ON-LINE PROBLEMS OF MINIMIZING MAKESPAN ON A SINGLE BATCH PROCESSING MACHINE WITH NONIDENTICAL JOB SIZES

The on-line problem of scheduling on a batch processing machine with nonidentical job sizes to minimize makespan is considered. The batch processing machine can process a number of jobs simultaneously as long as the total size of these jobs being processed does not exceed the machine capacity. The processing time of a batch is given by the longest processing time of any job in the batch. Each job becomes available at its arrival time, which is unknown in advance, and its processing time becomes known upon its arrival. The paper deals with two variants： the case only with two distinct arrival times and the general case. For the first case, an on-line algorithm with competitive ratio 119/44 is given. For the latter one, a simple algorithm with competitive ratio 3 is given. For both variants the better ratios can be obtained if the problem satisfies proportional assumption.

A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a foundry.In this process,a batch is initially formed,placed in a sandbox,and then the sandbox is positioned on a BPM formoulding.The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume.To minimize the makespan,a new cooperated imperialist competitive algorithm(CICA)is introduced.In CICA,the number of empires is not a parameter,and four empires aremaintained throughout the search process.Two types of assimilations are achieved:The strongest and weakest empires cooperate in their assimilation,while the remaining two empires,having a close normalization total cost,combine in their assimilation.A new form of imperialist competition is proposed to prevent insufficient competition,and the unique features of the problem are effectively utilized.Computational experiments are conducted across several instances,and a significant amount of experimental results show that the newstrategies of CICAare effective,indicating promising advantages for the considered BPMscheduling problems.

AutoSeqMan:batch assembly of contigs for Sanger sequences

With the wide application of DNA sequencing technology, DNA sequences are still increasingly generated through the Sanger sequencing platform. SeqMan （in the LaserGene package） is an excellent program with an easy-to-use graphical user interface （GUI） employed to assemble Sanger sequences into contigs. However, with increasing data size, larger sample sets and more sequenced loci make contig assemble complicated due to the considerable number of manual operations required to run SeqMan. Here, we present the ＇autoSeqMan＇ software program, which can automatedly assemble contigs using SeqMan scripting language. There are two main modules available, namely, ＇Classification＇ and ＇Assembly＇. Classification first undertakes preprocessing work, whereas Assembly generates a SeqMan script to consecutively assemble contigs for the classified files. Through comparison with manual operation, we showed that autoSeqMan saved substantial time in the preprocessing and assembly of Sanger sequences. We hope this tool will be useful for those with large sample sets to analyze, but with little programming experience. It is freely available at https：//github.com/ Sun-Yanbo/autoSeqMan.

Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a ＂global＂ nonlinear long-range prediction model through the fuzzy conjunction of a number of ＂local＂ linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.

Online Batch Process Monitoring Based on Just-in-Time Learning and Independent Component Analysis

A new method was developed for batch process monitoring in this paper.In the developed method,just-in-time learning(J1TL) and independent component analysis(ICA) were integrated to build JITL-ICA monitoring scheme.JITL was employed to tackle with the characteristics of batch process such as inherent timevarying dynamics,multiple operating phases,and especially the case of uneven length stage.According to new coming test data,the most correlated segmentation was obtained from batch-wise unfolded training data by JITL.Then,ICA served as the principal components extraction approach.Therefore,the non-Gaussian distributed data can also be addressed under this modeling framework.The effectiveness and superiority of JITL-ICA based monitoring method was demonstrated by fed-batch penicillin fermentation.

Genetic Algorithm for Scheduling Problem of Chemical Batch Process with Due Date Constraints

The intense competition in the current marketplace ha s forced firms to reexamine their methods of doing business, using superior manu facturing practices in the form of just-in-time (JIT), production with JIT pra ctices pursue completion on time and zero inventory, which is often instruct ed according to the custom’s demand or the sale contract. Earliness and tardine ss are undesirable because both of them will bring the extra cost, cost will als o be increased by some factors such as operation condition, intermediate storage , clean method, etc, to minimize the total cost is often the main scheduling objective, but sometime it is most important for factories to eliminate the tar diness cost in order to maintain the commercial credit and to avoid penalty, the refore, minimum of tardiness cost becomes the first objective. It is more import ant to select a reasonable objective by the actual condition during scheduli ng. In this paper scheduling problem of chemical batch process with due date is studied, two different intermediate storage policies and two different productio n modes are also discussed, production scheduling with different intermediate st orage policy and different production mode is proposed and the result is compare d. In order to complete all products within the due date, not only earliness and tardiness but also holding problem is considered, the objective is to selec t a proper intermediate storage policy and production mode and to minimize the c ost resulted by the earliness and tardiness, even the cost result by the interme diate storage. Scheduling with multiple stage and multiple machine is known as a NP-hard problem, mathematical program (MP) method, such as branch-and-bound (BAB), mixed integer linear program (MILP), etc, is often used to solve the sche duling problem. But as is well known, MP method is not good for combination opti mization, especially for large scale and complex optimal problem, whereas geneti c algorithm (GA) can overcome the MP method’s shortcoming and is fit for solvin g such scheduling problem. In this paper a modified genetic algorithm with speci al crossover operator and mutation operator is presented to solve this schedulin g problem. The results show such problem can be solved effectively with the pres ented method.

