Variable Processing Times Research Articles

NPSCS: Non-Preemptive Stochastic Coflow Scheduling With Time-Indexed LP Relaxation

Coflows model a scheduling setting that is commonly found in a variety of applications in distributed and cloud computing. A stochastic coflow task contains a set of parallel flows with randomly distributed sizes. Further, many applications require non-preemptive scheduling of coflow tasks. This article gives an approximation algorithm on the weighted expected completion time for non-preemptive stochastic coflow scheduling. The proposed approach uses a time-indexed linear program relaxation, and uses its solution to come up with a feasible schedule. This algorithm is shown to achieve an approximation ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(2\log {m}{+}1)(1{+}\sqrt {m {\Delta }})(1{+}m\sqrt {{\Delta }}){(1{+}{\Delta })}$ </tex-math></inline-formula> for zero-release times, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2(2\log {m}{+}1)(1{+}\sqrt {m{\Delta }})(1{+}m\sqrt {{\Delta }})(1{+}{\Delta })$ </tex-math></inline-formula> for general release times, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\Delta }$ </tex-math></inline-formula> represents the upper bound of squared coefficient of variation of processing times, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> is the number of servers.

Job shop scheduling with AGVs under variable processing time

In this paper, a job shop scheduling problem with material handling (JSSMH) is analysed given variable job processing time. The material handling is conducted by automatic guided vehicles (AGVs). Optimisation models have been formulated to accommodate the processing time variability due to random effects and deterioration. With random processing time, the model is formulated as a stochastic programming-based JSSMH (SP-JSSMH) model, and with deteriorating processing time the model can be nonlinear under specific deteriorating functions. A case study was conducted to illustrate and validate the model. The SP-JSSMH models were solved with Pyomo and deteriorating JSSMH models were linearised and solved with CPLEX. By considering variable processing time, the JSSMH models can better adapt to real production scenarios.

Job shop scheduling with AGVs under variable processing time

Job shop scheduling with AGVs under variable processing time

Disability Insurance in the Great Recession: Ease of Access, Program Enrollment, and Local Hysteresis

Previous research has documented that Social Security Disability Insurance (SSDI) applications and awards increase during economic downturns and that expanded access to SSDI leads to a reduction in employment. We build on these insights and investigate to what extent differential access to SSDI during economic downturns leads to differential changes in SSDI enrollment and employment during the subsequent recovery. We exploit plausibly exogenous variation in SSDI appeals processing time (a measure of hassle or access) facing individuals living in ZIP codes that straddle Social Security Administration hearing office catchment borders. During the Great Recession, ZIP codes assigned to hearing offices with faster appellate processes saw a larger increase in SSDI enrollment than their cross-border neighbors. These enrollment effects are concentrated among ZIP code pairs that experienced more severe labor market downturns, and they persist as late as 2015. In the full sample, there is no clear effect of longer processing times on subsequent employment rates. However, we find some limited evidence that faster appellate processes may have weighed on the employment recovery in hard-hit ZIP codes that had high pre-recession rates of SSDI enrollment. Our findings highlight the importance of considering interaction effects between economic shocks and ease of access to the safety net. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Integrated Scheduling of Tasks and Preventive Maintenance Periods in a Parallel Machine Environment With Single Robot Server

In this study, the objective of minimizing makespan has been considered for a scheduling problem of identical parallel machines with a single server and unavailability constraints. The unavailability constraints correspond to preventive maintenance periods. In this study, the jobs and the maintenance periods are scheduled simultaneously. This scheduling problem has a wide range of potential application areas in the manufacturing environment. In addition, the studied problem is a challenging one from theoretical point of view, due to its NP-Hardness. To conduct the study, a lower bound ( LB ) for the problem, and three metaheuristics namely Simulated Annealing ( SA ), Tabu Search ( TS ), and Genetic Algorithm ( GA ) have been proposed. The best parameters settings of the proposed algorithms were conducted using pilot runs with a Taguchi design. The algorithms performance has been assessed by using a set of test problems generated randomly. These test problems are based on a literature benchmark. The size of the instances, or number of jobs, were up to 500. Along with the performance analysis of the proposed algorithms, the effect of varying processing times and unavailability periods on the performance of the proposed algorithms is studied. The present work provides strong evidence of the efficiency and the performance of the proposed algorithms.

