Multi-objective hybrid harmony search-simulated annealing for location-inventory-routing problem in supply chain network design of reverse logistics with CO2 emission
The advancement of supply chain network design in reverse logistics is gaining interest from the industries. In recent years, the multi-objective framework of the problem has been widely studied by researchers. This paper integrates three different levels of decision planning in supply chain network...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing Ltd
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/35378/1/Multi-objective%20hybrid%20harmony%20search-simulated%20annealing%20for%20location-inventory-routing%20problem%20in%20supply%20chain%20network.pdf http://umpir.ump.edu.my/id/eprint/35378/ https://doi.org/10.1088/1742-6596/1988/1/012054 |
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| Summary: | The advancement of supply chain network design in reverse logistics is gaining interest from the industries. In recent years, the multi-objective framework of the problem has been widely studied by researchers. This paper integrates three different levels of decision planning in supply chain network design: location-allocation problem for strategic planning, inventory planning management for tactical planning, and vehicle routing for operational planning. A location-inventory-routing problem based on the economic production quantity model with environmental concerns is considered. This study aims to minimise the total cost of operating facilities, inventory and distance travelled by the vehicles as the first objective while minimising the CO2 emission cost as the second objective. Due to the complexity of the problem, a non-dominated sorting and ranking procedure is applied into a Multi-Objective Hybrid Harmony Search-Simulated Annealing (MOHS-SA) algorithm to find the trade-off between these two objectives. Computational experiments on the benchmark instances indicate that the proposed MOHS-SA algorithm can produce well-distributed Pareto-optimal solutions for multi-objective problems. |
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