Robot selection using a fuzzy regression-based decision-making approach

Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously considered in a robust selection study. In this study, a decision model based on fuzzy linear regression is presented for industrial robot selection. Fuzzy linear regression provides an alternative approach to statistical regression for modelling situations where the relationships are vague or the data set cannot satisfy the assumptions of statistical regression. The results obtained by employing fuzzy linear regression are compared with those of earlier studies applying different analytical methods to a previously reported robot selection problem.     

A fuzzy clustering approach to manufacturing cell formation

Cell formation, one of the most important problems faced in designing cellular manufacturing systems, is to group parts with similar geometry, function, material and process into part families and the corresponding machines into machine cells. There has been an extensive amount of work in this area and, consequently, numerous analytical approaches have been developed. One common weakness of these conventional approaches is that they implicitly assume that disjoint part families exist in the data; therefore, a part can only belong to one part family. In practice, it is clear that some parts definitely belong to certain part families, whereas there exist parts that may belong to more than one family.

In this study, we propose a fuzzy c-means clustering algorithm to formulate the problem. The fuzzy approach offers a special advantage over conventional clustering. It not only reveals the specific part family that a part belongs to, but also provides the degree of membership of a part associated with each part family. This information would allow users flexibility in determining to which part family a part should be assigned so that the workload balance among machine cells can be taken into consideration. We have also developed a computer program to simplify the implementation and to study the impact of the model's parameters on the clustering results.     

Machine loading and part type selection in flexible manufacturing systems

This paper presents a new approach to the loading problem in flexible manufacturing systems. It focuses on the existence of alternatives routes for each part type. Also, the optimal number of copies of each tool type to be loaded into each tool magazine is directly determined. Thus, the decision variables are the routing mix and the tool allocation. The loading objective is to balance machine workloads. Constraints on the number of available tools and on tool magazine capacities can be imposed. The problem is modelled as a mixed-integer linear program. Also, an extension of the model is formulated that includes part type selection.     

A due date-based approach to part type selection in flexible manufacturing systems

Nearly all of the literature on part type selection for flexible manufacturing systems has focused on maximizing throughput. We present an approach for part type selection in which meeting due dates is the primary objective. The approach is based on the idea of using information from the solution of an approximate, aggregate scheduling problem as the basis for determining release priorities. The need for the approximation and aggregation arises because the exact machine configuration (partitioning of identical machines into groups, and loading of tools) cannot be decided until the parts are selected. We develop and evaluate several different policies for setting release priorities in a context where the approximate, aggregate schedule is constructed using list scheduling (dispatching) rules. The results indicate that using information from such a schedule to set release priorities performs far better than using simpler procedures.     

Evaluating flexible manufacturing systems using a combined multiple attribute decision making method

This paper presents a logical procedure to evaluate alternative flexible manufacturing systems for a given industrial application. The procedure is based on a combined multiple attribute decision making method using TOPSIS and AHP methods together. A ‘flexible manufacturing system suitability index’ is proposed that evaluates and ranks flexible manufacturing systems for the given industrial application. The methodology is illustrated by means of an example.     

A novel multiple attribute material selection approach with uncertain membership linguistic information

In engineering design, the decision to select an optimal material has become a challenging task for the designers, and the evaluation of alternative materials may be based on some multiple attribute decision making (MADM) methods. However, the current methods for material selection may induce the information losing and cannot represent the real preference of decision maker precisely. Therefore, in this paper, inspired by the idea of the intuitionistic linguistic variables, we define a new fuzzy variable called uncertain membership linguistic variable (UMLV) which composes two linguistic variables and membership degrees of elements to the linguistic variables. Meanwhile, the operational laws, score function, accuracy function and comparison rules of the UMLV are defined. Then, some aggregation operators are developed for aggregating the uncertain membership linguistic information such as the uncertain membership linguistic weighted average (UMLWA) operator, the uncertain membership linguistic weighted geometric (UMLWG) operator, the uncertain membership linguistic ordered weighted average (UMLOWA) operator and the uncertain membership linguistic ordered weighted geometric (UMLOWG) operator, and some desirable properties of these operators are discussed. Based on the proposed operators, an approach is proposed for material selection problems under uncertain membership linguistic environment. Finally, two numerical examples for material selection are given to illustrate the application of the proposed approach.     

Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives

Considering the high required capital outlay and moderate risk of a flexible manufacturing system (FMS) investment, economic justification techniques are insufficient by themselves since they cannot cope with the benefits such as flexibility and enhanced quality offered by advanced manufacturing technologies. A robust decision-making procedure for evaluating FMS requires the consideration of both economic and strategic investment measures. A distance-based fuzzy multicriteria decision-making (MCDM) framework based on the concepts of ideal and anti-ideal solutions is presented for the selection of an FMS from a set of mutually exclusive alternatives. The proposed method provides the means for integrating the economic figure of merit with the strategic performance variables. The multicriteria decision approach presented here enables us to incorporate data in the forms of linguistic variables, triangular fuzzy numbers and crisp numbers into the evaluation process of FMS alternatives. Linguistic variables are also used to indicate the criteria's importance weights assigned by the decisionmakers. A comprehensive example illustrates the application of the multicriteria decision analysis.     

Multiple attribute decision-making solution to material selection problem based on modified fuzzy axiomatic design-model selection interface algorithm

Material selection, one of the mostly encountered decision problems in material science literature, is still an onerous task for manufacturing organisations. Achieving accurate solution to this issue, the paper developed a model selection interface to enable analytical solutions to different problem concepts in material selection under multiple attribute decision-making (MADM) environments. Specifically, the generic framework of the fuzzy axiomatic design-model selection interface (FAD-MSI) is modified and successfully applied to the different material selection problem concepts. Consequently, the derived problem-model sets can be referred to accomplish further proposals on this era.     

Operation allocation and materials-handling system selection in a flexible manufacturing system: A sequential modelling approach

Materials-handling systems are an integrating component of manufacturing operations and as such must be considered within a common framework in manufacturing systems design. This work proposes a first approach to the simultaneous consideration of the operation allocation and the materials-handling system selection problems in a flexible manufacturing system. The objective of the operation allocation model is to select a group of machines where the operations of the part types will be performed and then to assign those operations to the selected machines. The operation allocation model interfaces with the materials-handling system selection model by providing input data in the form of the manufacturing operations to be performed at each machining centre. The selection of the materials-handling system is centred on the matching of the parts visiting a machining centre to perform a manufacturing operation and the abilities of the handling equipment to perform the required materials handling functions of those part types. The objective is to select an optimal group of materials-handling equipment to be assigned to a cell.     

Maintenance policy selection in manufacturing firms using the fuzzy MCDM approach

In manufacturing firms, there is a critical need for proper maintenance of manufacturing facilities. The maintenance process enhances customer satisfaction and reliability of the products, and increases the profit of the manufacturer. Therefore, a proper maintenance policy selection is a critical issue for manufacturers, as an inefficient maintenance policy affects not only the direct cost of the firm but also the other aspects. In the present study, maintenance policy selection at the level of the firm rather than the equipment level is shown, and for selection various criteria have been identified. The presented work not only provides the best alternatives but also provides an alternative ranking, which facilitates decision-makers in choosing alternatives according to their constraints. These selection criteria are different in nature, as some give a crisp value, whereas others are defined in linguistic terms. To select the appropriate maintenance policy, a distance-based fuzzy multicriteria decision-making (MCDM) approach has been employed. The proposed method provides the means for integrating the economic figure of merit with the strategic performance variables. The MCDM approach is efficient in incorporating data, in the form of linguistic variables, triangular fuzzy numbers, and crisp numbers, into the evaluation process of maintenance policy alternatives. A comprehensive example illustrates the application of the distance-based fuzzy MCDM approach.     

