore, algorithm AL-7 is shown to perform the best regardless of the extreme distributions considered. Hence, it is recommended as the best algorithm.

Robust identical parallel machine scheduling with two-stage time-of-use tariff and not-all-machine option

Time-of-use (TOU) tariff has been implemented in the manufacturing industry to improve energy efficiency by regulating the electricity imbalance between supply and demand. Besides, not-all-machine (NAM) option is another way of energy-saving by using only a subset of all the available machines. This study investigates a robust identical parallel machine scheduling problem with a two-stage TOU tariff and NAM option. Only interval bounds on job processing times are known. The problem is first formulated into a min–max regret model to maximise the robustness. Based on problem properties, both an iterative relaxation-based exact algorithm and a memetic differential evolution-based heuristic are developed to solve the problem. Computational experiments on 240 randomly generated instances with up to 20 jobs are conducted to evaluate the performance of the developed methods. Besides, 900 large-sized randomly generated instances with up to 150 jobs are tested for sensitivity analysis and to identify managerial insights for achieving energy-efficient schedules.

Privacy Attacks on Schedule-Driven Data

Schedules define how resources process jobs in diverse domains, reaching from healthcare to transportation, and, therefore, denote a valuable starting point for analysis of the underlying system. However, publishing a schedule may disclose private information on the considered jobs. In this paper, we provide a first threat model for published schedules, thereby defining a completely new class of data privacy problems. We then propose distance-based measures to assess the privacy loss incurred by a published schedule, and show their theoretical properties for an uninformed adversary, which can be used as a benchmark for informed attacks. We show how an informed attack on a published schedule can be phrased as an inverse scheduling problem. We instantiate this idea by formulating the inverse of a well-studied single-machine scheduling problem, namely minimizing the total weighted completion times. An empirical evaluation for synthetic scheduling problems shows the effectiveness of informed privacy attacks and compares the results to theoretical bounds on uninformed attacks.

Time-flexible min completion time variance in a single machine by quadratic programming

In the context of job scheduling, the time required to complete a task is related not only to the intrinsic difficulty of the task, but also to the operator’s willingness to speed up (or slow down) its execution. In fact, service operators are sometimes authorized to flexibly calibrate job processing times when this is beneficial for the efficient design of services and production plans. In this paper, we show that some forms of time flexibility have major consequences on the operator’s ability to efficiently solve the problem of scheduling non-preemptive jobs to minimize the variance of their completion times. In fact, although this remains a challenging combinatorial problem, authorizing forms of processing time flexibility allows for solving it up to optimality by convex quadratic programming approaches, with a view to tackling large-scale instances, where no exact algorithm can be applied. Our contribution allows establishing a form of least flexibilization of job processing times while guaranteeing the solvability of the resulting problem by convex quadratic programming approaches. To this end, we provide novel bounding conditions for the characterization of an optimal sequence that strengthen and integrate state-of-the-art dominance properties. Our numerical tests indicate that this new methodology is capable of approaching the solution of the original min completion time variance problem with a max relative difference of about 0.05% (on average), with respect to the time-flexible solution.

Generalizing max pooling via [formula omitted]-grouping functions for Convolutional Neural Networks

Due to their high adaptability to varied settings and effective optimization algorithm, Convolutional Neural Networks (CNNs) have set the state-of-the-art on image processing jobs for the previous decade. CNNs work in a sequential fashion, alternating between extracting significant features from an input image and aggregating these features locally through “pooling” functions, in order to produce a more compact representation.Functions like the arithmetic mean or, more typically, the maximum are commonly used to perform this downsampling operation. Despite the fact that many studies have been devoted to the development of alternative pooling algorithms, in practice, “max-pooling” still equals or exceeds most of these possibilities, and has become the standard for CNN construction.In this paper we focus on the properties that make the maximum such an efficient solution in the context of CNN feature downsampling and propose its replacement by grouping functions, a family of functions that share those desirable properties. In order to adapt these functions to the context of CNNs, we present (a,b)-grouping functions, an extension of grouping functions to work with real valued data. We present different construction methods for (a,b)-grouping functions, and demonstrate their empirical applicability for replacing max-pooling by using them to replace the pooling function of many well-known CNN architectures, finding promising results.

