Uniform Machines Research Articles

MTWSI-Net: Towards Improved Multi-Task Whole Slide Image Classification with Contrastive Learning

Pathology is a digital technology for the capture and analysis of high-resolution Whole Slide Images (WSIs). WSI entails scanning glass pathology slides, typically containing tissue samples, into a digital format. This facilitates comprehensive digital viewing, analysis, and interpretation by pathologists. Despite the undeniable utility of this method, the inherent time-consuming and labor-intensive nature of traditional pathology image analysis, which is heavily reliant on expert pathologists, is well-known. Recent years have seen a surge in research aiming to address these challenges through the development of automated systems using machine learning approaches. While promising, such systems often exhibit bias towards specific datasets and struggle to transition effectively to real-world scenarios. For this reason, there is a need to establish a uniform machine learning training approach for generating more robust and consistent results. In this paper, I introduce a contrastive learning-based multi-task whole slide image classification system. The proposed system excels at extracting consistently reliable features for identical cancer categories, thereby enhancing accuracy in downstream tasks. Through extensive experiments, the results demonstrate that the proposed system outperforms pre-existing state-of-the-art machine learning models. I expect that the proposed system can significantly contribute to pathologists by offering valuable cancer screening capabilities in WSIs.

Approximation algorithms for job scheduling with block-type conflict graphs

The problem of scheduling jobs on parallel machines (identical, uniform, or unrelated), under incompatibility relation modeled as a block graph, under the makespan optimality criterion, is considered in this paper. No two jobs that are in the relation (equivalently in the same block) may be scheduled on the same machine in this model.The presented model stems from a well-established line of research combining scheduling theory with methods relevant to graph coloring. Recently, cluster graphs and their extensions like block graphs were given additional attention. We complement hardness results provided by other researchers for block graphs by providing approximation algorithms. In particular, we provide a 2-approximation algorithm for P|G=block graph|Cmax and a PTAS for the case when the jobs are unit time in addition. In the case of uniform machines, we analyze two cases. The first one is when the number of blocks is bounded, i.e. Q|G=k − block graph|Cmax. For this case, we provide a PTAS, improving upon results presented by D. Page and R. Solis-Oba. The improvement is two-fold: we allow richer graph structure, and we allow the number of machine speeds to be part of the input. Due to strong NP-hardness of Q|G=2 − clique graph|Cmax, the result establishes the approximation status of Q|G=k − block graph|Cmax. The PTAS might be of independent interest because the problem is tightly related to the NUMERICAL k-DIMENSIONAL MATCHING WITH TARGET SUMS problem. The second case that we analyze is when the number of blocks is arbitrary, but the number of cut-vertices is bounded and jobs are of unit time. In this case, we present an exact algorithm. In addition, we present an FPTAS for graphs with bounded treewidth and a bounded number of unrelated machines.The paper ends with extensive tests of the selected algorithms.

Scheduling a Distributed Permutation Flowshop With Uniform Machines and Release Dates

Scheduling a Distributed Permutation Flowshop With Uniform Machines and Release Dates

Estimation of Shape Error with Monitoring Signals

Recently, extensive research has actively been conducted in relation to intelligent manufacturing systems. During the machining process, various factors, such as geometric errors, vibrations, and cutting force fluctuations, lead to shape errors. When a shape error exceeds the tolerance, it results in improper assembly or functionality issues in the assembled part. Predicting shape errors before or during the machining process helps increase production efficiency. In this paper, we propose a methodology that uses monitoring signals and on-machine measurement (OMM) results to predict machining quality in real time. We investigate the correlation between monitoring signals and OMM results and then construct a machine learning model for shape error estimation. The developed model implements a tool offset compensation strategy. The performance of the proposed method is evaluated under various sliding window sizes and the compensation weights. The experimental results confirmed that the proposed algorithm is effective for obtaining a uniform machining quality.

Cost-based hybrid flow shop scheduling with uniform machine optimization using an improved tiki-taka algorithm

ABSTRACT Cost is the foremost factor in decision-making for profit-driven organizations. However, hybrid flow shop scheduling (HFSS) research rarely prioritizes cost as its optimization objective. Existing studies primarily focus on electricity costs linked to machine utilization. This paper introduces a comprehensive cost-based HFSS model, encompassing electricity, labor, maintenance, and penalty costs. Next, the Tiki-Taka Algorithm (TTA) is improved by increasing the exploration capability to optimize the problem. The cost-based HFSS model and TTA algorithm have been tested using benchmark and case study problems. The results indicated that the TTA consistently outperforms other algorithms. It delivers the best mean fitness and better solution distribution. In industrial contexts, the TTA able to reduces costs by 2.8% to 12.0% compared to other approaches. This holistic cost-based HFSS model empowers production planners to make more informed decisions. Furthermore, the improved TTA shows promise for broader applicability in various combinatorial optimization domains.

