Processing Times Of Operations Research Articles

A Two-Individual-Based Evolutionary Algorithm for Flexible Assembly Job Shop Scheduling Problem with Uncertain Interval Processing Times

The assembly job shop is a prevalent production organization mode in manufacturing enterprises. During the processing and assembly of products, operation processing times are influenced by numerous factors, leading to significant uncertainty. This paper investigates the flexible assembly job shop scheduling problem (FAJSP) with uncertain processing times, where processing times are represented as variable interval numbers. We develop a robust optimization model for the FAJSP, utilizing confidence level estimation to determine the ranges of processing times and reformulating the model based on the chance-constrained method. A two-individual-based master–apprentice evolutionary (MAE) algorithm is proposed. Two effective encoding schemes are designed to prevent the generation of infeasible solutions under assembly sequence constraints. Additionally, a decoding method based on interval scheduling theory is devised to accurately represent interval processing times. Case studies are conducted to validate the effectiveness of the proposed robust optimization model and demonstrate the superiority of the MAE algorithm.

Cold Plasma Techniques for Sustainable Material Synthesis and Climate Change Mitigation: A Review

In recent years, the emission of greenhouse gases (GHGs) has increased significantly, contributing to global warming. Among these GHGs, CH4, CO2, and CO are particularly potent contributors. Remediation techniques primarily rely on materials capable of capturing, storing, and converting these gases. Catalytic processes, particularly heterogeneous catalysis, are essential to chemical and petrochemical industries as well as environmental remediation. Due to the growing demand for catalysts, efforts are being made to reduce energy consumption and make technologies more environmentally friendly. Green chemistry emphasizes minimizing the use of hazardous reactants and harmful solvents in chemical processes. Achieving these principles should be paired with processes that reduce time and costs in catalyst preparation while improving their efficiency. Non-thermal plasma (NTP) has been widely used for the preparation of supported metal catalysts. NTP has attracted significant attention for its ability to improve the physicochemical properties of catalysts, enhancing process efficiency through low-temperature operation and shorter processing times. NTP has been applied to various catalyst synthesis techniques, including reduction, oxidation, metal oxide doping, surface etching, coating, alloy formation, surface treatment, and surface cleaning. Plasma-prepared transition-metal catalysts offer advantages over conventionally prepared catalysts due to their unique material properties. These properties enhance catalytic activity by lowering the activation energy barrier, improving stability, and increasing conversion and selectivity compared to untreated samples. This review demonstrates how plasma activation modifies material properties and, based on extensive literature, illustrates its potential to combat climate change by converting CO2, CH4, CO, and other gases, showcasing the benefits of plasma-treated materials and catalysts. A succinct introduction to this review outlines the advantages of plasma-based synthesis and modification over traditional synthesis techniques. The introduction also highlights the various types of plasma and their physical characteristics across different factors. Additionally, this review addresses methods by which materials are synthesized and modified using plasma. The latter section of this review discusses the use of non-thermal plasma for greenhouse gas mitigation, covering applications such as the dry reforming of CH4, CO and CH4 oxidation, CO2 reduction, and other uses of plasma-modified catalysts.

Optimisation of process plan generation for reconfigurable manufacturing systems: efficient heuristics and lower bounds

ABSTRACT We address the process plan generation problem in a reconfigurable manufacturing environment. The objective is to minimise the total production time of a given part which includes the processing times of operations, as well as the changeover times for machines, configurations, and tools. A new efficient 0–1 mathematical programming formulation is proposed, together with a heuristic and two lower bounds. The formulation is based on one main decision variable (1MDV). It outperforms a well known two-main decision variable formulation (2MDV) from the literature. Using a commercial solver, the 1MDV formulation is on average almost five times faster than the 2MDV formulation across all instances where optimality was achieved. The proposed heuristic is a decomposition mathematical programming-based heuristic. For small benchmark instances solved to optimality using 1MDV formulation, the heuristic obtains solutions at less than 1% distance on average from optimum in few seconds. The best of the two proposed lower bounds, LB2, is obtained in less than 0.56 seconds, across all tested instances, while the 1MDV model needs about 30 seconds on average to reach the same result. These lower bounds are used to measure the performance of the heuristics on large size instances

A deep reinforcement learning model for dynamic job-shop scheduling problem with uncertain processing time

