Machining of hardened work materials with appropriate levels of process parameters is still a burning issue in manufacturing sectors and challenging. It is because of pressing demand of surface quality which adversely affected by evolution of tool wear. Therefore the present investigation is undertaken to make a decision on parametric optimization of multi-responses such as flank wear and surface roughness during machining hardened AISI 52100 steel (55±1) steel using mixed ceramic insert under dry environment through grey relational analysis combined with Taguchi approach. Also predicted mathematical models of 1st and 2nd order have been developed for responses and checked for its accuracy. Second order mathematical model presented higher R2 value and represents best fit of the model and adequate compared to first order model. Model indicates good correlations between the experimental and predicted results. The proposed grey-based Taguchi methodology has been proved to be efficient for solving multi-attribute decision making problem as a case study in hard machining environment.

The present study endeavors to show an application of the multi objective optimization on the basis of ratio analysis (MOORA) method and technique for order performance by similarity to ideal solution (TOPSIS) method to select optimal process parameters in sheet hydroforming process. The right choice of the process parameters is critical to produce a final part with proper quality. In order to meet this characteristic, the important properties are the cup final thickness (FT), required forming force (FF) and radial stress (RS) at cup wall region. Nine alternatives for selecting the process parameters were taken into consideration based on Taguchi L9 orthogonal array. The limit drawing ratio (LDR), maximum pressure and prebulge pressure were selected as input variables. To solve the problem of process parameters’ selection, the two mentioned methods were used. A compromised weighting approach composed of Entropy and analytic hierarchy process (AHP) methods were used to weight all criteria. The alternatives ranking were performed using MOORA and TOPSIS methods and then the results were compared. The results achieved in both of the assessment represent that the alternative number 3, leads to the best multi performance features of the process among the 9 experiments. In this experiment LDR is 1.81, maximum pressure and prebulge pressure are 37 MPa and 15 MPa, respectively.

Recently, Symbiotic Organisms Search (SOS) algorithm is being used for solving complex problems of optimization. This paper proposes an Improved Symbiotic Organisms Search (I-SOS) algorithm for solving different complex unconstrained global optimization problems. In the improved algorithm, a random weighted reflective parameter and predation phase are suggested to enhance the performance of the algorithm. The performances of this algorithm are compared with the other state-of-the-art algorithms. The parametric study of the common control parameter has also been performed.

The selection of air compressor is a Multiple Criteria Decision Making (MCDM) problem including conflicting criteria and various alternatives. Selecting the appropriate air compressor is an important decision for the company as it affects the energy consumption and operating cost. To aid the decision making process in the companies, MCDM methods are proposed in the literature. In all MCDM methods, the main goal is to select the best alternative or to rank a set of given alternatives. In this paper, the air compressor is selected for a spinning mill of a textile company with an integrated approach based on MACBETH (Measuring Attractiveness by a Categorical Based Evaluation TecHnique) and COPRAS (COmplex PRoportional ASsessment) methods. MACBETH method is utilized to determine the weights of the criteria. Then COPRAS method is used to determine the ranking of the alternatives and select the best one.

As construction projects are becoming more deployed and more complicated at the same time, having an instrument for anticipation of success has become a primary requirement for every stakeholder. On this basis, several models have been introduced which implement different methods for anticipation of the entire goals or a series of goals of projects. In this research, at the first step, 16 criteria as instruments of anticipation of success and 33 factors as required instruments for obtaining success were extracted through library studies, semi-structured interviews and the Delphi method. At the next step, by having 169 questionnaires filled by senior managers of construction projects, the importance and priority of each of these 16 criteria and 33 factors for the initial phases of projects were determined according to Iran’s local conditions. Ultimately, through modeling of data by a propagation neural network including 35 hidden layers, the anticipator model for success of construction projects during their initial phases was developed with Performance and Regression. This model is able to anticipate the level of realization of projects’ success criteria according to the level of realization of success factors at the initial phase.