Implementation of robotic electrochemical machining in freeform surface machining with material removal rate prediction using different machine learning algorithms

Robots are used as machine tools via some advanced properties such as versatility, adaptation and mobility with other control systems. These advantages can be the solution to overcome the encountered problems in electrochemical machining (ECM). In this manner, the usability of a new machining method that is Robotic electrochemical machining (RECM) in freeform surface machining is investigated. Firstly, ECM parameters and their effects on the material removal rate (MRR) is discussed and compared with the literature. Experimental results showed that MRR variation for RECM acts similar characteristics with conventional ECM processes. The increase in voltage, electrolyte conductivity and feed rate increase MRR. The highest effect on MRR for both materials is voltage, electrolyte conductivity, and feed rate, respectively. Secondly, three different machine learning models are designed as Gaussian Process Regression (GPR), regression tree and deep neural network (DNN) models and the relationships between their actual and predicted values are discussed. The performance of the DNN model is better than other models with a correlation of determination values equal for Inconel 718 and AISI 1040. Furthermore, the DNN model with four input parameters outperforms developed models such as GPR and DTR with optimized hyperparameters regarding the R2, MSE, RMSE, and MAE. The change in height in the Z-axis and the surface roughness of the machined surfaces are also measured. The results show that the decrease in the Z axis is homogeneous, that is, uniform machining occurs and also the surface roughness has improved. Therefore, RECM can be a solution to machine free-form surfaces for different materials with high predictability.

Semi-online early work maximization problems on two hierarchical uniform machines with partial information of processing time

Semi-online early work maximization problems on two hierarchical uniform machines with partial information of processing time

Multi-objective energy-efficient hybrid flow shop scheduling using Q-learning and GVNS driven NSGA-II

The urgent mission for carbon peak and carbon neutrality is demanding greater industrial sustainability. Energy-efficient hybrid flow shop scheduling problem (EEHFSP) has been raising increasing attention in recent years. This paper studies a new EEHFSP with uniform machines to minimize total tardiness, total energy cost, and carbon trading cost. Time-of-use tariffs and power down strategies are simultaneously adopted. A novel multi-objective mixed-integer nonlinear programming model for the problem is proposed. To solve the model, we propose a Q-learning and general variable neighborhood search (GVNS) driven non-dominated sorting genetic algorithm II (QVNS-NSGA-II). The novelty of the algorithm is that we incorporate Q-learning into GVNS to guide premium adaptive operator selection throughout the shaking and local search processes. A distinguishing feature is that the states and actions of Q-learning are set as neighborhood structures and local search operators. The Q-learning-driven GVNS is embedded into NSGA-II to promote the exploration and exploitation capability. Experimental results show that the proposed QVNS-NSGA-II outperforms NSGA-II, improved Jaya, and modified MOEA/D in terms of the quantity, quality of Pareto solutions, and computational efficiency. Sensitivity analysis also derives several managerial implications. The proposed approach can be applied to improve sustainability and productivity for hybrid flow shop manufacturers.

A common due-date assignment problem with job rejection on parallel uniform machines

We study a common due-date assignment problem on two parallel uniform machines. The jobs are assumed to have identical processing times, and job-dependent and asymmetric earliness and tardiness unit costs. The scheduler may process only a subset of the jobs, i.e. the option of job-rejection is allowed. The objective function consists of three cost components: the total earliness-tardiness cost of all scheduled jobs, the cost of the common due-date, and total rejection cost. For a given number of rejected jobs and a given due-date, the problem is reduced to a non-standard linear assignment problem. Consequently, the optimal solution is shown to be obtained in polynomial time in the number of jobs. The case of a given (possibly restrictive) due-date, the extension to a setting of more than two machines, and a number of special cases, are also discussed.

Job scheduling for maximum revenue on uniform, parallel machines with major and minor setups and job splitting

We consider a revenue maximization scheduling problem where jobs can be split on parallel, uniform machines. By assuming that only major and minor setups exist between jobs and that these can be grouped into families, we reformulate the scheduling problem as a continuous knapsack problem with setup time. We then create the Revenue Rate Heuristic to solve the maximum revenue scheduling problem for both the partial and binary revenue cases. This makes use of a revenue rate – the ratio between total revenue that can be obtained from a job and the time required to achieve the revenue. The Revenue Rate Heuristic schedules the job with the highest revenue rate first and updates all the revenue rates accordingly based on existing scheduled families and remaining capacity. In cases where there are no setup times or if there is only one machine and an ordering of job revenue and production time, we are able to show that the Revenue Rate Heuristic is optimal. Numerical studies show that the Revenue Rate Heuristic performs well with an average optimality gap of 3.47% across a large set of parameters and when the setup times across families are different from each other. In a case study based on firm-level data, we also show that the average optimality gap remains small.

