A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study

Meisam Nasrollahi; Jafar Razmi; Reza Ghodsi

doi:10.7307/ptt.v30i6.2779

Meisam Nasrollahi University of Tehran
Jafar Razmi University of Tehran
Reza Ghodsi Professor, Industrial Engineering Department, University of Tehran & Professor, Engineering Department, Central Connecticut State University, USA

DOI: https://doi.org/10.7307/ptt.v30i6.2779

Keywords: healthcare, greenhouse effect, supply network, carbon emissions, Monte Carlo

Abstract

Measuring carbon emissions is an essential step in taking required action to fight global warming. This research presents a computational method for measuring transport related carbon emissions in a healthcare supply network. The network configuration significantly impacts carbon emissions. First, a multi-objective mathematical programing model is developed for designing a healthcare supply network in the form of a two-graph location routing problem under demand and fuel consumption uncertainty. Objective functions are minimizing total cost and minimizing total fuel consumption. In the presented model, the demand of each customer must be completely satisfied in each time period, and backlog is not permitted. The number and capacity of vehicles are determined, and vehicles are heterogeneous. Furthermore, fuel consumption depends on traveling distance, vehicle and road conditions, and the load of a vehicle. The centroid method is applied to face demand uncertainty. Next, a multi-objective non-dominated ranked genetic algorithm (M-NRGA) is proposed to solve the model. Then, a Monte Carlo based approach is presented for measuring
transport-related carbon emissions based on fuel consumption in supply network. Finally, the proposed approach is applied to the case of a healthcare supply network in the Fars province in Iran. The obtained results illustrate that the proposed approach is a practical tool in designing healthcare supply networks and measuring transport-related carbon emissions in the network.

Author Biographies

Meisam Nasrollahi, University of Tehran

Meisam Nasrollahi is Ph.D. candidate at the Industrial engineering school of the University of Tehran. His research area is Healthcare supply network design, green supply chain, transportation network design and time series. He has published several papers in leading journals such as Management Science and Practice, Production & Operations Management, Applied Mathematics in Engineering, Management and Technology.

Jafar Razmi, University of Tehran

Prof. Dr. Jafar Razmi is Professor at Department of Industrial Engineering, College of Engineering, University of Tehran. His research area include Supply Chain Management, Production Planning, Operations Management, Operations Research, Health Engineering, etc. He has published in leading journals such as Omega, Transportation Research Part E: Logistics and Transportation Review, Expert systems with applications, Computers & Industrial Engineering, International Journal of Production Research, Soft Computing, International Journal of Production Economics, Journal of Cleaner Production.

Reza Ghodsi, Professor, Industrial Engineering Department, University of Tehran & Professor, Engineering Department, Central Connecticut State University, USA

Dr. Reza Ghodsi is an associate professor of industrial engineering at university of Tehran & associate professor of Mechanical Engineering at the Engineering, Science and Technology school of the Central Connecticut State University in New Britain, Connecticut USA. His research area include Sustainable Energy; Advanced Manufacturing; Rapid Prototyping; Operations Research; Optimization; Data mining and analysis; Health Engineering. He has published in leading journals such as International Journal of Production Research, International Journal of Assembly Automation, International Journal of Management Practice, Expert Systems with Applications, International Journal of Operational Research, and Applied Mathematical Modeling.

References

P. Martens, Health and climate change: modelling the impacts of global warming and ozone depletion. Routledge, 2014.

A. Ali, S. E. Amin, H. H. Ramadan, and M. F. Tolba, “Enhancement of OMI aerosol optical depth data assimilation using artificial neural network,” Neural Computing and Applications, vol. 23, no. 7–8, pp. 2267–2279, 2013.

R. K. Pachauri et al., “Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,” 2014.

P. P. Reddy, “Causes of Climate Change,” in Climate Resilient Agriculture for Ensuring Food Security, Springer, 2015, pp. 17–26.

A. Acquaye, A. Genovese, J. Barrett, and S. C. L. Koh, “Benchmarking carbon emissions performance in supply chains,” Supply Chain Management: An International Journal, vol. 19, no. 3, pp. 306–321, 2014.

