Service-oriented disassembly sequence planning for electrical and electronic equipment waste

Disassembly sequence planning plays an important role in the end-of-life treatment of electrical and electronic equipment waste (e-waste). Effective planning methods can improve recovery rates and reduce environmental impacts of e-waste. In previous work, neither mathematical models nor optimization algorithms offered a satisfactory solution for this multi-objective disassembly problem. We present a multi-objective model for the problem and a modified teaching-learning-based optimization (MTLBO) algorithm to find the Pareto-optimal frontier. We use numerical simulations to demonstrate and verify the effectiveness and robustness of the algorithm. To do effective disassembly planning, all the participants in the lifecycle of e-waste should work together. Disassembly and recovery of e-waste involve complex processes across the lifecycle. Information support services, disassembly modeling and optimization services must be integrated using computer networks. We also propose a service-oriented framework to support business integration for the participants in the e-waste lifecycle. Effective and optimized disassembly planning can be achieved by invoking the related distributed services. The proposed framework is a novel e-business application for the end-of-life treatment of e-waste

Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested