Bijan Sarkar

Integrating AHP with QFD for robot selection under requirement perspective

Selection of a robot is an important task, as improper selection may adversely affect a firms production by reducing the quality of the product, thereby reducing productivity as well as profitability. To effectively select a robot for a specified job, several factors have to be considered. The objective of this paper is to explain how, using a combined AHP/QFD model, the authors are able to determine if the deployment of robots in industry helped in performance enhancement from requirement perspective. Incorporating a simple and novel cost factor measure in the proposed integrated AHP/QFD model aids justification of the implementation of a robotic system in a manufacturing firm from an economic point of view also. The proposed integrated approach also identifies technical requirements followed by customer requirements. In this paper, an integrated model combining AHP and QFD has been delineated for the industrial robot selection problem. Seven technical requirement factors have been considered for the case study.

Distance-based consensus method for ABC analysis

A distance-based multi-criteria consensus framework on the concepts of ideal and negative-ideal solutions is presented for the ABC analysis of inventory items. This article demonstrates a way of classifying inventory items using the TOPSIS (‘Technique for Order Preference by Similarity to Ideal Solution’) model. The methodology has been applied in a pharmaceutical company located in the heart of Kolkata, India. The technique takes into account various conflicting criteria having incommensurable units of measurement. Unit cost, lead time, consumption rate, perishability of items and cost of storing of raw materials have been considered for the case study. By using TOPSIS, the items are ranked in categories A, B and C. The suitability, practicability and effectiveness of the TOPSIS method used in ABC classification have been judged using the analysis of variance (ANOVA) technique. A simulation model has been used to compare the proposed model with that of the traditional ABC classification technique.

Detection of level of satisfaction and fuzziness patterns for MCDM model with modified flexible S-curve MF

The present research work deals with a logistic membership function (MF), within non-linear MFs, in finding out fuzziness patterns in disparate level of satisfaction for Multiple Criteria Decision-Making (MCDM) problem. This MF is a modified form of general set of S-curve MF. Flexibility of this MF in applying to real world problem has also been validated through a detailed analysis. An example illustrating an MCDM model applied in an industrial engineering problem has been considered to demonstrate the veracity of the proposed technique. The approach presented here provides feedback to the decision maker, implementer and analyst and gives a clear indication about the appropriate application and usefulness of the MCDM model. The key objective of this paper is to guide decision makers in finding out the best candidate-alternative with higher degree of satisfaction with lesser degree of vagueness under tripartite fuzzy environment.

The TOC-Based Algorithm for Solving Multiple Constraint Resources

The theory of constraints (TOC) emphasizes the exploitation of resource constraints in order to increase the product throughput of an organization. The product-mix decision is one application of the five steps in the TOC. This paper considers an integrated heuristic model comprising the analytic hierarchy process (AHP) and the TOC in a decision model in which priority of product and resource center optimizes the product throughput in a multiple constraint resource environment. An AHP component allows the decision maker to incorporate tangible and intangible criteria into the decision-making process and use the priority rankings of the AHP to represent a measure of value in the TOC model. The TOC identifies the constraints and the product ranking of the AHP maximizes the product throughput in a multiple constraint resource environment. The methodology incorporates sensitivity analysis to provide the decision maker with additional information regarding the robustness of the model so that he or she can make a better decision. The model compares three alternatives: the standard TOC, Integer Linear Programming (ILP), and our own solution. The numerical result shows that the proposed approach is superior to TOC and to ILP analysis and provides a measure of the models performance.

Application of an adaptive neuro-fuzzy system for the prediction of cotton yarn strength from HVI fibre properties

Abstract This paper presents the application of a hybrid neuro-fuzzy system for the prediction of cotton yarn strength from HVI fibre properties. The proposed system possesses the advantages of both artificial neural networks and fuzzy logic, and is thus more intelligent. HVI fibre test results are used to train the neuro-fuzzy inference system and its prediction performance is compared with those of artificial neural network and regression models. The prediction accuracy of the proposed neuro-fuzzy system is superior to that of a conventional multiple regression model and comparable with an artificial neural network model.

Selection of opencast mining equipment by a multi-criteria decision-making process

