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Reliability allocation is a critical process to ensure the reliability of numerical control (NC) machines during the design phase. However, there are a few issues with the reliability allocation techniques that are currently in use. First, the reliability allocation of NC machine tools still along the lines of "System-Component-Part," which is difficult to apply to the motion characteristics of machine tools and guide the product design effectively. Second, the consistency and objectivity of the reliability allocation technique based on multicriteria decision-making (MCDM) are inadequate due to the reliance on expert knowledge. Third, the reliability allocation criteria and reliability benefit are not taken into account by the allocation approach based multi-objective optimization (MOO), which leads to conservative allocation results. Therefore, this paper proposes a hybrid reliability allocation method based on meta-action for NC machine tools that integrates meta-action theory, MCDM, and MOO. In addition, the "conservative allocation" problem in the allocation process is addressed using the reliability benefit function. Finally, the rationality, effectiveness, and applicability of the proposed method are verified with the hobbing machine as an example through discussion and comparison. [ABSTRACT FROM AUTHOR]

Modern manufacturing's objective is to guarantee quality while lowering production costs and increasing productivity. In this context, the selection of cutting materials is important in increasing productivity due to their wide range of applications. In this paper, when machining AISI 4140 alloy steel, an experimental study is presented to find the cutting conditions that result in minimum Ra, Vb, and maximum MRR values. Different optimization methods are used, namely, Taguchi, grey relational analysis (GRA), technique by order of preference by similarity to ideal solution (TOPSIS), and multi-objective optimization ratio analysis (MOORA). Taguchi's L16 design was used to arrange 16 experiments. Cutting speed (Vc), feed rate (f), and depth of cut (ap) were the input parameters, with four levels for each cutting parameter. The feed rate and cutting speed had the greatest effect on Ra and Vb, according to an ANOVA analysis of the experimental results. MRR was significantly affected by the depth of cut and feed rate. To achieve the minimum surface roughness, flank wear, and maximum material removal rate, the cutting speed, feed rate, and depth of cut were required to be 250 m/min, 0.11 mm/rev, and 1.4 mm, respectively, according to the optimization results. [ABSTRACT FROM AUTHOR]

In recent years, competition among the Indian manufacturing industries (IMI) has increased enormously in the global market. The current uncertainty in the market context is characterised and governed by the customised requirements of the customers. Thus, the manufacturing system in the industries should be capable of adapting the parameters like flexibility in scalability, variety, agility, system responsiveness, inter-connectivity, automatic data exchange with communication among the manufacturing systems, transparency and human–machine interaction, which are the main components and principles of Industry 4.0 (I4.0). Thus, adopting I4.0 plays a vital role to corroborate its long-term survival in the global marketplace. However, very few research work considerations contribute towards the issues induced during the adoption of I4.0 in manufacturing industries. This paper aims to minimise the gap between the existing Industrial System Requirements (ISRs) and the challenges faced during implementing I4.0 technologies in existing Industries. The identified ISRs and barriers were evaluated and analysed based on the data set collected from a questionnaire-based survey. Fuzzy multi-criteria analysis is conducted to identify the most weighted ISRs and barriers and ranked them concerning their importance. Furthermore, the inter-item correlation between both of them is analysed. This research work offers the researchers, practitioners, and industrialists an opportunity to formulate multi-criteria decision making (MCDM) problems through numerous case studies and prioritise the top barriers, system requirements and the inter-relationship shared between them. [ABSTRACT FROM AUTHOR]

Self-piercing riveting (SPR) as a new type of dissimilar material joining technology has been widely used in the field of automobile manufacturing. In this paper, the smooth particle Galerkin (SPG) algorithm was used to establish the SPR 3D finite element model connecting steel and aluminum sheets to simulate the forming and cross-tension of the joint, and the accuracy of the simulation was verified by experiment. The effects of rivet length, rivet blade angle, rivet shank thickness, die diameter, and die depth on the cross-sectional dimensions and cross-tension strength of the joint were studied. The technique for order preferences by similarity to ideal solution (TOPSIS) combined with entropy method was proposed to solve the current problem, which is the difficulty to select a combination of rivet and die that can obtain the best joint quality objectively and accurately. The results showed that the best joint quality can be obtained when a combination of rivet and die with rivet length of 6.5 mm, rivet blade angle of 50°, rivet shank thickness of 1.2 mm, die diameter of 10 mm, and die depth of 1.7 mm was selected. In comparison with the benchmark combination, this combination increased by 90.9% in undercut, reduced by 44.7% in the remaining thickness of the bottom, increased by 16.2% in rivet shank flaring, and increased by 2.5% in cross-tension strength. This research provides references for the model selection and size design of rivets and dies. [ABSTRACT FROM AUTHOR]

In contemporary supply chain management, the performance of potential suppliers is evaluated against multiple criteria. In this paper, a fuzzy multi-objective programming model is outlined to propose supplier selection taking quantitative, qualitative, and risk factors into consideration. Also quantity discount has been considered to determine the best suppliers and to place the optimal order quantities among them. The mixed integer derivative nonlinear programming is obtained from fuzzy multi-objective programming model by chance-constrained method. To solve this problem, an innovative method is proposed. In addition, several 'what if' scenarios are facilitated. Finally, a real-life sample is used to validate the proposed model. [ABSTRACT FROM AUTHOR]

Control charts are widely implemented in firms to establish and maintain statistical control of a process which leads to the improved quality and productivity. Therefore, designs of control charts have gained particular attention from the outset. Design of control charts requires that the engineer selects a sample size, a sampling frequency, and the control limits for the chart. In this paper, a possible combination of design parameters is considered as a decision-making unit which is identified by three attributes: hourly expected cost, detection power of the chart, and in-control average run length. Subsequently, optimal design of control charts is formulated as a multiple-objective decision-making problem. Moreover, the cost function is extended from single to multiple assignable causes because there exist multiple assignable causes in real practice. An algorithm using data envelopment analysis is applied to solve the multiple-objective decision-making (MODM) model. Some numerical and experimental analyses are provided to illustrate the algorithm procedure. Sensitivity analysis is carried out to investigate the robustness of the model, and comparisons with other related published papers are made. It is shown that the proposed MODM model can overcome some drawbacks attached to the previous models and approaches. [ABSTRACT FROM AUTHOR]

