83 Papers
283 Citations
Jun Pei is an academic researcher from Hefei University of Technology. The author has contributed to research in topics: Job shop scheduling & Computer science. The author has an hindex of 19, co-authored 77 publications. Previous affiliations of Jun Pei include University of Florida & Chinese Ministry of Education.
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Papers
Operating room planning and surgical case scheduling: a review of literature
Shuwan Zhu,Shuwan Zhu,Fan Wenjuan,Fan Wenjuan,Shanlin Yang,Shanlin Yang,Jun Pei,Jun Pei,Jun Pei,Panos M. Pardalos +9 more
TL;DR: It is shown that mathematical programming and heuristics are frequently applied in the complex linear and combinatorial optimization problems.
191
Iterated local search embedded adaptive neighborhood selection approach for the multi-depot vehicle routing problem with simultaneous deliveries and pickups
TL;DR: In this paper, a metaheuristic based on iterated local search is developed for multi-depot vehicle routing problem with simultaneous deliveries and pickups (MDVRPSDP) is often encountered in real-life scenarios of transportation logistics, it has received little attention so far.
115
Serial batching scheduling of deteriorating jobs in a two-stage supply chain to minimize the makespan
Jun Pei,Jun Pei,Panos M. Pardalos,Xinbao Liu,Xinbao Liu,Wenjuan Fan,Wenjuan Fan,Shanlin Yang,Shanlin Yang +8 more
TL;DR: The results show that the proposed algorithm is superior to other four approaches in the literature and can effectively and efficiently solve both small-size and large-size problems in a reasonable time.
93
A hybrid ABC-TS algorithm for the unrelated parallel-batching machines scheduling problem with deteriorating jobs and maintenance activity
TL;DR: A hybrid ABC-TS algorithm combining artificial bee colony (ABC) and Tabu Search (TS) to solve the parallel machine scheduling problem with deteriorating maintenance activities, parallel-batching processing, and deteriorating jobs is developed.
81
A multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning approach
TL;DR: A multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning (ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services are proposed.