Fixed Processing Time Research Articles

Investigation into shrinkage of stretchable denim fabric and its consumption in marker making by varying drying temperatures

This investigation was carried out to minimize Cotton and Cotton–polyester stretchable denim fabrics consumption for garment development through marker making. It was achieved by controlling fabric shrinkage by reducing drying temperature after washing in fixed processing time, without compromising properties of denim; Tensile strength, Stretch (growth and recovery at constant and cyclic loading) and colour strength (K/S) of required shade. warp faced 3x1 and 2x1 denim containing elastin were constructed and then washed using same recipe as per industrial practice however drying temperatures were set at 50oC, 60oC and 70oC, separately. After measuring shrinkage and evaluating tensile strength, Stretch, Growth and recovery at constant and cyclic loading and colour strength (K/S) using standard test methods; trouser marker layout was developed by employing Gerber technology to investigate fabrics consumption. The obtained shrinkage % demonstrated a significant coefficient of determination (R2=0.9931) against drying temperatures. Relation of tensile strength, colour strength and growth were insignificant however stretch, growth and recovery at static and cyclic loading were significantly affected by changing temperatures. Average shrinkage % of stretchable denim fabric was 1.71% in warp and 8.24% in weft at 50oC. It increased to 12.05% in weft at 60oC however remained same in warp while against 70oC it was 1.82% in warp and 14.08% in weft. 10 minutes drying time was kept same for selected drying temperatures. It was found that fabric consumption reduced to 1.25 m per trouser from 1.32 m per trouser by setting drying temperature at 50oC. Present research work provides guideline to denim industries to process stretchable denim at lower drying temperature i.e., 50oC because it not only reduced consumption of elastin containing denim fabric, but it also makes process cost effective as well as environmentally friendly.

Parallel assembly lines worker assignment and balancing problem: A mathematical model and an artificial bee colony algorithm

In today’s competitive world, assembly lines are one of the most important industrial manufacturing systems, which can accommodate the massive increase in customer demands and product range, for the production industry. One possible way to increase the capacity and efficiency of assembly lines is to design a parallel assembly line system, consisting of two or more assembly lines placed next to each other. In the literature, the assembly lines are usually balanced according to tasks with fixed processing times without considering worker–task compatibility. In real-life applications, however, since the processing time of each task may vary according to the capability of each worker, the assignment of tasks to stations in the assembly line depends on the skills of the worker assigned to the related station. Considering the worker and task assignments together for each assembly line will provide a more realistic perspective. This paper, to the best of our knowledge, is the first study that considers the minimization of joint cycle time for the worker assignment and assembly line balancing problem in parallel assembly lines (PALWABP-II). Accordingly, a binary linear mathematical programming (BLP) model is developed and an artificial bee colony (ABC)-based solution approach is proposed for medium and large-sized problems. A new set of test problems is presented to the literature for a computational experiment. In addition, a summary of the literature is presented. An example problem is solved using the BLP model. In order to evaluate the effectiveness of the proposed model and approach, they are compared with the classical particle swarm optimization (PSO) algorithm and ten different priority rules. The computational results on test problems prove the effectiveness of the proposed ABC algorithm for the PALWABP-II.

Combined ultrasound cavitation and persulfate for the treatment of pharmaceutical wastewater.

In recent years industrialization has caused magnificent leaps in the high profitable growth of pharmaceutical industries, and simultaneously given rise to environmental pollution. Pharmaceutical processes like extraction, purification, formulation, etc., generate a large volume of wastewater that contains high chemical oxygen demand (COD), biological oxygen demand, auxiliary chemicals, and different pharmaceutical substances or their metabolites in their active or inactive form. Its metabolites impart non-biodegradable toxic pollutants as a byproduct and intense color, which increases ecotoxicity into the water, thus this requires proper treatment before being discharged. This study focuses on the feasibility analysis of the utilization of ultrasound cavitation (20 kHz frequency) together with a persulfate oxidation approach for the treatment of complex pharmaceutical effluent. Process parameters like pH, amplitude intensity, oxidant dosage were optimized for COD removal applying response surface methodology-based Box-Behnken design. The optimum value observed for pH, amplitude intensity and oxidant dosage are 5, 20% and 100 mg/L respectively with 39.5% removal of COD in 60 min of fixed processing time. This study confirms that a combination of ultrasound cavitation and persulfate is a viable option for the treatment of pharmaceutical wastewater and can be used as an intensification technology in existing effluent treatment plants to achieve the highest amount of COD removal.

