Development of a heuristic procedure for balancing mixed-model parallel assembly line type II

The single-model assembly line is not efficient for today’s competitive industry because to respond the customer’s expectation, companies need to produce mixedmodel products. On the other hand, using the mixed-model products increases the assembly complexity and makes it difficult to assign tasks to workstations because of the variety in model characteristics. As a result, the mixed-model products suffer from delays, limitations in the line workflow and longer lines. Parallel assembly lines as a production system in ALBPs which consists of a number of assembly lines in a parallel status, which by considering the cycle time of each line certain products are manufactured. This thesis takes advantages of the parallel assembly lines to produce mixed-model in order to assemble more than one model in each parallel assembly line and allocating tasks of models to workstations and balancing each parallel line to reduce the cycle times. To solve these problems, two heuristic algorithms were developed and coded in MATLAB®. The first one allocates each model to only one parallel assembly line and achieves the initial arrangement of tasks with the minimum number of workstations for each line. The second one called Tabu search Mixed-Model Parallel Assembly Line Balancing (TMMPALB), calculates final balancing tasks of different model in parallel lines with optimum cycle time for each line which tasks of each model can be allocated to more than one parallel assembly line through the TMMPALB. The main advantages of employing TS are using a flexible memory structure during the search process, and intensification and diversification strategies, which help to make a comprehensive search in the solution space. Fourteen data sets create 81 test problems that were solved to validate the performance of the TMMPALB. Each test problem consisted of the number of tasks, process time for each task (time unit), and the precedence relationship, minimum number of station and cycle time for each model. By considering that 80 out of the 81 test problems include three models and the remaining one has four models, 244 cycle times is made, which TMMPALB tries to minimize. The computational results showed that 205 cycle times out of the 244 cycle times have been improved. These results demonstrated that by arranging mixed-model through the parallel assembly lines with minimum number of workstations, the minimum cycle times are achieved in comparing with the single line

Development of an imperialist competitive algorithm (ICA)-based committee machine to predict bit penetration rate in oil wells of Iran

Drilling operation of a well is one the most expensive and time consuming procedures of oil and gas exploitation. Oil companies are always seeking for safe and cost-effective techniques for drilling. The main goal and motivation of drilling optimization is achieving the highest efficiency of work. Optimization and minimization of operational costs is one of the most important prerequisites of any engineering project. Rate of penetration is a crucial factor n drilling controlling cost and time of drilling. In the current research, capabilities of single independent intelligent models are employed for developing a hybrid committee machine that can predict bit penetration bit with high accuracy. To get this goal, three single intelligent models, including neural network, fuzzy logic and neuro-fuzzy, are trained. In the second step, the outputs of these models are integrated by imperialist competitive algorithm (ICA). Finally, a linear equation is achieved which gets outputs of single models as inputs and integrate them somehow the final results is closer to the actual value. The developed ICA-based committee machine is tested by 145 real data points gathered from the drilled wells in an oil field. Correlation of actual and predicted value of ROP obtained from committee machine shows that the model predicts ROP with accuracy of 88 percent. Such model can be used for optimization of drilling parameters in future drilling operations

Mechanical vibration technique for enhancing mechanical properties of particulate reinforced aluminium alloy matrix composite

The effects of subjecting solidifying particulate reinforced aluminium alloy matrix composite to various sources of vibration on the resulting casting quality, a mechanical vibration technique for inducing vibration resulting in enhanced mechanical properties, such as impact properties is devised. TiC particulate reinforced LM6 alloy matrix composites are fabricated by different particulate weight fraction of titanium dioxide and microstructure studies were conducted to determine the impact strength and density, respectively. Preliminary works show that the mechanical properties have been improved by using vibration mold during solidification compared to gravity castings without vibration

Particular model for improving failure mode and effect analysis (FMEA) by using of overall equipment efficiency (OEE)

Almost all of factories use failure mode and effect analysis (FMEA) technique to reduced risk priority number (RPN). This paper proposes a new model to reduce RPN by improving overall equipment efficiency (OEE) by using of heuristic mathematic model based on total productive maintenance index. Three factors are considered: (i) probability of failure (Occurrence), (ii) severity and (iii) distinction. A textile industry is used as a test case that produces some product for seat cover. It was shown that the RPN decreased in FMEA

Application of MATLAB to create initial solution for Tabu Search in parallel assembly lines balancing

In comparison with the exact mathematical methods, the heuristic models are different to provide solutions close to the optimal one, saving time of processing. With the appearance of the Tabu Search by Fred Glover in 1986, different applications have been arisen from the procedure to solve different problems for the classic problems of assembly line balancing and parallel mixed model assembly line. Here, an adaptation to an existing code appears which is applied to the problem of allocating and assigning mixed model tasks for the reconfiguration of distributed generation and balance with parallel assembly line. The model provides optimal or near optimal results in terms of results obtained from calculations compared to the other methods

Presenting a model for determining and discovering the causal relationships between the effective risks of the product family developing process in the Iranian automotive industry

Companies are always faced with risks in competing to response diversified needs of markets. Determining the probability of occurrence and how to manage and recognize the risks that cause other risks is always a challenge. The purpose of this study is to manage the effect of uncertainty on expected results and increase of product family developing process success through addressing causal risks that have bigger Conditional probability In this article, the risks of each stage of the product family development process are identified by focusing on the grounded theory based on the responses gathered from 18 experts in Iranian automotive industry Also the effect of the variables was determined through fuzzy cognitive map based on the 18 supplementary questionnaire data in these companies. Then, the conditional probability tables were formed and the probability of each variable was calculated systematically with the help of the Bayesian Belief Networks and causal risk of other risks were identified. The results show that the clustering of customers risk, parts design feature technical risk and modularity risk are specified to this process. The model output, also indicates that the needs risk with the probability of (19.7%), requirement risk (10.52%), and parts design feature technical risk (6.32%) As the causal risk with the highest conditional probability of a negative aspect. The executive managers could achieve greater success by focusing on controlling these three risks that act as the root cause of the next step risks , getting more success and make progress with more certainty

Supplier Selection in Three Echelon Supply Chain & Vendor Managed Inventory Model Under Price Dependent Demand Condition

This paper, considers the supplier selection in three echelon supply chain with Vendor Managed Inventory (VMI) strategy under price dependent demand condition. As there is a lack of study on the supplier selection in VMI literature, this paper presents a VMI model in supply chain including multi supplier, one distributer and multi retailer that distributer selects suppliers. Two class models (traditional vs. VMI) are presented and we compare them to study the impact of VMI on supply chain and supplier selection. As the proposed model is a NP-hard problem, a meta-heuristics namely Harmony Search is employed to optimize the proposed models. We show that how the VMI system can effect on supplier selection and can change the set of selected suppliers. Finally the conclusion and further studies are presente