Mô hình BiDAF trong bài toán phân tích cảm xúc

Sentiment analysis is a critical job in natural language processing. Controlling and evaluating customer feedback on their goods is a task that companies are especially interested in. For reading comprehension problems including attention processes, the BiDAF model is developed. Attention processes have recently been expanded and effectively used for natural language processing problems. In this study, we use the BiDAF model to perform sentiment analysis on Amazon product evaluations at the sentence level. The BiDAF model is a multilayered processing model that reflects context at multiple levels and uses the BiLSTM model. Furthermore, we investigate the sentence's attention weight distribution using the attention mechanism. With a recall measure, the model achieves an accuracy of up to 99.9%. We discovered that the attention weights of important phrases are equivalent to, if not higher than, the attention weights of sentiment words in the sentence.

GeaFlow: A Graph Extended and Accelerated Dataflow System

GeaFlow is a distributed dataflow system optimized for streaming graph processing, and has been widely adopted at Ant Group, serving various scenarios ranging from risk control of financial activities to analytics on social networks and knowledge graphs. It is built on top of a base with full-fledged stateful stream processing capabilities, extended with a series of graph-aware optimizations to address the space explosion and programming complexity issues of conventional join-based approaches. We propose new state backends and streaming operators that facilitate processing on dynamic graph-structured datasets, reducing space consumed by states. We develop a hybrid domain-specific language that embeds Gremlin into SQL, supporting both table and graph abstractions over streaming data. In addition to streaming workloads, GeaFlow is also extensively used for some batch processing jobs. In the largest deployments to date, GeaFlow is able to process tens of millions of events per second and manage hundreds of terabytes of states.

Balanced-DRL: A DQN-Based Job Allocation Algorithm in BaaS

Blockchain as a Service (BaaS) combines features of cloud computing and blockchain, making blockchain applications more convenient and promising. Although current BaaS platforms have been widely adopted by both industry and academia, concerns arise regarding their performance, especially in job allocation. Existing BaaS job allocation strategies are simple and do not guarantee load balancing due to the dynamic nature and complexity of BaaS job execution. In this paper, we propose a deep reinforcement learning-based algorithm, Balanced-DRL, to learn an optimized allocation strategy in BaaS based on analyzing the execution process of BaaS jobs and a set of job scale characteristics. Following extensive experiments with generated job request workloads, the results show that Balanced-DRL significantly improves BaaS performance, achieving a 5% to 8% increase in job throughput and a 5% to 20% decrease in job latency.

Methylene diphenyl diisocyanate occupational exposure data in industry (1998-2020): A descriptive summary from an industrial hygiene perspective.

This paper provides an overview of airborne methylene diphenyl diisocyanate (MDI) concentrations in workplaces across North America and Europe. A total of 7649 samples were collected between 1998 and 2020 by producers of MDI during product stewardship activities at customer sites, primarily using validated OSHA or ISO sampling and analysis techniques. As would be expected from the low vapor pressure of MDI, 80% of the concentrations were less than 0.01mg/m3 (1 ppb) and 93% were less than 0.05mg/m3 (5 ppb). Respiratory protection is an integral part of Industrial Hygiene practices; therefore, its use was studied and summarized. While covering a variety of MDI applications, a large number of samples was obtained from composite wood manufacturing facilities, offering specific insight into potential exposures associated with different process sections and job types in this industry sector. Given the potential presence in industrial processes of MDI-containing dust or aerosols, future work should place increased emphasis on also investigating dermal exposure. The data reported in this paper provide valuable information for product stewardship and industrial hygiene purposes throughout the MDI-processing industry.

Generic Edge Computing System for Optimization and Computation Offloading of Unmanned Aerial Vehicle

Several Unmanned Aerial Vehicles (UAVs) offer ground equipment low-latency edge computing services. On the other hand, low-Earth orbit satellites offer all-encompassing cloud computing services. The officially stated joint optimization problem is a mixed nonlinear programming problem, leading to the development of a two-layer optimization technique. It is suggested that a particle swarm optimization (PSO) algorithm can be used in the upper layer of the algorithm along with genetic algorithm (GA) operators to optimize the UAV's deployment position. The greedy algorithm is applied to optimize the offloading of processing jobs in the lowest layer of the algorithm. The feasibility and effectiveness of the proposed strategy have been validated by numerous numerical simulation tests. The findings show that it is similar to other benchmark algorithms. The proposed approach can also reduce the usual job response time of the system.

