International Journal of Industrial Engineering & Production Research

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At the last decade of the 20^th century, Womack et. Al introduced Lean concept to the industrial world. Since 1990 up to now, existed studies mostly have focused on lean production in the step of manufacturing, but in this research leanness concept has developed in the plant life cycle. In this paper leanness concept will be described as elimination of wastes in the phases of investment, plant design & construction(hardware), organization & systems design (software) and these three steps will be added to, elimination of previously described  seven wastes in production step. For this purpose at first, the types of wastes in the above mentioned phases are defined by using Axiomatic Design methodology. After defining the types of wastes, a model for assessment of leanness is submitted. In this quantitative model, amount of leanness in each phase will be determined and combined to make a unique measure for total leanness. Dimensions of leanness are shown for quick understanding, by using a spider diagram. In the last section of the paper, the results of an example of the application of this model in fan industry are brought. This example shows the simplicity and powerfully of the model to determine the leanness in before production phases. © 2008 Authors all rights reserved.

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Microcasting Shape-Deposition-Manufacturing is an approach to Solid-Freeform-Fabrication (SFF) process which is a novel method for rapid automated manufacturing of near-net-shape multi-material parts with complex geometries. By this method, objects are made by sequentially depositing molten metal droplets on a substrate and shaping by a CNC tool, layer by layer. Important issues are concerned with remelting dept of substrate, cooling rate and stress build up. In the present study attempts were made to numerically model the heat transfer and phase change within the droplet/substrate, making a better understanding of process performance. Thus, making a brief literature review, a 2-D transient heat transfer Finite Element Analysis was carried out by the use of ANSYS multiphysics, in which solidification is handled using apparent capacity method. Verification was done by available experimental data in the open literature to ensure model predictions. The model was run under various process parameters and obtained results presented in the form of temperature fields, solidification profiles, cooling curves and remelting history curves. Solidification profile studies predict a columnar dendritic solidified structure in the vertical orientation which was in agreement with metallographic sections published earlier. Parametric studied were also carried out under different boundary conditions, initial temperature of the droplet and Substrate temperature. It was concluded that 1) the process is not sensitive to convection/radiation effects from the surface. 2) the main parameter that can control the maximum remelting dept is initial temperature of the droplet. the more drop temperature, the more remelting dept. This parameter also affects cooling rate during solidification. 3) Increasing substrate temperature showed a decreased cooling rate in solid, which can be used to reduce residual stresses, but it had a minor effect on the cooling rates during solidification .

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Dynamic cellular manufacturing systems,   Mixed-integer non-linear programming,   Production planning, Manufacturing attributes

This paper presents a novel mixed-integer non-linear programming model for the design of a dynamic cellular manufacturing system (DCMS) based on production planning (PP) decisions and several manufacturing attributes. Such an integrated DCMS model with an extensive coverage of important design features has not been proposed yet and incorporates several manufacturing attributes including alternative process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine depot, machine capacity, lot splitting, material flow conservation equations, inflation coefficient, cell workload balancing, budget constraints for cell construction and machine procurement, varying number of formed cells, worker capacity, holding inventories and backorders, outsourcing part-operations, warehouse capacity, and cell reconfiguration. The objective of the integrated model is to minimize the total costs of cell construction, cell unemployment, machine overhead and machine processing, part-operations setup and production, outsourcing, backorders, inventory holding, material handling between system and warehouse, intra-cell and inter-cell movements, purchasing new machines, and machine relocation/installation/uninstallation. A comprehensive numerical example taken from the literature is solved by the Lingo software to illustrate the performance of the proposed model in handling the PP decisions and to investigate the incorporated manufacturing attributes in an integrated DCMS .