Recent research is recognizing that multiple criteria analysis should take account of the concepts of uncertainty and risk. In some cases, precise determination of the exact value of alternatives and weights of criteria is difficult. Consequently, to deal with these potential problems, their values are regarded as fuzzy and intervals. This paper proposes an interval-valued fuzzy multiple criteria complex proportional assessment (IVF-COPRAS) method that can reflect both a subjective judgment and objective information in real-life situations. In this method, the performance rating values versus selected criteria as well as the weights of conflicting criteria are linguistic variables represented by interval-valued triangular fuzzy numbers. Moreover, performances of alternatives against subjective criteria are described via linguistic variables and then transformed into interval-valued triangular fuzzy numbers. Finally, an application example for the robot selection problem is given in this paper to show this decision-making steps and the effectiveness of the proposed method in manufacturing companies. [ABSTRACT FROM AUTHOR]

Group decision making with multiple criteria is the most popular method for ranking a set of alternatives. In this regard, all alternatives are compared based on a common criteria set. Meanwhile, decision makers sometimes encounter special situations, for example, having to select from among a set of alternatives without a set of criteria or with a set of criteria that are grouped/related to various alternatives. Thus, it may be impossible to select from among a set of alternatives using typical methods. Hence, in this study, a new, modified VIKOR method is proposed to address the lean tool selection problems in manufacturing systems. In this study, a model was developed to help practitioners improve their ability to solve problems when the possible solutions have their own individual criteria. In fact the modified VIKOR method can be applied to rank alternatives in threefold: alternatives with common criteria, without common criteria, with integrated common, and uncommon criteria. This paper offers numerical examples of the model, using a case study to illustrate an application of the proposed model and properly assess the validity of this new method. The results demonstrate the usefulness and effectiveness of the modified new method. The model covers the lack existing in the current literature to assess effectiveness of applying lean tools. [ABSTRACT FROM AUTHOR]

Recent research is recognizing that multicriteria decision making should take into account the concepts of uncertainty, risk, and confidence. In some cases, precise determination of the exact value of the attributes is difficult. Consequently, to surmount this potential problem, their values are considered as fuzzy and intervals. The elimination and choice translating reality (ELECTRE) is one of the most widely used methods to rank a set of alternatives versus a set of criteria to reflect the decision maker’s preference. What is customary in almost all recent papers regarding ELECTRE is the utilization of fuzzy concepts in decision-making process. In this paper, we propose a novel algorithmic ELECTRE method which assimilates the concepts of interval weights and data for the facilitation of evaluation of a set of alternatives against a set of criteria in this situation. Finally, the executive procedure of our proposed ELECTRE method is illustrated by applying it to the problem of supplier selection. [ABSTRACT FROM AUTHOR]

Following the environmental crises of recent decades, a turning point in the awareness of the fragility of ecosystems has been marked, i.e., environmental awareness. This has contributed to the development of various environmental laws and regulations such as the "Waste Electrical and Electronic Equipment," the "Restriction of Hazardous Substances," and the "Registration, Evaluation, Authorization and Restriction of Chemicals" regulations and the "Energy Using Products" Directives. Our work contributes to the development of eco-friendly product manufacturing processes. In order to estimate and optimize the environmental impacts of a product, most of the methodologies, concepts, and tools that integrate computer-aided design (CAD) and life cycle assessment systems generally exploit the feature technology at the level of each feature independently of the others, i.e., "microplanning." The feature interaction technology (FIT) is treated only in few studies, but it is pivotal in the eco-manufacturing process. In this paper, we propose a new manufacturing-scenarios-based methodology by using FIT and a Multi-criteria Decision Support Method (MCDSM), which helps manufacturers maintain their marketplaces by producing goods in an eco-friendly way. In fact, this methodology helps designers choose from the CAD design phase the most ecological manufacturing process from possible existent scenarios in real time. [ABSTRACT FROM AUTHOR]

In this paper, a tri-objective optimization problem related to sequencing in multi-model reconfigurable assembly systems (RAS) is addressed. RASs are also designed around product families similar to the more generic reconfigurable manufacturing systems (RMSs). A multi-model assembly line needs reconfiguration after assembling each batch of the products from the family. Further, the changeover of assembly line from one product type to another is also of varying degree of complexity and reconfiguration cost. Two novel indices for commonality and reconfiguration complexity are proposed here to incorporate this feature of RAS. Optimizing the reconfiguration time/cost is taken as the first objective while fulfilling the product due dates and the work load balance among the assembly stations as the second and third objective functions, respectively. This optimization problem is solved using a Multi-objective Self-Organizing Migrating Algorithm (MOSOMA) metaheuristic to find the non-dominated Pareto optimal solutions representing optimum sequencing of products in the family. An approach for identifying the best solution from the multiple Pareto optimal solutions using aggregate fitness is also proposed. A numerical illustration along with computational procedure is presented on a hypothetical RAS model. [ABSTRACT FROM AUTHOR]

