The present paper is aimed at multi-objective scheduling in an agent based holonic manufacturing system to satisfy the goal of several communities namely the product, the resource, and the organization simultaneously. In this attempt, first a multi criteria based priority rule is developed following Simple Additive Weight (SAW) method under Multi Criteria Decision Making (MCDM) environment to rank the products. Accordingly, the products are allowed to select a particular resource for execution by negotiation considering minimum time as criterion. The interests of different communities are accomplished by allocating the ordered rank of products to the ordered rank of resources. Conflict, if arises between products and resources, are resolved by introducing the concept of Early Finish Time (EFT) as criterion for task allocation. A scheduling algorithm is proposed for execution of the rule. In view of machine failure, a cooperation strategy is evolved that also optimizes reallocation of the incomplete task. It is concluded that the proposed scheduling algorithm together with the disturbance handling algorithm are poised to satisfy the agent’s local objective as well as organization’s global objective concurrently and are commensurable with multi agent paradigm.

Automation in service delivery has increased the consumers’ expectation with regard to service quality and subsequently the perception of the same. Technology-driven services redefined quality dimensions and their subsequent impact on the behavioural outcomes of the consumers with specific reference to attitudinal loyalty and propensity to switch. Customer Relationship Management (CRM) has further reinforced the operational aspects of a service provider by integrating the behavioural perspectives with technology. This paper attempts to explore the nomological link between automated service quality and its behavioural consequences with specific reference to consumers’ attitudinal loyalty and their intention to switch or defect from their present service provider. The study further takes into consideration the moderating effects of the performance of the dimensions and attributes of customer relationship management by introducing a novel approach to CRM performance indexing. The cross-sectional study was carried out with the customers of State Bank of India at Asansol, Durgapur, Bolpur and Santiniketan in West Bengal, India. The study used structural equation modeling (SEM) to assess and validate the nomological relationship between the variables.

Highway accident data analysis provides probability of occurrence of road accidents by associating different accident factors using data mining algorithms. Analysis can be improved by using advanced data mining algorithms that compute relationships with minimum processing time. As accident datasets are very heterogeneous in nature, it is difficult to identify the relationship between critical factors responsible for road accidents without data mining algorithms. In this study, K-modes for clustering and frequent pattern growth algorithms to extract relationships between critical accident factors have been used. The accomplished result concludes better relationships with better accuracy than earlier implemented data mining algorithms and has found meaningful hidden situations that would be beneficial for future work in decreasing the number of highway accidents.

In this paper, we presents an inventory problem where initially, a retailer purchases Q(=P+R) units and after fulfilling the backlogged quantities, there is a P unit of the on-hand inventory. It continuously declines to meet the customer’s demand, which depends on the on-hand inventory level up to the time t = t1. After that the inventory level declines by constant demand up to t = t2. Thereafter, shortage occurs and it accumulates at the rate ?(T-t) till t = T when the next batch arrives. This whole cycle repeats itself after the cycle length T. The proposed model of this paper is investigated under various conditions and the implementation of the proposed model is presented through some numerical examples.

This paper presents an integrated granular framework of wavelet decomposition, DCC-GARCH, ADCC-GARCH, Diks-Panchenko nonlinear Granger’s causality and Diebold-Yilmaz spillover assessment techniques to understand temporal correlation, causal interplay and spillovers among volatile financial time series data exhibiting nonparametric behavior. The exercise has been carried out on daily closing observations of eight financial time series. Wavelet decomposition has been used to generate time varying components in which the other research models are applied to extract the interactive pattern of interaction to ascertain short and long run nexus. The findings rationalize the effectiveness of the presented research framework.

Cash is the life blood of the organizations and cash management is the important aspect of any organization. Corporate cash management boosts the companies from small to giant in the competitive environment. In this study, we highlighted three factors of a good cash management practices; namely cash conversion cycle, cash holding and credit score. Influence of one factor to other help organizations manage their corporate cash more appropriately. In this study, we collected data from Capitaline corporate data base of Mumbai over the period 2002-2011. In this study we observed that due to higher credit score, companies were forced to minimize their cash conversion cycle and helped them maintain lower levels of working capital.

With the ever-increasing demands for high surface finish and complex shape geometries, conventional metal removal methods are now being replaced by non-traditional machining (NTM) processes. These NTM processes use energy in its direct form to remove materials in the form of atoms or molecules to obtain the required accuracy and burr-free machined surface. In order to exploit the optimal capabilities of the NTM processes, it is often required to determine the best possible combinations of their controllable parameters. Different non-conventional optimization techniques have been used for dealing with these process optimization problems because of their inherent advantages and capabilities for arriving at the almost global optimal solutions. This paper reviews the applications of different non-conventional optimization techniques for parametric optimization of NTM processes. It is observed that electrical discharge machining processes have been optimized most number of times, followed by wire electrical discharge machining processes. In most of the cases, the past researchers have preferred to maximize material removal rate. Genetic algorithm has been found to be the most popular non-conventional optimization technique.

Activity based costing (ABC) is a new age accounting system developed to overcome the limitations of traditional costing system, which in today’s technologically advanced world has lost its ability to provide accurate and useful cost information often required in taking various managerial decisions, like setting of selling price, ascertaining profitability of products as well as customers, taking a make or buy decision, eliminating the non-beneficial activities, and rewarding the employees. In this paper, an ABC model is developed for a government hospital in the state of Bihar in India to quantify its various public health related services. Although, there are limited published research papers related to application of ABC model in a specific department or method of a healthcare system, none of them has investigated its scope for organization-wide implementation. The results obtained from the developed ABC model in the public hospital would provide more comprehensive and accurate cost information, and radically contribute to the hospital administration in effective planning, controlling and decision making. This paper also elucidates about various obstructions and difficulties encountered during the implementation phase of ABC model in an Indian healthcare setting.

The concept of green inventory systems is very important for economic growth and development in the era of sustainable development. There is a special need for green inventory systems to identify and manage perishable products since spoilage and deterioration can lead to significant losses of items, which negatively affect the satisfaction of consumers. As perishable products decay continuously (such as vegetables, fruits, milk, juices, frozen foods, baked foods), their demand is adversely affected as well as customers' purchasing decisions. The more realistic assumption is a price-sensitive demand. As well as deterioration rates, perishable products have an expiration date-dependent deterioration rate. Further, inventory holding, and the deterioration of perishables contribute significantly to carbon emissions when operating the inventory system. A carbon tax policy is more flexible and effective when it comes to reducing carbon emissions due to its environmental conscious nature. We develop two sustainable inventory procedures for perishable items based on a practical scenario in which the buyer has a limited storeroom. So, to achieve sustainability goals, a model for inventory management for perishable products based on expiration dates is presented in this paper. We distinguish between two inventory schemes: (i) one that allows shortages and fractional backlogs, and (ii) one that does not allow shortage. In both schemes, both the decay rate and demand function show an upward trend against storage time. Since the decay rate increases with storage time, it is assumed that the cost of storing items is linearly related to storage time. numerical examples along with a real-life case study are presented to validate the inventory schemes after several decision-making findings have been derived.

The main purpose of this paper is to know about the recent status of big data analytics (BDA) on various manufacturing and reverse supply chain levels (RSCL) in Indian industries. In particular, it emphasizes on understanding of BDA concept in Indian industries and proposes a structure to examine industries’ development in executing BDA extends in reverse supply chain management (RSCM). A survey was conducted through questionnaires on RSCM levels of 500 industries. Of the 500 surveys that were mailed, 125 completed surveys were returned, corresponding to a re-sponse rate of 25 percent, which was slightly greater than previous studies. The information of Indian industries with respect to BDA, the hurdles with boundaries to BDA-venture reception, and the connection with reverse supply chain levels and BDA learning were recognized. A structure was presented for the selection of BDA ventures in RSCM. This paper gives bits of knowledge to professionals to create activities including big data and RSCM, and presents utilitarian and predict-able direction through the BDA-RSCM triangle structure as an extra device in the execution of BDA ventures in the RSCM factors. This paper does not provide outside legitimacy owing to limitations for the speculation of the outcomes even in the Indian surroundings, which originates from the present test. Future research ought to enhance the understanding in this area and spotlight on the effect of big data on reverse supply chains in developed countries.