Sanat Kumar Mukherjee

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.

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.

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.

RELIABILITY ANALYSIS OF SHOVEL MACHINES USED IN AN OPEN CAST COAL MINE

Performance of a mining machine depends on reliability of the equipment used and other factors. Now the machine reliability is not only considered to be a major performance barometer but also an integral part of an engineering design. So machine reliability study is necessary for equipment design and modification. In this paper, shovel machine has been divided into six subsystems. Trend test and serial correlation test have been done for three shovel and their subsystems from failure data. The parameters of three idealised probability distributions, namely exponential, lognormal, Weibull distribution have been estimated. An investigation has also been made to determine which of these distributions do best fit for characterising failure pattern of shovels and their subsystems. Reliability of a shovel and its subsystems have been estimated at different mission time with taking their best fit distribution. Non-independently and identically distributed (non-iid) data set have been treated by power law process. Some aspects of failure behaviour of machine are analysed briefly for ongoing machine improvement.

Reliability assessment of hydraulic shovel system using fault trees

Abstract The reliability of a hydraulic shovel system has been assessed through the application of fault-tree methodology. Fault-tree analysis assists in the evaluation of the reliability of a system or alternative designs and identifies critical components for system success and potential causes of system failure. It uses deductive reasoning to identify system failure. A reliability block diagram and fault tree of the shovel system were developed. An algorithm for obtaining the minimum cut set as well as the minimum path set from the fault tree is presented and the reliability of the machine and its sub-systems with time is evaluated. The analysis indicates room for improvement in different aspects of the machine.

Virtual manufacturing of gears with chip formation

The manufacturing process of gears is fairly complicated owing to the presence of various simultaneous motions of the cutter and the job. In this paper, an attempt is made to simulate the spur and helical-gear manufacturing process, including the chip formation, in a virtual manufacturing environment. The user has the option to manipulate the various parameters necessary for the manufacturing operation. The integrated process may also help to develop an optimised product. For better understanding of the operational principle, an animation facility in the form of a movie is included in the package.

Performance Evaluation of a Dragline Machine in a Surface Mine

Abstract Performance of a dragline machine depends on reliability, availability, maintainability(RAM) characteristics of the machine, working environment and other techno-managerial decisions. To improve the performance of the dragline, therefore, different parameters of its operational behaviour such as utilisation, idle time, availability breakdown etc must be understood and analysed. This paper discusses the operational behaviour of a dragline machine using Markov model. The applied model was compared with actual statistical data available from mine operation. Appropriate conclusion has been highlighted on the basis of the analysis.