Manufacturing machine parts of high quality with high productivity and low cost is the most important goal of the production in metalworking industry. For the realization of production goals, single-objective optimization of the machining processes is a good way but multi-objective optimization is the right way. Turning is the most widely used machining process. Turning operation is usually realized through a multi-pass roughing and single-pass finishing. In this paper, multi-objective optimization of turning operation which consists of multi-pass roughing and single-pass finishing AISI 1064 steel with carbide cutting tool, in terms of material removal rate and machining cost, was studied. For multi-pass roughing, optimization problem with two objectives (material removal rate and machining cost), three factors (depth of cut, feed and cutting speed), and five machining constraints (cutting force, torque, cutting power, tool life, and cutting ratio) was studied. For single-pass finishing, optimization problem with two objectives (material removal rate and machining cost), four factors (tool nose radius, depth of cut, feed, and cutting speed), and three machining constraints (surface roughness, tool life, and cutting ratio) was studied. The optimization problem is solved using three techniques: (i) iterative search method, (ii) multi-objective genetic algorithm (MOGA), and (iii) genetic algorithm (GA). With the iterative search method, the values of objectives for all combinations of factor levels were calculated and an optimal solution was selected. With a multi-objective genetic algorithm, a set of optimal solutions named "Pareto optimal set" was defined and an optimal solution was selected. With a genetic algorithm, the optimal solution was determined by using the weighted-sum-type objective function. [ABSTRACT FROM AUTHOR]

Due to industrialization and globalization, automotive sectors in both professional and societal applications have increased manufacturing and have resulted in higher production of virgin tires. These hikes in virgin tire production subsequently results in more end-of-life (EOL) tires, as well as lower quality, shorter tire lifespan, and a restricted availability of new model tires. Many developed nations have started to address EOL tire management issues through various strategies and codes of conduct, but because the environment is a global concern shared both by developed and developing nations, this study examines the issue of EOL tire management in India, a highly populated developing country. This paper proposes a framework to analyze the motivating factors of EOL tire management; it is validated in the Indian scenario with the assistance of a multi-criteria decision-making (MCDM) approach. Existing literatures are limited to the study of recycling and remanufacturing techniques. This study also provides the interrelationship between drivers and their respective influence with sound managerial implications. Finally, the paper concludes with the most influential driver of EOL tire management among all common drivers. We examine its limitations, and we shed light on the prospects of greater sustainability in EOL tire management in the future. [ABSTRACT FROM AUTHOR]

Storage includes all activities involved in holding products at a point for temporary custody and subsequent distribution. The criteria that must be considered in determining the location in a storage facility for each product are often conflicting. Thus, the main objective of this paper is to determine the best alternative for assigning a product to a warehouse storage location. A class formation and allocation model and a preference ranking organization method for enrichment evaluation (Promethee) multicriteria method are used to achieve this goal. By using the model proposed, the manager can learn of all the possible non-dominated allocations of the products in the warehouse and can then change the allocation when needed. The Promethee method is efficient at ranking alternatives which facilitates the decision process. [ABSTRACT FROM AUTHOR]

The product mix problem is one of the most important problems in production systems. Several algorithms to determine the product mix under the theory of constraints have been developed. Most of the previous works focused on one bottleneck (dominant bottleneck) and considered the product mix problem with exact data. In this paper, all bottlenecks are used in order to determine the aggregated priority of each product, and a multi-criteria decision-making approach is proposed for product mix problem with interval parameters. The proposed approach involves bottlenecks identification, determination of the production priority and the weights vector of bottlenecks, application of interval TOPSIS to calculate the aggregated priority of each product, and use of reducing and increasing process to improve the production plan. At the end, a numerical example is presented to illustrate the procedure of the proposed approach. The results obtained from the computational study have shown that the proposed algorithm is an effective approach to solve the product mix problem. [ABSTRACT FROM AUTHOR]

Today, in the competitive markets, supply chain works as an interrelating network of suppliers, manufacturers, distributors, and customers, to satisfy customer demands. Coordinating all parts of the supply chain is a multi-level process and needs important decisions that affect on the performance of each part and whole supply chain and some conflicting objectives should be considered. In this paper, the supplier selection problem is integrated with production decision and distributor location problems and a new mathematical model is proposed. Delivery time, total cost, and quality are the objective functions considered and weighting method is used to solve the multi-objective problem. A modified genetic algorithm is proposed to obtain Pareto optimal solution sets. Computational experiments are included for validation of proposed algorithm. The results show that the proposed algorithm gives high-quality solutions as well as better computational times. [ABSTRACT FROM AUTHOR]

An ever increasing trend in today's dynamic and competitive world markets, manufacturing organizations should provide the right amount of flexibility at the right time in the right direction. Among many manufacturing strategies available, reconfigurable manufacturing system is emerging as an attractive alternative to meet this goal. Organizations in pursuit of reconfigurations are often presented with a number of options and hence face a problem of evaluating the feasible alternative configurations before choosing the best one. The study reported in this paper is related to an organization interested in reconfiguring the present manufacturing setup. A number of alternative configurations were identified. Simulation models were developed for these configurations, and the results of the simulation experiments were analyzed to assess the suitability of each configuration in the point of view of five performance measures. The simulation experiments, method used to analyze the simulated results, and the outcome of the study are reported in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective formulation of the buffer allocation problem in unreliable production lines. Majority of the solution methods for buffer allocation problems assume that the process times, time between failures, and repair times are deterministic or exponentially distributed. This paper relaxes these restrictions by proposing a simulation-based methodology which can consider general function distributions for all parameters of production lines. Factorial design has been used to build a meta-model for estimating production rate based on a detailed, discrete event simulation model. We use genetic algorithm combined to line search method to solve the multiobjective model and determining the optimal (or near optimal) size of each buffer storage. [ABSTRACT FROM AUTHOR]

