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A systems perspective of waste management allows an integrated approach not only to the five basic functional elements of waste management itself (generation, reduction, collection, recycling, disposal), but to the problems arising at the interfaces with the management of energy, nature conservation, environmental protection, economic factors like unemployment and productivity, etc. This monograph separately describes present practices and the problems to be solved in each of the functional areas of waste management and at the important interfaces. Strategies for more efficient control are then proposed from a systems perspective. Systematic and objective means of solving problems become possible leading to optimal management and a positive contribution to economic development, not least through resource conservation. India is the particular context within which waste generation and management are discussed. In considering waste disposal techniques, special attention is given to sewage and radioactive wastes.

Knowledge management (KM) has recently received considerable attention in the computer information systems community and is continuously gaining interest by industry, enterprises and government. Decision support and KM processes are interdependent activities in many organizations. In all cases, decision makers always combine different types of data and knowledge available in various forms in the organization. One of the key – but also criticized – building blocks for advancing this field of knowledge management and consequently supporting the decision making is artificial intelligence (AI). In this framework, this paper aims to improve understanding of AI towards knowledge management. It examines and discusses both the potential and the limitations of basic AI technologies in terms of their capability to support the KM process and shares thoughts and estimations on further research on the development of the next generation decision support environments.

From the very earliest moments in the modern history of the computer, scientists have dreamed of creating advanced systems which would simulate human meditation and reasoning. Of all the modern technological quests, this research to create artificially intelligent computer systems has been one of the most ambitious and fascinating. Although attempts had been made more than 30 years ago to develop and apply such systems to the medical sciences, the field languished for decades. In this framework, this paper aims to share thoughts and estimations on the important role of expert systems in medicine and address their future as well as the trends that are foreseen in this area.

Companies deal with many decision‐making processes whose impact on the global performance can be very strong. As a consequence, the role of the decision support systems (DSSs) within the organization is critical. Considering the imprecise or fuzzy nature of the data in real‐world problems, it becomes obvious that the ability to manage uncertainty turns out to be a crucial issue for a DSS. In this framework, this paper discusses the key role of fuzzy logic (FL) in the DSSs, presents new applications of FL in DSSs in various sectors and identifies new challenges and new directions for further research.

Intelligent solutions, based on expert systems (ES), to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. Expert systems are being developed and deployed worldwide in myriad applications, mainly because of their symbolic reasoning and its explanation capabilities. Provides an overview for the operations researcher of the expert systems methodology, as well as their historical and current use in business. Aims to present and focus on the wide range of business areas of ES applications, avoiding an in‐depth analysis of all the applications ‐‐ with varying success ‐‐ recorded in the literature. Reveals that, although still regarded as a novel methodology, ES are shown to have matured to the point of offering real practical benefits in many of their applications.

Intelligent solutions, based on expert systems (ES), to solve complicated practical problems in various sectors are becoming more and more widespread. However, the role of expert systems in the improvement of service industry has not yet been studied in the research community. The primary objective of this paper is to discuss the important role that ES can play in the improvement of service industry.

Based on the diplomacy, preparedness and engagement responsibilities with the aid of technology ((DPEAT) model proposed by Agnihothri et al. the article presents how ES can be used in order to create an effective service organization.

The key findings of the study show that carrying out ES‐based DPEAT can result in superior service outcomes.

Service industries are growing in importance in all over the world. These industries operate in a very competitive market where the need to differentiate a service from the competitor is analogous to survival. The effective use of information technology (IT) to help service companies improve service quality, financial performance, customer satisfaction and productivity is a very crucial issue nowadays. However, the role of expert systems in the improvement of service industry has not yet been studied in the research community. The value of this paper is that it describes how the DPEAT model can be used in order to create effective service organization.

The purpose of this paper is to investigate the effects of industrialization and urbanization on CO₂ emissions in 20 African countries for the period 1980 to 2013.

In order to correct for cross-sectional dependence, this study adopts the use of pooled mean group. Also, the study contributes to the literature by estimating the direct, indirect and total effects of industrialization and urbanization on carbon emission.

The results show that industrialization and urbanization directly increase environmental degradation. Interestingly, industrialization and urbanization were also found to reduce environmental degradation through their indirect effects on per capita income. In general, the authors conclude that the indirect effect of industrialization will overcrowd the direct effect, and this will lead to a decline in the overall effect of industrialization on carbon emission. Also, the positive direct effect of urbanization outweighs the negative indirect effect, thus the overall effect of urbanization will endanger carbon emission in the long run.

The existing studies on emission, industrialization and urbanization have typically been biased toward Africa. This present study filled this gap. The choice of African countries is based on the notion that the continent is desirous of expanding her industrialization level. This has coincidentally led to the increase in urbanization growth rate as well as income level of former rural dwellers. The second contribution of this study is the “effects decomposition” into direct, indirect and total effects. This is to reveal some inherent information that might be missing.

This is a preliminary study on the modal split of reducing environmental contaminants to a minimum. In this paper, we focus on NO_x as a typical air pollutant and make clear the structure of energy flow with regard to freight transport using input‐output analysis. Then we present a mathematical model for the optimal modal split considering the input‐output structure of energy flow. The goal of this model is to minimize NO_x which is emitted in the processes of freight transport, energy transport and energy production subject to maintaining the status quo of freight volume transported in Japan. This model is applied to the actual freight transport problem in Japan and the validity of this model is evaluated. As the result of that, we could suggest a policy of the modal shift, which can reduce the volume of NO_x emissions in the transportation sector by approximately 30 per cent compared to the current circumstances in Japan.

The purpose of this paper is to propose a decision support system (DSS) that stabilizes the flow of fixtures in manufacturing systems. The proposed DSS assists decision-makers to reuse or adapt the available fixtures or to manufacture new fixtures depending upon the similarity between the past and new cases. It considers the cost effectiveness of the proposed decision when an adaptation decision is passed.

The research problem is addressed by integrating case-based reasoning, rule-based reasoning and fuzzy set theory. Cases are represented using an object-oriented (OO) approach to characterize them by their feature vectors. The fuzzy analytic hierarchy process (FAHP) and the inverse of weighted Euclidean distance measure are applied for case retrieval. A machining operation is illustrated as a computational example to demonstrate the applicability of the proposed DSS.

The problems of fixture assignment and control have not been well-addressed in the past, although fixture management is one of the complex problems in manufacturing. The proposed DSS is a promising approach to address such kinds of problems using the three components of an artificial intelligence and FAHP.

Although the DSS is tested in a laboratory environment using a numerical example, it has not been validated in real industrial systems.

The DSS is proposed in terms of simple rules and equations. This implies that it is not complex for software development and implementation. The illustrated numerical example indicates that the proposed DSS can be implemented in the real-world.

Demand-driven fixture retrieval and manufacture to assign the right fixtures to planned part-orders using an intelligent DSS is the main contribution. It provides special consideration for the adaptation of the available fixtures in a system.