Fault-Diagnosis Method Based on Support Vector Machine and Artificial Immune for Batch Process

A new fault-diagnosis method to be used in batch processes based on multi-phase regression is presented to overcome the difficulty arising in the processes due to non-uniform sample data in each phase.Support vector machine is first used for phase identification,and for each phase,improved artificial immune network is developed to analyze and recognize fault patterns.A new cell elimination role is proposed to enhance the incremental clustering capability of the immune network.The proposed method has been applied to glutamic acid fermentation,comparison results have indicated that the proposed approach can better classify fault samples and yield higher diagnosis precision.

Run-to-run product quality control of batch processes

Batch processes have been increasingly used in the production of low volume and high value added products. Consequently, optimization control in batch processes is crucial in order to derive the maximum benefit. In this paper, a run-to-run product quality control based on iterative learning optimization control is developed. Moreover, a rigorous theorem is proposed and proven in this paper, which states that the tracking error under the optimal iterative learning control （ILC） law can converge to zero. In this paper, a typical nonlinear batch continuous stirred tank reactor （CSTR） is considered, and the results show that the performance of trajectory tracking is gradually improved by the ILC.

Predictive Sliding Mode Control to a Thermal Process for Batch Dyeing

The batch dyeing process is a typical nonlinear process with time-delay,where precise controlling of temperature plays a vital role on the dyeing quality.Because the accuracy and robustness of the commonly used proportion integration differentiation(PID) algorithm had been limited,a novel method was developed to precisely control the heating and cooling stages for batch dyeing process based on predictive sliding mode control(SMC) algorithm.Firstly,a special predictive sliding mode model was constructed according to the principle of generalized predictive control(GPC);secondly,an appropriate reference trajectory for SMC was designed based on the improved approaching law;finally,the predictive sliding mode model and the Diophantine equation were used to predict the output and then the optimized control law was derived using the generalized predictive law.This method combined GPC and the SMC with their respective advantages,so it could be applied to time-delay process,making the control system more robust.Simulation experiments show that this algorithm can well track the temperature variation for the batch dyeing process.

Influence of temperature on performance of anaerobic digestion of municipal solid waste

The influence of temperature on the performance of anaerobic reactors for treating the organic fraction of municipal sohd waste （OFMSW） was studied. Batch digestion of OFMSW was carded out for 32 d at different temperature （25℃, 35℃, 45℃ and 55℃） conditions for total solid concentrations （TS） 17% with the ratio of total organic carbon to nitrogen （C/N） being, 25：1 respectively, while keeping other parameters constant such as inoculum, start-up pH, reactor volume （2 L） and so on. Temperature can influence the methanogenic bacteria activity, accordingly inhibiting the OFMSW biodegradation and stabilization efficiency. Anaerobic reactors excelled at TS reduction, total volatile solid reduction, chemical oxygen demand reduction, increasing cumulative biogas production, whose rate was at temperature （35℃ and 55℃） conditions. Methane concentration in the biogas was above 65% in four reactors. In addition, the fluctuation of temperatures resulted in the biogas production variation. The data obtained indicated that temperature had a significant influence on anaerobic process.