Online load balancing on uniform machines with limited migration

Consider that one job after another arrives online and must be placed on machines immediately, without knowing which jobs might arrive later. The goal is to balance the load of the machines – the sizes of placed jobs divided by the machine speed. Each job contributes a migration potential proportional to its size, which can be used to relocate some previously placed jobs. This paper presents results with improved competitiveness compared to the pure online case using only very limited migration.

On the complexity of scheduling unrelated parallel machines with limited preemptions

We consider the problem of finding a minimum-length preemptive schedule for n jobs on m parallel machines. The problem is solvable in polynomial time, whether the machines are identical, uniform or unrelated. For identical or uniform machines, it is easy to obtain an optimal schedule in which the portion of a job that is assigned to a single machine is processed without interruption. We show that imposing this condition in the case of unrelated machines makes the problem NP-hard.

Faster algorithms for bicriteria scheduling of identical jobs on uniform machines

<p style='text-indent:20px;'>This paper studies the problem of scheduling <inline-formula><tex-math id="M1">\begin{document}$ n $\end{document}</tex-math></inline-formula> jobs with equal processing times on <inline-formula><tex-math id="M2">\begin{document}$ m $\end{document}</tex-math></inline-formula> uniform machines to optimize two criteria simultaneously. The main contribution is an <inline-formula><tex-math id="M3">\begin{document}$ O(n\log m+n^3) $\end{document}</tex-math></inline-formula>-time algorithm for two general min-max criteria, improving the previous <inline-formula><tex-math id="M4">\begin{document}$ O(n\log m+n^4) $\end{document}</tex-math></inline-formula> time complexity. For a particular min-sum criterion (total weighted completion time or total tardiness) in combination with a general min-max criterion, <inline-formula><tex-math id="M5">\begin{document}$ O(n\log m+n^3) $\end{document}</tex-math></inline-formula>-time algorithms are also obtained, improving the previous <inline-formula><tex-math id="M6">\begin{document}$ O(n\log m+n^3\log n) $\end{document}</tex-math></inline-formula> time complexity. The algorithms can produce all Pareto optimal points together with the corresponding schedules.</p>

Depth Model and 5-Axis Variable-Angle Laser Engraving Experiment Based on the Energy Conservation Principle.

To ensure the consistency of laser engraving depth in chemical milling, the precise control of 5-axis variable-angle laser engraving was the focus of research. Based on the energy conservation principle, the depth model of 5-axis variable-angle laser engraving is established, and the relationships among the laser engraving depth, laser power, scanning velocity, and beam axis angle are proposed. A depth-constraint real-time adaptive control method of laser power is proposed considering the variable scanning velocity and beam axis angles. The depth model parameters are identified by an orthogonal experiment, and a variable-angle laser engraving experiment with adaptive control of laser power is carried out. The coefficient of determination of the proposed depth model is 0.977, which means that the engraving depth model established in this paper predicts the engraving depth effectively and reliably. The depth-constraint adaptive control method of laser power obtains stable and uniform machining results under abrupt changes in scanning velocity and beam axis angles.

Overview of finish machining methods for the internal surfaces of rectangular pipe parts

The article deals with the proble of finish machining the internal surfaces of straight and curved pipe parts of rectangular cross-section. There is carried out analysis of technological capabilities of special finish machining technologies of internal surfaces in the Russian Federation. General information, disadvantages and advantages of these methods are presented. The selection criterion is the uniformity of machining on straight and curved parts of the pipe part. There are proved the main advantages of choosing an abrasive flow machining method as a fairly inexpensive method of finish machining. A scheme for finish machining the internal surfaces of rectangular parts is proposed. The composition of the working medium has been chosen, which allows for uniform machining, including in the corners of a rectangular cross-section.