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In recent years, Many manufacturing industries for promoting their efficiency have tended to use the automatic manufacturing systems. Expanding automatic systems and to increase their complexity are representing the necessity of studying a proper functional quality and using reliable equipment in such systems more than ever. In this direction, the technique of fault tree analysis (FTA), along with using other techniques such as failure mode and effect analysis (FMEA) reveals the incorrect performance states (modes) in system in order to know these modes exactly may prevent their occurance and increase their function quality. In this study, the approaches may increase the reliability of performance in an industrial robot are studied by FTA technique as a case study to show improvement in performance of equipments on automatic systems to reduce their destruction (fault) during the work, and finally access to an automatic manufacturing systems with high reliability.

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This paper deals with the problem of forbidden states in discrete event systems modeled by Petri Net. To avoid the forbidden states, some constraints which are called Generalized Mutual Exclusion Constraints can be assigned to them. Enforcing these constraints on the system can be performed using control places. However, when the number of these constraints is large, a large number of control places must be connected to the system which complicates the model of controller. In this paper, the objective is to propose a general method for reducing the number of the mentioned constraints and consequently the number of control places. This method is based on mixing some constraints for obtaining a constraint verifying all of them which is performed using the optimization algorithms. The obtained controller after reducing the number of the control places is maximally permissive.

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Generally, human resources play an important role in manufacturing systems as they can affect the work environment. One of the most important factors affecting the human resources is being an interactional interest among the workers in the workshops. If the workers in a manufacturing cell have the highest surface of the interactional interest level, it causes a significant raise in coordination and cooperation indicators and in long time periods. In this paper, a new concept of being an interactional interest between workers in a manufacturing cell besides the ability to work with its machines is proposed and a bi-objective mathematical model to carry out this new point of view in cellular manufacturing systems is presented. Applying the ε-constraint method as an optimization tool for multi-objective mathematical programming, a comprehensive numerical example is solved to exhibit the capability of the presented model.

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The manufacturing organizations today are having a competition of supply chain versus supply chain. Existing research work fails to relate human metrics with supply chain performance. The authors intend to empirically assess the effects of human metrics on supply chain performance in the context of Indian manufacturing organizations. A rigorous literature review has identified 12 variables. The variables are individually measured and later on reduced in number by factor analysis. As a pilot study, primary data is collected from 100 manufacturing organizations by means of a questionnaire, both offline and online, which is administered across India and a scale is developed. t-test and factor analysis resulted in 3 factors related to human metrics. The outcomes of the research work provide valuable implications for the Indian manufacturing organizations to understand the factors affecting supply chain performance.

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There has been considerable number of papers published related to Six Sigma applications in manufacturing and service organizations. However, very few studies are done on reviewing the literature of Six Sigma in all the areas including manufacturing, construction, education, financial service, BPOs and healthcare etc. Considering the contribution of Six Sigma in recent time, a more comprehensive review is presented in this paper. The authors have reviewed Six Sigma literature in the way that would help research academicians and practitioners to take a closer look at the growth, development, and applications of this technique. The authors have reviewed various journal papers and suggested different schemes of classification. In addition, certain gap areas are identified that would help researchers in further research.

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This article addresses a single row facility layout problem where the objective is to optimize the arrangement of some rectangular facilities with different dimensions on a line. Regarding the NP-Hard nature of the considered problem, a hybrid meta-heuristic algorithm based on simulated annealing has been proposed to obtain a near optimal solution. A number of test problems are randomly generated and the results obtained by the proposed hybrid meta-heuristic are compared with exact solutions. The results imply that the proposed hybrid method provides more efficient solutions for the large-sized problem instances.

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The concept of "complexity" is familiar to most of project's managers, but it is not comprehended in the same way. although the complexity highlights negative points, but it may bring positive advantages which support the project. Researches conducted on this field show that the understanding of "complexity" between the researchers is different and it is mainly depends on their points of view. In fact, many identified aspects of the complexity in the literature are related to the aims of the research. In this paper, an attempt was made to describe the positive / negative features of the complexity of project using three approaches research literature (manufacturing and project complexity), interviews (deep interview with 20 experts) and questionnaire. The research was conducted on the Complex product and system (CoPS) projects. in addition, WH question technique was used. In conclusion, a 5p model (Purpose, Product, Process, People, Peripheral) has been introduced as the outcome of the study.