Determination of optimal cutting parameters is one of the vital modules in process planning of parts since economy of machining operations plays an important role in increasing productivity and competitiveness. Recently, Gadakh and Shinde (Int J Adv Manuf Technol ) presented a graph theory and matrix approach (GTMA) and few other multiple attribute decision making (MADM) methods for selection of cutting parameters in side milling operation in their paper 'Selection of cutting parameters in side milling operation using graph theory and matrix approach' Gadakh and Shinde (Int J Adv Manuf Technol DOI , ). The authors had presented the experimental results of Chiang-Kao and Lu (Int J Adv Manuf Technol 34:440-447, ) to demonstrate the proposed methods. However, the systematic procedures of GTMA and the other MADM methods were already reported exhaustively by Rao (), and Gadakh and Shinde (Int J Adv Manuf Technol ) had reproduced the related contents. Many computational and fundamental mistakes were also present in their work. Furthermore, the authors had presented the incorrect values of metal removal rate (MRR) and these values were different from the values presented by Chiang-Kao and Lu (Int J Adv Manuf Technol 34:440-447, ). This note discusses these mistakes and presents the correct approaches and the results. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a model for selecting of the global supplier by analytical hierarchy process (AHP) and analytical network process (ANP) based on linguistic variable weight. The fuzzy AHP and fuzzy ANP methods are suggested to be a good tool for solving our proposed multiple-criteria decision-making problem. Fuzzy AHP-based methodology evaluated different decision criteria such as service quality, cost, risk factors, and supplier's characteristics involved in the selection of the best supplier in a global supply chain and ANP represented an effective tool for providing a suitable solution for managers. The linguistic levels of comparisons produced by the experts with triangular fuzzy numbers for constructing fuzzy pairwise comparison matrices. The implementation of the system was demonstrated by a problem having four stages of hierarchy which contains four criteria and 28 attributes. In conclusion, the fuzzy AHP and fuzzy ANP results were compared. [ABSTRACT FROM AUTHOR]

In today's competitive world, manufacturing firms require to meet demand, increase quality, and decrease cost due to continuous changes in the market. Because of the importance of flexible manufacturing system, the optimum operator allocation problem in cellular manufacturing systems (CMSs) is a challenging issue. Hence, the aim of this paper is presenting a decision making approach based on Fuzzy Analytical Hierarchy Process (Fuzzy AHP), Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), and computer simulation to determine the most efficient number of operators and the efficient measurement of operator assignment in CMS. Also, the proposed approach is performed by employing the number of operators, average lead time of demand, average waiting time of demand, number of completed parts, operator utilization, and average machine utilization as criteria for decision making. An actual case is considered and a computer simulation which considers various operators layout is developed with respect to the purpose of this study and 36 scenarios is produced. In order to find the best scenarios among 36 alternatives, a combined Fuzzy AHP and TOPSIS is employed. The Fuzzy AHP method is applied to determine the importance weight of the criteria. Finally, the TOPSIS method is utilized to rank and analyze scenarios. Also, a sensitivity analysis is carried out for validating the obtained results. [ABSTRACT FROM AUTHOR]

ound the globe, manufacturers are increasingly trying to design products that are as ecological as possible, which is leading to the establishment of new design methods focused on environmentally conscious design (ECD). Materials selection, as a key element in ECD, differs from traditional design by introducing three additional requirements: life cycle viewpoint, consideration on environmental impact and cost, and uncertainties in the product life cycle scenario. This paper presents a new multi-criteria decision making (MCDM) model and uncertainty analysis method for the environmentally conscious materials selection problem. The model is based on engineering analysis and life cycle simulation, and is constructed in such a way so that it contains uncertainties to be explored and may be executed during product design; it considers such issues as manufacturing and post-use processes such as recycling. The TOPSIS method is employed, and uncertainty analyses are performed for model flexibility. The model is applied to material selection for PC housing. The case study shows that the decision-making analysis can provide useful design guidelines and a criterion for materials selection to achieve Environmentally Conscious Design. [ABSTRACT FROM AUTHOR]

Even small improvements in storage operations can bring significant benefits in customer response time, increasing the level of service offered. However, a company cannot offer high level of service for all products. In this sense, one of the ways used to optimize the activities for order picking is the study of storage location assignment problem (SLAP). Thus, this paper proposed a multi-criteria decision model to perform the products classification and to solve the SLAP in a multi-layer warehouse. The ELECTRE TRI method is used to define the shelf level for each product, and the ELECTRE III method is used to establish a fixed location for each product on the shelf it belongs to. The great advantage of this model is that there is not trade-off among the criteria, and it is able to consider several criteria simultaneously. However, the main goal is not optimization, but the search for a storage operations performance balance, considering multiple criteria, allowing for a better inventory management when considering profitability criteria, for example, and improvement in the order picking operations, through the demand and space required criteria. In addition, it considers the preferences of the decision maker. [ABSTRACT FROM AUTHOR]

Supplier selection is a crucial multi-criteria decision-making (MCDM) problem which requires a tradeoff between multiple criteria denoting vagueness and imprecision with the involvement of a group of experts. Fuzzy MCDM methods allow for modeling the decision processes involving imprecise and subjective information expressed as linguistic variables or fuzzy numbers by decision-makers. In this paper, considering information fusion between criteria and linguistic terms, a robust supplier selection algorithm based on the hybrid use of a modified fuzzy Analytic Hierarchical Process (FAHP) and generalized Choquet fuzzy integral (GCFI) methods is proposed. The FAHP is employed in order to obtain importance weights of the evaluation criteria and then the GCFI is used to achieve the overall performance values of supplier alternatives. The suggested methodology has been conducted in a real case application for supplier selection problem in a steel-producing company operating in Turkey. We also showed the robustness of the devised methodology through a sensitivity analysis and compared the obtained results with other MCDM methods. Consequently, it is concluded that the proposed integrated approach can effectively handle supplier selection and other MCDM problems especially when there are interdependent sub-criteria in a complex hierarchy of evaluation criteria. [ABSTRACT FROM AUTHOR]

Equipment selection is a complex task that requires the consideration of multiple criteria with different measurement units. A number of decision-making methods have been proposed for analysing equipment selection problems, each having their own distinctive advantages and limitations. Despite the number of decision-making techniques available, few comparative studies exist that evaluate two or more methods with a singular problem. This paper evaluates three multi-attribute decision-making methods for an equipment selection problem in the early stages of a chemical manufacturing process. A software framework which incorporates analytical hierarchy process (AHP), multi-attribute range evaluations (MARE) and ELimination Et Choix Traduisant la REalité trois (ELECTRE III) was developed and distributed to a technology manager at Fujifilm Imaging Colorants Ltd (FFIC). The manager, within a team of nine people, examined the same decision problem using the three decision analysis methods. The results of the study are examined in respect to assessing each method's ability to provide accurate representations of the decision-makers' preferences and the ability to comprehend the uncertainty present. The decision-makers identified MARE as their preferred method, AHP was found to be comparatively more time-consuming and showed the highest variation of results while ELECTRE III was unable to provide a conclusive best result. [ABSTRACT FROM AUTHOR]

Electric power consumption by sanitation companies represents the second largest operating cost item, which can be explained by the many electric motors that operate in their facilities. An electric motor is an electric device that is capable of transforming electrical energy to mechanical energy. These devices are responsible for more than 80 % of all electricity consumed in the world. Because they are machines, they can fail, and consequently, repairs are needed. Many companies choose to outsource the repair service of broken electric motors, and most capital cities have many suppliers. Selecting or classifying these suppliers into groups to fulfill strategic goals is a significant challenge. This paper presents a model to classify businesses that provide electric motor repair services. This model can be used to pre-qualify suppliers. To this end, the PROMSORT multicriteria classification method was applied to classify suppliers in a non-compensatory way according to the evaluation criteria. The applied method enabled the classification of the evaluated companies in four groups based on attributes related to their facilities, their professionals, and their experience. The results showed that the model is effective for this application and it can be applied in other contexts also related to classification problem. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new fuzzy group multi-criteria decision making method and apply it to determine the critical path in a project network. The criteria used here are time (expected duration), cost, risk, and quality of the project activities that are considered critical in project management. As each criterion has its independent level of importance in the critical path selection, the weights of the project criteria are also considered in the analysis. Considering that the information in terms of various project activities and criteria weights are often incomplete and/or uncertain in real-world situations, the essential information in terms of the criteria and project activities are obtained using triangular fuzzy numbers and/or linguistic variables that are mapped to triangular fuzzy numbers, wherever appropriate. The proposed method involves fuzzy evaluation based on the fuzzy information of the possible project paths on each criterion leading to the strength and weakness index scores of the project paths. We define a measure of criticality termed as the total performance score of each project path obtained using its strength and weakness index scores. The path that has the highest measure of criticality is selected as the critical path. A numerical illustration is provided to demonstrate working of the proposed methodology. Further, a case study from manufacturing engineering industry is also presented to better justify the applicability and potentials of the proposed methodology. A comparison with closely related fuzzy multi-criteria decision methods for the critical path selection is done to analyze the performance of the proposed methodology. [ABSTRACT FROM AUTHOR]

Supplier selection is a multiple criteria decision making (MCDM) problem which is affected by several conflicting factors. In the business market of flaming competition in recent years, more attention has been paid to this problem. In this paper, a model is proposed in two phases. At first, suppliers are evaluated according to both qualitative and quantitative criteria arising from strengths, weaknesses, opportunities, and threats (SWOT) analysis. SWOT, as a useful technique in strategic management, is utilized to determine criteria and deal with suppliers' situation in a competitive market. Moreover, fuzzy set theory is employed to deal with the vagueness of human thought. Then, fuzzy logic is integrated with technique for order preference by similarity to ideal solution (TOPSIS) technique to calculate weights of criteria. In the second phase, results from fuzzy TOPSIS (FTOPSIS) are used as an input for linear programming to allocate orders. Finally, a case study has been used simultaneously to validate the proposed model. [ABSTRACT FROM AUTHOR]

Supplier selection is one of the most critical activities of purchasing management in a supply chain because of the key role of supplier's performance in achieving the objectives of a supply chain. Supplier selection problem requires a trade-off between multiple criteria exhibiting vagueness and imprecision with the involvement of a group of experts. This paper presents a multiple criteria group decision-making approach for supplier selection problem in the context of interval type-2 fuzzy sets. A new method for ranking interval type-2 fuzzy numbers, based on the centroid of fuzzy sets, is proposed and compared with some methods. The proposed ranking method is used for extending complex proportional assessment (COPRAS) method for group decision-making with interval type-2 fuzzy numbers. The developed method uses a stepwise procedure for ranking and evaluating the alternatives, in terms of significance and utility degree, and selects the best solution considering both the positive-ideal and the negative-ideal solutions. To demonstrate the applicability of the proposed approach in supplier selection problems, an illustrative example is presented and the results are analyzed. [ABSTRACT FROM AUTHOR]

Like other manufacturing processes, plasma spraying also has a non-linear behavior due to contribution of many coating parameters. This characteristic makes finding optimal factor combination difficult. Hence, the problem can be solved through effective and strategic statistical methods integrated with systematic experimental data. The purpose of this paper is to study the tribological and mechanical performance of coated surface deduced by plasma spraying process. Taguchi experimental design and analysis of variances (ANOVA) were used to investigate the influence of plasma spraying factors (spraying layers, accelerating voltage, arc current, travel speed, stand-off distance, powder feed rate, carrier gas flow rate, and primary gas flow rate) on oxidant percent and hardness. To find optimal combination of factors to reach maximum oxidation and hardness, gray relational analysis (GRA) coupled with principal component analysis (PCA) are applied on experimental data. Here, the PCA is used to find the most appropriate weighing factors necessary for construction gray relational grade. Results indicated that stand-off-distance is the most significant factor having the greatest influence on gray relational grade, followed by powder feed rate, traverse speed, spraying layers, and primary gas flow rate. Furthermore, GRA coupled with PCA can effectively acquire the optimal combination of plasma spraying parameters and the proposed approach can be a useful tool to increase the coating performance. [ABSTRACT FROM AUTHOR]