Material selection for thin-film solar cells using multiple attribute decision making approach

This paper presents a material selection approach for selecting absorbent layer material for thin-film solar cells (TFSCs) using multiple attribute decision making (MADM) approach. In this paper, different possible materials for absorbent layer and their properties like band gap, absorption coefficient, diffusion length, thermodynamic compatibility and recombination velocity is taken into consideration and MADM approach is applied to select the best material for thin-film solar cells. It is observed that Copper Indium Gallium Diselinide (CIGS) is the best material for the absorbent layer in thin-film solar cells out of all possible candidates. It was observed that the proposed result is in accordance with the experimental findings thus justifying the validity of the proposed study.     

A scheduling approach for a flexible manufacturing system

This paper discusses the scheduling problem of a particular flexible manufacturing system (FMS). The two main components of the FMS are a CNC turret lathe and a CNC machining centre. In the system a wide range of different jobs has to be processed. Each job consists of one or more processing operations on one or both machines. Important characteristics of the scheduling problem are sequence-dependent change-over times (on the turret lathe) and transfer times (on both machines and between the machines). The change-over times are caused by the need to exchange tools in the turret when a new part is going to be processed. The transfer times reflect the time needed to perform manual transportation and clamping activities between two subsequent processing (machining) operations of a part. In this paper a branch and bound algorithm is described based on an active schedule strategy. Solutions are compared to results obtained by a simple dispatching rule     

Measuring manufacturing competence: a fuzzy approach

Over the last decade a large body of literature has pointed out the crucial role played by manufacturing in the achievement of the overall goals of a company. Some authors have attempted to summarise this role by introducing the concept of production competence: it measures a manufacturer's ability to support and implement a product-market specific business strategy and is calculated on the basis of current manufacturing performances. Such a measure of production competence can be associated with a short term perspective, as it disregards the future evolution of manufacturing performance. In fact, in many of today's industries, characterised by high turbulence, the current manufacturing performances could not be representative of the future ones and, consequently, of the manufacturing's overall ability to support the company's strategy in the long term. The purpose of this article is twofold: (1) to propose a new framework of production competence (which we will refer to as manufacturing competence), based on manufacturing critical capabilities and infrastructural resources, that considers also the potential of manufacturing for improving the company's performance in the future; and (2) to suggest a consistent measurement framework, derived from the fuzzy ses theory.     

Multi-agent-based approach to solve part selection and task allocation problem in flexible manufacturing systems

Agent technology is currently being considered as an important approach for developing intelligent manufacturing systems. It offers a new way of thinking about many of the classical problems in manufacturing engineering. A multi-agent-based approach for solving the part allocation problems in flexible manufacturing systems (FMS) is presented that can easily cope with the dynamic environment. Four agents were involved in carrying out the tasks of allocating parts on different machines: communicator, machine, part and material handling device (MHD). Upon arrival in the manufacturing facility, the part informs the communicator agent about the task requirements. The communicator agent divides the task into subtasks and sends a call-for-bids message to the machine and MHD agents. Each machine responds in accordance with its process capabilities and buffer limit. This response may be for the whole task or for one or more subtasks and it contains the price and cost details for these subtasks along with the performance index and acceptance ratio of the machine. The final allocation is made based on the objective function that includes processing and transportation costs and time. An algorithm is presented that is used by the communicator agent for allocating parts to different machines. An illustrative example is given to solve the task allocation on five machines, with each machine having different performance index and acceptance ratio.     

Part selection policy for a flexible manufacturing cell feeding several production lines

Analysis of the part selection policy in the case of a flexible manufacturing cell producing different parts for several emanating lines is presented. The discrete response control problem is formulated as an undiscounted semi-Markovian decision problem whose decision epochs occur when the cell completes a part and must decide what type of part to fabricate next. Design objective is to minimize the expected shortage penalty per unit time incurred by the production lines when they run out of inputs. Optimal production policies are characterized, and a discussion of related industrial implementation issues is given.     