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Scheduling has become an attractive area for artificial intelligence researchers. On other hand, in today's real-world manufacturing systems, the importance of an efficient maintenance schedule program cannot be ignored because it plays an important role in the success of manufacturing facilities. A maintenance program may be considered as the heath care of manufacturing machines and equipments. It is required to effectively reduce wastes and have an efficient, continuous manufacturing operation. The cost of preventive maintenance is very small when it is compared to the cost of a major breakdown. However, most of manufacturers suffer from lack of a total maintenance plan for their crucial manufacturing systems. Hence, in this paper, we study a maintenance operations planning optimization on a realistic variant of parallel batch machines manufacturing system which considers non-identical parallel processing machines with non-identical job sizes and fixed/flexible maintenance operations. To reach an appropriate maintenance schedule, we propose solution frameworks based on an Artificial Immune Algorithm (AIA), as an intelligent decision making technique. We then introduce a new method to calculate the affinity value by using an adjustment rate. Finally, the performance of proposed methods are investigated. Computational experiments, for a wide range of test problems, are carried out in order to evaluate the performance of methods.

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Nowadays, the necessity of manufacturers’ response to their customers’ needs and their fields of activities have extended widely. The cellular manufacturing systems have adopted reduced costs from mass-production systems and high flexibility from job-shop manufacturing systems, and therefore, they are very popular in modern manufacturing environments. Manufacturing systems, in addition to proper machinery and equipment, workforces and their performance play a critical role.

Staff creativity is an important factor in product development, and their interest in cooperating with each other in the work environment can help the growth and maturity of this factor. In this research, two important aspects of cellular manufacturing take into consideration: Cell formation and workforce planning. Cell formation is a strategic decision, and workforce planning is a tactical decision. Practically, these two sectors cannot be planned simultaneously, and decision making in this regard is decentralized. For this reason, a bi-level mathematical model is proposed. The first level aims to reduce the number of voids and exceptional elements, and the second level tends to promote the sense of interest between the workforces for working together, which will result in synergy and growth of the organization.

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The fundamental function of a cellular manufacturing system (CMS) is based on definition and recognition of a type of similarity among parts that should be produced in a planning period. Cell formation (CF) and cell layout design are two important steps in implementation of the CMS. This paper represents a new nonlinear mathematical programming model for dynamic cell formation that employs the rectilinear distance notion to determine the layout in the continuous space. In the proposed model, machines are considered unreliable with a stochastic time between failures. The objective function calculates the costs of inter and intra-cell movements of parts and the cost due to the existence of exceptional elements (EEs), cell reconfigurations and machine breakdowns. Due to the problem complexity, the presented mathematical model is categorized in NP-hardness; thus, a genetic algorithm (GA) is used for solving this problem. Several crossover and mutation strategies are adjusted for GA and parameters are calibrated based on Taguchi experimental design method. The great efficiency of the proposed GA is then demonstrated via comparing with particle swarm optimization (PSO) and the optimum solution via GAMS considering several small/medium and large-sized problems. 

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This paper focused on scheduling problems arising in a two-machine, identical parts robotic cell configured in a flow shop. Through current research, a mathematical programming model on minimizing cycle time as well operational cost, considering availability of robotic cell as a constraint, is proposed to search for the optimum allocation and schedule of operations to these two machines. Two solution procedures, including weighted sum method and ∊-constraint method are provided. Based on the weighted sum method, like some previous studies, sensitivity analysis on model parameters were done and the optimum solutions were compared with previous results, while the ∊-constraint method can find the Pareto optimal solutions for problems with up to 18 operations in a reasonable time.