Supplier selection, a multi-criteria decision making (MCDM) problem, is one of the most important strategic issues in supply chain management (SCM). A good solution to this problem significantly contributes to the overall supply chain performance. This paper proposes a new integrated mixed integer programming - data envelopment analysis (MIP-DEA) model for finding the most efficient suppliers in the presence of imprecise data. Using this model, a new method for full ranking of units is introduced. This method tackles some drawbacks of the previous methods and is computationally more efficient. The applicability of the proposed model is illustrated, and the results and performance are compared with the previous studies. [ABSTRACT FROM AUTHOR]

Selecting the best supplier has been turned into a strategic subject in the competitive market, in the recent years. Supplier selection is one of the significant multiple criteria decision-making problems, which is studied in this paper. This research applies the multi-segment goal programming formulation, which considers different positions in the goal programming. This new kind of goal programming is revised in this investigation. The advantage of the proposed method is that it allows decision makers to set multiple aspiration levels for the coefficient of variables. Hence, the decision makers obtain more revenue and match with the reality of their problems. Unlike the multi-segment goal programming proposed in the literature, the revised formulation is linearized in the current study. The supplier selection problem is solved by this new formulation in a special circumstance. Finally, some concluding notes are remarked. [ABSTRACT FROM AUTHOR]

Staff scheduling is one of the most relevant issues among production planning managers. The problem is to set up an appropriate schedule for various employees to maximize the performance measurement. There are different conflicting criteria with any scheduling problem such as cost minimization, efficiency maximization, etc. The proposed model of this paper develops a new multiobjective decision-making scheduling problem, and the resulted problem is solved using two different techniques of goal programming and augmented epsilon constraint. The implementation of the new proposed model is demonstrated with a real-world case study, and they are analyzed. The preliminary results indicate that the epsilon-constraint method somewhat performs better than goal programming technique. [ABSTRACT FROM AUTHOR]

Sequencing is one of the major research areas in increasing manufacturing productivity. In case of multiple items to be manufactured using multiple processes, complex sequencing may also be considered as a queuing problem where the products are processed with respect to the priorities which can be developed by static and dynamic fashion depending on nature of the problem. In this paper, a dynamic priority approach is incorporated for a group-based single machine scheduling problem using multi-criteria decision-making technique regarding setup times and machine utilization. After running the model, sequences of groups are obtained and schedule is developed with respect to priorities. Results are satisfactory as compared to random process and existing implementation within the company. [ABSTRACT FROM AUTHOR]

Reverse logistics has emerged as an important dimension for organizations to build their strategic advantage. Part of this effort relies on potentially outsourcing these activities. With this competitive issue in mind, this paper presents a multistep process to select a third-party reverse logistic provider (3PRLP). Criteria for evaluation are drawn from the literature and practical input from experts and decision makers within a case company. The process requires that an initial screening of criteria is completed through the analytical hierarchy process. The second stage of the process, 3PRLP selection, is completed using the analytic network process. An illustrative example is provided to demonstrate the solutions obtained by the proposed process within an automobile case company. A sensitivity analysis is also provided for a robustness check. The results obtained from the proposed model provide some interesting managerial implications to the case company and others wishing to apply the process. [ABSTRACT FROM AUTHOR]

This paper presents a multi-stage decision-making process for multiple attributes analysis under an interval-valued fuzzy environment. The proposed method does not demand weights of conflicting attributes through the decision-making process under uncertainty. The performance ratings of potential alternatives with respect to selected conflicting attributes are first described by linguistic terms and then are represented as interval-valued triangular fuzzy numbers. Second, an outranking matrix is proposed to denote the frequency with which one potential alternative dominates all the other alternatives according to each selected attribute. Consequently, the outranking matrix is triangularized in order to provide an implicit preordering or provisional order of potential alternatives under uncertainty. Finally, the tentative order of alternatives undergoes different operations of the screening and balancing that requires sequential application of a balancing principle to the advantages-disadvantages table. It hybridizes the conflicting attributes with the pair-wise comparisons of the potential alternatives for the multiple attributes analysis. Furthermore, an application example is used for the decision-making in a selection problem to illustrate the feasibility and applicability of proposed method in an interval-valued fuzzy environment. [ABSTRACT FROM AUTHOR]

Manpower scheduling is a complicated problem to solve that strives to satisfy employers' objectives and employees' preferences as much as possible by generating fairly desirable schedules. But sometimes, objectives and preferences may not be determined precisely. This problem causes manpower scheduling takes the fuzzy nature. This paper presents a new fuzzy multi-objective mathematical model for a multi-skilled manpower scheduling problem considering imprecise target values of employers' objectives and employees' preferences. Hence, a fuzzy goal programming model is developed for the presented mathematical model and two fuzzy solution approaches are used to convert the fuzzy goal programming model to two single-objective models. Since the complexity of a manpower scheduling problem is NP-hard, the single-objective models are solved by two meta-heuristics, namely particle swarm optimization and elite tabu search. Eventually, the performance of the proposed algorithms is verified and the results are compared with each other to select the best schedules. [ABSTRACT FROM AUTHOR]

Aggregate planning is a medium-range capacity planning that suggests the production strategies in order to meet the forecasted demand considering the capacity constraints. As the period of planning length increases, the uncertainty of information will grow. Regarding this point, in this paper, a multi-objective model is proposed for aggregate planning problem in which the parameters of the model are expressed in the form of grey numbers. The suggested grey multi-objective model is solved based on a goal programming problem with fuzzy aspiration levels. The model is applied in a real-world problem, and its results are illustrated. The obtained results give a range for decision variables, and decision makers can handle the inevitable uncertainty of information by using these ranges. [ABSTRACT FROM AUTHOR]