제어가능한 공정시간과 납기 지연 조건을 고려한 선점적 단일 머신 일정계획 문제

In classical scheduling problems, jobs are usually assumed to be non-preemptive with constant processing times. In many practical cases, however, restrictions on preemption can be released by outsourcing and the job processing times are controllable by allocating additional resources to the job operations. In this paper, we consider a single- machine scheduling problem with preemption such that each job processing time can be reduced linearly by using additional resource. The objective is to minimize the sum of total late work and total compression cost. We show that the problem can be solved in polynomial time.

Minimising total weighted completion time for semi-online single machine scheduling with known arrivals and bounded processing times

This paper addresses the semi-online scheduling problem of minimising the total weighted completion time on a single machine, where a combination of information on jobs release dates and processing times is considered. In this study, jobs can only arrive at known future times and a lower bound on jobs processing times is known in advance. A new semi-online algorithm is presented and is shown to be the best possible for the considered problem. In order to make this statement, a new lower bound on the competitive ratio of any semi-online algorithm for the problem is developed and, using competitive analysis, the proposed semi-online algorithm is shown to have a competitive ratio that matches the lower bound.

Women’s Participation in Firms’ Management and Their Impact on Financial Performance: Pre-COVID-19 and COVID-19 Period Evidence

At a time when gender equality is a key priority of all international organizations, this paper can be considered a remarkable contribution to the role of women executives in firms’ performance. More specifically, this study focuses on the effect of women holding positions of responsibility on firms’ performance worldwide. For the purposes of our research, we applied cross-sectional and panel data analysis for all sectors at an international level from 2019, the year preceding the breakout of the pandemic crisis, to 2021, while the indicators used to measure the participation of women in executive positions are classified as ESG indices. The empirical analysis findings end up showing that the participation of women in executive positions positively affects firms’ performance over time, while there is no material change observed before and during the COVID-19 pandemic period. More specifically, when the percent of women processing job positions of responsibility increases by 10%, then the index of profitability will increase from 1.4% to 1.8%, regardless of the measurement of female participation in executive positions used. The results of this study constitute a remarkable contribution to the promotion of the creative economy, the progress of societies, and sustainable development. The research’s outcome can be primarily used by policymakers drawing up policies for achieving gender equality in the labor market and workplaces and by shareholders and firms’ managers in order to trust females in executive positions in favor of their firms’ financial performance. The current study is unique in that it focuses on the period before and during the COVID-19 period, as a period of high volatility in economic activity worldwide, while the sample includes firms from large and mid-cap companies belonging to developed and emerging markets. The above approach will contribute to providing more credible information related to the role of women executives in firms’ performance.

An Algebraic Approach to the Solutions of the Open Shop Scheduling Problem

The open shop scheduling problem (OSSP) is one of the standard scheduling problems. It consists of scheduling jobs associated with a finite set of tasks developed by different machines. In this case, each machine processes at most one operation at a time, and the job processing order on the machines does not matter. The goal is to determine the completion times of the operations processed on the machines to minimize the largest job completion time, called Cmax. This paper proves that each OSSP has associated a path algebra called Brauer configuration algebra whose representation theory (particularly its dimension and the dimension of its center) can be given using the corresponding Cmax value. It has also been proved that the dimension of the centers of Brauer configuration algebras associated with OSSPs with minimal Cmax are congruent modulo the number of machines.

Effective league championship algorithm and lower bound procedure for scheduling a single batch-processing machine with non-identical job sizes and job rejection

We address the scheduling problem of a set of non-identical size jobs on a single batch-processing machine (SBPM) wherein the scheduler can make decision whether to schedule a job in batches or not to schedule it with a job-dependent penalty. The processing time of a batch is the greatest job processing time in that batch (parallel batching or p-batching). The scheduler wants to minimize a given objective function f, where f is the total rejection penalties of the rejected jobs (rejection cost) plus the makespan of the scheduled ones. We formulate the aforementioned problem as a 0–1 mixed integer programming model. We also apply an effective dynamic programming algorithm (DPA) to calculate a lower bound (LB) on the optimal cost of the problem. To tackle the problem, we propose a grouping algorithm, based on league championship algorithm (LCA), with new updating equations maintaining the major characteristics of the original updating equations of the LCA and well-suited to the structure of the problem. For small problems, performance of the proposed LCA is compared with GAMS/CPLEX solver. For large-scale instances, a genetic algorithm is adopted as a basis for comparison. Simulated experiments confirm the performance of the proposed methods.