The dynamic job-shop scheduling problem (DJSP) is a type of scheduling tasks where rescheduling is performed when encountering the uncertainties such as the uncertain operation processing time. However, the current deep reinforcement learning (DRL) scheduling approaches are hard to train convergent scheduling policies as the problem scale increases, which is very important for rescheduling under uncertainty. In this paper, we propose a DRL scheduling method for DJSP based on the proximal policy optimization (PPO) with hybrid prioritized experience replay. The job shop scheduling problem is formulated as a sequential decision-making problem based on Markov Decision Process (MDP) where a novel state representation is designed based on the feasible solution matrix which depicts the scheduling order of a scheduling task, a set of paired priority dispatching rules (PDR) are used as the actions and a new intuitive reward function is established based on the machine idle time. Moreover, a new hybrid prioritized experience replay method for PPO is proposed to reduce the training time where samples with positive temporal-difference (TD) error are replayed. Static experiments on classic benchmark instances show that the make-span obtained by our scheduling agent has been reduced by 1.59% on average than the best known DRL methods. In addition, dynamic experiments demonstrate that the training time of the reused scheduling policy is reduced by 27% compared with the retrained policy when encountering uncertainties such as uncertain operation processing time.

MEASUREMENT AND CONTROL OF THE THICKNESS OF QUARTZ TUBES BY OPTICAL METHOD

Quartz glass tubes are widely used in electronic, chemical, lighting and other industries. They have good heat resistance, light transmission, electrical insulation and chemical stability. To avoid breakage and potential harm to the user, the wall thickness of the quartz glass tube must be checked. At the same time, the control of the parameters of the tubes, in particular the thickness, is usually done manually. This article presents the method and algorithm of the system for monitoring and measuring the thickness of quartz glass tubes in the process of their production based on technical vision. An important advantage of the method is the ability to measure the thickness of the tube even in the presence of possible fluctuations and vibrations during pulling, and it possible to control the difference in thickness along the tube's length. The method provides real-time operation (processing time for one tube image does not exceed 30 ms) with an accuracy of no more than 3 – 5 %.

Effect of Setup Times and Workload Trigger Levels on Order Review and Release Policies Performance in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Time — A Simulation Approach

Order review and release (ORR) in a job shop considering sequence-dependent setup time (SDST) is among the most challenging job shop problems. This study investigates the effect of varying setup times and workload trigger levels on ORR policies performance in a stochastic dynamic job shop considering SDST. It is essential to examine the effect of varying setup times and workload trigger levels on ORR policies, because, for different ORR policies, workload trigger level plays a major role to control the work-in-process on the shop floor. Further, in manufacturing industries, less setup time is required in some cases, but it can also be equal to or greater than the processing time in others. Promodel[Formula: see text] simulation software is used to develop the simulation model for conducting experiments. The performance of the system is evaluated in terms of mean lead time (MLT), mean throughput time (MTT), number of tardy jobs (NOTJ), and total setups (TS) performance measures. From results, it is found that for a given ORR policy and at a given setup time level, lower workload trigger level provides minimum MTT and maximum MLT, TS, and NOTJ performance measures. As the workload trigger level increases, MTT performance measure increases, whereas MLT, TS, and NOTJ performance measures decrease. Further, for a given ORR policy and workload trigger level, as the setup time level increases, MTT and MLT performance measures increase. In contrast, TS and NOTJ performance measures decrease. A stochastic dynamic job shop considering ORR and SDST where setup time is either less than, equal to or more than operation processing time is the novelty of the work.

Post cryosurgical rehabilitation of ear chondroma. Case from practice

A typical place of localization of chondroma is the cartilaginous tissue of the auricle, which rarely develops and most often in people at a young age. Existing treatment methods are effective and widespread, however, any injury to the tumor accelerates its growth. Therefore, significant development of chondromas is observed after a trial excision or incomplete removal. Cryosurgical treatment refers to gentle, less traumatic methods. Тhe authors describe a case from practice: removal of the chondroma of the outer ear using a СAO-2 cryoapparatus with an individual polyethylene cap, which allows for accelerating and simplifying the process of thawing and re of the instrument. This intervention is outpatient and does not require special preparation. Taking into account the undesirable complications after cryotherapy (swelling, pain due to increased tissue turgor, local deterioration of blood circulation) on the one hand, and taking into account the antitoxic, calming, relaxing, and analgesic effect of magnesium on the body as a whole, the authors used balneotherapy in the form of a course of applications with a natural balneological agent with a high content of magnesium chloride on the zone of cryotherapy, as well as its drinking course consumption. The observation showed that the intervention using a cryoapplicator is a gentle method, slightly damaging the surrounding tissues, minimizing the possibility of intraoperative bleeding, allowing to accelerate the operation process and healing time, and, accordingly, reducing the patient's incapacity for work. Applying the results in clinical practice will significantly reduce the likelihood of deformation of the outer ear and maintain its primary functions.

Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops

The flexible job shop is a well-known scheduling problem that has historically attracted much research attention both because of its computational complexity and its importance in manufacturing and engineering processes. Here we consider a variant of the problem where uncertainty in operation processing times is modeled using triangular fuzzy numbers. Our objective is to minimize the total energy consumption, which combines the energy required by resources when they are actively processing an operation and the energy consumed by these resources simply for being switched on. To solve this NP-Hard problem, we propose a memetic algorithm, a hybrid metaheuristic method that combines global search with local search. Our focus has been on obtaining an efficient method, capable of obtaining similar solutions quality-wise to the state of the art using a reduced amount of time. To assess the performance of our algorithm, we present an extensive experimental analysis that compares it with previous proposals and evaluates the effect on the search of its different components.

Blockchain-Based Privacy Preservation for IoT-Enabled Healthcare System

Blockchain technology provides a secure and reliable platform for managing data in various application areas, such as supply chain management, multimedia, financial sector, food sector, Internet of Things (IoT) , healthcare, and many more. The recent emergence of blockchain with IoT provides significant growth in the healthcare industry to improve security, privacy, efficiency, and transparency with more business opportunities. Nevertheless, conventional healthcare schemes suffer from various security attacks like collusion, phishing, masquerade, etc. Therefore, a privacy-preserving Distributed Application (DA) is proposed in this paper using blockchain technology to create and maintain healthcare certificates. Here, the distributed application provides an interface between the blockchain network and system objects like healthcare centers, verifiers, and regular authorities to generate and issue medical documents. In addition, it also ensures security by specifying rules using various smart contracts. To evaluate the performance of the proposed scheme, various experimental tests are conducted using the Etherscan tool for measuring operation cost, latency, and processing time. Here, the efficiency of the proposed system is also compared to the existing systems in terms of latency, throughput, and response time. The experimental results and comparative analysis show that the proposed work is more efficient than the existing techniques.

IMPROVE BUILDING DATABASE ON THE OPERATION PROCESS AND PERFORMANCE TIME FOR SEWING OPERATIONS OF KNITTED GARMENT PRODUCTS

This paper presents the findings of the study of building and completion of a standard database on the operation process and sewing time for 02 typical products from knitted fabrics, namely Polo-Shirt and TShirt. The study process is carried out based on applying MTM (Methods Time measurement) standard time analysis method and predetermined time system GSD (General Sewing Data). In this research, we have inherited the results from previous studies including Classifying the main parts sewing linkages, formulated sewing technology process and theoretical analysis of the process of manipulating sewing of the main parts, linkages of the 02 classical textile products including Polo-Shirt and T-Shirt by MTM standard time analysis method and GSD predetermined time system; The studies work of the group of authors on the experimental research content determines the simultaneous influence of a group of factors: distance to place the sewing element (cm), the rotation angle of the sewing element (˚), the size of the sewing element, the number of element layers involved in the sewing, the light intensity (lux) and the skills of sewing workers (grade worker) to the sewing time of knitting products and research simultaneous influences of a group of technological factors including: seam length (cm) and stitches per centimeter (stitches/cm), experiment on 4 sewing devices such as 1-needle lockstitch machine, overlock machine (1 needle and 3 threads) and (2 needles and 4 threads), coverstitch machine (2 needles and 3 threads); and with 3 kinds of single jersey fabrics, which are thin, medium, and thick fabrics to sewing time on the machine of Polo-Shirt and T-Shirt products. The above research results show that there is a big difference between the actual values and theoretically calculated values according to MTM method, GSD predetermined time system, the authors have determined a set of adjustment coefficients for the former and the latter for two values of sewing preparation operation time and sewing time on the machine. We have tested the above research results in 03 enterprises: Hanoi Star Fashion Co., Ltd., Tinh Loi Garment Company and Ha Nam Hanosimex Company Limited and received a lot of practical comments from businesses.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ МАСШТАБУВАННЯ АРХІТЕКТУРИ ХМАРНИХ ДОДАТКІВ

Abstract. The areas of improvement of the method of dynamic load balancing for very heavy calcula-tions and tasks with scaling on cloud infrastructure, which differ from known methods, are considered. An analysis of existing methods and algorithms that provide solutions to load balancing problems in cloud in-frastructures was carried out. Methods of load balancing are considered. A model of the load distribution system for cloud infrastructure services based on a modification of the dynamic load balancing method has been developed. Experimentally verified characteristics of the created model. Comparison of available load balancing methods. The practical implementation of tracking results allows to reduce the processing time of tasks that come from the messaging system and enables flexible configuration of servers and applications. The conducted experimental studies showed that the processing time of operations decreases when using the proposed method of load balancing. Keywords: cloud applications, cloud infrastructure, servers, load balancing, message exchange system.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ МАСШТАБУВАННЯ АРХІТЕКТУРИ ХМАРНИХ ДОДАТКІВ

