Decision Analysis of Disturbance Management in the Process of Medical Supplies Transportation after Natural Disasters
Abstract
:1. Introduction
2. Literature Review
2.1. Disturbance Management in Transportation
2.2. Distribution Optimization of Medical Supplies
3. Model Formulation
3.1. Problem Description
- As mentioned above, after a natural disaster, any one of a number of factors, such as vehicles, cargoes, paths, demands, and others, may be disturbed in the process of cold-chain distribution, which will influence the distribution task. In this paper, we assume that the transport capacity is disturbed (vehicle breakdown) to study the distribution optimization after a single type of disturbance event occurs.
- After a natural disaster, the rescuers dispatched by the government restore basic communication and traffic as soon as possible and rescue the survivors simultaneously. On the basis of unimpeded communication and roads, medical supply distribution can be carried out to ensure timely medical assistance to the injured and to reduce the risk of secondary disasters caused by the outbreak of epidemic situation. So, in this paper, we assume that the communication and roads are basically unimpeded during the distribution process of medical supplies.
- The location and service time windows of TMPs are known.
- The initial distribution scheme is known, and the same type of refrigerated vehicle is used.
- In a distribution task, each TMP is only served once.
3.2. Disturbance Processing Strategy
3.3. Parameters and Variables
3.4. Measurement of Disturbance
3.4.1. The Cost Disturbance
3.4.2. The Time Disturbance
3.5. The DMMSD Model Setting
4. Algorithm Design
5. Numerical Experimental Design
5.1. Model Experiment
5.1.1. Experimental Parameters
5.1.2. Initial Distribution Scheme
5.1.3. A Disturbance Occurs
5.2. Experimental Results
5.2.1. Results under Different Objective Weights
- (1)
- Different weight coefficients of the objective functions correspond to different disturbance recovery schemes. As shown in Table 6, we set the weight coefficients of the time and cost objective functions to four different sets of numerical values (; ); then, four different disturbance recovery distribution schemes were obtained.
- (2)
- The number of vehicles used in different disturbance recovery distribution schemes is discrepant. As shown in Table 6, there were different numbers of vehicles in the four distribution schemes. The original distribution vehicles continued to be used in the first two distribution schemes; vehicle 1 was dispatched to rescue disturbed vehicle 3 and was responsible for the remaining TMPs that had not been served in the delivery tasks of vehicles 1 and 3 (i.e., the red route in Figure 6a,b). Vehicle 4 was added in the latter two distribution schemes to assist vehicle 1 in completing the service to the remaining TMPs that had not been served in the delivery tasks of vehicles 1 and 3 (i.e., the red route belongs to vehicle 1 and the purple route belongs to vehicle 4 in Figure 6c,d).
- (3)
- For the time disturbance subobjective, the disturbance gradually decreased with an increase in its weight, but the disturbance of the distribution cost gradually increased. As seen from the results in Table 6, gradually increased with the decrease in , and continuously decreased with the increase in . In other words, as the weight coefficient of the cost objective function decreased, the cost of the disturbance recovery distribution scheme gradually increased, but the disturbance of the rescue scheme to time gradually decreased.
- (4)
- The different weight coefficient settings of objective functions resulted in the same fitness value. From the results in Table 6, we found that the same value of could be obtained from different values of by calculations using Formula (14). This means that the different weight coefficient settings of the objective functions have no influence on the fitness value of the optimal solution, but different values of cost and time disturbance subobjective functions are formed; in addition, different disturbance recovery schemes are obtained at the same time. Therefore, in the face of actual disturbance events, emergency rescue decision makers should set the appropriate weight combination of objective function according to the current disturbance situation.
