Title: Productivity improvement of an eco friendly warehouse using multi objective optimal robot trajectory planning
Authors: S. Mahalakshmi; A. Arokiasamy; J. Fakrudeen Ali Ahamed
Addresses: Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611-002, Tamil Nadu, India ' Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611-002, Tamil Nadu, India ' Engineering Department, Nizwa College of Technology, P.O. Box 477, 611 Nizwa, Sultanate of Oman
Abstract: Production of environment is one of the top rated objectives of all countries. 10% emission of CO2 in the world is from logistics industries due to freight transports and warehousing operations. Green logistics is an important step to minimise ecological impacts of the logistics operations. Green environment in a warehouse plays a mandatory role in green logistics. Automation, robotics and smart systems give a good contribution in making the warehouse environment clean and green. A method for productivity improvement of a green warehouse using multi objective optimal trajectory planning of a warehouse robot is proposed in this paper. Mixed load palletising operation (build-to-order palletising) is considered. Two multi objective optimisation algorithms such as multi objective particle swarm optimisation (MOPSO) and multi objective differential evolution (MODE) are used. A numerical example on an Industrial robot (MTAB ARISTO 6XT robot) is presented. An economic and productivity analysis is carried out. The obtained results proved that the multi objective optimisation on warehouse robot trajectory planning enhances supply chain productivity and profits.
Keywords: green supply chain; green logistics; productivity improvement; mixed load palletising; eco friendly warehouse robot; multi objectives; optimal trajectory planning; MOPSO; multi objective differential evolution; MODE.
International Journal of Productivity and Quality Management, 2019 Vol.27 No.3, pp.305 - 328
Received: 28 Feb 2018
Accepted: 17 May 2018
Published online: 11 Aug 2019 *