Knowledge and Behavior‐Driven Fruit Fly Optimization Algorithm for Field Service Scheduling Problem with Customer Satisfaction

Abstract

The field service scheduling problem (FSSP) is the key problem in field services. Field service pays particular attention to customer experience, that is, customer satisfaction. Customer satisfaction described by customer behavior characteristics based on the prospect theory is considered as the primary optimization goal in this paper. The knowledge of the insertion feasibility on the solution is analysed based on the skill constraint and time window. According to the knowledge, an initialization method based on the nearest heuristic algorithm is constructed. Based on the prior knowledge of the FSSP and the endowment of the Fruit Fly Optimization Algorithm, two operators are defined according to the matrix encoding method. Based on these two operators, three search strategies are then proposed, and the smell-based search strategy and vision-based search strategy for the FOA are redesigned. To verify the performance of the algorithms, the proposed operators and strategies are tested and analysed in the well-known benchmark. Through comparison with the state-of-the-art algorithms, the results show that the proposed HFOA is an effective and efficient method to solve the FSSP with customer satisfaction.

Dynamic Scheduling Algorithm Based on Evolutionary Reinforcement Learning for Sudden Contaminant Events Under Uncertain Environment

“…Lü et al [23] proposed an extensible framework combining the advantages of RL, EC, and imitation learning, which improves the learning process of agents in traditional ERL methods. Gupta et al [24] introduced a deep ERL framework, in which different agent forms evolve to learn challenging motor and operational tasks in a complex environment.…”