S. J. O’Shea et al., “Area fluxes of carbon dioxide, methane, and carbon monoxide derived from airborne measurements around Greater London: A case study during summer 2012,” Journal of Geophysical Research: Atmospheres, vol. 119, no. 8, pp. 4940–4952, Apr. 2014.

K. Carling, M. Han, J. Håkansson, X. Meng, and N. Rudholm, “Measuring transport related CO 2 emissions induced by online and brick-and-mortar retailing,” TRANSPORTATION RESEARCH PART D, vol. 40, pp. 28–42, 2015.

J. C. Minx et al., “Input–output analysis and carbon footprinting: an overview of applications,” Economic Systems Research, vol. 21, no. 3, pp. 187–216, 2009.

A. Jaegler and P. Burlat, “Carbon friendly supply chains: a simulation study of different scenarios,” Production Planning & Control, vol. 23, no. 4, pp. 269–278, 2012.

Y. Tian and Q. Liu, “The Study about the Calculation Method of Product Carbon Footprint during the Flow Manufacturing Process,” Journal of Low Carbon Economy 低碳经济, vol. 3, pp. 1–6, 2014.

L. Mogensen, T. Kristensen, T. L. T. Nguyen, M. T. Knudsen, and J. E. Hermansen, “Method for calculating carbon footprint of cattle feeds–including contribution from soil carbon changes and use of cattle manure,” Journal of Cleaner Production, vol. 73, pp. 40–51, 2014.

H. Kaydani, M. Najafzadeh, and A. Hajizadeh, “A new correlation for calculating carbon dioxide minimum miscibility pressure based on multi-gene genetic programming,” Journal of Natural Gas Science and Engineering, vol. 21, pp. 625–630, 2014.

B. Y. Ryu, H. J. Jung, and S. H. Bae, “Development of a corrected average speed model for calculating carbon dioxide emissions per link unit on urban roads,” Transportation Research Part D: Transport and Environment, vol. 34, pp. 245–254, 2015.

T. H. Ortmeyer and P. Pillay, “Trends in transportation sector technology energy use and greenhouse gas emissions,” Proceedings of the IEEE, vol. 89, no. 12, pp. 1837–1847, 2001.

W. R. Morrow, K. S. Gallagher, G. Collantes, and H. Lee, “Analysis of policies to reduce oil consumption and greenhouse-gas emissions from the US transportation sector,” Energy Policy, vol. 38, no. 3, pp. 1305–1320, 2010.

M. M. Lotfi and R. Tavakkoli-Moghaddam, “A genetic algorithm using priority-based encoding with new operators for fixed charge transportation problems,” Applied Soft Computing, vol. 13, no. 5, pp. 2711–2726, 2013.

A. Babazadeh, M. F. Langerudi, N. Afkar, and K. F. Shahandashti, “Parameter Selection In Particle Swarm Optimization For Transportation Network Design Problem,” arXiv preprint arXiv:1412.7185, 2014.

K. F. Wong and J. E. Beasley, “Vehicle routing using fixed delivery areas,” Omega, vol. 12, no. 6, pp. 591–600, 1984.

N. Norouzi, M. Sadegh-Amalnick, and M. Alinaghiyan, “Evaluating of the particle swarm optimization in a periodic vehicle routing problem,” Measurement, vol. 62, pp. 162–169, 2015.

Z. Huang and K. Geng, “Local search for dynamic vehicle routing problem with time windows,” in Instrumentation and Measurement, Sensor Network and Automation (IMSNA), 2013 2nd International Symposium on, 2013, pp. 841–844.

A. Nadizadeh, H. Hosseini Nasab, and H. H. Nasab, “Solving the dynamic capacitated location-routing problem with fuzzy demands by hybrid heuristic algorithm,” European Journal of Operational Research, vol. 238, no. 2, pp. 458–470, 2014.

B. Vahdani, D. V. N. Shekari, and S. M. Mousavi, “Multi-objective , multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair,” Neural Computing and Applications, 2016.

H. Ewbank, P. Wanke, and A. Hadi-Vencheh, “An unsupervised fuzzy clustering approach to the capacitated vehicle routing problem,” Neural Computing and Applications, pp. 857–867, 2015.

V. M. Dalfard, M. Kaveh, and N. E. Nosratian, “Two meta-heuristic algorithms for two-echelon location-routing problem with vehicle fleet capacity and maximum route length constraints,” Neural Computing and Applications, vol. 23, no. 7–8, pp. 2341–2349, 2013.

