Daily Production Schedule Research Articles

The Application of Data Mining Technology in the Predictive Maintenance for Oil and Gas Equipment

Data mining is a new research topic in intelligent predictive maintenance by searching, counting, and processing a large amount of data to obtain valuable information. With the rapid development of the petroleum industry in recent years, automation and intelligent oil and gas equipment have been constantly updated and applied with a significant role in the daily production scheduling process control in the oil and gas fields. In this context, this work first discussed the basic concept of data mining technology and predictive maintenance, then presented the implementation route of data mining technology in predictive maintenance, and finally proposed the predictive maintenance scheme of deep hole drilling tools based on the two-mode algorithm. This study mainly discussed the basic problems and key technologies of data mining technology in the predictive maintenance of oil and gas equipment, aiming to provide new solutions to the equipment fault prediction.

Hybrid simulation method by cooperating between manufacturing system simulation and computational fluid dynamics simulation (Evaluation for simultaneous operation of production lines and compressed air systems, and evaluation for energy consumption per unit of production throughput)

For enhancing the energy efficiency of a manufacturing system as a whole, it is critical to optimize energy consumption including that in the utility systems, which supplies compressed air, and that in each production line. Compressed air is used in the manufacturing domain in a wide variety of systems such as those that fix and hold works through pneumatic equipment. Compressed air systems account for approximately 10% of the industrial electricity consumption in the European Union. Therefore, a method for minimizing the energy used in compressed air systems is sought. From the point of view of energy saving in the operation of a compressed air system, a method is available for reducing the output of the compressed air system. However, if the supply of compressed air becomes insufficient by the reduction in the amount supplied, it can result in extension of the cycle time of the production line or in defective products; thus, it can eventually cause lower productivity. Therefore, businesses ensure adequate supply from compressed air systems in order to prevent the adverse impact of insufficient supply of compressed air on productivity; however, this results in wastage of energy. Therefore, it is necessary to simultaneously evaluate productivity and compressed air feed. However, there has been no progress in the research of methods for evaluation. Hence, in this research, a method, which is a hybrid of a manufacturing system simulation and computational fluid dynamics simulation, is proposed for evaluating the operation of both production lines and compressed air systems simultaneously. The proposed method is used to evaluate the deficiencies and excesses in the supply from a compressed air system and the reliability of the supply of a compressed air system in response to the daily production schedule. Moreover, the energy consumption per unit of production throughput is evaluated.

Scheduling and Operator Control in Reconfigurable Assembly Systems

Pushed by the recent market trends, companies need to adapt to changeable demands, regarding both mix and volume, in order to keep their competitiveness. Modular and reconfigurable assembly systems offer an efficient solution to these changes, providing economies of scale and also economies of scope. In the previous works of the authors, novel methods were presented to solve strategic level system configuration, and tactical mid-term production planning problems related to modular, reconfigurable assembly systems. The paper relies on these results, and aims at extending the previously proposed planning hierarchy on the short-term, daily production scheduling. The objective is to minimize the total operator headcount, considering the production lot sizes calculated on a higher, planning level on a working shift basis. The analyzed scheduling problem requires novel models, as important constraints in the scheduling problem are the reconfigurations consuming time as well as resources. In the paper, constraint programming and metaheuristics models are formulated and compared, resulting in production schedules that specify the production sequences, and the operator allocations. Conclusively, the operator controls can be also obtained from the results, specifying a work plan and tasks for a given operator within a working shift. The proposed methods are compared by using real industrial problem instances.

Modeling and analysis of a mixed‐model assembly line with stochastic operation times

AbstractWe consider a mixed‐model assembly line (MMAL) comprised a set of workstations and a conveyor. The workstations are arranged in a serial configuration. The conveyor moves at a constant speed along the workstations. Initial units belonging to different models are successively fed onto the conveyor, and they are moved by the conveyor to pass through the workstations to gradually generate final products. All assembling tasks are manually performed with operation times to be stochastic. An important performance measure of MMALs is overload times that refer to uncompleted operations for operators within their work zones. This paper establishes a method to analyze the expected overload times for MMALs with stochastic operation times. The operation processes of operators form discrete time nonhomogeneous Markov processes with continuous state spaces. For a given daily production schedule, the expected overload times involve in analyzing the Markov processes for finite horizon. Based on some important properties of the performance measure, we propose an efficient approach for calculating the expected overload times. Numerical computations show that the results are very satisfactory. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007

A rule-based heuristic finite capacity scheduling system for semiconductor backend assembly

