11,883 research outputs found

    Performance optimization of a leagility inspired supply chain model: a CFGTSA algorithm based approach

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    Lean and agile principles have attracted considerable interest in the past few decades. Industrial sectors throughout the world are upgrading to these principles to enhance their performance, since they have been proven to be efficient in handling supply chains. However, the present market trend demands a more robust strategy incorporating the salient features of both lean and agile principles. Inspired by these, the leagility principle has emerged, encapsulating both lean and agile features. The present work proposes a leagile supply chain based model for manufacturing industries. The paper emphasizes the various aspects of leagile supply chain modeling and implementation and proposes a new Hybrid Chaos-based Fast Genetic Tabu Simulated Annealing (CFGTSA) algorithm to solve the complex scheduling problem prevailing in the leagile environment. The proposed CFGTSA algorithm is compared with the GA, SA, TS and Hybrid Tabu SA algorithms to demonstrate its efficacy in handling complex scheduling problems

    Scheduling With Alternatives Machine Using Fuzzy Inference System And Genetic Algorithm.

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    As the manufacturing activities in today's industries are getting more and more complex, it is required for the manufacturing company to have a good shop floor production scheduling to plan and schedule their production orders. Industri pengeluarcim kini telah berkembang pesat dan aktiviti pengeluarannya semakin kompleks, dengan itu syarikat pengeluar memerlukan jadual lantai pengeluaran (shop floor) yang terbaik untuk merancang permintaan pengeluaran (product)

    Performance Analysis of An Experimental Micro Flexible Manufacturing System (FMS)

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    Due to advanced technology, it is very important the performance of FMS for sensivity, production quality, repeatability and energy consumptions. Flexible manufacturing systems (FMSs) are the most automated and technologically sophisticated of the machine cell types used to implement cellular manufacturing. An FMS usually has multiple automated stations and is capable of variable routings among stations, while its flexibility allows it to operate as a mixed model system. The FMS concept integrates many of the advanced technologies that we met in previous units, including flexible automation, CNC machines, distributed computer control, and automated material handling and storage. In this experimental investigation, vibration and accelerations analysis of an experimental FMS with 5 degrees of freedom robot manipulator are presented. Firstly, experimental measurement of accelerations and vibrations are trained with a vibration measurement system and sensors. However, the process of production of part is a cycle of exact production time

    Analusis and Modeling of Flexible Manufacturing System

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    Analysis and modeling of flexible manufacturing system (FMS) consists of scheduling of the system and optimization of FMS objectives. Flexible manufacturing system (FMS) scheduling problems become extremely complex when it comes to accommodate frequent variations in the part designs of incoming jobs. This research focuses on scheduling of variety of incoming jobs into the system efficiently and maximizing system utilization and throughput of system where machines are equipped with different tools and tool magazines but multiple machines can be assigned to single operation. Jobs have been scheduled according to shortest processing time (SPT) rule. Shortest processing time (SPT) scheduling rule is simple, fast, and generally a superior rule in terms of minimizing completion time through the system, minimizing the average number of jobs in the system, usually lower in-process inventories (less shop congestion) and downstream idle time (higher resource utilization). Simulation is better than experiment with the real world system because the system as yet does not exist and experimentation with the system is expensive, too time consuming, too dangerous. In this research, Taguchi philosophy and genetic algorithm have been used for optimization. Genetic algorithm (GA) approach is one of the most efficient algorithms that aim at converging and giving optimal solution in a shorter time. Therefore, in this work, a suitable fitness function is designed to generate optimum values of factors affecting FMS objectives (maximization of system utilization and maximization of throughput of system by Genetic Algorithm (GA) approach

    Dynamic scheduling in a multi-product manufacturing system

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    To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

    Stochastic-optimization of equipment productivity in multi-seam formations

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    Short and long range planning and execution for multi-seam coal formations (MSFs) are challenging with complex extraction mechanisms. Stripping equipment selection and scheduling are functions of the physical dynamics of the mine and the operational mechanisms of its components, thus its productivity is dependent on these parameters. Previous research studies did not incorporate quantitative relationships between equipment productivities and extraction dynamics in MSFs. The intrinsic variability of excavation and spoiling dynamics must also form part of existing models. This research formulates quantitative relationships of equipment productivities using Branch-and-Bound algorithms and Lagrange Parameterization approaches. The stochastic processes are resolved via Monte Carlo/Latin Hypercube simulation techniques within @RISK framework. The model was presented with a bituminous coal mining case in the Appalachian field. The simulated results showed a 3.51% improvement in mining cost and 0.19% increment in net present value. A 76.95yd³ drop in productivity per unit change in cycle time was recorded for sub-optimal equipment schedules. The geologic variability and equipment operational parameters restricted any possible change in the cost function. A 50.3% chance of the mining cost increasing above its current value was driven by the volume of material re-handled with 0.52 regression coefficient. The study advances the optimization process in mine planning and scheduling algorithms, to efficiently capture future uncertainties surrounding multivariate random functions. The main novelty includes the application of stochastic-optimization procedures to improve equipment productivity in MSFs --Abstract, page iii

    Tool flow management in batch manufacturing systems for cylindrical components

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    The objective of the research is to study the design of and operating strategies for advanced tool flow systems in highly automated turning systems. A prototype workstation has been built to aid this process. The thesis consists of three main parts. In the first part the current flexible manufacturing technology is reviewed with emphasis laid on tool flow and production scheduling problems. The 'State-of-the-Art' turning systems are studied, to highlight the requirement of the computer modelling of tool flow systems. In the second part, the design of a computer model using fast modelling algorithms is reported. The model design has concentrated on the tool flow system performance forecasting and improving. Attention has been given to the full representation of highly automatic features evident in turning systems. A number of contemporary production scheduling rules have been incorporated into the computer model structure, with the objectives of providing a frontend to the tool flow model, and to examine the tool flow problems interactively with the production scheduling rules. The user-interface of the model employs conversational type screens for tool flow network specification and data handling, which enhances its user friendliness greatly. An effective, fast, and easy to handle data base management system for tool, part, machine data entries has been· built up to facilitate the model performance. The third part of the thesis is concerned with the validation and application of the model with industry supplied data to examine system performance, and to evaluate alternative strategies. Conclusions drawn from this research and the recommendations for further work are finally indicated

    Agent-based transportation planning compared with scheduling heuristics

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    Here we consider the problem of dynamically assigning vehicles to transportation orders that have di¤erent time windows and should be handled in real time. We introduce a new agent-based system for the planning and scheduling of these transportation networks. Intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. We use simulation to compare the on-time delivery percentage and the vehicle utilization of an agent-based planning system to a traditional system based on OR heuristics (look-ahead rules, serial scheduling). Numerical experiments show that a properly designed multi-agent system may perform as good as or even better than traditional methods
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