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Yaping Ren
Researcher at Jinan University
Publications - 55
Citations - 1547
Yaping Ren is an academic researcher from Jinan University. The author has contributed to research in topics: Computer science & Scheduling (production processes). The author has an hindex of 14, co-authored 44 publications receiving 729 citations. Previous affiliations of Yaping Ren include Northeast Forestry University & Jilin University.
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Modeling and Planning for Dual-Objective Selective Disassembly Using and / or Graph and Discrete Artificial Bee Colony
TL;DR: This work proposes an effective triple-phase adjustment method to produce feasible disassembly sequences based on an AOG graph that is capable of rapidly generating satisfactory Pareto results and outperforms a well-known genetic algorithm.
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Dual-Objective Scheduling of Rescue Vehicles to Distinguish Forest Fires via Differential Evolution and Particle Swarm Optimization Combined Algorithm
TL;DR: A novel multiobjective scheduling model to handle forest fires subject to limited rescue vehicle (fire engine) constraints is presented, in which a fire-spread speed model is introduced into this problem to better describe practical forestry fire.
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MILP models for energy-aware flexible job shop scheduling problem
TL;DR: Six new mixed integer linear programming (MILP) models with turning Off/On strategy are proposed based on two different modeling ideas namely idle time variable and idle energy variable to help the enterprises rationalize production so as to reduce energy consumption and costs.
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An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem
TL;DR: A novel efficient approach based on gravitational search algorithm (GSA) is proposed to solve the PPDLBP, and a mathematical model of this problem is established, which is to achieve the maximisation of profit for dismantling a product in DLBP.
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Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines
TL;DR: The results demonstrate that the IGA is more effective than the genetic algorithm (GA), simulating annealing algorithm (SA) and migrating birds optimisation algorithm (MBO) and within no more than 10% of the running time of the best MILP model.