Staff scheduling for a courier distribution centre using evolutionary algorithm

Pua Si Ying, Zeratul Izzah Mohd Yusoh

Abstract


Staff scheduling is a combinatorics optimization problem and companies face this complex task on daily basis in constructing a schedule fitting all conditions. In a courier distribution center, staffs are assigned to work in processes of a continuous workflow. Staffs have varying work ability for each process. Instead of generating staff schedule instinctively, it is an advantage to optimize staff’s schedule by measuring the performance of each staff. An optimized schedule improves the operation’s efficiency and fully utilize staffs’ work ability, hence, minimizing the cost. This paper proposed evolutionary algorithm, namely genetic algorithm as the solution to courier center staff scheduling. Based on the result, the produced schedule can reduce up to 30% of the staff in schedule while not affecting operation workflow. The cut down on number of working staffs could amount to a substantial reduction of operation cost every month. The generated schedule is significantly customized and take less time to complete an operation. Although the proposed solution is specific to the use case of a courier distribution center, it is however, potentially a generalize model for the logistics industry, introducing a more effective staff scheduling system to cope with the industry’s ever-rising demands.

Keywords


Courier distribution centre; Evolutionary algorithm; Genetic algorithm; Optimization; Staff scheduling;

Full Text:

PDF


DOI: http://doi.org/10.11591/ijeecs.v27.i2.pp1043-1050

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

shopify stats IJEECS visitor statistics