5.2.2. The Convergence of the Algorithm
5.3. Analysis of Experimental Results
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Han, W.; Chen, L.; Jiang, B.; Ma, W.; Zhang, Y. Major natural disasters in China, 1985–2014: Occurrence and damages. Int. J. Environ. Res. Public Health 2016, 13, 1118. [Google Scholar] [CrossRef] [PubMed]
- Chan, E.Y.Y.; Gao, Y.; Griffiths, S.M. Literature review of health impact post-earthquakes in China 1906–2007. J. Public Health 2010, 32, 52–61. [Google Scholar] [CrossRef] [PubMed]
- Chan, E.Y.Y.; Guo, C.; Lee, P.; Liu, S.; Mark, C.K.M. Health emergency and disaster risk management (health-EDRM) in remote ethnic minority areas of rural China: The case of a flood-prone village in Sichuan. Int. J. Disaster Risk Sci. 2017, 8, 156–163. [Google Scholar] [CrossRef]
- Lo, S.T.T.; Chan, E.Y.Y.; Chan, G.K.W.; Murray, V.; Abrahams, J.; Ardalan, A.; Kayano, R.; Yau, J.C.W. Health emergency and disaster risk management (health-EDRM): Developing the research field within the Sendai framework paradigm. Int. J. Disaster Risk Sci. 2017, 8, 1–5. [Google Scholar] [CrossRef]
- Nelson, C.M.; Wibisono, H.; Purwanto, H.; Mansyur, I.; Moniaga, V.; Widjaya, A. Hepatitis B vaccine freezing in the Indonesian cold chain: Evidence and solutions. Bull. World Health Organ. 2004, 82, 99–105. [Google Scholar] [PubMed]
- Haidari, L.A.; Brown, S.T.; Ferguson, M.; Bancroft, E.; Spiker, M.; Wilcox, A.; Ambikapathi, R.; Sampath, V.; Connor, D.L.; Lee, B.Y. The economic and operational value of using drones to transport vaccines. Vaccine 2016, 34, 4062–4067. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jozefowiez, N.; Mancel, C.; Mora-Camino, F. A heuristic approach based on shortest path problems for integrated flight, aircraft, and passenger rescheduling under disruptions. Eur. J. Inf. Syst. 2013, 64, 384–395. [Google Scholar] [CrossRef]
- Kohla, N.; Larsenb, A.; Larsenc, J.; Rossd, A.; Tiourinee, S. Airline disruption management—Perspectives, experiences and outlook. J. Air Transp. Manag. 2007, 13, 149–162. [Google Scholar] [CrossRef] [Green Version]
- Ambulkar, S.; Blackhurst, J.; Grawe, S. Firm’s resilience to supply chain disruptions: Scale development and empirical examination. J. Oper. Manag. 2015, 33–34, 111–122. [Google Scholar] [CrossRef]
- Revilla, E.; Sáenz, M.J. Supply chain disruption management: Global convergence vs. national specificity. J. Bus. Res. 2014, 67, 1123–1135. [Google Scholar] [CrossRef]
- Yuan, J.; Mu, Y. Rescheduling with release dates to minimize makespan under a limit on the maximum sequence disruption. Eur. J. Oper. Res. 2007, 182, 936–944. [Google Scholar] [CrossRef]
- Wang, K.; Choi, S.H. A decomposition-based approach to flexible flow shop scheduling under machine breakdown. Int. J. Prod. Res. 2012, 50, 215–234. [Google Scholar] [CrossRef]
- Cacchiani, V.; Huisman, D.; Kidd, M.; Kroon, L.; Toth, P.; Veelenturf, L.; Wagenaar, J. An overview of recovery models and algorithms for real-time railway rescheduling. Transp. Res. B Methodol. 2014, 63, 15–37. [Google Scholar] [CrossRef]
- Yu, G.; Qi, X. Disruption Management: Framework, Models and Applications; World Scientific: Singapore, 2004. [Google Scholar]
- Zeimpekis, V.; Giaglis, G.M.; Minis, I. In A dynamic real-timefleet management system for incident handling in city logistics. In Proceedings of the IEEE Vehicular Technology Conference, Stockholm, Sweden, 30 May–1 June 2005. [Google Scholar]
- Potvin, J.Y.; Xu, Y.; Benyahia, I. Vehicle routing and scheduling with dynamic travel times. Comput. Oper. Res. 2006, 33, 1129–1137. [Google Scholar] [CrossRef]
- Taniguchi, E.; Shimamoto, H. Intelligent transportation system based dynamic vehicle routing and scheduling with variable travel times. Transp. Res. Part C 2004, 12, 235–250. [Google Scholar] [CrossRef]
- Ruan, J.; Wang, X. Disruption management of emergency medical supplies intermodal transportation with updated transit centers. Oper. Res. Manag. Sci. 2016, 25, 114–124. [Google Scholar]
- Ding, Q.; Hu, X.; Jiang, Y. A model of disruption management based on prospect theory in logistic distribution. J. Manag. Sci. China 2014, 17, 1–9. [Google Scholar]
- Liu, C.; Zhu, Z.; Liu, L. Disruption management of location-routing problem (LRP) for emergency logistics system in early stage after earthquake. Comput. Eng. Appl. 2017, 53, 224–230. [Google Scholar]