A Provisional Labels-Reduced, Real-Time Connected Component Labeling Algorithm for Edge Hardware

Connected component labeling (CCL) is an important operation in computer vision tasks, widely used for real-time image processing on edge hardware. The existing connected component labeling algorithms always generate plenty of provisional labels. Solving the equivalence of provisional labels needs much memory access and processing time. The greater difficulty is that the processing time varies with the number and shape of the connected components. To solve the problems, this paper proposes a new two-scan CCL algorithm. It consists of novel provisional label predicting and provisional label assignment methods based on predicting table, linked lists, and equivalent table. The provisional label predicting method reduces the number of provisional labels effectively. The provisional label assignment method can implement quickly the assignment operation of CCL by combination of the first scan and the second scan with fixed processing time. We have implemented the proposed algorithm on Field Programmable Gate Arrays (FPGA) for raster scan images. The results show that its processing speed can achieve 726fps@225MHz for 640×480 image resolution, and its resource utilization is less than 15% of compared implementation. It is suitable for real-time edge applications. Compared with other algorithms, the proposed algorithm reduces provisional labels efficiently and completes the CCL operation with a fixed number of iterations. Therefore, it shortens processing time and makes the processing time constant.

An ant colony optimization approach for the proportionate multiprocessor open shop

Multiprocessor open shop makes a generalization to classical open shop by allowing parallel machines for the same task. Scheduling of this shop environment to minimize the makespan is a strongly NP-Hard problem. Despite its wide application areas in industry, the research in the field is still limited. In this paper, the proportionate case is considered where a task requires a fixed processing time independent of the job identity. A novel highly efficient solution representation is developed for the problem. An ant colony optimization model based on this representation is proposed with makespan minimization objective. It carries out a random exploration of the solution space and allows to search for good solution characteristics in a less time-consuming way. The algorithm performs full exploitation of search knowledge, and it successfully incorporates problem knowledge. To increase solution quality, a local exploration approach analogous to a local search, is further employed on the solution constructed. The proposed algorithm is tested over 100 benchmark instances from the literature. It outperforms the current state-of-the-art algorithm both in terms of solution quality and computational time.

Influence of rf power on growth, structural and optical properties of ZnO synthesized via vapor transport in inductively coupled plasma

The aim of this work was to examine the influence of radio frequency (rf) power on ZnO films synthesized by means of chemical vapor transport technique in inductively coupled plasma (ICP). As the rf power increased, with a fixed processing time of only 8 min, the temperature inside the reactor tube, the thickness and surface roughness of the formed polycrystalline ZnO films increased. The morphology of the ZnO films changed to oblate, webby like cloud-grains, nanoslices, and a mixture of nanorods and nanograins with changing the rf power from 300 to 500 W. Mixed phases of hexagonal ZnO and hexagonal Zn were found for samples synthesized at 300 and 500 W whereas single hexagonal ZnO was synthesized at 350, 400 and 450 W. The optical band gap decreased from 3.35 eV to 3.05 eV with increasing the rf power from 300 W to 500 W. The refractive index and extinction coefficient values were mainly increased with increasing the rf power. The electrical resistivity was generally decreased with increasing rf power and has values between 1.38 × 10−2 - 2.83 × 10−3 Ωcm. The obtained results might be important for useful applications of ZnO films grown by chemical vapor transport with the assistance of ICP.

A Neutrosophic Number-Based Memetic Algorithm for the Integrated Process Planning and Scheduling Problem With Uncertain Processing Times

