This paper presents our research works on integrating disassembly sequence planning with cost model for end-of-life (EOL) product. This paper has two objectives. The first objective is to optimize disassembly sequence of the EOL product. We integrate a traveling salesman problem approach with genetic algorithm in finding the optimal disassembly sequence for disassembling the EOL product. Based on this optimal sequence, the second objective is to identify the best EOL option. We employ EOL profits and net present value of parts and subassemblies of the EOL product to determine the best EOL option of components and parts of the EOL product. The predicted results showed that the developed cost model has reached a good correspondence with the established methods.

In this paper, a single facility centre location problem with a line barrier, which is uniformly distributed on a given horizontal route in the plane is proposed. The rectilinear distance metric is considered. The objective function minimizes the maximum expected barrier distance from the new facility to all demand points in the plane. An algorithm to solve the desired problem is proposed where a mixed integer nonlinear programming needs to be solved. The proposed model of this paper is solved using some already existed benchmark problem in the literature and the results are compared with other available methods.

Supplier evaluation and selection has been a vital issue of strategic importance for long time. Different multi-criteria decision making (MCDM) approaches have been proposed by the researchers in past, to solve the supplier evaluation and selection problem. In this paper, we present a review of various MCDM methodologies reported in the literature for solving the supplier evaluation and selection process. The review is solely based on sixty-eight research articles, including eight review articles in the academic literature from 2000 to 2011. We try to find out the most prevalent approach in the articles and thereby present the future scope of arriving at an optimal solution to the problem, based on the specifications, the strategies and the requirements of the buyers. The study presents that with the change in processes and the requirements, how the approach of the manufacturing industry has shifted from striving for operational effectiveness to the strategic partnership in the dyadic relationship.

In the current global economic scenario, inflation plays a vital role in deciding optimal pricing of goods in any business entity. This paper develops a two-echelon (manufacturer-buyer) supply chain model taking into account inflation and time value of money. The present value of the total cost of the supply chain is derived when the manufacturer produces a number of lots, the sum of which is equal to the buyer’s total demand over a finite time horizon and the manufacturer’s each production lot is delivered to the buyer in n shipments. The optimal solution of the model is obtained for a numerical example after some adjustments (required to exhibit feasibility) in the derived solution. Sensitivity analysis is also carried out in order to examine the effects of changes in model-parameters on the optimal solution.

This paper presents the experimental study, development of mathematical model and parametric optimization for surface roughness in turning D2 steel using TiN coated carbide insert using Taguchi parameter design and response surface methodology. The experimental plan and analysis was based on the Taguchi L27 orthogonal array taking cutting speed (v), feed (f) and depth of cut (d) as important cutting parameters. The influence of the machining parameters on the surface finish has also been investigated and the optimum cutting condition for minimizing the surface roughness is evaluated. The optimal parametric combination for TiN coated cutting insert is found to be v3-f1-d3. The ANOVA result shows that feed the most significant process parameter on surface roughness followed by depth of cut. The cutting speed is found to be insignificant from the study. The RSM model shows good accuracy between predicted values and experimental values with 95% confidence intervals and adequate. It is concluded that the developed RSM model can be effectively utilized to predict the surface roughness in turning D2 steel.

Industrial robots are mainly employed to perform repetitive and hazardous production jobs, multi-shift operations etc. to reduce the delivery time, improve the work environment, lower the production cost and even increase the product range to fulfill the customers’ needs. When a choice is to be made from among several alternative robots for a given industrial application, it is necessary to compare their performance characteristics in a decisive way. As the industrial robot selection problem involves multiple conflicting criteria and a finite set of candidate alternatives, different multi-criteria decision-making (MCDM) methods can be effectively used to solve such type of problem. In this paper, ten most popular MCDM methods are considered and their relative performance are compared with respect to the rankings of the alternative robots as engaged in some industrial pick-n-place operation. It is observed that all these methods give almost the same rankings of the alternative robots, although the performance of WPM, TOPSIS and GRA methods are slightly better than the others. It can be concluded that for a given industrial robot selection problem, more attention is to be paid on the proper selection of the relevant criteria and alternatives, not on choosing the most appropriate MCDM method to be employed.

The paper discusses about the effect of outliers and trends on the response surface design fitted to the experiments results. The common way to analyze the response surface is to fit the polynomial regression to the response variable by ordinary least square method and to find the significant controllable variables by ANOVA. In this case, the outliers can have confusing effect on the regression model, which derives the experiment results and lead to wrong interpretation of the data. The proposed moving average iterative method (MAIW) of this paper is a robust approach to decrease the effect of these faulty points by considering the previous data to detect the outliers or detect the probable trends in residuals. The iterative weighting method is used to estimate the coefficients of the regression model and a numerical example illustrates the proposed approach.

One of the primary issues in line balancing problems is the uncertainty associated with the processing times. There are different reasons for having uncertain processing times such as task deterioration, failure in machines, etc. On the other hand, there are different objectives, such as cycle time, number of workstations in an assembly line balancing. In this paper, we present a multi-objective decision making assembly line balancing which minimizes different objectives such as cycle time and number of workstations. The resulted problem is formulated based on Lp-norm mixed integer programming and a meta-heuristic approach is also presented to solve the resulted model. The problem formulation is solved for some test examples and the results are discussed under different conditions.

The XPRESS project introduces a completely new scalable concept of a manufactronic networked factory, which is composed by a co-ordinated team of specialized autonomous objects (Manufactrons), each knowing how to do a certain process optimally. This knowledge based concept integrated the complete chain: production configuration (decrease of ramp-up time of at least 50%), multi-variant production line (varying types and volumes on a single line) and 100% quality monitoring. The manufactronic networked architecture allows continuous process improvement, and will be able to anticipate and to respond to rapidly changing consumer needs, producing high-quality products in adequate quantities while reducing costs. This concept is demonstrated in the automotive, aeronautics and electrical industry but can be transferred to nearly all production processes.

This study presents optimal ordering policies for retailer when supplier offers cash discount and two progressive payment schemes for paying of purchasing cost. If the retailer pays the outstanding amount before or at first trade credit period M, the supplier provides r_1cash discount and does not charge any interest. If the retailer pays after M but before or at the second trade period N offered by the supplier, the supplier provides r_2 cash discount and charges interest on unpaid balance at the rate ?Ic?_1 . If retailer pays the balance after N, (N & GT; M) then the supplier does not provide any cash discount but charges interest on unpaid balance at the rate ?Ic?_2. The primary objective of this paper is to minimize the total cost of inventory system. This paper develops an algebraic approach to determine the optimal cycle time, optimal order quantity and optimal relevant cost. Numerical example are also presented to illustrate the result of propose model and solution procedure developed.