• 산업경영시스템학회지
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• 제32권3호
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• pp.1-9
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• 2009

Processing mail objects in a mail processing center involves several steps and operations, in particular dispatching as well as sorting by destination. The purpose of this paper is to present a model for the part-time worker staffing and allocation problem as it arises at the parcel sorting area in a mail processing center. The problem is formulated as a mixed integer linear program model to minimize the variable part-time workforce related cost. Hot only the characteristics of the sorting operations but also the dispatching requirements of the vehicles are reflected into the model. Six example problems with three different daily amounts of arriving mail are solved with LINGO to demonstrate the effectiveness of the 7-level induction option for the parcel sorting machine over the current 3-level one. The results indicate that measurable savings can be achieved by departing from current practice.

• 산업경영시스템학회지
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• 제43권4호
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• pp.76-83
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• 2020

Postal logistics organizations are characterized as having high labor intensity and short response times. These characteristics, along with rapid change in mail volume, make load scheduling a fundamental concern. Load analysis of major postal infrastructures such as post offices, sorting centers, exchange centers, and delivery stations is required for optimal postal logistics operation. In particular, the performance of mail traffic forecasting is essential for optimizing the resource operation by accurate load analysis. This paper addresses a traffic forecast problem of postal parcel that arises at delivery stations of Korea Post. The main purpose of this paper is to describe a method for predicting short-term traffic of postal parcel based on self-similarity analysis and to introduce an application of the traffic prediction model to postal logistics system. The proposed scheme develops multiple regression models by the clusters resulted from feature engineering and individual models for delivery stations to reinforce prediction accuracy. The experiment with data supplied by main postal delivery stations shows the advantage in terms of prediction performance. Comparing with other technique, experimental results show that the proposed method improves the accuracy up to 45.8%.