Selection Of Flexible Manufacturing System Research Articles

DEA cross‐efficiency aggregation based on preference structure and acceptability analysis

AbstractAlthough a considerable amount of efforts have been made to overcome the drawbacks of traditional equal aggregation in the Data Envelopment Analysis cross‐efficiency evaluation, the endogenous preference structures in the cross‐efficiency matrix are still largely unexplored. We first untangle both preferential differences denoting the preference degrees among different Decision Making Unit (DMU), and preferential priorities denoting the favorite ranking of the DMUs. A revised cross‐efficiency matrix is formulated. We then apply the stochastic multicriteria acceptability analysis for group decision‐making method to obtain a set of holistic acceptability indices corresponding to different DMUs. Finally, we develop a satisfaction index to evaluate the satisfactory level of the proposed method. Two numerical examples with respect to the Flexible Manufacturing System selection problem and industrial robot selection problem are conducted to validate the proposed methodology. The superiority of our methodology is manifested in terms of an improvement of the overall satisfactory level as 18.69% and 8.48%, respectively.

Fast and meta heuristics for part selection in flexible manufacturing systems with controllable processing times

This study addresses a part selection problem for flexible manufacturing systems in which part processing times are controllable to optimize the total cost associated with energy consumption, operational performance, and so on. The problem is to determine the set of parts to be produced, part processing times and the number of tools for each tool type in each period of a planning horizon while satisfying processing time capacity, tool magazine capacity and tool life restrictions. The objective is to minimize the sum of part processing, earliness/tardiness, tool and subcontracting costs. Tool sharing among part types is also considered. After an integer programming model is developed, two types of solution algorithms are proposed, that is, fast heuristics useful when decision time is critical and variable neighborhood search algorithms when solution quality is important. Computational experiments were conducted on a number of test instances and the best fast heuristics are specified, together with reporting how much the variable neighborhood search algorithms improve the fast heuristics.

Interval valued multi criteria decision making methods for the selection of flexible manufacturing system

Interval valued multi criteria decision making methods for the selection of flexible manufacturing system

Entropi Temelli ROV Yöntemi İle Esnek Üretim Sistemi Seçimi

With increasing global competition, most manufacturing businesses turn to increase their production technologies to gain competitive advantage. Flexible manufacturing systems are also one of these production technologies. Flexible manufacturing systems have the capacity to produce a variety of products in a short time and in specified quantities. With the widespread use of advanced technology, manufacturing companies have begun using flexible manufacturing system to take advantage of benefits of this system. When evaluating or selecting of flexible manufacturing systems, many criteria, which are conflicted, should be considered. Hence, multi-criteria decision making methods are used in the evaluation or selection of flexible manufacturing systems. In this study, the flexible manufacturing systems will be selected with the Entropy based range of value (ROV) method. The objective weights of the criteria will be obtained by the Entropy method and alternatives will be ranked by the ROV method. Eight different alternatives are ranked with respect to their performance by using of this method. Since there is no study using ROV in Turkish literature, this study is thought to contribute to Turkish literature.

Investigating the Effect of Normalization Norms in Flexible Manufacturing Sytem Se-lection Using Multi - Criteria Decision - Making Methods

The main objective of this paper is to assess the effect of different normalization norms within multi-criteria decisionmaking (MADM) models. Three well accepted MCDM tools, namely, preference ranking organization method for enrichment evaluation (PROMETHEE), grey relation analysis (GRA) and technique for order preference by similarity to ideal solution (TOPSIS) methods are applied for solving a flexible manufacturing system (FMS) selection problem in a discrete manufacturing environment. Finally, by the introduction of different normalization norms to the decision algorithms, its effct on the FMS selection problem using these MCDM models are also studied.

Flexible manufacturing system selection using preference ranking methods : A comparative study

Flexible manufacturing systems (FMSs) offer opportunities for the manufacturers to improve their technology, competitiveness and profitability through a highly efficient and focused approach to manufacturing effectiveness. Justification, evaluation and selection of FMSs have now been receiving significant attention in the manufacturing environment. Evaluating alternative FMSs in the presence of multiple conflicting criteria and performance measures is often a difficult task for the decision maker. Preference ranking tools are special types of multicriteria decision-making methods in which the decision maker’s preferences on criteria are aggregated together to arrive at the final evaluation and selection of the alternatives. This paper deals with the application of six most potential preference ranking methods for selecting the best FMS for a given manufacturing organization. It is observed that although the performances of these six methods are almost similar, ORESTE (Organization, Rangement Et Synthese De Donnes Relationnelles) method slightly outperforms the others. These methods use some preference function or utility value or Besson ranking of criteria and alternatives, to indicate how much an alternative is preferred to the others. Most of these methods need quantification of criteria weights or different preference parameters, but ORESTE method, being an ordinal outranking approach, only requires ordinal data and attribute rankings according to their importance. Therefore, it is particularly applicable to those situations where the decision maker is unable to provide crisp evaluation data and attribute weights.