J. W. Escobar, R. Linfati, M. G. Baldoquin, and P. Toth, “A Granular Variable Tabu Neighborhood Search for the capacitated location-routing problem,” Transportation Research Part B: Methodological, vol. 67, pp. 344–356, 2014.

V. C. Hemmelmayr, “Sequential and parallel large neighborhood search algorithms for the periodic location routing problem,” European Journal of Operational Research, vol. 243, no. 1, pp. 52–60, 2015.

M. Rahimi, A. Baboli, and Y. Rekik, “Multi-objective inventory routing problem: A stochastic model to consider profit, service level and green criteria,” Transportation Research Part E: Logistics and Transportation Review, vol. 101, pp. 59–83, 2017.

E. Marcon, S. Chaabane, Y. Sallez, T. Bonte, and D. Trentesaux, “Simulation Modelling Practice and Theory A multi-agent system based on reactive decision rules for solving the caregiver routing problem in home health care,” Simulation Modelling Practice and Theory, vol. 74, pp. 134–151, 2017.

J. Mańdziuk and M. Świechowski, “UCT in Capacitated Vehicle Routing Problem with traffic jams,” Information Sciences, vol. 406, pp. 42–56, 2017.

M. S. Puga and J. Tancrez, “A heuristic algorithm for solving large location – inventory problems with demand uncertainty,” vol. 0, pp. 1–11, 2016.

E. Dehghani, nima Behfar, and M. S. ‎ Jabalameli, “Optimizing location, routing and inventory decisions in an integrated supply chain network under uncertainty,” Journal of Industrial and Systems Engineering, vol. 9, no. 4, p. 0, 2016.

M. Nasrollahi, A. S. Amiri, J. Razmi, and H. Nasrollahi, “Bullwhip Effect in Different Network Configuration,” vol. 3, no. 4, pp. 261–267, 2015.

M. Safaei and K. D. Thoben, “Measuring and evaluating of the network type impact on time uncertainty in the supply networks with three nodes,” Measurement: Journal of the International Measurement Confederation, vol. 56, pp. 121–127, 2014.

S. Rezvani, “Ranking generalized exponential trapezoidal fuzzy numbers based on variance,” Applied Mathematics and Computation, vol. 262, pp. 191–198, 2015.

S. M. Mousavi, N. Alikar, S. T. A. Niaki, and A. Bahreininejad, “Two tuned multi-objective meta-heuristic algorithms for solving a fuzzy multi-state redundancy allocation problem under discount strategies,” Applied Mathematical Modelling, vol. 39, no. 22, pp. 6968–6989, 2015.

O. Al Jadaan, L. Rajamani, and C. R. Rao, “NON-DOMINATED RANKED GENETIC ALGORITHM FORSolving constrained multi-objective optimization problems using non-dominated ranked genetic algorithm,” Journal of Theoretical and Applied Information Technology, pp. 113–118, 2008.

H. S. Kilic, S. Zaim, and D. Delen, “Selecting ‘The Best’ ERP system for SMEs using a combination of ANP and PROMETHEE methods,” Expert Systems with Applications, vol. 42, no. 5, pp. 2343–2352, 2015.

V. M. Athawale and S. Chakraborty, “Facility Location Selection using PROMETHEE II Method,” in International Conference on Industrial Engineering and Operations Management Dhaka, 2010, pp. 59–64.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.

S. H. R. Pasandideh, S. T. A. Niaki, and K. Asadi, “Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA,” Information Sciences, vol. 292, pp. 57–74, 2015.

J. Moore and R. Chapman, “Application of particle swarm to multiobjective optimization,” Department of Computer Science and Software Engineering Department, Auburn University, pp. 1–4, 1999.

Azadeh, M. Ravanbakhsh, M. Rezaei-Malek, M. Sheikhalishahi, and A. Taheri-Moghaddam, “Unique NSGA-II and MOPSO Algorithms for Improved Dynamic CMS by Considering Human Factors,” Applied Mathematical Modelling, 2017.

G. C. Montgomery, D. C., & Runger, Applied statistics and probability for engineers, 6th ed. NJ: Wiley, 2007.