Finite capacity scheduling (FCS) has been widely implemented in manufacturing companies for detailed shop floor operation management. Most of the traditional and artificial intelligence-based FCS systems lack the ability to provide a satisfying and practical solution for the large-scale scheduling problem in a complex environment. This paper describes a rule-based FCS system for the daily production scheduling (DPS) of a semiconductor backend assembly company. DPS is the FCS module of the gintic scheduling system (GSS), which has been enhanced and implemented successfully in a semiconductor backend assembly company, which is a world market leader in semiconductor packaging technology. Since the focus of this paper is on the DPS module, the overall framework and the other core modules of GSS are briefly covered. The detailed design of the six DPS modules, namely data input/integration, model configuration, data pre-processing, job prioritization (JP) executive, machine selection (MS) executive and result output, are presented in detail. The inference engines of JP and MS executive modules are discussed, which perform a unique rule-based job prioritization and machine selection process of the work order list and the assignments on the machines to achieve the desired scheduling objectives specified by the end user. Group technology is incorporated into the system to categorise the resources and parts for set-up/changeover reduction consideration. The successful deployment of the system in the industry endorsed the fact that the implemented DPS system has the capability to solve large-scale scheduling problems and performs well in the complex industrial environment. Favourable feedbacks received from the company include reduction in changeover and set-up, improved on-time delivery, increased machine utilization, decreased cumulative cycle time of work order, etc.

A Cutting Stock and Scheduling Problem in the Copper Industry

This case study was carried out for Thomas Bolton Ltd, a copper component manufacturer. The focus was on the first major production operation that is carried out in the foundry. This operation consists of three processes — melting scrap metal, casting it as ‘logs’ and cutting logs into ‘billets’. The timely production of the billets is essential as these feed a bottleneck process. The objective of the study was to investigate alternative methods of generating a production plan for the foundry that minimized costs whilst meeting the demand for billets at the bottleneck. The production plan was required to include a daily production schedule and a list of the cutting patterns to use when cutting the logs into billets. Thus, both the scheduling and cutting stock problems were addressed. A two-stage solution procedure was proposed. Alternative heuristic methods were investigated at the first stage and an optimal solution using Integer Programming (IP) was proposed for the second stage. It is shown that current performance could be improved using all of the heuristics considered at the first stage, but that using an IP-based heuristic method gives the best results.

A Cutting Stock and Scheduling Problem in the Copper Industry

This case study was carried out for Thomas Bolton Ltd, a copper component manufacturer. The focus was on the first major production operation that is carried out in the foundry. This operation consists of three processes — melting scrap metal, casting it as ‘logs’ and cutting logs into ‘billets’. The timely production of the billets is essential as these feed a bottleneck process. The objective of the study was to investigate alternative methods of generating a production plan for the foundry that minimized costs whilst meeting the demand for billets at the bottleneck. The production plan was required to include a daily production schedule and a list of the cutting patterns to use when cutting the logs into billets. Thus, both the scheduling and cutting stock problems were addressed. A two-stage solution procedure was proposed. Alternative heuristic methods were investigated at the first stage and an optimal solution using Integer Programming (IP) was proposed for the second stage. It is shown that current performance could be improved using all of the heuristics considered at the first stage, but that using an IP-based heuristic method gives the best results.

A Multiple Objective Model for a Just‐in‐Time Manufacturing System Environment

Presents a linear goal programming (LGP) model, which can be used to derive an optimal daily production schedule for JIT production systems. Also provides an explanation of how a detailed post‐optimal LGP analysis can enable a decision maker to examine the effects of production scheduling in a JIT “mixed‐model” production environment. To illustrate the informational efficacy of the proposed JIT‐based LGP model, presents an illustrative example.

Production Planning in the New Zealand Dairy Industry

From 1977 onwards, D.S.I.R. has been developing an L.P. model to help the largest N.Z. dairy company plan its medium-term production. This time-staged model finds the most profitable daily production schedule of the various powder, casein, cheese and butter products. Computer reports and graphics show Company management how to allocate the milk available in the various farming regions to the Company's factories, and where to send the by-products for further processing. The L.P. models seasonal fluctuations in wholemilk availability and quality, as well as transport costs, factory capacities and costs, and product-yields and prices. Comparison runs have shown that the L.P. increases the profitability of the manual plan by over $5000 per day (because the computer can handle changing yields, factory costs and by-product processing). The model gives management the ability to adjust production plans quickly in response to improved information, new market opportunities and emergencies like plant breakdown.

Material Routing to the Car-Assembly Line

Abstract In this paper the author tells how the particular problem of delivering parts to the car-assembly line, that can not be handled efficiently by conveyors, is taken care of in the Chrysler plant. The quantities of materials to be handled are obtained from the daily-production schedule. Loads were standardized, and moved at regular intervals by jitneys. Requisition sheets for material are made up according to specified loads to meet transportation requirements, and are issued to the stock man at pick-up points. A copy of the requisition serves as a transportation order for the jitney operators.