- Ramezanian, R.; Behboodi, Z. Blood supply chain network design under uncertainties in supply and demand considering social aspects. Transp. Res. Part E 2017, 104, 69–82. [Google Scholar] [CrossRef]
- Wang, K.; Ma, Z.; Zhou, Y. A two-phase decision-making approach for emergency blood transferring problem in public emergencies. J. Transp. Syst. Eng. Inf. Technol. 2013, 13, 169–178. [Google Scholar]
- Chen, Y.; Lv, W.; Jiang, J. Research on cold-chain delivery model of multi-vaccines based on time constraint. Logist. Sci.-Tech. 2017, 40, 24–26. [Google Scholar]
- Campbell, A.M.; Vandenbussche, D.; Hermann, W. Routing for relief efforts. Transp. Sci. 2008, 42, 127–145. [Google Scholar] [CrossRef]
- Battini, D.; Peretti, U.; Persona, A.; Sgarbossa, F. Application of humanitarian last mile distribution model. J. Humanit. Logist. Supply Chain Manag. 2014, 4, 131–148. [Google Scholar] [CrossRef]
- Ruan, J.; Wang, X.; Shi, Y. A two-stage approach for medical supplies intermodal transportation in large-scale disaster responses. Int J. Environ. Res. Public Health 2014, 11, 11081–11109. [Google Scholar] [CrossRef] [PubMed]
- Caunhye, A.M.; Nie, X.; Pokharel, S. Optimization models in emergency logistics: A literature review. Socio-Econ. Plan. Sci. 2011, 46, 4–13. [Google Scholar] [CrossRef]
- Galindo, G.; Batta, R. Review of recent developments in or/ms research in disaster operations management. Eur. J. Oper. Res. 2013, 230, 201–211. [Google Scholar] [CrossRef]
- Najafi, M.; Eshghi, K.; Dullaert, W. A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transp. Res. Part E 2013, 49, 217–249. [Google Scholar] [CrossRef]
- Omair, M.; Sarkar, B. Minimum quantity lubrication and carbon footprint: A step towards sustainability. Sustainability 2017, 9, 714. [Google Scholar] [CrossRef]
- Habib, M.S.; Sarkar, B. An integrated location-allocation model for temporary disaster debris management under an uncertain environment. Sustainability 2017, 9, 716. [Google Scholar] [CrossRef]
- Ahmed, W.; Sarkar, B. Impact of carbon emissions in a sustainable supply chain management for a second generation biofuel. J. Clean. Prod. 2018, 186, 807–820. [Google Scholar] [CrossRef]
- Sarkar, B.; Ahmed, W.; Kim, N. Joint effects of variable carbon emission cost and multi-delay-in-payments under single-setup-multiple-delivery policy in a global sustainable supply chain. J. Clean. Prod. 2018, 185, 421–445. [Google Scholar] [CrossRef]
- Sarkar, B. Supply chain coordination with variable backorder, inspections, and discount policy for fixed lifetime products. Math. Probl. Eng. 2016, 2016, 1–14. [Google Scholar] [CrossRef]
- Sarkar, B.; Ganguly, B.; Sarkar, M.; Pareek, S. Effect of variable transportation and carbon emission in a three-echelon supply chain model. Transp. Res. Part E 2016, 91, 112–128. [Google Scholar] [CrossRef]
- Sarkar, B. A production-inventory model with probabilistic deterioration in two-echelon supply chain management. Appl. Math. Model. 2013, 37, 3138–3151. [Google Scholar] [CrossRef]
- Sarkar, B.; Sana, S.S.; Chaudhuri, K. An inventory model with finite replenishment rate, trade credit policy and price-discount offer. J. Ind. Eng. 2013, 2013, 18. [Google Scholar] [CrossRef]
- Moon, I.; Shin, E.; Sarkar, B. Min-max distribution free continuous—Review model with a service level constraint and variable lead time. Appl. Math. Comput. 2014, 229, 310–315. [Google Scholar] [CrossRef]
- Sarkar, B.; Saren, S.; Sinha, D.; Sun, H. Effect of unequal lot sizes, variable setup cost, and carbon emission cost in a supply chain model. Math. Probl. Eng. 2015, 2015, 1–13. [Google Scholar] [CrossRef]
- He, Z.; Chen, P.; Liu, H.; Guo, Z. Performance measurement system and strategies for developing low-carbon logistics: A case study in China. J. Clean. Prod. 2017, 156, 395–405. [Google Scholar] [CrossRef]
- He, Z.; Guo, Z.; Wang, J. Integrated scheduling of production and distribution operations in a global MTO supply chain. Enterp. Inf. Syst. 2018, 19, 94–122. [Google Scholar] [CrossRef]
- Guo, Z.; Zhang, D.; Liu, H.; He, Z.; Shi, L. Green transportation scheduling with pickup time and transport mode selections using a novel multi-objective memetic optimization approach. Transp. Res. D 2016, 60, 137–152. [Google Scholar] [CrossRef]
- Wang, S.; Tao, F.; Shi, Y. In Optimization of air freight network considering the time window of customer, In Proceedings of the IEEE International Conference on Industrial Technology and Management, Oxford, UK, 7–9 May 2018.