Shewhart charts are the most popular control charts that can be used to monitor variable quality characteristics in a production process. In this paper, a multi-objective model of the economic statistical design of the X-bar control chart is first proposed by incorporating the Taguchi loss function and the intangible external costs. The model minimizes the mean hourly loss cost while minimizing out-of-control average run length and maintaining reasonable in-control average run length. A multi-objective evolutionary algorithm, namely NSGA-II, is then developed and used to obtain the Pareto optimal solution of the model. Some sensitivity analyses are next performed to investigate the effect of parameter estimation on the chart performances. Finally, a comparison study with a traditional economic design model reveals that the proposed multiple objective design of the X-bar control chart offers a better approach and more practical outcomes for the practitioners. [ABSTRACT FROM AUTHOR]

Material selection is an onerous process of design activities which needs to be carefully carried out in order to increase the probability of success. A lot of multi-criteria decision-making methods have been proposed in material selection, many of which require quantitative weights for the attributes. Since weights play a very significant role in the ranking results of the materials, this paper presents a framework for determining importance degree of criteria to overcome the shortcomings of this subject in material selection. Furthermore, the suggested framework covers the situation of interdependent relationship between the criteria which has not been surveyed in material selection yet. An example was considered to illustrate how this framework is conducted. On the basis of the numerical results, it can be concluded that the proposed method can soundly deal with the material selection problems. [ABSTRACT FROM AUTHOR]

The choice of materials plays an important role in the decision-making process of the manufacturing organizations. The materials affect many aspects of a product and the manufacturing process too. The improper selection of material can result in a defective final product. Thus, if satisfactory results are to be expected, immense importance must be given for proper selection of the materials. There are numerous choices and various criteria influencing the selection of material for a particular application. These criteria range from mechanical, electrical, and physical properties to corrosion resistance and economic considerations of the materials. The large number of available materials, together with the complex relationships between various selection parameters, often makes the selection process a difficult task. The problem of selecting a material for an engineering application from among two or more alternatives on the basis of several criteria can be treated as a multi-criteria decision-making (MCDM) problem. This paper mainly focuses on solving two real-time material selection problems using utility additive (UTA) method, which is an almost unexplored MCDM tool to solve such type of complex decision-making problems. It is observed that the ranking performance of the UTA method is quite comparable with the other MCDM methods as adopted by the past researchers. [ABSTRACT FROM AUTHOR]

In recent years, going green has become a strategic priority in manufacturing which has evolved from the growing awareness of the need for environmentally friendly processes and products. Recent trends in developing new machining strategies able to support environmental protection and prevention of pollution in balance with socioeconomic needs and technical requirements inevitably require significant efforts in fundamental understanding of the actual energy and material flows needed to meet the machining requirements. This paper describes the methodology and software, Global Reasoning for Eco-Evaluation of Machining, developed to evaluate the use phase of a machine tool system with respect to a consistent set of technical, economical, and environmental criteria, as a part of the NEXT European project. The evaluation, based on the analytic hierarchy process, is conducted at two levels: (a) the process/part level which considers the local cutting environment along with the 'actors' that are directly involved in the cutting area, their relationships, and the phenomena that occur from their interactions and (b) the system level which gathers together the main specifications of the machine tool system and the energy requirements associated with various activities performed in order to support the material removal processes. Two database structures were defined in order to store the entire amount of data needed to perform the analysis at each level. A detailed example of the evaluation carried out at the first level demonstrated the superior performance of the dry and minimum quantity lubrication alternatives over their wet machining counterpart when cutting power, cutting fluid consumption, and the machining time were considered as criteria for the performance evaluation of various milling tests. [ABSTRACT FROM AUTHOR]

This paper presents an approach to consensual cell formation in cellular manufacturing design. The cell formation is a multi-measurable criteria problem. However, a number of factors limit the possibility of simultaneously considering multiple criteria during the cell formation. During the cellular manufacturing system design, the engineers must know the cells responding individually to each criterion. Thus, for discerning the consensus in multiple-criteria cell formation problem, a formal approach is proposed. The problem of consensus searching is formulated as the problem of searching the best overlap between potential solutions, called conjecture, under some constraints. Two models are proposed. The first model is devoted to the consensus conjecture which satisfies globally the set of measurable criteria. The second model addresses the problem of nucleus recognition. Thus, the problem of consensus searching is an alternative to the multiple-criteria problem optimisation. The results confirm the assumption that the cell formation is a process of searching and evaluating the consensual information. [ABSTRACT FROM AUTHOR]

This paper presents a statistical technique to optimise process conditions for multiple quality criteria in micro-injection moulding. A sample hierarchical component with micro-features was replicated where it was required to improve the process conditions for both complete mould filling and variability in mass. A design-of-experiments approach was used to investigate the effect of five processing parameters on both criteria. It was found that holding pressure, melt temperature and injection velocity were statistically significant for part mass, whereas injection velocity alone was significant for mass variation. Desirability functions were used to predict processing conditions that improved both requirements within pre-set conditions. The technique was validated by experiment and it was shown to be applicable for process parameters for multiple criteria. [ABSTRACT FROM AUTHOR]

Most welding processes present large sets of correlated quality characteristics. With this particularity in mind, we present a multi-objective optimization technique based on Principal Component Analysis (PCA) and response surface methodology (RSM). This two-fold technique utilizes PCA to factorize the original welding responses. The original responses—obtained through a Central Composite Design—are then replaced by the resulting principal component scores. The technique’s advantage is that it reduces the data set and still considers the correlation among the responses. Quite often, however, the first principal component alone cannot explain the amount of variance–covariance structure of the welding responses. In this paper, we remedy this shortfall by proposing an objective function established in terms of the most significative principal component scores (weighted by their respective eigenvalues). Experimental results were obtained with a multiresponse pulsed gas metal arc welding process. These results, when compared with other strategies of multiresponse combination, verify the adequacy of our proposed approach. [ABSTRACT FROM AUTHOR]