FasParser:a package for manipulating sequence data

A computer software package called ＇FasParser＇ was developed for manipulating sequence data. It can be used on personal computers to perform series of analyses, including counting and viewing differences between two sequences at both DNA and codon levels, identifying overlapping regions between two alignments, sorting of sequences according to their IDs or lengths, concatenating sequences of multiple loci for a particular set of samples, translating nucleotide sequences to amino acids, and constructing alignments in several different formats, as well as some extracting and filtrating of data for a particular FASTA file. Majority of these functions can be run in a batch mode, which is very useful for analyzing large data sets. This package can be used by a broad audience, and is designed for researchers that do not have programming experience in sequence analyses. The GUI version of FasParser can be downloaded from https：//github.com/Sun-Yanbo/FasParser, free of charge

Flow-Shop Scheduling with Transportation Capacity and Time Consideration

Planning and scheduling is one of the most important activity in supply chain operation management.Over the years,there have been multiple researches regarding planning and scheduling which are applied to improve a variety of supply chains.This includes two commonly used methods which are mathematical programming models and heuristics algorithms.Flowshop manufacturing systems are seen normally in industrial environments but few have considered certain constraints such as transportation capacity and transportation time within their supply chain.A two-stage flowshop of a single processing machine and a batch processing machine are considered with their capacity and transportation time between twomachines.The objectives of this research are to build a suitable mathematical model capable of minimizing the maximum completion time,to propose a heuristic optimization algorithm to solve the problem,and to develop an applicable program of the heuristics algorithm.AMixed Integer Programming(MIP)model and a heuristics optimization algorithmwas developed and tested using a randomly generated data set for feasibility.The overall results and performance of each approach was compared between the two methods that would assist the decision maker in choosing a suitable solution for their manufacturing line.

Experimental Research of Dual-Polarization Passive Radar Based on DTTB Signal

The passive radar is a hot research topic. A multi-channel wideband passive radar experimental system is designed and the digital television terrestrial broadcasting (DTTB) signal is chosen to carry out the target detection experiment of civil aviation aircraft. The polarization and spatial filtering methods are used to solve the strong direct path interference suppression problems brought by the receiving system location;combined with the characteristics of DTTB signal, the block length selection interval in the block batch processing method for range-Doppler images calculation is given;the clutter suppression performance is compared through the experimental data receiving from different bistatic polarization channels, the conclusion is different from the monostatic radar and it can guide the passive radar experiment.

SEVERAL PROBLEMS IN DESIGNING DBS SYSTEMS FOR AIRBORNE PULSE DOPPLER RADAR

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Fabrication scheduling on a single machine to minimize the weighted sum of product completion time

In this paper, a fabrication scheduling problem concerning the production of components at a single manufacturing facility was studied, in which the manufactured components are subsequently assembled into a finite number of end products. Each product was assumed to comprise a common component to all jobs and a unique component to itself. Common operations were processed in batches and each batch required a setup time. A product is completed when both its two operations have been processed and are available. The optimality criterion considered was the minimization of weighted flow time. For this scheduling problem, the optimal schedules were described in a weignted shortest processing time first （WSPT） order and two algorithms were constructed corresponding to the batch availability and item availability, respectively.

Monitoring Saccharification Process in Brewery Industry Using Quality Control Charts

The aim of this study was to establish a control system for saccharification process using quality control charts. To achieve this goal, temperature, pH and brix were measured at 12 minutes intervals for 15 consecutive batches which took 2 hours each. The time variations for three process parameters were assessed to establish a good understanding of the saccharification process. The temperature varied between 58℃ and 62℃ while the pH decreased slowly due to oxidation, values of which varied between 5.7 and 5.0. Brix values increased linearly with time. The initial and final values of the three parameters varied from one batch to another. Of the three parameters, brix was not well represented on the quality control charts due to wide difference between initial and final values during saccharification. The final brix values varied between batches, from 10.6% to 11.6%. The control charts used in this study were X-bar and Range charts. The rules for interpreting control charts were implemented for both X-bar and R charts, results of which showed that the process was out of control, although some rules were not violated due to little number of batches studied. The values of for temperature and pH data (2.27℃ and 0.35, respectively) were lower compared to brix data (11.2%). The corresponding values of span between control limits, SPx and SPR for temperature and pH were also comparatively lower than those established from brix data. Due to larger values of for brix measurements, the corresponding control charts for brix were insensitive in identifying out-of-control points during saccharification process.