Sophisticated, capital-intensive, high-capacity earthmoving machinery is now being used in opencast coal mines to meet demand in the face of increasing pressure from competitors. The selection of equipment for coal extraction and overburden removal is a determining factor in the viability and profitability of an opencast operation, seeing that extraction and haulage account for 50–70% of the total costs.1 Mines can achieve the targeted production at the minimum unit cost and gain a competitive edge through selection of the most appropriate equipment. The selection of equipment for mining applications is not a well-defined process and because it involves the interaction of several subjective factors or criteria, decisions are often complicated and may even embody contradictions. Traditionally, procurement costs become elevated through a system of public tendering to appear as the primary criterion and the major costs of looking after the equipment during its useful life are not taken into account.2,3 The cheapest procurement, however, is not always the best and the most economic approach. Life-cycle cost (LCC) analysis helps mine management to justify equipment selection on the basis of the total costs over its useful life rather than the initial purchase price. Rao and co-workers4 and Sharma5 have presented accounts of the methodology for mining equipment selection through LCC analysis. LCC analysis again considers only the cost parameters of similar equipment and other parameters are either predetermined or not considered. Various types of cost model have been proposed for applicaton to the selection of mining equipment.6–9 Hrebar6 and Sevim and Sharma7 used net present-value analysis for selection of a dragline and surface transportation system. Use of a linear breakeven model has been proposed.8 Models for equipment selection and evaluation described by Celebi9 were aimed at selection of the equipment fleet on the basis of minimizing the unit stripping cost and maximizing production. Linear programming10 and decision-making tools11 may be applied. General guidelines and a survey related to the selection of surface mining equipment were discussed by Martin et al.12 and Srajer et al.13 and Chanda14 reviewed the fundamental concepts of equipment selection. Erdem and coworkers presented an extended bench model by means of which the optimal dragline selection may be made.15 Hall et al.16 illustrated how reliability analysis can provide mine management with quantitative information of value for decision-making about surface mining equipment. Some researchers1,17,18 have advocated the use of knowledge-based expert systems. The application of modelling in the selection of a suitable equipment fleet was discussed by Sturgul and Jacobson19 and simulation in the context of selecting an ore haulage system was reported by Lebedev and Staples.20 Most of these decision-making tools either rely on objective input data, with little or no subjective judgement, or focus on a single parameter. Multi-criteria decision-making (MCDM) techniques, such as the Analytical Hierarchy Process (AHP), can, however, be very useful in encompassing several subjective criteria with conflicting objectives to arrive at an eclectic decision. Whereas AHP is well-established as an operations research technique for decision-making in engineering applications,21–24 there has been a dearth of development and application to mining problems. A method of selecting heavy earthmoving machinery for opencast mining use has now been developed on the basis of AHP and is presented here.

A fully fuzzified, intelligent theory-of-constraints product-mix decision

The present research work outlines a fuzzified approach using fuzzy linear programming (FLP) using a suitably designed smooth logistic membership function (MF) for finding fuzziness patterns at disparate levels of satisfaction for theory of constraints-based (TOC) product-mix decision problems. The objective of the present work is to find fuzziness patterns of product-mix decisions with disparate levels of satisfaction of the decision-maker (DM). Another objective is to provide a robust, quantified monitor of the level of satisfaction among DMs and to calibrate these levels of satisfaction against DM expectations. Product-mix decision should take into account considerations such as the DMs level of satisfaction (sometimes called ‘emotions’) in order to make the decision a robust one. Sensitivity of the decision has been focused on a bottleneck-free, optimal product-mix solution of a TOC problem. The inefficiency of traditional linear programming (LP) in handling multiple-bottleneck problems using TOC is discussed using an illustrative example. Relationships among the degree of fuzziness, level of satisfaction and the throughput of modified TOC guide decision-makers (DM) under tripartite fuzzy environment in obtaining their product-mix choice trading-off with a pre-determined allowable fuzziness.

MCA BASED PERFORMANCE EVALUATION OF PROJECT SELECTION

Multi-criteria decision support systems are used in various fields of human activities. In every alternative multi-criteria decision making problem can be represented by a set of properties or constraints. The properties can be qualitative & quantitative. For measurement of these properties, there are different unit, as well as there are different optimization techniques. Depending upon the desired goal, the normalization aims for obtaining reference scales of values of these properties. This paper deals with a new additive ratio assessment method. In order to make the appropriate decision and to make a proper comparison among the available alternatives Analytic Hierarchy Process (AHP) and ARAS have been used. The uses of AHP is for analysis the structure of the project selection problem and to assign the weights of the properties and the ARAS method is used to obtain the final ranking and select the best one among the projects. To illustrate the above mention methods survey data on the expansion of optical fibre for a telecommunication sector is used. The decision maker can also used different weight combination in the decision making process according to the demand of the system .

Determination of quality value of cotton fibre using hybrid AHP-TOPSIS method of multi-criteria decision-making

Abstract This paper presents a new multi-criteria decision-making approach to determine the quality value of cotton fibre. Major cotton fibre properties were considered and their relative importance or weights were determined by a typical pair-wise comparison method. Cotton fibres were ranked according to their relative closeness with respect to the best and worst possible alternatives. This ranking was compared with the ranking of yarn tenacity in two different yarn counts. It was found that there is significant agreement between the two rankings.

Forecasting of engineering manpower through fuzzy associative memory neural network with ARIMA: a comparative study

Abstract The smooth working of industry depends on the availability of proper engineering manpower. If proper qualified and experienced technical personnel are not available, the industry cannot run in the most efficient way. Here, an effort is made to assess the engineering manpower requirement (personnel belonging to mechanical engineering) in certain industry group (steel manufacturing) in the state of West Bengal in India for the next 5 years. The method of auto regressive integrated moving average (ARIMA) and the fuzzy associative memory (FAM) neural network model are tested and based on error analysis (calculation of average error) the model with minimum error is selected and used for assessment of futuristic engineering manpower. Certain statistical functions, i.e. regression analysis using a least square technique based on linear, exponential, curvilinear (parabolic) equations and the tables of Orthogonal Polynomial are applied on the estimated data value calculated earlier. The particular statistical model is chosen based on the average error of estimated date generated using statistical models with the actual data over span of years. The said statistical model based on the estimated data using the selected model of ARIMA or FAM neural network can be used for the generation of futuristic forecasted engineering manpower.