Identifying weather regimes for regional‐scale stochastic weather generators

AbstractWeather regime based stochastic weather generators (WR‐SWGs) have recently been proposed as a tool to better understand multi‐sector vulnerability to deeply uncertain climate change. WR‐SWGs can distinguish and simulate different types of climate change that have varying degrees of uncertainty in future projections, including thermodynamic changes (e.g., rising temperatures, Clausius‐Clapeyron scaling of extreme precipitation) and dynamic changes (e.g., shifting circulation and storm tracks). These models require the accurate identification of WRs that are representative of both historical and plausible future patterns of atmospheric circulation, while preserving the complex space–time variability of weather processes. This study proposes a novel framework to identify such WRs based on WR‐SWG performance over a broad geographic area and applies this framework to a case study in California. We test two components of WR‐SWG design, including the method used for WR identification (Hidden Markov Models (HMMs) vs. K‐means clustering) and the number of WRs. For different combinations of these components, we assess performance of a multi‐site WR‐SWG using 14 metrics across 13 major California river basins during the cold season. Results show that performance is best using a small number of WRs (4–5) identified using an HMM. We then juxtapose the number of WRs selected based on WR‐SWG performance against the number of regimes identified using metastability analysis of atmospheric fields. Results show strong agreement in the number of regimes between the two approaches, suggesting that the use of metastable regimes could inform WR‐SWG design. We conclude with a discussion of the potential to expand this framework for additional WR‐SWG design parameters and spatial scales.

Order release planning with predictive lead times: a machine learning approach

An essential task in manufacturing planning and control is to determine when to release orders to the shop floor. One key parameter is the lead time which is the planned time that elapses between the release of an order and its completion. Lead times are normally determined based on the observed time orders previously took to traverse the production system (flow times). Traditional order release models assume static lead times, although it has been shown that they should be set dynamically to reflect the dynamics of the system. Therefore, we present a flow time estimation procedure to set lead times dynamically using an artificial neural network. Additionally, we implement a safety lead time to incorporate the underlying cost ratio between finished inventory holding and backorder costs in the order release model. We test our proposed approach using a simulation model of a three-stage make-to-order flow-shop and compare the forecast accuracy and the cost performance to other forecast-based order release models from the literature. We show that our proposed model using artificial neural networks outperforms the other tested approaches, especially for scenarios with high utilisation and high variability in processing times.

A graphene oxide Cookbook: Exploring chemical and colloidal properties as a function of synthesis parameters

Herein, we describe the synthesis of graphene oxide (GO) over a large range of conditions, exploring the effects of reaction temperature, reaction time, oxidant ratio, and sonication time on the chemical and colloidal properties of the product. As a function of reaction parameters, modified from Hummers’ method, GO products were characterized and described via a suite of spectroscopic, structural, and morphological techniques, including TEM, UV–vis spectroscopy, XPS, Raman spectroscopy, FTIR, and DLS. Average carbon oxidation state and the yield (upon sonication) were chosen as the two criteria to evaluate synthesized GO materials. It was observed that as reaction temperature increased, GO oxidation state and yield of the sonication step both increased. Further, increasing reaction time and oxidant ratio not only increased the oxidation state, but also had a pronounced effect on the final yield. As synthesized, GO with higher degrees of oxidization exhibited higher negative ζ-potential, slightly smaller hydrodynamic diameter, and higher critical coagulation concentration(s). Data sets collectively demonstrate that carbon oxidation state, functional group ratios, and the aggregation kinetics of GO products can be readily controlled by varying processing time and conditions with expected changes in aqueous behavior(s), including stability/aggregation.

Integration of scheduling and control for batch process based on generalized Benders decomposition approach with genetic algorithm

Batch process plays an important role in various fields of industrial production, such as chemical engineering and pharmacy, given its characteristics such as small batch size, flexible production, and additional value of the product. Efforts to integrate the scheduling and process control for improving the benefits of batch process are recent. The integration of scheduling and process control is described by state equipment network which is closely related to the processing variables due to the feature of the network structure of the batch process where material splitting and mixing are allowed. The integrated formulation invokes logical disjunctions and operational dynamics which represents a typical mixed-logic dynamic optimization (MLDO) problem. To solve such a MLDO problem, we transform it into a mixed-integer nonlinear program (MINLP) using the Big M reformulation and the simultaneous collocation method. Then, the MINLP problems are solved through a generalized Benders decomposition (GBD) approach and genetic algorithm. The decomposed master problem is a scheduling problem with variable processing times, processing costs, and the Benders cut. Accordingly, the genetic algorithm is implemented to increase benefit. The primal problem comprises a set of separable dynamic optimization problems in the processing units. By collaboratively optimizing the process scheduling and dynamics, the proposed method substantially improves the overall economic performance of the batch production. At last, the feasibility and superiority of the proposed integration model and optimization algorithm can be determined by dealing with specific production instances.