Process planning and scheduling are two crucial components in a flexible manufacturing system. Lots of novel meta-heuristics have been applied to the integrated process planning and scheduling (IPPS) problem for an efficient utilization of manufacturing resources; nevertheless, the tricky part in real life stems from the uncertainty in processing times. Existing publications regarding IPPS problems mainly focus on cases with nominal or fixed processing times; nevertheless, processing time fluctuations will certainly result in an intolerable deviation between the actual makespan and the nominal one. This research focuses on the IPPS problem with uncertain processing times to hedge against the uncertainty in makespan. The novel neutrosophic numbers are first introduced to model the uncertain processing times. A neutrosophic number based mixed integer linear programming (MILP) model is established; due to the non-deterministic polynomial (NP)-hardness and the complexity in solving the model, a variable neighborhood search (VNS) incorporated memetic algorithm (MA) is then developed to facilitate more robust solutions. In the proposed algorithm, the nominal makespan criterion and the deviation (robustness) criterion have been considered in a weighted sum manner. The well-known Kim's benchmark is adopted to test the performance of the proposed algorithm and different degrees of fluctuations are also defined in experiments. Computational results reveal that the VNS based local search method is powerful in capturing promising solutions; competitive solutions with superior nominal makespan and robustness have been obtained. This research presents a novel perspective or methodology to seek more robust solutions for the uncertain IPPS problem.

Effect of Waiting Time and Process Temperature on the Quality of Commercially Sterilized Flavored Liquid Milk

In Indonesia, commercially sterilized flavored liquid milk is regulated requiring minimum F0-value of 3.0 minutes. Due to batch thermal processing operation, a waiting time, period for product holding before sterilization process at room temperature, is necessary. Waiting time is identified as a critical factor due to potential increase in initial microbial load. Consequently, increase of waiting time might be associated with the need of higher F0-value. The objective of this study was to evaluate the effect of waiting time (0, 1, 2, 3, 4, 5, and 6 hours) and process temperatures (121.9, 122.6, and 123.9˚C) for fixed processing time (10 minutes) on the selected quality parameters of commercially sterilized flavored liquid milk. Our result showed no significant increase on the number of microbes, pH, and viscosity of the milk after waiting time up to 6 hours. After sterilization, all products tested with waiting time up to 6 hours and processing temperature at 121.9˚C (F0–value of 11.00-13.54 minutes), 122.6˚C (F0–value of 15.49-17.01 minutes), 123.9˚C (F0–value of 20.53-21.46 minutes) were organoleptically acceptable. However, decrease of vitamin B1 (6-7, 9.6-10.4, 22-23%), vitamin C (45-50, 100, and 100%) were observed at processing temperature of 121.9, 122.6, and 123.9˚C, respectively. These findings shows that the use of processing temperature of 121.9˚C (F0-value of 11.00-13.54 minutes) is consider to be the best alternative to produce commercially sterilized flavored liquid milk having waiting time up to 6 hours.

Effect of Gas Content and Treatment Temperature on the Characteristic of Surface Layers of Low Temperature Plasma Nitrided 316L Austenitic Stainless Steel

The effect of gas content and treatment temperature on the surface characteristics of hardened layers of low temperature plasma nitrided 316L Austenitic Stainless Steel was investigated. The process was performed at fixed processing time at 15Hr and changing the N2 content from 10% to 25%, changing the treatment temperature from 370 °C to 430 °C. The surface hardness and the thickness of expanded austenite (S-Phase) layer increase with increasing both temperature and nitrogen content. At 430°C processing temperature and 25% N2 content, the S-phase layer thickness increased up to 13 μm and surface hardness increased about 3.5 times of that of untreated sample which is 880 HV0.1 (before treatment 250HV0.1 hardness). However, the XRD pattern of the this sample shows the presence of γ'-(Fe,Cr)4N precipitation on the surface which is the reason for the increased corrosion current density that results in increased corrosion rate. Thus the corrosion resistance degrades and presents even worst behavior comparing that of before treatment. Therefore, for increasing the corrosion behavior, further research was conducted by fixing the N2 content at 10% with changing the CH4 content from 0% to 5%. The best results were found when treated at 430°C with 5% CH4 content. At this condition the S-phase thickness increase to around 17μm and surface hardness (980 HV0.1) is improved about 3.9 times of the hardness of untreated one. This sample also showed superior corrosion resistance than the other treated samples and the untreated one due to much higher pitting potential.

Sequential linear regression with online standardized data.

The present study addresses the problem of sequential least square multidimensional linear regression, particularly in the case of a data stream, using a stochastic approximation process. To avoid the phenomenon of numerical explosion which can be encountered and to reduce the computing time in order to take into account a maximum of arriving data, we propose using a process with online standardized data instead of raw data and the use of several observations per step or all observations until the current step. Herein, we define and study the almost sure convergence of three processes with online standardized data: a classical process with a variable step-size and use of a varying number of observations per step, an averaged process with a constant step-size and use of a varying number of observations per step, and a process with a variable or constant step-size and use of all observations until the current step. Their convergence is obtained under more general assumptions than classical ones. These processes are compared to classical processes on 11 datasets for a fixed total number of observations used and thereafter for a fixed processing time. Analyses indicate that the third-defined process typically yields the best results.

