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Due to growing awareness about environment protection, firms are forced to implement environmental practices to enhance their green image. In recent times, academicians and practitioners have shown interest in green marketing and green supply chain management (GSCM). Fields of green branding and sustainability have seen special interest from different business disciplines including information management, marketing, supply chain management, etc. Due to economical and ecological impact, there is a growing concern for the environment and related critical issues. Pressure on the environment is dynamic and diverse, and demands new levels of accountability, financial commitment and supply chain capabilities. Indian manufacturing industries have started adopting green concepts in their supply chain management giving special attention to environmental issues based on pressures from different directions, e.g. customer pressure, government regulations etc. Yet, industries struggle hard to identify essential pressures for implementation of GSCM. This work focuses mainly on identifying such pressures for implementation of GSCM. Initially 65 pressures were identified through detailed literature and categorised into six groups. Then common acceptable pressures were identified through a questionnaire survey from different industrial sectors in Phase 1. Finally, essential pressures are prioritised with the help of analytic hierarchy process in Phase 2. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of simultaneous scheduling of machines and two identical automated guided vehicles (AGVs) in a flexible manufacturing system (FMS). For solving this problem, a new meta-heuristic differential evolution (DE) algorithm is proposed. The problem consists of two interrelated problems, scheduling of machines and scheduling of AGVs. A simultaneous scheduling of these, in order to minimise the makespan will result in a FMS being able to complete all the jobs assigned to it at the earliest time possible, thus saving resources. An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs as an integral part of the overall scheduling activity. The algorithm is tested by using problems generated by various researchers and the makespan obtained by the algorithm is compared with that obtained by other researchers and analysed. [ABSTRACT FROM AUTHOR]

The objective of this paper is to find a sequence of jobs for the permutation flow shop to minimise the makespan. The shop consists of 10 machines. A feed-forward back-propagation artificial neural network (ANN) is used to solve the problem. The network is trained with the optimal sequences for five-, six- and seven-job problems. This trained network is then used to solve a problem with a greater number of jobs. The sequence obtained using the neural network is used to generate the initial population for the genetic algorithm (GA) using the random insertion perturbation scheme (RIPS). The makespan of the sequence obtained by this approach (ANN-GA-RIPS) is compared with that obtained using GA starting with a random population (ANN-GA). It was found that the ANN-GA-RIPS approach performs better than ANN-GA starting with a random population. The results obtained are compared with those obtained using the Nawaz, Enscore and Ham (NEH) heuristic and upper bounds of Taillard's benchmark problems. ANN-GA-RIPS performs better than the NEH heuristic and the results are found to be within 5% of the upper bounds. [ABSTRACT FROM AUTHOR]

There are many reasons for the growing interest in reverse logistics. The most prominent reasons are the growing concern for the environment and cost reduction. Next to environment, consumers demand for clean manufacturing and recycling. Hence, customers and retailers expect original equipment manufacturers to set up a proper reverse logistics system and expect the returned products to be processed and recovered in an environmentally responsible way and another reason is cost reduction. A well-managed reverse logistics programme can provide important cost savings in procurement, disposal, inventory carrying and transportation. In this context, looking at the entire supply chain is the best starting point for solutions. Supply chain management aims at the integration of traditional 'forward' supply chain processes, avoiding local optimisation by emphasising integrality. The main objective of this paper is to design an integrated forward logistics multi-echelon distribution inventory supply chain model (FLMEDIM) and closed loop multi-echelon distribution inventory supply chain model (CLMEDIM) for the built-to-order environment using genetic algorithm and particle swarm optimisation. In this paper, the proposed model is validated by considering two case studies: one for a tyre manufacturer and the other for a plastic goods manufacturer both located in the southern part of India. This paper utilises the multi-echelon distribution inventory supply chain model proposed by Haq and Kannan (2006a) for the FLMEDIM. The software used was written in the Java programming language. [ABSTRACT FROM AUTHOR]

The paper deals with the development of an integrated supplier selection and multi-echelon distribution inventory model (MEDIM) for the original equipment manufacturing company in a built-to-order supply chain environment using fuzzy analytical hierarchy process (FAHP) and a genetic algorithm. The main objective is to design the integrated qualitative decision-making of the supplier selection model using FAHP with that of the quantitative mathematical model for the distribution inventory supply chain using a genetic algorithm to the built-to-order environment. The proposed model is validated by considering the case study in a tyre industry in Southern India. The software used for programming is C++ language. [ABSTRACT FROM AUTHOR]