Data-Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing

Fault detection and classification (FDC) is important for semiconductor manufacturing to monitor equipment’s condition and examine the potential cause of the fault. Each equipment in the semiconductor manufacturing process is often accompanied by a large amount of sensor readings, also called status variable identification (SVID). Identifying the key SVIDs accurately can make it easier for engineers to monitor the process and maintain the stability of the process and wafer productive yields. This article proposes using the random forests algorithm to analyze the importance of SVIDs of equipment sensors, automatically filters the key SVID by using ${k}$ -means, and integrates various machine learning methods to verify the key SVIDs and identify key processing time and steps. Upon the key parameters are identified, the key processing time and steps are investigated subsequently. The ensemble models constructed on ${k}$ -nearest neighbors ( ${k}$ NNs) and naive Bayes classifiers are presented for classifying wafers as normal or abnormal. Data visualization of multidimensional key SVIDs is performed by using ${t}$ -distributed stochastic neighbor embedding ( ${t}$ -SNE) to create a graphical aid in FDC for the process engineer. An empirical study is conducted to validate the proposed data-driven framework for fault detection and diagnostic. The experimental results demonstrate that the proposed framework can detect abnormality effectively with highly imbalanced classes and also gain insightful information about the key SVIDs and corresponding key processing time and steps. Note to Practitioners —The challenges of equipment sensor data analytics in semiconductor manufacturing include building the classifier to detect wafer abnormality correctly, identification of key status variable identifications (SVIDs) and processing time and steps of abnormality, and data visualization of the abnormality in a high-dimensional feature space. This article proposes a data-driven framework for fault detection and classification (FDC) during the wafer fabrication process by incorporating several useful machine learning approaches. Experimental results demonstrate that the proposed data-driven framework can supply quality fault detection performances and provide valuable information regarding the critical SVIDs and associated key processing time for fault diagnostic. The engineers can utilize the extracted fault patterns to perform a prognosis of the aging effect on process tools or modules for health management.

Exact lexicographic scheduling and approximate rescheduling

In industrial resource allocation problems, an initial planning stage may solve a nominal problem instance and a subsequent recovery stage may intervene to repair inefficiencies and infeasibilities due to uncertainty, e.g. machine failures and job processing time variations. In this context, we investigate the minimum makespan scheduling problem, a.k.a. P||Cmax, under uncertainty. We propose a two-stage robust scheduling approach where first-stage decisions are computed with exact lexicographic scheduling and second-stage decisions are derived using approximate rescheduling. We explore recovery strategies accounting for planning decisions and constrained by limited permitted deviations from the original schedule. Our approach is substantiated analytically, with a price of robustness characterization parameterized by the degree of uncertainty, and numerically. This analysis is based on optimal substructure imposed by lexicographic optimality. Thus, lexicographic optimization enables more efficient rescheduling. Further, we revisit state-of-the-art exact lexicographic optimization methods and propose a lexicographic branch-and-bound algorithm whose performance is validated computationally.

Experimental and computational analysis of microbial inactivation in a solid by ohmic heating using pulsed electric fields

Pulsed electric field technology (PEF) has traditionally been used as a technique to inactivate microorganisms in liquid foods at temperatures below those used in heat treatments; however, application of high-intensity PEF (E>1 kV/cm) at high frequencies (>10 Hz) can allow rapid and volumetric solid food electrical heating in order to replace traditional convection/conduction heating that progresses from the heating medium to the inside of the product. This investigation is the first one to evaluate the inactivation of Salmonella Typhimurium 878 in a solid product (cylinder of technical agar used as reference solid) by applying PEF treatments (2.5 and 3.75 kV/cm, and up to 9000 microseconds) at 50 Hz. The evolution of temperature in different locations of the agar cylinder was measured by observing the variability of heating rates depending on location and PEF intensity. Microbial inactivation was determined and compared with isothermal heat treatments that predicted similar inactivation values, but did not detect additional inactivation. Computational analysis enabled us to predict temperature and microbial inactivation for any spatial and temporal distribution of the cylinder agar by detecting the coldest point in the transition zone between the high-voltage electrode, the agar, and the plastic container of the treatment chamber. In order to evaluate the variability of the temperature, computational predictions were done each 0.5-mm. The difference between the coldest and the hottest point (e.g. at the center of the cylinder) resulted in around 10 ∘C and 10 second variation in temperature and processing time, respectively. In any case, it was possible to obtain 5-log10-reductions after 60 s of PEF treatments when using 2.5 kV/cm and 50% reduction for 3.75 kV/cm. These results suggested the potential of PEF technology as a rapid heating system based on ohmic heating for microbial inactivation in solid food products.

Prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine

Papermaking wastewater accounts for a large proportion of industrial wastewater, and it is essential to obtain accurate and reliable effluent indices in real-time. Considering the complexity, nonlinearity, and time variability of wastewater treatment processes, a dynamic kernel extreme learning machine (DKELM) method is proposed to predict the key quality indices of effluent chemical oxygen demand (COD). A time lag coefficient is introduced and a kernel function is embedded into the extreme learning machine (ELM) to extract dynamic information and obtain better prediction accuracy. A case study for modeling a wastewater treatment process is demonstrated to evaluate the performance of the proposed DKELM. The results illustrate that both training and prediction accuracy of the DKELM model is superior to other models. For the prediction of the quality indices of effluent COD, the determinate coefficient of the DKELM model is increased by 27.52 %, 21.36 %, 10.42 %, and 10.81 %, compared with partial least squares, ELM, dynamic ELM, and kernel ELM, respectively.

Event-related brain-oscillatory and ex-Gaussian markers of remission and persistence of ADHD.

Attention-deficit/hyperactivity disorder (ADHD) often persists into adolescence and adulthood, but the processes underlying persistence and remission remain poorly understood. We previously found that reaction time variability and event-related potentials of preparation-vigilance processes were impaired in ADHD persisters and represented markers of remission, as ADHD remitters were indistinguishable from controls but differed from persisters. Here, we aimed to further clarify the nature of the cognitive-neurophysiological impairments in ADHD and of markers of remission by examining the finer-grained ex-Gaussian reaction-time distribution and electroencephalographic (EEG) brain-oscillatory measures in ADHD persisters, remitters and controls. A total of 110 adolescents and young adults with childhood ADHD (87 persisters, 23 remitters) and 169 age-matched controls were compared on ex-Gaussian (mu, sigma, tau) indices and time-frequency EEG measures of power and phase consistency from a reaction-time task with slow-unrewarded baseline and fast-incentive conditions ('Fast task'). Compared to controls, ADHD persisters showed significantly greater mu, sigma, tau, and lower theta power and phase consistency across conditions. Relative to ADHD persisters, remitters showed significantly lower tau and theta power and phase consistency across conditions, as well as lower mu in the fast-incentive condition, with no difference in the baseline condition. Remitters did not significantly differ from controls on any measure. We found widespread impairments in ADHD persisters in reaction-time distribution and brain-oscillatory measures. Event-related theta power, theta phase consistency and tau across conditions, as well as mu in the more engaging fast-incentive condition, emerged as novel markers of ADHD remission, potentially representing compensatory mechanisms in individuals with remitted ADHD.

Analysis of Backward Sequence for Single-Armed Cluster Tools With Processing Time Variations

This article analyzes the backward sequence for single-armed cluster tools with processing time variations. The backward sequence is popularly used to operate a single-armed cluster tool in practice, but its performance has not been analyzed when processing time variations are introduced. To address the problem, we first define a fundamental cycle and derive a formula for cycle time analysis considering processing time variations. We then develop conditions for which the backward sequence is optimal for a certain cycle or all cycles. We also analyze the upper bound of the average cycle time with the backward sequence. Finally, the performance of the backward sequence with processing time variations is investigated experimentally. Note to Practitioners —The backward sequence, which is widely used for scheduling single-armed cluster tools, is analyzed with processing time variations in processing modules (PMs). The backward sequence is proven to be optimal in many cases with deterministic processing times. However, in reality, the wafer processing time in a PM is not deterministic and varies within a given time range. With regard to this issue, we analyze the performance of the backward sequence with processing time variations. This study can be very helpful for not only tool engineers but also for researchers who are interested in scheduling automated manufacturing systems.

Optimization of NaCl concentration and electrocoagulation processing time to reduce the level of chromium heavy metal solutions

The process of reduction of Cr from the wastewater of the metal coating industry can be carried out by electrocoagulation process. The purpose of this research was to reduce the levels of Cr in solutions, containing Cr as a preliminary study for the determination of NaCl levels added as electrolyte media and the determination of optimal electrocoagulation processing time. This research was laboratory experiments with a continuous electrocoagulation process using iron electrodes, with variations in addition of NaCl and processing time. Determination of Cr in solutions was carried out before and after electrocoagulation using an Atomic Absorption Spectrophotometer (AAS). Two-way ANOVA was used to analyze the result and showed that the significance value of the variation of NaCl concentration and processing time are decrease concentration of heavy metal Cr in solutions was 0.001 (p <0.05). These results indicate that there is a significant difference in the concentration of Cr which is significant in terms of variations in NaCl concentration and variations in electrocoagulation processing time. Based on these results, it can be concluded that the continuous system electrocoagulation process with the addition of 1.20 % w/v a time of 60 minutes gave the most optimum reduction of Cr.