Increasing Throughput for a Class of Two-Machine Robotic Cells Served by a Multifunction Robot

The multifunction robotic cell scheduling problem has been recently studied in the literature. The main assumption in the pertaining literature is that a multifunction robot performs an operation on the part during any loaded move between two adjacent processing stages. For a two-machine cell, these stages are the input hopper, the first machine, the second machine and the output hopper. Consequently, the multifunction robot performs three operations with fixed processing times. In contrast, we assume a class of two-machine cells where none of the processing times of three operations are fixed. However, their summation is fixed and equivalent to the processing time of the unique operation. The processing mode of the unique operation performed by the multifunction robot is “stop resume.” Thus, regardless of the gap interrupts during operations by two machines, the robot continues performing the unique operation of the part when it is reloaded to the robot without any loss of time. The focus lies on $n$ -unit cycles due to their popularity. It is proven one-unit cycles have better performance for the problem under study. The cycle time of one-unit cycles are obtained and optimality conditions are determined for different pickup criteria: free, interval, and no-wait.

Approximate algorithms for unrelated machine scheduling to minimize makespan

We study two unrelated machine scheduling problems with machine dependent release dates to minimize the makespan.For the case with fixed processing time where processing job $j$ on machine $i$ requires time $p_{ij}$ and incurs a cost of $c_{ij}$,we derive a $2$-approximation algorithm. For the problem with variable processing times where the cost increases linearly as the processing time decreases, we propose a $(2+\epsilon)$-approximation algorithm.

Capacity optimization and scheduling of a multiproduct manufacturing facility for biotech products.

A general mathematical framework has been proposed in this work for scheduling of a multiproduct and multipurpose facility involving manufacturing of biotech products. The specific problem involves several batch operations occurring in multiple units involving fixed processing time, unlimited storage policy, transition times, shared units, and deterministic and fixed data in the given time horizon. The different batch operations are modeled using state-task network representation. Two different mathematical formulations are proposed based on discrete- and continuous-time representations leading to a mixed-integer linear programming model which is solved using General Algebraic Modeling System software. A case study based on a real facility is presented to illustrate the potential and applicability of the proposed models. The continuous-time model required less number of events and has a smaller problem size compared to the discrete-time model.

Scheduling: Agreement graph vs resource constraints

We investigate two scheduling problems. The first is scheduling with agreements (SWA) that consists in scheduling a set of jobs non-preemptively on identical machines in a minimum time, subject to constraints that only some specific jobs can be scheduled concurrently. These constraints are represented by an agreement graph. We extend the NP-hardness of SWA with three distinct values of processing times to only two values and this definitely closes the complexity status of SWA on two machines with two fixed processing times. The second problem is the so-called resource-constrained scheduling. We prove that SWA is polynomially equivalent to a special case of the resource-constrained scheduling and deduce new complexity results of the latter.

An Online Load Balancing Algorithm for Virtual Machine Allocation with Fixed Process Intervals

To improve the performance and reliability of resource in Cloud data centers, it is significant to apply load balancing strategy for resource scheduling. In a dynamic changing traffic environment, workload of a Cloud data center has real-time and fixed process time characteristic. One of the challenging scheduling problems in Cloud datacenters is to take the real-time allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. In general, load-balance scheduling is NP-hard problem as proved in many open literatures. We introduce an Online Load-balance Resource Scheduling Algorithm (OLRSA) for Cloud datacenters considering realtime and multi-dimensional resources. Unlike traditional load balance scheduling algorithms which often do not consider lifecycle and fixed interval constraints, OLRSA treats life cycles and fixed intervals for both physical machines and virtual machines. We develop and apply integrated measurement for each server and a Cloud datacenter. New metrics such as integrated imbalance level, capacity makespan, capacity skew are defined for Cloud data centers. Simulation results show that OLRSA has better performance than a few related load balancing algorithms with regard to total imbalance level, makespan, overall load efficiency as well as capacity makespan.

