Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization

This paper aims to simultaneously minimize logistics costs and carbon emissions. For this purpose, a mathematical model for a three-echelon supply chain network is created considering the relevant constraints such as capacity, production cost, transport cost, carbon emissions, and time window, which will be solved by the proposed quantum-particle swarm optimization algorithm. The three-echelon supply chain, consisting of suppliers, distribution centers, and retailers, is established based on the number and location of suppliers, the transport method from suppliers to distribution centers, and the quantity of products to be transported from suppliers to distribution centers and from these centers to retailers. Then, a quantum-particle swarm optimization is described as its performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to balance the economic benefit and environmental effect.

[1]  Gwo-Hshiung Tzeng,et al.  New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management , 2016 .

[2]  Mark Goh,et al.  Policy insights from a green supply chain optimisation model , 2015 .

[3]  Miguel A. Figliozzi,et al.  Lifecycle Modeling and Assessment of Unmanned Aerial Vehicles (Drones) CO2e Emissions , 2017 .

[4]  Xiaojun Wu,et al.  Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection , 2012, Evolutionary Computation.

[5]  Tamás Bányai,et al.  Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions , 2018, Energies.

[6]  Juan Huang,et al.  Synergy Degree Evaluation Based on Synergetics for Sustainable Logistics Enterprises , 2018, Sustainability.

[7]  Mark Goh,et al.  Production , Manufacturing and Logistics A stochastic model for risk management in global supply chain networks , 2007 .

[8]  Ágota Bányai,et al.  Smart Scheduling: An Integrated First Mile and Last Mile Supply Approach , 2018, Complex..

[9]  Syed Abdul Rehman Khan,et al.  The Green Logistics Impact on International Trade: Evidence from Developed and Developing Countries , 2018 .

[10]  Wu Deng,et al.  A novel collaborative optimization algorithm in solving complex optimization problems , 2016, Soft Computing.

[11]  Chin-Shan Lu,et al.  Evaluating Green Supply Chain Management Capability, Environmental Performance, and Competitiveness in Container Shipping Context , 2013 .

[12]  Maziar Hedayati,et al.  Hybrid quantum particle swarm optimisation to calculate wideband Green's functions for microstrip structures , 2016 .

[13]  Congdong Li,et al.  A Location-Inventory Problem in a Closed-Loop Supply Chain with Secondary Market Consideration , 2018, Sustainability.

[14]  Armand Baboli,et al.  Production , Manufacturing and Logistics A stochastic aggregate production planning model in a green supply chain : con ‐ sidering flexible lead times , nonlinear purchase and shortage cost functions , 2013 .

[15]  P. Tamás,et al.  Innovative Business Model for Realization of Sustainable Supply Chain at the Outsourcing Examination of Logistics Services , 2018 .

[16]  Ana Paula Barbosa-Póvoa,et al.  Combining Supplier Selection and Production-Distribution Planning in Food Supply Chains , 2014 .

[17]  K. Rameshkumar,et al.  Application of particle swarm intelligence algorithms in supply chain network architecture optimization , 2012, Expert Syst. Appl..

[18]  Daqing Wu,et al.  A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization , 2012 .

[19]  D. Q. Wu,et al.  Vehicle Routing Problem with Time Windows Using Multi-Objective Co-Evolutionary Approach , 2016 .

[20]  M. Jaber,et al.  Carbon emissions and energy effects on a two-level manufacturer-retailer closed-loop supply chain model with remanufacturing subject to different coordination mechanisms , 2017 .

[21]  Shiyou Yang,et al.  A Modified Particle Swarm Optimization Algorithm for Global Optimizations of Inverse Problems , 2016, IEEE Transactions on Magnetics.

[22]  W. C. Benton,et al.  Research opportunities in purchasing and supply management , 2012 .