Unravel the potential of zinc oxide nanoparticle-carbonized sawdust matrix for removal of lead(Ⅱ) ions from aqueous solution

Zinc oxide nanoparticles(ZnOnp) are molecular nanoparticles synthesized by a chemical precipitation method from zinc nitrate tetrahydrate and sodium hydroxide.Carbonized sawdust(CSD) was prepared from sawdust obtained from a local wood mill.The matrix of both provides a better material as an adsorbent.The present study applied the functionality of ZnOnp,CSD,and ZnOnp-CSD matrix as adsorbent materials for the removal of Pb(Ⅱ) ions from aqueous solution.The method of batch process was employed to investigate the potential of the adsorbents.The influence of pH,contact time,initial concentration of adsorbate,the dosage of adsorbents,and the temperature of adsorbate-adsorbent mixture on the adsorption capacity were revealed.The adsorption isotherm studies indicate that both Freundlich and Langmuir isotherms were suitable to express the experimental data obtained with theoretical maximum adsorption capacities(q_(m)) of 70.42,87.72,and 92.59 mg·g^(-1) for the adsorption of Pb(Ⅱ) ions onto ZnOnp,CSD,and ZnOnp-CSD matrix,respectively.The separation factors(R_(L)) calculated showed that the use of the adsorbents for the removal of Pb(Ⅱ) ions is a feasible process with R_(L) <1.The thermodynamic parameters obtained revealed that the processes are endothermic,feasible,and spontaneous in nature at 25-50℃.Evaluation of the kinetic model elected that the processes agreed better with pseudo-second order where the values of rate constant(k_2) obtained for the adsorption of Pb(Ⅱ) ions onto ZnOnp,CSD,and ZnOnp-CSD matrix are 0.00149,0.00188,and 0.00315 g·mg^(-1)·min^(-1),respectively.The reusability potential examined for four cycles indicated that the adsorbents have better potential and economic value of reuse and the ZnOnp-CSD matrix indicates improved adsorbent material to remove Pb(Ⅱ) ions from aqueous solution.

Modeling and Solving Lot-Splitting Scheduling Problem Based on Process

In flexible job-shop batch scheduling problem, the optimal lot-size of different process is not always the same because of different processing time and set-up time. Even for the same process of the same workpiece, the choice of machine also affects the optimal lot-size. In addition, different choices of lot-size between the constrained processes will impact the manufacture efficiency. Considering that each process has its own appropriate lot-size, we put forward the concept of scheduling with lot-splitting based on process and set up the scheduling model of lot-splitting to critical path process as the core. The model could update the set of batch process and machine selection strategy dynamically to determine processing route and arrange proper lot-size for different processes, to achieve the purpose of optimizing the makespan and reducing the processing batches effectively. The experiment results show that, comparing with lot-splitting scheduling scheme based on workpiece, this model optimizes the makespan and improves the utilization efficiency of the machine. It also greatly decreases the machined batches （42%） and reduces the complexity of shop scheduling production management.

Dynamic I/O-Aware Scheduling for Batch-Mode Applications on Chip Multiprocessor Systems of Cluster Platforms

Efficiency of batch processing is becoming increasingly important for many modern commercial service centers, e.g., clusters and cloud computing datacenters. However, periodical resource contentions have become the major performance obstacles for concurrently running applications on mainstream CMP servers. I/O contention is such a kind of obstacle, which may impede both the co-running performance of batch jobs and the system throughput seriously. In this paper, a dynamic I/O-aware scheduling algorithm is proposed to lower the impacts of I/O contention and to enhance the co-running performance in batch processing. We set up our environment on an 8-socket, 64-core server in Dawning Linux Cluster. Fifteen workloads ranging from 8 jobs to 256 jobs are evaluated. Our experimental results show significant improvements on the throughputs of the workloads, which range from 7% to 431%. Meanwhile, noticeable improvements on the slowdown of workloads and the average runtime for each job can be achieved. These results show that a well-tuned dynamic I/O-aware scheduler is beneficial for batch-mode services. It can also enhance the resource utilization via throughput improvement on modern service platforms.

Optimal Control of Service Rates of Discrete-Time(s,Q)Queueing-Inventory Systems with Finite Buffer

This article,we develop an optimal policy to control the service rate of a discrete time queueing-inventory system with finite buffer.The customers arrive according to a Bernoulli process and the service time for the customers are geometric.Whenever the buffer size attains its maximum,any arriving new customers are considered to be lost.The customers are served one by one according to FCFS rule and each customers request random number of items.The inventory is replenished according to a(s,Q)inventory policy with geometric lead time.The main objectives of this article are to determine the service rates to be employed at each slot so that the long run expected cost rate is minimized for fixed inventory level and fixed buffer size and to minimize the expected waiting time for a fixed inventory level and fixed buffer size.The problems are modelled as Markov decision problem.We establish the existence of a stationary policy and employ linear programming method to find the optimal service rates.We provide some numerical examples to illustrate the behaviour of the model.