International Journal of Industrial Engineering: Theory, Applications and Practice
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A SURVEY ON BIG DATA: INFRASTRUCTURE, ANALYTICS, VISUALIZATION AND APPLICATIONS
No full textBig data is a collection of heterogeneous and autonomous data sources which are available abundantly in multiple formats. Knowledge discovery and decision-making from the ever-growing abundant data are challenging tasks for data-oriented business organizations. Big data analytics investigates a large amount of data to find correlations among the available data and finally provides useful information to business organizations. Big data analytics facilitates application domains such as business, banking, healthcare, transportation, social media, agriculture, and government sectors. Existing surveys on big data are based on either of the following dimensions: characteristics, analytics, and visualization, or discuss the applications of big data analytics in the field of agriculture, transportation, etc., separately. However, this article provides a survey of big data, which integrates different dimensions of big data, such as infrastructure, analytics, visualization, and their applications. It also discusses the technologies and tools involved in the domains mentioned above
RESEARCH ON QUANTIFICATION OF HAZOP DEVIATION BASED ON A DYNAMIC SIMULATION AND NEURAL NETWORK
Get PDFHazard and operability (HAZOP) analysis has become more significant as the complexity of process technology has increased. However, traditional HAZOP analysis has limitations in quantifying the deviations. This work introduces artificial neural networks (ANNs) and Aspen HYSYS to explore the feasibility of HAZOP deviation quantification. With the proposed HAZOP automatic hazard analyzer (HAZOP-AHA) method, the conventional HAZOP analysis of the target process is first carried out. Second, the HYSYS dynamic model of the relevant process is established to reflect the influence of process parameters on target parameters. Third, to solve the problem of deviation identification based on multi-attribute and a large dataset, we use the ANN to process the input data. Finally, HAZOP deviation can be quantified and predicted. The method is verified by the industrial alkylation of benzene with propene to cumene. The results show that the predicted deviation severity can be close to the actual deviation severity, and the accuracy of prediction can reach nearly 100%. Thus, the method can diminish the probability of conflagration, burst, and liquid leakage
A NEW LOSS FUNCTION FOR THE SELECTION OF PRODUCER SPECIFICATION LIMITS
No full textThis paper presents a new approach to deteIlDining economical values for manufacturer lower and upper specification limits using a newly derived hybrid function for expected cost. The identification and use of specification limits are essential in protecting the producer from shipping defective products that pass unnoticed due to measurement error. This cost function is composed of four parts: a generic Taguchi loss function a function for rework cost, a function for scrap cost, and a function that describes a variance to cost tradeoff. The expected cost function does not assume the process mean to be equal to the target value of the process nor is it restricted to being symmetrical. The cost function is implemented among different production systems and optimal values of manufacturer lower and upper specification limits are determined for each system
A Mathematical Model for a Multi-Commodity, Two-Stage Transportation and Inventory Problem
Get PDFThis paper presents a mathematical model for two-stage planning of transportation and inventory for many sorts of products (multi-commodity). The situation considered in this paper, which happens in a local furniture manufacturing firm, is that the total supply in origins exceeds the current-stage’s total demand from all destinations (markets). Therefore, the problem is how to arrange the current-stage’s shipping in consideration of next-stage’s (that is, future’s) inventory in both origins and destinations. A mathematical model is proposed for the problem with the objective of minimizing the total cost of both shipping and inventory for all products within two stages. Meanwhile, since the next-stage’s shipping costs usually are unknown, this paper presents a new concept of rational unit shipping cost: a forecasted average cost with weight of next-stage’s shipping amount. Finally, a numerical example extracted from the furniture manufacturing company with 4 origins, 4 destinations and 4 commodities is illustrated in the paper
Solving the Reentrant Permutation Flow-Shop Scheduling Problem with a Hybrid Genetic Algorithm
Get PDFMost production scheduling-related research assumes that a job visits certain machines once at most, but this is often untrue in practical situations. A reentrant permutation flow-shop (RPFS) describes situations in which a job must be processed on machines in M1, M2, …, Mm, M1, M2, …, Mm, …, and M1, M2, …, Mm order and no job is allowed to pass a previous job. This study minimizes makespan by using the genetic algorithm to move from local optimal solutions to near-optimal solutions for RPFS scheduling problems. In addition, the hybrid genetic algorithm (HGA) improves the genetic algorithm’s performance in solving RPFS
A Comparative Study of the Three Predictive Tools for Forecasting a Transfer Line's Throughput
Get PDFA study comparing the performance of three predictors of a manufacturing system’s future state based upon its current state is presented. The three predictors are constructed using statistical regression, neural networks and case-based reasoning techniques. An asynchronous transfer line with unreliable machines is considered for this study. The line’s current Work-In-Process (WIP) is used to forecast future throughput. A simulation model of the system is used to generate training data for constructing the predictors as well as data used for validation. The impacts of the number of machines and the number of line partitions on forecast accuracy are investigated. Results indicate that the predictors based on statistical regression and neural network techniques offered comparable performance, and both performed better than the predictor based on case-based reasoning technique. Future work includes development of a prediction model to assist in day-to-day operational tasks such as scheduling of opportunistic maintenance and manpower planning
MODELING OF TURNAROUND OPERATIONS FOR NEXT GENERATION SPACE TRANSPORTATION ARCHITECTURES
No full textThis paper presents an approach to space transportation turnaround operations modeling using knowledge based complexity functions. This project is of signiticant relevance to the field of industrial engineering as it deals with knowledge based estimation of processing operations based on the design characteristics of an extremely complex product. The modeling approach uses experts knowledge to predict the ground cycle time and costs of a new concept launch vehicle by functions that estimate the complexity and reliability of the system based on its design characteristics. The operational requirements include the interactions between the launch vehicle and its ground infrastructure: the spaceport. Finally, the paper discusses the implementation of the modeling concept into a tool called Architecture Assessment Tool enhanced (AA Te) used byNASA and its contractors
A SIMULATED ANNEALING HEURISTIC FOR A CRANE SEQUENCING PROBLEM
No full textWhen maintenance activities during outages at electric power plants are scheduled using project managemenr software, the locations of the resources required to perform the acrivities are determined such that the total distance the resources travel during the outage is minimized. This problem is defined as the dynamic space allocation problem (DSAP). Once the DSAP solution (i.e., assignment of activities and their required resources to workspaces and of idle resources to storage spaces) is obtained, the problem then is to determine the sequence in which the crane moves the resources to their required locations at the beginning of each period (i.e., change in the schedule) such that the total distance the crane travels is mininlized. In this paper, this problem is defined as the crane sequencing problem (CSP), and a simulated annealing heuristic is presented for this problem
AN EXACT ALGORITHM MINIMIZING THE MAKESPAN FOR THE TWO-MACHINE FLOWSHOP SCHEDULING UNDER RELEASE DATES AND BLOCKING CONSTRAINTS
No full textThis paper describes the problem of two-machine permutation flowshop scheduling with release dates where blocking constraint is authorized. The objective is the minimization of the makespan. This problem is proved as an NP-hard problem. Four lower bounds were developed in this paper to test experimental results with different classes. An optimal solution is also proposed based on the mathematical formulation and solved using the Cplex program.
The Usage of Artificial Neural Networks For Finite Capacity Planning
Get PDFIn this study finite scheduling and artificial neural networks are applied for finite capacity planning. Utilisation of artificial neural networks on solving finite scheduling problems is examined. Also a model is developed by using multi layer perceptron (MLP) networks and carried out to solve a real world problem in a job shop scheduling system, in an automotive firm