Development of a Profit-Based Air Cargo Loading Information System.

IEEE Trans. Industrial Informatics 01/2006; 2:303-312.
Source: DBLP
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    ABSTRACT: Despite the rapid advance of information technology (IT), a fully automated Enterprise Information System (EIS) for designing and planning of complex products and assemblies is still not available, and human interventions are required in one way or another. Therefore, one of the challenges in developing an EIS for complex multidisciplinary design is how to design human-machine interfaces and integrate experts' knowledge in decision-making. In this paper, a rule-based methodology is proposed to take into consideration experts' experience; the rules are applied based on human designers' visual perception. The strength of IT and human experts can be synthesized to find a suboptimized solution efficiently. To illustrate the performance of the developed methodology, an EIS for aero-engine pipe routing is used as a case study. Pipe routing is a typical no-deterministic polynomial-time hard (NP-hard) problem; in addition, many factors, such as fluid dynamics, sequences of pipes, installation, maintenance, machinability, stability, and vibration, have to be taken into consideration simultaneously. Experts' guidance is essential to find a vital solution in the confined 3-D space. In the implementation, virtual objects have been determined to define feasible spaces for pipes based on the analysis of fluid structure interaction. A new algorithm is developed to find the possible graphs of pipes routes and improve the solutions with the help of human visual perception. The effectiveness of the proposed methodology has been verified.
    IEEE Transactions on Industrial Informatics 11/2013; 9(4):2187-2199. DOI:10.1109/TII.2013.2257805 · 8.79 Impact Factor
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    ABSTRACT: This study adopts a hybrid approach that integrates the genetic algorithm (GA) and fuzzy logic in order to assist in the generation of an optimal pallet loading plan. The proposed model enables the maximisation of profits for freight forwarders through the most efficient use of space and weight in pallet loading. The model uses fuzzy controllers to determine the numbers and size of cargo units on a pallet as well as the mutation rate in the GA approach within the optimisation process and enables the capture of tacit knowledge vested in industry practitioners. The pragmatic use of the model is illustrated using a freight-forwarding scenario that demonstrates the inherent limitations of the standard GA method, followed by the application of the proposed fuzzy GA model. To further demonstrate the benefits of the hybrid model, simulated annealing and Tabu search are used to benchmark the results achieved using various approaches; the proposed hybrid model is demonstrated to exceed these other approaches in overall performance. The application of the proposed hybrid approach across a range of scenarios is also discussed.
    International Journal of Production Research 12/2014; · 1.32 Impact Factor
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    ABSTRACT: This paper investigates uncertainties in complex supply chain situations and proposes a fuzzy-based decision support model for determining the chance of meeting on-time delivery in a complex supply chain environment. It integrates fuzzy logic principles and unitary structure-based supply chain model and enables addressing uncertainties associated with key inputs of on-time delivery performance for effective decision making process. The proposed pragmatic model deals with the fuzziness of the key inputs including, variations in demand forecasting, materials shortages and distribution lead time, and combines a fuzzy reasoning approach for monitoring on-time delivery of finished products. In systematically dealing with the uncertainties of complex supply chains, this model supports the minimizing of business losses that result from penalties and customer dissatisfaction, and the consequent reduced market share. Application of the proposed model is illustrated using a textile industry case study.
    European Journal of Operational Research 03/2013; 225(3):507–517. DOI:10.1016/j.ejor.2012.10.010 · 1.84 Impact Factor