A novel 0-1 linear integer programming model and NSGA-II for dynamic machine tool selection and operation allocation problem in FMS

Flexible manufacturing systems (FMS) consist of some multi functional machines that are linked together through material-handling system and the whole of the system control by a central computer. One of the most important problems in production planning of FMS is machine tool selection and operation allocation problem that directly influences the production costs and times. Here, a novel 0-1 linear integer programming model is presented in such a way that each part and each tool can move during the production phase. The objective of this model is to determine a machine-tool combination for each operation of the part type. The objective function calculates some production costs, such as machining costs, setup costs, material-handling costs, tool movement costs and maximum transformation times. This paper uses an elitist multi-objective evolutionary algorithm based on the non-dominated sorting genetic algorithm-II (NSGA-II), for solving the dynamic machine tool selection and operation allocation problem in FMS. Experimental results indicate effectiveness of the proposed algorithm for solving the problem.

Evaluation and selection of flexible manufacturing systems using MACBETH method

Flexible manufacturing system (FMS) is a centrally controlled manufacturing unit consisting of numerically controlled machine tools and material handling equipment. Selection of an appropriate FMS is a complicated task as it involves huge capital investment. Due to tough competition in the globalised market, decisions regarding these investments become crucial for the survival of any manufacturing industry. Decision for selection of an FMS is observed to be based on numerous conflicting tangible and intangible criteria. In this paper, the application of measuring attractiveness by a categorical-based evaluation technique (MACBETH) is illustrated by solving two real time FMS selection problems. In this method, semantic scales are used for pair-wise comparison of performance of the alternatives and also to determine the criteria weights. Further, each category of semantic scales is converted to normalised quantitative values and ranking of FMS alternatives is obtained by the weighted mean method. Even though, MACBETH method is well known for qualitative performance measures, it is also capable to deal with quantitative performance measures. The derived FMS rankings are observed to be quite satisfactory and well acceptable to the decision makers.

The selection of flexible manufacturing system using preference selection index method

The selection of appropriate flexible manufacturing systems (FMSs) is one of the most critical decisions in the automated manufacturing industries. In this paper, a systematic and effective multi-criteria decision-making (MCDM) methodology is presented and implemented for the selection of appropriate FMS alternative. The proposed methodology is based on the preference selection index (PSI) method. In addition, the proposed method easily handles qualitative criteria involved in the decision-making process, and an appropriate flexible FMS alternative is selected without considering the relative importance between FMS selection criteria. Three different types of FMS selection problems are considered and examined to demonstrate, validate and check the reliability of the proposed method. The study has concluded that the FMS selection methodology based on PSI method is simple, logical and more appropriate for the FMS selection problems.

Evaluating flexible manufacturing systems using Euclidean distance-based integrated approach

A flexible manufacturing system (FMS) is designed to combine the efficiency of a mass-production line and the flexibility of a job shop to produce a variety of products on a group of machines. Even though a number of studies were proposed in the past to address the issue of evaluation and selection of FMSs, there is a need for a simple and systematic method to guide user organisations in taking a proper decision. In this paper, a new multiple attribute decision making (MADM) method, named as ‘Euclidean distance-based approach (EDBA)’, is presented for the selection of an FMS from a set of mutually exclusive alternatives. The results are compared with the results obtained by using other decision making methods. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. Three examples are presented to illustrate the potential of the proposed approach.

Flexible Manufacturing System selection based on grey relation under uncertainty

Flexible Manufacturing System (FMS) selection is a Multi-Criteria Decision-Making (MCDM) problem. To effectively select an FMS, several criteria need to be considered which may be objective or subjective. Objective criteria are quantitative (tangible) while subjective criteria are qualitative (intangible) in nature. As the assessment values have various types of vagueness, one cannot always use the classical decision-making techniques for such decision problems. Grey theory is one of the methods used to study uncertainty. In this paper, a fuzzy MCDM approach based on the concept grey theory has been proposed to systematically evaluate alternatives. A numerical example is used to illustrate the efficiency of the proposed approach.

A comment on “A comment on ‘A fuzzy DEA/AR approach to the selection of flexible manufacturing systems”’ and “A fuzzy DEA/AR approach to the selection of flexible manufacturing systems”

In a recent paper in this journal by Jahanshahloo et al. [Jahanshahloo, G. R., Sanei, M., Rostamy-Malkhalifeh, M., & Saleh, H. (2009). A comment on ‘A fuzzy DEA/AR approach to the selection of flexible manufacturing systems’. Computers & Industrial Engineering, 56(4), 1713–1714]. They correct the proof of the Proposition 1 for finding lower and upper bounds of the α -cut of E ∼ r appeared in the previous paper by Liu [Liu, S. T. (2008). Fuzzy DEA/AR approach to the selection of flexible manufacturing systems. Computers & Industrial Engineering, 54, 66–79]. However, we find that both of the above proofs omit the verification of constraints of assurance regions, which makes them incredible. In this paper, we correct the proof of the proposition.

Flexible manufacturing system selection using a combinatorial mathematics-based decision-making method

A methodology based on a combinatorial mathematics-based decision-making method is proposed for evaluation of alternative flexible manufacturing systems. The methodology is developed to judge the relative merits of different flexible manufacturing systems for the industrial application considered. The proposed ‘FMS selection index’ evaluates and ranks flexible manufacturing systems for a given FMS selection problem. Two examples are included to illustrate the approach.

Flexible Manufacturing System selection using an improved compromise ranking method

A Flexible Manufacturing System (FMS) is designed to combine the efficiency of a mass-production line and the flexibility of a job shop to produce a variety of products on a group of machines. Even though a number of studies were proposed in the past to address the issue of evaluation and selection of FMSs, there is a need for a simple and systematic method to guide user organisations in taking a proper decision. This is considered in this paper using an improved compromise ranking method in conjunction with Analytic Hierarchy Process (AHP). The proposed 'FMS Selection Index' (FMS-SI) evaluates and ranks FMSs for a given FMS selection problem. Two examples are included to illustrate the approach.

A comment on “A fuzzy DEA/AR approach to the selection of flexible manufacturing systems”

In a recent paper in this journal by Liu [Shiang-Tai Liu. (2008). A fuzzy DEA/AR approach to the selection of flexible manufacturing system. Computer & Industrial Engineering, 54, 66–76], he provided conditions to find lower and upper bounds of the α -cut of E ∼ r and then, by these conditions, he introduced two models. In this paper we correct the proof of his theorem and also, we correct the presented models for finding lower and upper bounds of the α -cut of E ∼ r .

A fuzzy DEA/AR approach to the selection of flexible manufacturing systems

Flexible Manufacturing System (FMS) offers opportunities for manufacturers to improve their technology, competitiveness, and profitability through a highly efficient and focused approach to manufacturing effectiveness. Data envelopment analysis (DEA) has been utilized as a multiple criteria tool for evaluation of FMSs. The concept of the assurance region (AR) is restricting the ratio of any two weights to some range to avoid the evaluated alternatives from ignoring or relying too much on any criterion in evaluation. In this paper, we develop a fuzzy DEA/AR method that is able to evaluate the performance of FMS alternatives when the input and output data are represented as crisp and fuzzy data. Based on Zadeh’s extension principle, a pair of two-level mathematical programs is formulated to calculate the lower and upper bounds of the fuzzy efficiency score of the alternatives. We transform this pair of two-level mathematical programs into a pair of conventional one-level DEA/AR method to evaluate the FMS performance. An example illustrates the application of the proposed methodology.

A decision-making framework model for evaluating flexible manufacturing systems using digraph and matrix methods

In the present work, a methodology based on digraph and matrix methods is developed for evaluation of alternative flexible manufacturing systems. A ‘flexible manufacturing system selection index’ is proposed that evaluates and ranks flexible manufacturing systems for a given industrial application. The methodology is illustrated by an example.

Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS

Due to the global competition in manufacturing environment, firms are forced to consider increasing the quality and responsiveness to customization, while decreasing costs. The evolution of flexible manufacturing systems (FMSs) offers great potential for increasing flexibility and changing the basis of competition by ensuring both cost effective and customized manufacturing at the same time. Some of the important planning problems that need realistic modelling and quicker solution especially in automated manufacturing systems have assumed greater significance in the recent past. The language used by the industrial workers is fuzzy in nature, which results in failure of the models considering deterministic situations. The situation in the real life shop floor demands to adopt fuzzy-based multi-objective goals to express the target set by the management. This paper presents a fuzzy goal programming approach to model the machine tool selection and operation allocation problem of FMS. An ant colony optimization (ACO)-based approach is applied to optimize the model and the results of the computational experiments are reported.

Tool allocation in flexible manufacturing systems with tool alternatives

In this paper, a heuristic approach for tool selection in flexible manufacturing systems (FMS) is presented. The proposed approach utilizes the ratio of tool life over tool size ( L/ S) for tool selection and allocation. The proposed method selects tool types with high L/ S ratios by considering tool alternatives for the operations assigned to each machine. The performance of the method is demonstrated in sample problems as static examples, as well as in a simulation study for further analysis. This study also presents a survey of several approaches related to loading and tool allocation problems in FMS, highlights the importance of tooling, and discusses the practical aspects of tool-oriented decision-making. An extended framework, which expands on the L/ S concept, is also presented.

Operation sequence and tool selection in flexible manufacturing systems under dynamic tool allocation

In order to adapt to a rapid changes of manufacturing orders, flexible manufacturing systems (FMS) advances into the direction that machines become further versatile functionally and that tools are controlled by fast tool delivery device. Versatile machine can perform a variety of operations when it is supplied with the required tools. Tool management is among the essential elements in the successful operation of the FMS studied in this research. In this research, we propose an integrated model that performs operation sequence and tool selection simultaneously into the direction that minimizes tool waiting time when the tool is absent. The effectiveness of the model is demonstrated through a series of simulation experiments, and interpretations of the results are also presented.