Integrated project scheduling and staff assignment with controllable processing times.

This paper addresses a decision problem related to simultaneously scheduling the tasks in a project and assigning the staff to these tasks, taking into account that a task can be performed only by employees with certain skills, and that the length of each task depends on the number of employees assigned. This type of problems usually appears in service companies, where both tasks scheduling and staff assignment are closely related. An integer programming model for the problem is proposed, together with some extensions to cope with different situations. Additionally, the advantages of the controllable processing times approach are compared with the fixed processing times. Due to the complexity of the integrated model, a simple GRASP algorithm is implemented in order to obtain good, approximate solutions in short computation times.

MFM imaging of expanded austenite formed on 304 SS and CoCrMo alloys

New data related to the magnetic nature of the expanded austenite layers on CoCrMo and austenitic stainless steel by nitrogen plasma immersion ion implantation (PIII) are presented. Implantations were performed in the temperature range between 300 and 550°C for a fixed processing time of 1h. Magnetic properties, nitrogen distribution, implanted layer phases, and surface topography were studied with a combination of experimental techniques involving magnetic force microscopy, SIMS, XRD, SEM and AFM. As a function of the processing temperature, phase evolution stage for both alloys follows the same trend: (1) initial stage of the expanded phase formation, γN; (2) its full development; and (3) its decomposition into CrN precipitates and the Cr-depleted matrix, fcc γ-(Co, Mo) for CoCrMo and bcc α-(Fe, Ni) for 304 SS. MFM imaging reveals distinct, stripe-like ferromagnetic domains for the fully developed expanded austenite layers both on CoCrMo and 304 SS alloys. Weak domain structures are observed for the CoCrMo samples treated at low and high processing temperatures. The images also provide strong evidence for grain orientation dependence of magnetic properties. The ferromagnetic state for the γN phase observed here is mainly linked to large lattice expansions due to high N content.

A genetic algorithm for two-dimensional bin packing with due dates

This paper considers a new variant of the two-dimensional bin packing problem where each rectangle is assigned a due date and each bin has a fixed processing time. Hence the objective is not only to minimize the number of bins, but also to minimize the maximum lateness of the rectangles. This problem is motivated by the cutting of stock sheets and the potential increased efficiency that might be gained by drawing on a larger pool of demand pieces by mixing orders, while also aiming to ensure a certain level of customer service. We propose a genetic algorithm for searching the solution space, which uses a new placement heuristic for decoding the gene based on the best fit heuristic designed for the strip packing problems. The genetic algorithm employs an innovative crossover operator that considers several different children from each pair of parents. Further, the dual objective is optimized hierarchically with the primary objective periodically alternating between maximum lateness and number of bins. As a result, the approach produces several non-dominated solutions with different trade-offs. Two further approaches are implemented. One is based on a previous Unified Tabu Search, suitably modified to tackle this revised problem. The other is randomized descent and serves as a benchmark for comparing the results. Comprehensive computational results are presented, which show that the Unified Tabu Search still works well in minimizing the bins, but the genetic algorithm performs slightly better. When also considering maximum lateness, the genetic algorithm is considerably better.

Minimizing the number of tardy jobs for the single machine scheduling problem: MIP-based lower and upper bounds

This paper considers the problem of scheduling n jobs on a single machine. A fixed processing time and an execution interval are associated with each job. Preemption is not allowed. The objective is to find a feasible job sequence that minimizes the number of tardy jobs. On the basis of an original mathematical integer programming formulation, this paper shows how good-quality lower and upper bounds can be computed. Numerical experiments are provided for assessing the proposed approach.

Approximation algorithms for parallel machine scheduling with linear deterioration

This paper deals with a parallel machine scheduling problem. Different from fixed processing time assumption in the classical scheduling, a job’s processing time is a simple linear increasing function of its starting time. The aim is makespan minimization, and our focus is on the case with an arbitrary number of parallel machines. We prove that LIST rule is (1+bmax)m−1m-approximation where m is the number of machines and bmax is the maximum deteriorating rate of job. We then propose one heuristic LDR (Largest deteriorating Rate first). The heuristic is proved by (1+bmin)m−1m-approximation where bmin is the minimum deteriorating rate. We further show that this ratio is tight when m=2,3 and 4.