A fuzzy approach to process plan selection

Process planning is the systematic determination of the detailed methods by which parts can be manufactured from raw material to finished product. In a real manufacturing environment, usually several different parts need to be manufactured in a single facility sharing constrained resources. The existence of alternative process plans for each part makes the selection of process plan a very important issue in manufacturing. The objectives in process plan selection might be imprecise and conflicting. In this paper, a fuzzy approach is used to deal quantitatively with the imprecision of the process plan selection problem. Each process plan is evaluated and its contribution to shopfloor performance is calculated using fuzzy set theory. A progressive refinement approach is used to first identify the set of process plans that maximize the contributions, and then consolidate the set to reduce the manufacturing resources needed.     

A study of scheduling rules of flexible manufacturing systems: A simulation approach

This study examines the effects of scheduling rules on the performance of flexible manufacturing systems (FMSs). Several machine and AGV scheduling rules are tested against the mean flowtime criterion. In general, scheduling rules are widely used in practice ranging from direct applications as a stand-alone scheduling scheme to indirect application as a part of complicated scheduling systems. In this paper, we compare the rules under various experimental conditions by using an FMS simulation model. Our objective is to measure sensitivity of the rules to changes in processing time distributions, various levels of breakdown rates, and types of AGV priority schemes. A comprehensive bibliography is also presented in the paper.     

A branch-and-bound approach for machine selection in just-in-time manufacturing systems

Equipment selection issues are very important in the early stages of implementation of just-in-time (JIT) manufacturing systems. This paper addresses the problem of determining the number of machines for each stage of a JIT system by minimizing production, imbalance and investment costs. The problem is modelled as a mixed-integer nonlinear optimization program and a branch-and-bound algorithm is developed for its solution. This algorithm guarantees the global optimum of the problem and is enhanced by simple, yet very effective, upper bounding heuristics. The solutions obtained by the developed branch-and-bound approach are compared to solutions that have appeared in the literature using heuristic approaches. The comparisons indicate that the proposed algorithm leads to significant economic savings, averaging 17% on a set of problems from the literature. The paper also considers the application of the algorithm to large-scale, industrially-relevant, problems with up to 10 stages and 200 products. Even for the largest of these problems, the search for the integer optimum requires modest computational times. This demonstrates the potential practical impact of the proposed methodology.     

A new DEA common-weight multi-criteria decision-making approach for technology selection

This paper addresses an advanced manufacturing technology selection problem by proposing a new common-weight multi-criteria decision-making (MCDM) approach in the evaluation framework of data envelopment analysis (DEA). We improve existing technology selection models by giving a new mathematical formulation to simplify the calculation process and to ensure its use in more general situations with multiple inputs and multiple outputs. Further, an algorithm is provided to solve the proposed model based on mixed-integer linear programming and dichotomy. Compared with previous approaches for technology selection, our approach brings new contributions. First, it guarantees that only one decision-making unit (DMU) (referring to a technology) can be evaluated as efficient and selected as the best performer while maximising the minimum efficiency among all the DMUs. Second, the number of mixed-integer linear programs to solve is independent of the number of candidates. In addition, it guarantees the uniqueness of the final optimal set of common weights. Two benchmark instances are used to compare the proposed approach with existing ones. A computational experiment with randomly generated instances is further proceeded to show that the proposed approach is more suitable for situations with large datasets.     

An integrated fuzzy approach for the selection of outsourcing manufacturing partners in pharmaceutical R&D

The selection of the outsourcing manufacturing partners (OMPs) is an important issue for research and development (R&D) in the pharmaceutical industry. The selection process considers several main criteria, as well as a few sub-criteria for each main criterion. Such problems can be formulated as hierarchical structures and can usually be resolved with multi-criteria decision-making approaches. This paper presents an integrated fuzzy approach for selecting a suitable OMP in pharmaceutical R&D. In the integrated approach, fuzzy concepts are used for decision-makers’ subjective judgments to reflect the vague nature of the selection process. Fuzzy AHP and fuzzy TOPSIS are included in the integrated approach. Fuzzy AHP is used to determine the fuzzy weights of criteria and sub-criteria because it can effectively determine various criteria's weights in a hierarchical structure. Fuzzy TOPSIS aims to find the OMP with the best performance with respect to the sub-criteria. We exemplify the integrated approach using a numerical application to demonstrate its feasibility.