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There is still a great deal of attention in cellular manufacturing systems and proposing capable metaheuristics to better solve these complicated optimization models. In this study, machines are considered unreliable that life span of them follows a Weibull distribution. The intra and inter-cell movements for both parts and machines are determined using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance. The objectives minimize the total cost of parts relocations and maximize the processing routes reliability due to alternative process routing. To solve the proposed problem, Genetic Algorithm (GA) and two recent nature-inspired algorithms including Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed. In addition, the main innovation of this paper is to propose a novel hybrid metaheuristic algorithm based on the benefits of aforementioned algorithms. Some numerical instances are defined and solved by the proposed algorithms and also validated by the outputs of exact solver. A real case study is also utilized to validate the proposed solution and modeling algorithms. The results indicate that the proposed hybrid algorithm is more appropriate than the exact solver and outperforms the performance of individual ones.

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The selection of an appropriate cutting tool for the production of different jobs in a flexible manufacturing system (FMS) can play a pivotal role in the efficient utilization of the FMS. The selection procedure of a cutting tool for different production operations becomes more significant with the availability of similar types of tools in the FMS. In order to select and allocate appropriate tool for various production operations in the FMS, the tool selection rules are commonly used. The application of tool selection rules is also observed to be beneficial when a system demands two or more tools for the production operations at different work centers at the same time in the FMS. In this paper, investigations are carried out to evaluate the performance of different tool selection rules in the FMS. The performance of the tool selection rules is evaluated by simulation with respect to different performance parameters in the FMS namely makespan, mean work center utilization (%) and mean automatic tool transporter (ATT) utilization (%).
 

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It is indispensable that any manufacturing system is consistent with potential changes such as fluctuations in demand. The uncertainty also makes it more essential. Routing Flexibility (RF) is one of the necessities to any modern manufacturing system such as Flexible Manufacturing System (FMS). This paper suggests three mixed integer nonlinear programming models for the Unequal–Area Stochastic Dynamic Facility Layout Problems (UA–SDFLPs) by considering the Routing Flexibility. The models are proposed when the independent demands follow the random variable with the Poisson, Exponential, and Normal distributions. To validation of the proposed models, many small-sized test problems has solved that derived from a real case in literature. The large-sized test problems are solved by the Genetic Algorithm (GA) at a reasonable computational time. The obtained results indicate that the discussed models for the UA–SDFLPs are valid and the managers can take these models to the manufacturing floor to adapt to the potential changes in today's competitive market.
 

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In today’s world, manufacturing companies are required to integrate their sources with manufacturing systems and use novel technologies in order to survive in the competitive world market. In this context, computer integrated manufacturing (CIM) and its related technologies are taken as novel and efficient schemes; therefore, selecting the best technology among them has been a challenging issue. Such an investment decision is, in nature, a multi-attribute problem. In fact, manufacturing technologies have various advantages and disadvantages which need to be considered in order to choose the best one. In this paper, we briefly study the structure and goals of computer integrated manufacturing systems, the role of different sectors in traditional and modern manufacturing systems, and the effect of information communication on them. Then, various options regarding the implementation of an integrated computer manufacturing technology are introduced and a  combined model of the fuzzy analytical hierarchy process and fuzzy TOPSIS is proposed to handle the above-mentioned multiple criteria decision making problem. Finally, the considered options for manufacturing technologies are ranked using a numerical example.
 

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Inventory management plays a vital role in attaining the desired service level and prevents excess capital from being tied up in the form of dead stock. This paper presents a framework to effectively determine the items subject to obsolescence in an automotive spare parts warehouse. The inventory management techniques are applied to minimize the costs and a framework is proposed based on ABC-XYZ and FSN analysis to prioritize the spare parts based on their criticality. Further, the importance of items in the warehouse is carried out to eliminate the dead stock. The ABC classification findings reveal that A-class items accounted for 10.39% and hold the highest inventory value grouping. XYZ classification concludes that much priority should be given to the management of 52.7% of items under the Z category as the demand trend of these items is highly fluctuating. The N category items have no demand in recent times and need immediate attention, thereby preventing further unnecessary procurement. Thus, based on the ABC-XYZ and FSN analysis, the non-critical items, i.e., the non-moving items having fluctuating demand, are sorted out.