Robust Scheduling of Hot Rolling Production by Local Search Enhanced Ant Colony Optimization Algorithm

Scheduling of a hot strip mill is an important decision problem in the steel manufacturing industry. Previous studies on the hot strip mill scheduling problem have mostly neglected the random factors in production. However, random variations in processing times are inevitable due to unpredictable delays and disturbances. In this article, we adopt a robust optimization approach to deal with the uncertainty in processing times. The advantage is that no assumption has to be made regarding the distribution of random data, and the obtained schedule will remain strictly feasible when the variations have not exceeded a predefined uncertainty set. First, a mixed-integer linear programming model is presented to formulate the robust scheduling problem. Then, a hybrid metaheuristic algorithm, which combines ant colony system (ACS) and enhanced local search, is proposed to provide an efficient solution to the problem. Finally, extensive computational experiments involving both randomly generated and real-world instances have been conducted to verify the effectiveness of the proposed algorithm. It is shown that the algorithm achieves optimality for small instances and outperforms two state-of-the-art metaheuristics when used to solve large instances.

Effect of Styrene Additives on the Cracking Kinetics of Components of High-Sulfur Vacuum Residue

In view of depleting light oil reserves, the involvement of heavy petroleum feedstocks (bitumens, atmospheric residues, vacuum residues) in oil refining is becoming increasingly promising. The conversion of these feedstocks requires the development of new methods for destroying macromolecular components (resins, asphaltenes), which will provide an effective production of distillate fractions and a decrease in the formation of a solid product (coke). The paper describes the results of high-sulfur vacuum residue cracking at temperatures of 400–500°C and varying process time; optimum cracking conditions providing an increase in the light fraction yield and a decrease in the amount of sulfur-containing components in liquid products are determined. Variations in the cracking product composition as a function of the amount of a radical-generating additive (styrene) are shown. Using a kinetic model of the cracking process, changes in the rate constants of thermal conversions of the components of Novokuibyshevsk refinery vacuum residue under various conditions have been determined.

Single-machine lot scheduling with variable lot processing times

This research addresses single-machine lot scheduling problems, with a focus on minimizing the total weighted completion time. The following properties for the lots are assumed: the lots have the same capacity, each lot may contain several orders of different sizes, the size of each order is less than or equal to the identical capacity, and orders must be processed at most in two consecutive lots. To date, lot scheduling researchers have assumed that all lots have identical processing times. In this study, variable lot processing times are considered; specifically, the setting where the processing time of the lots is position dependent, reflecting general position-dependent processing time properties, e.g. learning/ageing effects. Three special cases are examined: minimizing the total completion time, minimizing the makespan, and minimizing the linear combination of the makespan and the total completion time. All problems are shown to be solved in polynomial time.

Metallurgical Characterization of the Interfaces in Steel Plates Clad with Austenitic Steel or High Ni Alloys by Hot Rolling

An integrated experimental-theoretical approach to the metallurgical characterization of the interfaces in steel plates clad by hot rolling is proposed. Three different couplings of materials have been studied: ASTM A 515 Gr.60 low carbon steel clad with austenitic stainless steel AISI 304L; extra low carbon steel ASTM A283 clad with high Ni content Alloy 59; and, low carbon steel AISI 1010 clad with Cu-Ni Monel 400. Experimental investigations, which are addressed to analyse the microstructural changes near the interfaces and identify the present phases, have been carried out through scanning electron microscopy (SEM) observations, microanalytical measurements by energy dispersive spectroscopy (EDS), and Vickers microhardness tests. In all of the cases examined, the zones that are affected by detrimental microstructural changes results in being considerably less thick than the overall cladding layer. Simulations that are based on theoretical diffusion modelling have been integrated to the experimental characterization by introducing a cladding parameter that acts on the diffusion bonding efficiency, in order to evaluate the effects of process temperature and time variations on diffusion bonding efficiency and stability. In particular, this analytical investigation has shown how the shorter is the duration of the diffusion transient and the higher the temperature, the lower results the sensitivity of the diffusion processes to temperature fluctuations.