• Journal of Information Technology Applications and Management
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• v.12 no.1
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• pp.49-68
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• 2005

It is one of the crucial components of electronic logistics systems to manage logistics information of cargos and transportation companies and to mediate appropriate brokerage between them. Due to the advance of e-Commerce technologies many kinds of logistics transactions can be handled by means of EDI or XML/EDI applications. but the brokering processing relies mostly on the traditional processes and the research in this field is still at the initial stage. In this paper we study a logistics brokering system for ships and cargos and describe the design and implementation of the system. We analyze the brokering constraints for logistics of cargos and ships and construct an optimization model for their brokering. We also suggest a brokering procedure and a simple heuristic algorithm with respect to the proposed matching criteria. The experimental result shows that the proposed greedy-based heuristic algorithm performs very well. In its response time the proposed algorithm executed within a couple of seconds independently of the number of cargos and the container capacities of ships. The output of the algorithm is very close to that of the optimal solution. showing higher than 95% of approximation. The proposed system is implemented for the Web environment using JSP and PL/SQL.

• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.53-62
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• 2004

This paper presents a study on the optimization for brokering between cargos and ships for future e-logistics. The primary contribution of this research is that we establish an optimization model by formalizing the criteria for the brokering such as time constraints, weight constraints, and preference values between cargos and ships. Another important contribution is that we not only investigate the complexity and the tractability of the optimal brokering problem but present how to evaluate the performance of the optimization program through an experiment. We first derive the preference values between cargos and ships using the time and the weight constraints. These preference values between each pair of cargos and ships are assigned to corresponding binary decision variables as coefficients in the objective function. The optimization model selects pairs of cargos and ships in a way that the sum of the preference values is maximized while satisfying given constraints. Experiment shows that the Davis-Putnam based optimization program finds optimal solutions in reasonable time for the problems with less than 90 decision variables.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.219-230
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• 2014

In the cloud computing environment, cloud service users purchase and use the virtualized resources from cloud resource providers on a pay as you go manner. Typically, there are two billing plans for computing resource allocation adopted by large cloud resource providers such as Amazon, Gogrid, and Microsoft, on-demand and reserved plans. Reserved Virtual Machine(VM) instance is provided to users based on the lengthy allocation with the cheaper price than the one of on-demand VM instance which is based on shortly allocation. With the proper mixture allocation of reserved and on-demand VM corresponding to users' requests, cloud service providers are able to reduce the resource allocation cost. To do this, prior researches about VM allocation scheme have been focused on the optimization approach with the users' request prediction techniques. However, it is difficult to predict the expected demands exactly because there are various cloud service users and the their request patterns are heavily fluctuated in reality. Moreover, the previous optimization processing techniques might require unacceptable huge time so it is hard to apply them to the current cloud computing system. In this paper, we propose the cloud brokering system with the adaptive VM allocation schemes called A3R(Adaptive 3 Resource allocation schemes) that do not need any optimization processes and kinds of prediction techniques. By using A3R, the VM instances are allocated to users in response to their service demands adaptively. We demonstrate that our proposed schemes are able to reduce the resource use cost significantly while maintaining the acceptable Quality of Service(QoS) of cloud service users through the evaluation results.