Abstract. The areas of improvement of the method of dynamic load balancing for very heavy calcula-tions and tasks with scaling on cloud infrastructure, which differ from known methods, are considered. An analysis of existing methods and algorithms that provide solutions to load balancing problems in cloud in-frastructures was carried out. Methods of load balancing are considered. A model of the load distribution system for cloud infrastructure services based on a modification of the dynamic load balancing method has been developed. Experimentally verified characteristics of the created model. Comparison of available load balancing methods. The practical implementation of tracking results allows to reduce the processing time of tasks that come from the messaging system and enables flexible configuration of servers and applications. The conducted experimental studies showed that the processing time of operations decreases when using the proposed method of load balancing. Keywords: cloud applications, cloud infrastructure, servers, load balancing, message exchange system.

Robust permutation flow shop total weighted completion time problem: Solution and application to the oil and gas industry

This work proposes exact solution approaches for the m-machine robust permutation flow shop problem, where operation processing times are uncertain and vary in a given interval. Following the concept of budgeted uncertainty, the objective is to obtain a robust scheduling that minimizes the total weighted completion time of the restricted worst-case scenario, in which only a subset of operation processing times will deviate to worst-case values. We develop several robust counterpart formulations, which can be used to derive optimal solutions for medium-sized problem instances by using a Column-and-Constraint Generation algorithm. The efficacy of the solution methods is validated through experiments on three sets of randomly-generated instances. Finally, a case study for the maintenance schedule of Brazilian oil platforms is presented.

Improving Speed of MUX-FSM-based Stochastic Computing for On-device Neural Networks

We propose an acceleration technique for processing multiplication operations using stochastic computing (SC) in on-device neural networks. Recently, multiplexor driven finite state machine (MUX-FSM)-based SCs, which employ a MUX controlled by an FSM to generate a (repeated but short) bit sequence of a binary number to count up for a multiplication operation, considerably reduce the processing time of MAC operations over the traditional stochastic number generator (SNG) based SC. Nevertheless, the existing MUX-FSM-based SCs still do not meet the multiplication processing time required for the wide adoption of on-device neural networks in practice even though it offers a very economical hardware implementation. In this respect, this work proposes a solution that speeds up the conventional MUX-FSM-based SCs. Precisely, we analyze the bit counting pattern produced by MUX-FSM and replace the counting redundancy by shift operation, resulting in a shortening of the length of the required bit sequence significantly, together with analytically formulating the number of computation cycles. Through experiments, we have shown that the enhanced SC technique can reduce the processing time by 44.1% on average over the conventional MUX-FSM-based SCs.

Threat elimination algorithm for Dual Resource Constrained Flexible Job Shop Scheduling Problems

This paper proposes an expert knowledge based algorithm for solving the Dual Resource Constrained Job Shop Scheduling Problem (DRC-FJSSP), the Biggest Threat First (BTF). The main idea behind this algorithm is that by eliminating your biggest threat, you get much closer to achieving your goal. Moreover, the BTF algorithm considers an encoded representation of the schedule, which combines 2 vectors: the Blacklist and the Deployment. The Blacklist sorts the major threats in descending order, while the Deployment vector allocates a worker-machine pair to tackle each assigned task. Results show that this method utterly outperforms a state-of-the-art metaheuristic, the KGFOA, at makespan minimization problems when applied to the MK1-10 benchmark dataset. Future work may access the algorithm's performance in problems with different performance metrics, such as the weighted tardiness. Moreover, research may be carried to apply the BTF to problems where the processing time of operations varies with the allocated worker-machine pair.

WORK ANALYSIS OF PLATE BENDING WORKSHOP IN SHIPBUILDING INDUSTRY

Plate bending is one of the major processes in shipbuilding. The plates which are cut in specific dimensions are sent to plate bending work shop and they are curved by using various methods. These curved plates constitute bilge turn plates of a vessel which are curvilinear. In this study, plate bending work shop was considered and its simulation model was created by using SIMIO software. The aim of the study is to see the effects of some alterations on available situation of plate bending work shop and present some recommendations in order for the plate bending work shop to operate effectively. In this work, there are number of four alterations including the changing of bending processing time, number of cranes and bending machine. At the end of the study, the values of the most suitable crane number, bending machine number, and the bending operation processing time were recommended. It is believed that the plate bending work shop will operate in effective way if the shipyard administration implement the recommendations presented in this study.