- Wang, X.; Ruan, J.; Zhang, K.; Ma, C. Study on combinational disruption management for vehicle routing problem with fuzzy time windows. J. Manag. Sci. China 2011, 14, 2–15. [Google Scholar]
- Wang, S.; Tao, F.; Shi, Y.; Wen, H. Optimization of vehicle routing problem with time windows for cold chain logistics based on carbon tax. Sustainability 2017, 9, 694. [Google Scholar] [CrossRef]
- Wang, S.; Tao, F.; Shi, Y. Optimization of inventory routing problem in refined oil logistics with the perspective of carbon tax. Energies 2018, 11, 1437. [Google Scholar] [CrossRef]
- Liu, W.Y.; Lin, C.C.; Chiu, C.R.; Tsao, Y.S.; Wang, Q. Minimizing the carbon footprint for the time-dependent heterogeneous-fleet vehicle routing problem with alternative paths. Sustainability 2014, 6, 4658–4684. [Google Scholar] [CrossRef]
- Wang, S.; Tao, F.; Shi, Y. Optimization of location–routing problem for cold chain logistics considering carbon footprint. Int. J. Environ. Res. Public Health 2018, 15, 86. [Google Scholar] [CrossRef] [PubMed]
Parameters and Variables | Meaning |
---|---|
Collection of temporary medical points (TMPs) | |
Collection of TMPs that have been served when a disturbance event occurs | |
Collection of TMPs that have not been served when a disturbance event occurs | |
Collection of pseudo demand points | |
Collection of distribution vehicles | |
Collection of vehicles on their way to deliver cargoes | |
Collection of vehicles at the Medical Supplies Distribution Center (MSDC) | |
Starting point of the vehicle | |
End point of the vehicle after the delivery service is completed | |
Collection of all available vehicles | |
Collection of task points after the disturbance event occurs | |
Original distribution scheme | |
New distribution scheme, the collection of network nodes changes as | |
Total number of TMPs that have not been served | |
Total number of the vehicles on the way to deliver cargoes in the original distribution scheme | |
Collection of points, , where represents the TMPs that have not been served; is the location of the disturbed vehicle; represents the location of the vehicles on the way to deliver cargoes when a disturbance event occurs, that is, the pseudo distribution centers; is the location of the MSDC, which is the initial distribution center | |
Distance between and | |
Speed of the refrigerated vehicles | |
Time that the temperature inside the refrigerated box goes up by 1 °C when the refrigeration equipment fails to work | |
Critical temperature at which the medical supplies approach deterioration | |
Temperature inside the refrigerated box when the refrigerated vehicle is normal working | |
Service time of the vehicle for | |
Moment of the disturbance event occurs | |
Time for vehicle to reach TMP in the original plan | |
Time for vehicle to reach TMP after adjusting the distribution scheme | |
Time window of TMP , including the beginning and end point of the arrival time required by the TMP | |
Medical supply demands of TMP in the original distribution scheme | |
Medical supply demands of TMP after the disturbance event occurs | |
Maximum load allowed for refrigerated vehicle | |
Available load of vehicle that is on its way to deliver cargo | |
Transportation cost for a unit of distance for a refrigerated vehicle from TMP to TMP | |
Collection of distribution path edge in the new scheme | |
Collection of distribution path edge in the original scheme | |