Cellular manufacturing is considered as a lean technique of producing similar parts using sells or groups of team members, workstations, or equipment to facilitate operations by removing setup and unnecessary cost components among various operations. Cell formation and layout planning are the most components of the cellular manufacturing. This paper presents a dynamic method to minimize different costs including the total cost of movements within and between cells and exceptional parts. In this study, the Hierarchical Genetic Algorithm (HGA) is used for solving the resulted model and the results are compared with genetic algorithm. The results have indicated that the proposed method could reach optimal solutions for some small and medium sized problems in reasonable amount of time.

Green supply chain management (GSCM) has become an emerging concept among the environmental management topics during the last few years. There are some pressures that push industries to adopt GSCM like: governmental regulations, tough market competition for green image, pressure from Non-governmental organizations, media pressures and other pressures for environmental actions. These pressures can be more considerable for special industries like mining and mineral industries, because of their activities which can cause more damages to environment. As it is not possible to respond to all of the pressures at the same time, identifying and ranking the most important pressures can be very useful for managers & apos; decisions. This study aims to identify and evaluate the pressures for GSCM adopting according to Iranian mining experts & apos; opinions by using grey methodology.

This study develops an effective method to measure value chain performance and rank them based on qualitative criteria and to determine the ranking order of the various forms of performance under study. This approach integrates the advantage of grey systems theory and TOPSIS to evaluate and rank value chain performance. Grey-TOPSIS approach has been applied to measure and rank the value chain performance of various firms. The results indicate that the proposed model is useful to facilitate multi-criteria decision-making (MCDM) problem under the environment of uncertainty and vagueness. The model also provides an appropriate ranking order based on the available alternatives. The Grey-TOPSIS approach that will be useful to the managers to use for solving the similar type of decision-making problems in their firms in the future has been discussed. Even though, the problem of choosing a suitable performance option is often addressed in practice and research, very few studies are available in the literature of Grey-TOPSIS decision models. Also, Grey-TOPSIS model application in the tea processing firms is non-existence hence this study is the very first to apply this model in evaluating value chain performance in the tea processing firms.

Suitable project manager has a significant impact on successful accomplishment of the project. Managers should possess such skills in order to effectively cope with the competition. In this respect, selecting managers based on their skills can lead to a competitive advantage towards the achievement of organizational goals. selection of the suitable project manager can be viewed as a multi-criteria decision making (MCDM) problem and an extensive evaluation of criteria, such as Technical skills, experience skills, Personal qualities and the related criteria must be considered in the selection process of project manager. The fuzzy set theory and MCDM methods appears as an essential tools to provide a decision framework that incorporates imprecise judgments and multi criteria nature of project manager selection process inherent in this process. This paper proposes the joint use of the Fuzzy DEMATEL (FDEMATEL) and Fuzzy VIKOR methods for the decision-making process of selecting the most suitable managers for projects. First, with the opinions of the senior managers based on project management competency model (ICB-IPMA), all the criteria required for the selection are gathered. Then the FDEMATEL method is used to prioritize the importance of various criteria and FVIKOR used to rank the alternatives in a preferred order to select the best project managers from a number of alternatives. Next, a real case study used to illustrate the process of the proposed method. Finally, some conclusions are discussed at the end of this study.

This paper analyzes a supply chain, which consists of a manufacturer, a retailer and several suppliers in which the retailer orders jobs to the manufacturer and the suppliers provide the requiring parts. The manufacturer schedules and processes the orders and dispatches them to the retailer either individually or collectively in batches. The manufacturer incurs a penalty cost for each tardy job and a transportation cost for every delivered batch and therefore, searches for a schedule that yields minimum number of tardy jobs and batches. Moreover, the manufacturer tries to optimize its supplying cost through locating the suppliers that offer appropriate release times and costs for manufacturing parts. Since the release times of parts directly affect scheduling of orders, in this research, we develop an integrated mathematical model for the manufacturer that incorporates suppliers & apos; selection issue into the scheduling and batching decisions. Furthermore, we present a heuristic algorithm (greedy algorithm) and also a local search to quickly determine the optimal or near-optimal solutions. The computational analysis shows the importance of the integrated model and also the superiority and effectiveness of the heuristic algorithms.