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Minimizing cargo handling time and waiting time of ship are some of the most critical tasks for terminal operators during berth allocation planning. An efficient and effective berth allocation planning approach is not only significant for improving a terminal's productivity but worth even more in enhancing terminal serviceability. As berths are no longer leased by specific ship lines or ship companies in the majority of terminals, ships of various sizes and various cargo handling volume at a particular terminal of call are competing for the same berth for handling. As a result, there are always concerns from both terminal operators and ship companies regarding the service priority. This research contributes to deal with the dilemma terminal operators encountered in balancing service priority and terminal productivity maximization during berth allocation. This research deals with berth allocation problem which treats calling ships at various service priorities with physical constraints. The problem encountered is to determine how to cope with various ships with various attributes in the system, and the objective is to minimize the total service time of a set of given calling ships through proper berth allocation. This research adopts a chaotic genetic algorithm-based method to deal with the problem. The new formulation and method have been proposed and results obtained have been compared with the existing one in literature. The results show the improved feasibility of the proposed formulation and improved convergence speed of the proposed method over the existing one. Also, higher terminal serviceability is indicated. [ABSTRACT FROM AUTHOR]

This paper presents a supplier selection and order allocation (SSOA)model to solve the problemof amultiperiod supplier selection and then order allocation in the environment of short product life cycle and frequentmaterial purchasing, for example, fast fashion environment in apparel industry. At the first stage, with consideration of multiple decision criteria and the fuzziness of the data involved in deciding the preferences of multiple decision variables in supplier selection, the fuzzy extent analytic hierarchy process (FEAHP) is adopted. In the second stage, supplier ranks are inputted into an order allocation model that aims at minimizing the risk of material purchasing and minimizing the total material purchasing costs using a dynamic programming approach, subject to constraints on deterministic customer demand and deterministic supplier capacity. Numerical examples are presented, and computational results are reported. [ABSTRACT FROM AUTHOR]

Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.