In this study, a volume flexible inventory system for deteriorating items with stock & time dependent demand has been developed over a finite planning horizon. Shortages are permitted with partial backorder. Uncertainties are inherent in real inventory problems due to complexities of market situation. This uncertainty can be handled by the concept of randomness. As a result, backorder rate is taken as random and follows a probability distribution. All the costs are influenced by the learning effect. The optimal number of production cycles that minimize the total cost is considered. Numerical illustrations together with sensitivity analysis are given to elucidate the model. Furthermore, the numerical results of the finite planning horizon model have been plotted graphically.

This study attempts to assess the moderating impact of recently introduced tourist relationship management (TRM) framework on service quality perception-tourist satisfaction-destination loyalty link. Tourist relationship management framework draws inspiration from customer relationship management (CRM) model with validated addition of dimensions compatible to tourism dynamics. The study, carried out in Santiniketan, India, confirmed moderating impact of dimensional performance of tourist relationship management on perceived tourism service quality-tourist satisfaction-destination loyalty link.

This work deals with production scheduling problem in an assembly flow shop, having parts machining followed by their subsequent assembly operations. Limited heuristics available on the problem, are based on unrealistic assumption that every part is processed on all machines. In this paper, two heuristics NEH_BB and Disjunctive are proposed to solve assembly flow shop scheduling problem where every part may not be processed on each machine. Exhaustive computational experiments are conducted with 60 trials each. The methods are found to be applicable to large size problems. The objective functions used for comparison are makespan and computational time. Disjunctive method takes very less computational time as compared to NEH_BB and hence claimed to be the better among available approaches for finding solution in assembly flow shop problems.

Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance.

An integrated information system based DSS is developed for Open and Distance Learning (ODL) institutions in India. The system has been web structured with the most suitable newly developed modules. A DSS model has been developed for solving semi-structured and unstructured problems including decision making with regard to various programmes and activities operating in the ODLIs. The DSS model designed for problem solving is generally based on quantitative formulas, whereas for problems involving imprecision and uncertainty, a fuzzy theory based DSS is employed. The computer operated system thus developed would help the ODLI management to quickly identify programmes and activities that require immediate attention. It shall also provide guidance for obtaining the most appropriate managerial decisions without any loss of time. As a result, the various subsystems operating in the ODLI are able to administer its activities more efficiently and effectively to enhance the overall performance of the concerned ODL institution to a new level.

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.