Decision making is the process of finding the best option among the feasible alternatives. In classical multiple criteria decision-making (MCDM) methods, the ratings and the weights of the criteria are known precisely. Owning to vagueness of the decision data, the crisp data are inadequate for real-life situations. Since human judgments including preferences are often vague and cannot be expressed by exact numerical values, the application of fuzzy concepts in decision making is deemed to be relevant. In this paper, we proposed the application of a fuzzy distance formula in order to compute a crisp value for the standard deviation of fuzzy data. Then, we use this crisp value of the standard deviation to normalize the fuzzy data using the distance formula again. In our normalization approach, we have enough flexibility to consider various types of fuzzy numbers (such as triangular, trapezoidal, and interval). Finally, we use the technique for order preference by similarity to an ideal solution to determine the ranking order of the alternatives. A numerical example from the literature is solved to demonstrate this applicability of the proposed model. We also compare our proposed approach with similar methods in the literature using some examples with known results and a number of randomly generated test problems. The results point to the applicability of our method and signify its effectiveness in identifying solutions. [ABSTRACT FROM AUTHOR]

Sustainable product design is one of the key challenges for manufacturers to achieve sustainability, whereas it is not clear what products mean sustainability due to subjectivity of concept. This paper proposes a novel assessment methodology for evaluating product sustainability performance based on quantitative and qualitative information. Product sustainability performance index (PSPI) is defined to assist designers and engineers to evaluate and compare the relative sustainability performance of different product. An evaluation model is established based on six dimension sustainability elements (Environment, Economy, Society, Functionality, Manufacturability, and Reusability) in order to provide a new comprehensive sustainability assessment framework in a closed-loop life cycle system. Furthermore, the multi-level hierarchical structure based on criteria and corresponding sub-criteria is utilized in order to conduct more effective analysis. The improved fuzzy multi-criteria decision method is employed incorporating subjective judgment and objective information to determine weights of criteria and related sub-criteria in real decision-making situation. The positive-ideal and negative-ideal solutions are employed to satisfy the designers' desire to be as close as possible from the positive-ideal solutions. Sustainability performances of different manufactured products are assessed by integrating different levels sub-criteria into criteria. Illustrative example involving Bridgeport manual mill and Mori Seiki DuraVertical 5060 in three use environments is discussed to demonstrate the feasibility of the proposed methodology. [ABSTRACT FROM AUTHOR]

Conceptual design is a fundamental phase for developing optimal product configurations. During conceptual design, the degree of freedom in engineering choices can propose optimal solutions in terms of assembly, manufacturing, cost and material selection. Nevertheless, in current industrial practices, each aspect is analysed independently and a guided decision-making approach based on multi-objective criteria is missing. Multi-objective analysis is a way of combining each production aspect with the aim of choosing the best design option. The goal of this research work is to define a multiobjective design approach for the determination of optimal and feasible design options during the conceptual design phase. The approach is based on the concept of functional basis, module heuristics for defining product modules and the theory of multi-criteria decision-making for mathematical assessment of the best design option. The novelty of this approach lies in making the design process, currently based on company know-how and experience, systematic. A complex product (i.e. tool-holder carousel of a computer numerical control machine tool) is the case study used to assess the economic sustainability of different design options and to validate the proposed design workflow in a real manufacturing context. Different product modules have been re-designed and prototyped for comparing the assemblability, manufacturability and cost of the design solutions. [ABSTRACT FROM AUTHOR]

The reduction and elimination of maintenance-related waste is receiving increasing attention because of the negative effect of such waste on production costs. The overall goal of this research is to identify and prioritize factors that can be considered maintenance-related waste within the automotive manufacturing industry. Five manufacturing companies participated in a workshop to identify root causes of maintenance-related waste; 16 categories were found. The identified factors were heavily reliant on human factors as a root or major contributory cause at different levels affecting performance and productivity. For prioritization, the evidential reasoning (ER) approach which is one of the latest developments in multi-criteria decision-making is applied. A basic tree structure necessary for ER assessment is developed based on the workshop results as well as literature on human factors. Then, a survey on basic attributes at the lowest level of this tree is designed and performed at one of the companies participating in the workshop. The application of ER shows that, on an overall level, 'management condition' is in first order and 'maintainer condition' and 'working condition' are in second and third order respectively as the worst cases for creating maintenance-related waste. On the most delimited level 'inadequate resources' and 'weather/indoor climate' have the highest and lowest average scores respectively in ER ranking or prioritization. This methodology with its resulting ranking can be used as a tool to create awareness for managers seeking to reduce or eliminate maintenance-related waste. [ABSTRACT FROM AUTHOR]

Mathematically, each Pareto optimal point is an equally acceptable solution to a multiobjective optimization problem (MOP). However, the process of choosing a single preferred solution among all computed alternatives can be a difficult task because of a high cognitive effort required. Thus, the main objective of this work is to optimize the process of AISI H13 hardened steel turning with PCBN wiper tool using a robust multiple criteria decision making based on an entropic measure to choose the most preferred Pareto optimal point as the problem final solution. The responses of tool life ( T), surface roughness parameter ( Ra), and the ratio between material removal rate and cutting force ( MRR/ Fc) are modeled by using the response surface methodology (RSM), using as decision variables cutting speed ( Vc), feed rate ( f), and depth of cut ( d) . The normal boundary intersection (NBI) method along with the mixture design of experiments (MDE) are used to optimize these responses simultaneously. The optimal turning process conditions obtained were judged satisfactory since that parameter values for cost, quality, and productivity are acceptable. Moreover, the study was able to demonstrate that the weights used in the multiobjective optimization process influence the prediction variance. From the variability point of view, the final solution obtained is the robust one, since it leads to a region of minimum variance, less variability, and greater reliability. [ABSTRACT FROM AUTHOR]