Biswajit Mahanty

A decision framework for the analysis of green supply chain contracts

Highlights? We model supply chain maximizing both economical and environmental benefits. ? We model a contract between the agents to maximize their economical benefits. ? We use evolutionary game theory to achieve a win-win situation for all the agents. ? We use green branding to draw extra economical benefit for the agents. ? We use sustainability metric to show the environmental impact of the activities. The overwhelming growth in the struggle between economy and ecology has led managerial research to promote methodologies to reap profits ensuring sustainability of the ecosystem thus venturing into the novel domain incorporating sustainability with supply chain management and coordination. In this paper, we look for a synergetic alliance between the environmental and commercial benefits by establishing coordination between the producer and the retailer to adjudicate their strategies to trigger green practices with the focus on maximizing economic profits by leveraging upon the products greenness. A two player game has been developed to stand synonymous to the situation considered. Special considerations have been devised to augment the analysis with the consumer psychology towards the green products to establish a practical outlook involving the greenness and economic returns. Evolutionary dynamics has been adopted extensively to locate the optimal and the most stable point offering the best economic gains. The economically suitable solutions are then substantiated on an existing sustainability metric and hence we aim to offer a holistic view into supply chain dynamics from the perspective of environmental management. The insights drawn provide managerial implications and advantages of having environment as a key focus along with business motives.

Clustering Indian stock market data for portfolio management

In this paper a data mining approach for classification of stocks into clusters is presented. After classification, the stocks could be selected from these groups for building a portfolio. It meets the criterion of minimizing the risk by diversification of a portfolio. The clustering approach categorizes stocks on certain investment criteria. We have used stock returns at different times along with their valuation ratios from the stocks of Bombay Stock Exchange for the fiscal year 2007-2008. Results of our analysis show that K-means cluster analysis builds the most compact clusters as compared to SOM and Fuzzy C-means for stock classification data. We then select stocks from the clusters to build a portfolio, minimizing portfolio risk and compare the returns with that of the benchmark index, i.e. Sensex.

Aggregate procurement, production, and shipment planning decision problem for a three-echelon supply chain using swarm-based heuristics

Supplier selection is deemed as a crucial strategic decision-making activity in building a competitive edge. Firms prefer to operate with a few trusted suppliers, selected from a bigger pool of vendors. The chosen suppliers are the ones whose commitments are best oriented in realising the business goals of the company. At the same time enterprise targets cannot be achieved in the absence of cost-effective inventory management policies. This has created the inevitable need for aggregate production and distribution planning. Even more competitive strategy would be integrating procurement planning with production-distribution scheduling. We address the problem of integrated procurement, production and shipment planning for a supply chain, spanning over three echelons. Supplier order scheduling is combined with a production-shipment planning process to realise a minimum cost operations policy. Two recently developed swarm heuristics are employed to search for the near optimal solution of the mathematical model, which is developed to capture the aggregate planning problem.

Optimization of Hot Rolled Coil Widths Using a Genetic Algorithm

Abstract This paper focuses on the assortment problem in the steel industry; with the help of the genetic algorithm, it attempts to determine the optimum width of the parent stock given a set of forecasted customer widths so that the trim loss is minimized. For each given set of forecasted customer widths, an attempt is made to find a single width of the mother coil to be manufactured and kept in stock. In the genetic algorithm, six different selection schemes are considered. A number of test problems are taken up for different selection schemes. Evaluated against the maximum fitness value, the optimal mother coil width, and the generation number at which they are achieved, the elitism selection scheme shows consistently good results in all the test problems compared with the other selection mechanisms.

Manpower shortage crisis in Indian information technology industry

Indian IT industry has drawn attention of the world for its spectacular growth over last decade. Concurrent emergence of global economy and boom in Information Technology industry has brought unexpected opportunity for India. Because of a variety of reasons, Indian IT industry has become globally competitive and a frontrunner in software services exports. However Indias position as a leading IT nation could be seriously endangered soon for want of adequate skilled manpower. This paper assesses the manpower scenario of Indias IT industry and its effect on the industrys long term prospects.

Rule based optimization for a bulk handling port operations

In this paper a study on the operations of a bulk material port is carried out to develop a decision support model, which deals with the dynamics of port and aids in better decision making at different scenarios. Here, we describe various decisions taken by port authorities pertaining to the import of coal at the terminal. We optimize these decisions with the aim of minimizing the unloading time of ships at the port, congestion in the stockyard, and loading time of the rakes. A practice-oriented decision support model is proposed to assist port planners in making these decisions. Various operational rules are embedded within the model, for carrying out the modelling of various port operations. The utility of the developed model is demonstrated using a case study which helps in achieving efficient utilization of berths, stockyard and rake loading stations.

A Genetic-Algorithms-Based Multiobjective Approach for a Three-Dimensional Guillotine Cutting Problem

ABSTRACT A novel method based upon multiobjective genetic algorithms is presented for simultaneously minimizing the amount of scrap and the number of turns in a three-dimensional guillotine cutting. The concept of best orientation between two cuboids has been used to improve the efficiency of the minimization process. Two different evolutionary algorithms have been used and also compared for effectiveness.

Hybrid Approach to Optimal Packing Using Genetic Algorithm and Coulomb Potential Algorithm

It is difficult and computationally time-consuming to find the best possible solutions for blank packing problems, because they include a lot of underlying combinational conditions. This paper presents two approaches for packing two-dimensional irregular-shaped polygonal elements—a real-encoded genetic algorithm and a hybrid algorithm using a real-encoded genetic algorithm and a local optimization algorithm. The local optimization algorithm presented is a novel one utilizing the Coulomb potential technique. In the hybrid approach, the real-encoded genetic algorithm generates the order of the polygons while the coulomb potential algorithm determines the embodiment layout under the fixed combinations so as to minimize the scrap. The hybrid genetic algorithm is found to give better results for problems of larger size although it takes more computational time.

Service quality dimensions in designing profitable business strategies in Indian two-wheeler services

Identification of the pertinent service quality dimensions and formulating strategies accordingly, in a service, may not always be profitable. Every feature added to the current service incurs cost. The managements are not willing to invest in businesses until they have enough evidence of the profit and not all the customers may be willing to pay for every service feature. In the dynamic business environment of the organised two-wheeler service sector in India, the classification of customers was carried out based on price sensitivity and usage level. The dimensions of importance were identified for all categories of customers. This paper highlights how such a categorisation aids the managements in identifying potential target groups and formulating strategies to realise the bottom line.

Non-contact estimation of surface roughness in turning using computer vision and Artificial Neural Networks

Accurate non-contact estimation of surface roughness in turning operations plays an important role in the manufacturing industries. This paper investigates the effectiveness of using various surface image features, such as contrast, energy, homogeneity, entropy, range, and standard deviation for computer vision-based non-contact estimation of surface roughness in turning operations. A Bayesian Regularisation-aided Artificial Neural Network (ANN) model-based approach is proposed in this paper for accomplishing surface roughness estimation. Analyses of experimental data demonstrate that the proposed approach yields significant improvement in the accuracy level of computer vision-based non-contact estimation of surface roughness (without involving turning parameters) of turned workpieces.