Exact solutions for the two-machine robust flow shop with budgeted uncertainty

This work proposes an exact solution approach for the two-machine robust flow shop problem, where operation processing times are uncertain and vary in a given interval. Based on the concept of budgeted uncertainty, the objective is to obtain a robust scheduling that minimizes the makespan of the restricted worst-case scenario, where only a subset of job processing times will oscillate to their worst-case values. To our knowledge, this is the first work to obtain optimal solutions to this problem, by extending two classical MILP formulations for the deterministic case and combining them with a Column-and-Constraint Generation framework. For this purpose, a dynamic programming algorithm was also developed, allowing the identification of worst-case scenarios in polynomial time. Experiments on a set of literature instances confirm the efficacy of our approach, including a case study that shows little overhead in the expected solution value of obtained robust solutions.

A multi-objective multi-micro-swarm leadership hierarchy-based optimizer for uncertain flexible job shop scheduling problem with job precedence constraints

In realistic production scheduling, the processing time of operations and the due time of orders always fail to be precisely estimated as deterministic values due to fluctuating manufacturing environments and modest delay tolerance. When fabricating complex products that are assembled by multilevel parts, tree-structure dependencies between parts lead to hierarchical precedence constraints between corresponding jobs. Consequently, this paper studies an uncertain flexible job shop scheduling problem with job precedence constraints (U-FJSP-JPC). Uncertain processing time and due time are represented as interval grey number and trimmed triangular fuzzy number respectively. A tardiness index indicator is devised to assess the delay extent of grey completion time relative to fuzzy due time. To solve U-FJSP-JPC with minimizing three objectives simultaneously involving interval grey makespan, interval grey total machine workload and average tardiness index, this paper elaborately designs a novel multi-objective multiple-micro-swarm leadership hierarchy-based optimization algorithm (MOM2SLHO). This algorithm adopts a two-vector encoding scheme based on job and operation sequencing and a grey active decoding scheme based on heuristic machine assignment. In MOM2SLHO, the entire search agents are divided into multiple micro-swarms in which each one conducts an independent search based on leadership hierarchy and communicates with others by specific strategies. MOM2SLHO embodies an enhanced external grid archive to store and retrieve non-dominated Pareto optimal solutions. Extensive experiments and statistical analyses demonstrate that the proposed MOM2SLHO algorithm outperforms other well-known and state-of-art algorithms significantly for solving the studied U-FJSP-JPC.

An intelligent face mask integrated with high density conductive nanowire array for directly exhaled coronavirus aerosols screening

The current ongoing outbreak of Coronavirus Disease 2019 (COVID-19) has globally affected the lives of more than one hundred million people. RT-PCR based molecular test is recommended as the gold standard method for diagnosing current infections. However, transportation and processing of the clinical sample for detecting virus require an expert operator and long processing time. Testing device enables on-site virus detection could reduce the sample-to-answer time, which plays a central role in containing the pandemic. In this work, we proposed an intelligent face mask, where a flexible immunosensor based on high density conductive nanowire array, a miniaturized impedance circuit, and wireless communication units were embedded. The sub-100 nm size and the gap between the neighbored nanowires facilitate the locking of nanoscale virus particles by the nanowire arrays and greatly improve the detection efficiency. Such a point-of-care (POC) system was demonstrated for coronavirus ‘spike’ protein and whole virus aerosol detection in simulated human breath. Detection of viral concentration as low as 7 pfu/mL from the atomized sample of coronavirus aerosol mimic was achieved in only 5 min. The POC systems can be readily applied for preliminary screening of coronavirus infections on-site and may help to understand the COVID-19 progression while a patient is under prescribed therapy.

Two- and three-machine open shop scheduling using LAPT-like rules

In the paper, open shop scheduling problems with the objective to minimize the makespan and fixed or proportionally deteriorating operation processing times are considered. We solve the problems using three rules which are modifications of the Longest Alternate Processing Time (LAPT) rule that earlier was proved to be optimal for two-machine open shop scheduling problem with the same objective and fixed operation processing times. We prove that the first of the modified rules is optimal for two-machine open shop scheduling problem with proportionally deteriorating operation processing times. We also show, based on results of conducted numerical experiments, that the next two modified rules generate near-optimal schedules for three-machine open shop problems, regardless whether operation processing times are fixed or proportionally deteriorating.