Experimental investigation on machining characteristics of titanium processed using electrolyte sonicated µ-ECDM system

Micromachining of difficult-to-machine materials is of prime focus nowadays. One such material is Titanium, which has numerous applications in aerospace, chemical, and biomedical industries. The micromachining of Titanium has become the need of the day because of its exhilarating properties. This investigation employs a tailor-made electrolyte sonicated micro-electrochemical discharge machining (ES-µ-ECDM) system to generate microholes in a commercially pure titanium plate with a thickness of 1000 µm. The machining chamber is the ultrasonication unit (36 kHz) with process parameters voltage (V), concentration (wt%), and duty factor (DF) chosen at three levels. The FCC-RSM-based DOE is selected for experimentation to study the machining characteristics like material removal rate, overcut, and circularity. Through holes were achieved at parameters of 80 V, 25 wt%, and 60% DF and 80 V, 30 wt%, and 50% DF. The incorporation of ultrasonication into the system enhanced electrolyte replenishment and evacuation of the debris at the machining vicinity. The assistance technique improved the gas film stabilization around the tool enabling uniform machining. The multi-response optimization is performed using the MOJAYA algorithm to obtain Pareto optimal solutions, and the MADM (R-method) is employed to obtain the optimal parameter. The optimal parameter was found to be 69 V, 30 wt%, and 50% DF, at which the machined microhole was found to have a circularity of 0.9615 with minimal surface defects.

Scheduling on uniform machines with a conflict graph: complexity and resolution

AbstractThis paper deals with the problem of scheduling a set of unit‐time jobs on a set of uniform machines. The jobs are subject to conflict constraints modeled by a graph G called the conflict graph, in which adjacent jobs cannot be processed on a same machine. The objective considered herein is the minimization of maximum job completion time in the schedule, which is famous to be NP‐hard in the strong sense. The first part of this paper is an extensive study of the computational complexity of the problem restricted to several graph classes, namely: split graphs, interval graphs, forests, trees, paths and cycles. Afterward, we focus on the resolution of the problem with arbitrary conflict graphs. For this latter, a combination of a mixed integer linear programming (MILP) formulation, lower and upper bounds is proposed. A wild range of computational experiments proved the efficiency of this technique to tremendously reduce runtime and produce more optimal solutions (around 80% in average). Furthermore, a deep analysis of the resolution process based on both the density of the conflict graph as well as machine speeds (including identical machines) is thoroughly reported.

Uniform Machine Scheduling with Predictions

The revival in learning theory has provided us with improved capabilities for accurate predictions. This work contributes to an emerging research agenda of online scheduling with predictions by studying the makespan minimization in uniformly related machine non-clairvoyant scheduling with job size predictions. Our task is to design online algorithms that effectively use predictions and have performance guarantees with varying prediction quality. We first propose a simple algorithm-independent prediction error measurement to quantify prediction quality. To effectively use the predicted job sizes, we design an offline improved 2-relaxed decision procedure approximating the optimal schedule. With this decision procedure, we propose an online O(min{log eta, log m})-competitive algorithm that assumes a known prediction error. Finally, we extend this algorithm to construct a robust O(min{log eta, log m})-competitive algorithm that does not assume a known error. Both algorithms require only moderate predictions to improve the well-known Omega(log m) lower bound, showing the potential of using predictions in managing uncertainty.

Algorithms for Multi-Customer Scheduling with Outsourcing

There are two customers and two uniform machines. Each customer has a set of jobs. These jobs may be processed on a uniform machine or may be outsourced with an outsourcing cost. Every customer has an objective function for his jobs. Manufacturers want to find the best scheduling scheme for both customers. We present algorithms for these problems for the first time.

Scheduling with complete multipartite incompatibility graph on parallel machines: Complexity and algorithms

In this paper, the problem of scheduling on parallel machines with a presence of incompatibilities between jobs is considered. The incompatibility relation can be modeled as a complete multipartite graph in which each edge denotes a pair of jobs that cannot be scheduled on the same machine.The paper provides several results concerning schedules, optimal or approximate with respect to the two most popular criteria of optimality: Cmax (makespan) and ∑Cj (total completion time). The problems are considered for a variety of machine types: identical, uniform and unrelated. The results consist of delimitation of the easy (polynomial) and NP-hard problems within these constraints. Also, polynomial-time exact algorithms are provided for easier problems and algorithms with a guaranteed constant worst-case approximation ratio for harder ones.In particular, there is provided a polynomial-time approximation scheme (PTAS) for scheduling with respect to ∑Cj on uniform machines, when the number of parts (i.e. sets of vertices constituting the mentioned graph) is bounded. The problem is addressed by developing a linear programming relaxation technique with appropriate rounding. This technique together with an exhaustive search (albeit in a manner controlled by the precision parameter) allows to provide the desired algorithm. For Cmax a PTAS is provided for the case of unit time jobs, but when the number of parts is part of the input. Interestingly, the latter result shows a connection between the considered problem of scheduling and covering problems.