The deviation parameter of the path, when , indicates the path edge between TMP and TMP is shown in the original scheme but not in the new scheme; when , indicates the path edge between TMP and TMP is shown in the new scheme but not in the original scheme; when , indicates the path edge between TMP and TMP is shown in both the original and new schemes | |
A 0–1 variable, represents that vehicle is driven from TMP to TMP , otherwise | |
A 0–1 variable, represents that vehicle is driven from the pseudo distribution center to demand point , otherwise | |
A 0–1 variable, represents that vehicle is driven from the MSDC to demand point , otherwise |
Number | Longitude (° E) | Latitude (° N) | Demand (Box) | Acceptable Time Window | Service Time (min) |
---|---|---|---|---|---|
1 | 105.385 | 30.871 | 0 | 5:30–17:00 | 0 |
2 | 105.439 | 31.012 | 1.5 | 6:00–8:00 | 10 |
3 | 105.396 | 30.983 | 0.5 | 7:30–9:00 | 5 |
4 | 105.535 | 30.885 | 1.5 | 6:00–8:00 | 10 |
5 | 105.396 | 30.791 | 1.5 | 6:30–8:20 | 10 |
6 | 105.346 | 30.816 | 1 | 7:40–9:30 | 8 |
7 | 105.287 | 30.989 | 1 | 7:00–9:00 | 8 |
8 | 105.243 | 30.896 | 0.5 | 7:20–9:00 | 5 |
9 | 105.396 | 30.923 | 1 | 7:30–9:00 | 8 |
10 | 105.236 | 30.855 | 0.5 | 7:00–8:30 | 5 |
11 | 105.250 | 30.803 | 1 | 7:30–9:30 | 8 |
12 | 105.312 | 30.755 | 2 | 7:30–9:30 | 15 |
13 | 105.237 | 30.752 | 0.5 | 7:30–9:30 | 5 |
14 | 105.233 | 30.697 | 1.5 | 7:30–9:30 | 10 |
15 | 105.352 | 30.680 | 1.5 | 7:30–9:00 | 10 |
16 | 105.418 | 30.720 | 1.5 | 6:50–8:30 | 10 |
17 | 105.618 | 30.911 | 1.5 | 7:00–8:40 | 10 |
18 | 105.572 | 30.958 | 1.5 | 7:00–8:40 | 10 |
19 | 105.395 | 31.063 | 0.5 | 7:50–9:00 | 5 |
20 | 105.172 | 31.009 | 1 | 6:30–8:30 | 8 |
21 | 105.133 | 30.956 | 1 | 7:50–9:00 | 8 |
Parameter | Parameter Value | Parameter | Parameter Value |
---|---|---|---|
Outline dimension | 4845 × 2000 × 2500 (mm) | Container volume | 3 m3 |
Fastest speed | 135 km/h | Rated load capacity | 670 kg |
Engine type | SOFIM8140.43S4 | Fuel type | diesel oil |
Engine power | 95 kw | Engine emission volume | 2798 mL |
Parameter | Parameter Value |
---|---|
15 min | |
8 °C | |
2 °C | |
133 min | |
300 CNY | |
1000 CNY | |
60 CNY/h | |
80 CNY/h | |
PC | 0.8 |
PM | 0.2 |
LS | 200 |
Route Number | Service Order |
---|---|
1 | 1-5-16-15-14-12-6-1 |
2 | 1-3-19-2-18-17-4-9-1 |
3 | 1-7-20-21-8-10-11-13-1 |
Weights | ||||
---|---|---|---|---|
The number of vehicles that complete the remaining distribution Tasks | 2 | 2 | 3 | 3 |
Path of Vehicle 1 | 12-AP-11-13-6-1 | 12-13-AP-11-6-1 | 12-AP-11-1 | 12-11-AP-1 |
Path of Vehicle 2 | 17-4-9-1 | 17-4-9-1 | 17-4-9-1 | 17-4-9-1 |
Path of Vehicle 3 | AP | AP | AP | AP |
Path of Vehicle 4 | / | / | 1-13-6-1 | 1-13-6-1 |
/CNY | 1526.1 | 1819.7 | 2400.4 | 3627.5 |
/min | 153.7 | 103.4 | 99.7 | 80.5 |
Average time for solving (s.) | 8.4 | 8.1 | 8.9 | 10.2 |
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Shi, Y.; He, Z. Decision Analysis of Disturbance Management in the Process of Medical Supplies Transportation after Natural Disasters. Int. J. Environ. Res. Public Health 2018, 15, 1651. https://doi.org/10.3390/ijerph15081651
Shi Y, He Z. Decision Analysis of Disturbance Management in the Process of Medical Supplies Transportation after Natural Disasters. International Journal of Environmental Research and Public Health. 2018; 15(8):1651. https://doi.org/10.3390/ijerph15081651
Chicago/Turabian StyleShi, Yuhe, and Zhenggang He. 2018. "Decision Analysis of Disturbance Management in the Process of Medical Supplies Transportation after Natural Disasters" International Journal of Environmental Research and Public Health 15, no. 8: 1651. https://doi.org/10.3390/ijerph15081651