Hazardous waste management system for Thailand’s local administrative organization via route and location selection

Figures & data

Figure 1. Conceptual diagram of waste management optimization by DSS.

Table 1. The relevant literature on hazardous waste management system.

References	Objective	Solution Method	Decision Problem	Validation Methodology
Samanlioglu (2013)	Minimize simultaneously total cost, transportation risk and location risk	A lexicographic weighted Tchebycheff approach (CPLEX)	Location-routing problem	Case study
Berglund and Kwon (2014)	Minimize total cost (fixed facility cost, transportation cost and exposure risk)	An exact full enumeration method, Genetic algorithm	Location-routing problem	Numerical experiment
Jiang et al. (2014)	Multi-objective and multimodal model	Exact method (LINGO)	Location-routing problem	Case study
Li et al. (2015)	Optimal locating approach	Integer linear programming method	Location	Case study
Ghezavati and Morakabatchian (2015)	Multi-objective location-routing model	Exact method (LINGO)	Case study	Case study
Zhao et al. (2016)	Multi-objective continuous network flow model by minimizing jointly total cost and total risk	Scalarization-based multi-objective integer programming approaches i.e. weighted-sum approach, Tchebycheff approach and e-constraint approach	Location-routing problem	Numerical experiment and case study
Yılmaz, Kara, and Yetiş (2017)	Mixed-integer location/routing model by minimizing transportation cost and risks	Exact methos	Location-routing problem	Case study
Ma et al. (2018)	Multi-objective robust optimization model	A fuzzy C-means clustering particle swarm optimization algorithm	Vehicle Routing Problems (VRPs)	Case study
Rabbani, Heidari, and Yazdanparast (2019)	A stochastic mixed-integer nonlinear programming model	Non-dominated sorting genetic Algorithm (NSGA-II) and monte carlo simulation	Location-routing problem, and inventory	Numerical experiment
Araee and Aghamohammadi (2020)	Multi-objective model by minimizing travel time, travel risks, and economic aspects	Two meta-heuristic algorithms: multi-objective particle swarm optimization algorithm (MOPSO) and the non-dominated sorting genetic algorithm (NSGA II)	Vehicle Routing Problems (VRPs)	Numerical experiment
Homayouni and Pishvaee (2020)	A multi-objective robust optimization model by minimizing the total cost including transportation and operations costs, and total risk of transportation and operations	Augmented ε-constraint method	Location- allocation problem	Numerical experiment and case study
Yu et al. (2020)	Two single-objective optimization problems for cost and risk minimization	A sample average approximation based goal programming algorithm (SAA-GP)	Location	Numerical experiment and case study
Pourhejazy et al. (2021)	Mixed-integer Linear Programming MILP (Capacitated General Routing Problem with Time-Window, CGRPTW)	Two metaheuristic algorithms i.e. neighborhood search procedures and tabu search	Vehicle Routing Problems (VRPs)	Case study
Raeisi and Ghoushchi (2022)	Robust-fuzzy optimization model	Several meta-heuristic algorithms such as non-dominated sorting genetic algorithm-II, multi-objective particle swarm optimization, multi-objective invasive weed optimization, pareto envelope-based selection algorithm, multi-objective evolutionary algorithm based on decomposition and multi-objective gray wolf optimizer algorithms	Location-routing problem	Numerical experiment

Figure 2. Diagram of a decision support system via zero-one programming models (DSS-ZOPM).

Table 2. Parameters and decision variables including their definitions for route selection.

Parameter and Decision Variable	Definition
$a_{gr}$	Constant decision variable’s coefficient of the goal g the decision variable r
$d_g^{-}$	Underachievement deviation of the goal g
$d_g^{+}$	Overachievement deviation of the goal g
$G_g$	Value of the goal g
$w_g$	AHP weight of the goal g
$x_r$	Decision variable r with a value of 0 indicating no selection and 1 indicating selection

Table 3. Indices, parameters, and decision variables including their definitions for ZOLD.

Parameters and Decision Variables	Definitions
i	Local administration organization (LAO); i ∈ {1, 2, 3, …, 62}
j	Hub index; j ∈ {1, 2, 3, …, H}; H = 6 or 15
$C_j^F$	Fixed cost for hub j
$C_{j\leftarrow i}^T$	Six-wheel vehicle transport cost: LAO i → hub j
$C_{WDP\leftarrow j}^T$	Ten-wheel vehicle transport cost: hub j → WDP
$P_{j\leftarrow i}$	Penalty for not using full six-wheel vehicle capacity: LAO i → hub j
$P_{WDP\leftarrow j}$	Penalty for not using full ten-wheel vehicle capacity: hub j → WDP
$D_{j\leftarrow i}$	BSF distance: LAO i → hub j
$D_{WDP\leftarrow j}$	BSF distance: hub j → WDP
$R_{j\leftarrow i}$	The actual number of six-wheel vehicles used: LAO i → hub j $or{Q}_i/C{A}_{SWT}$
$R_{WDP\leftarrow j}$	The actual number of ten-wheel vehicles used: hub j → WDP $or\left({\sum }_{i=1}^{62-H}{Q}_i{X}_{ij}\right)/C{A}_{TWT}$
$N_{j\leftarrow i}$	Number of six-wheel vehicles used: LAO i → hub j
$N_{WDP\leftarrow j}$	Number of ten-wheel vehicles used: hub j → WDP
$Q_k$	Hazardous waste quantities in each province, k
$C{A}_{SWT}$	Six-wheel vehicles capacity
$C{A}_{TWT}$	Ten-wheel vehicles capacity
$Y_iϵ\left\{0,1\right\}$	$Y_i$: 1 if hub j is selected; 0 otherwise
$X_{ij}ϵ\left\{0,1\right\}$	$X_{ij}$: 1 if there is the transport: LAO i → hub j; 0 otherwise

Table 4. Alternative locations for the hubs using CoG.

Scenario	Alternative locations for hubs
A	Lampang, Sing Buri, Ratchaburi, Chonburi, Udon Thani, Buri Ram
B	Chiang Mai, Nan, Phitsanulok, Nakhon Sawan, Lop Buri, Saraburi, Ratchaburi, Samut Sakhon, Samut Prakan, Chonburi, Roi Et, Udon Thani, Sakon Nakhon, Nakhon Ratchasima, Ubon Ratchathani

Table 5. Pairwise comparison matrix for the route selection.

	C1	C2	C3	C4	C5
C1	1.000	2.000	3.000	3.000	3.000
C2	0.500	1.000	4.000	2.000	2.000
C3	0.333	0.250	1.000	2.000	0.333
C4	0.333	0.500	0.500	1.000	1.000
C5	0.333	0.500	3.000	1.000	1.000

Table 6. The objective function value (Z), individual deviation percentage from the goal, and BSF routes (Rt) from the 6 hubs to the waste disposal plant (WDP←H, Rt) for the first scenario.

H	R(WDP←H, Rt)	Z	Deviation Percentage for the goal, g
			1	2	3	4	5
Lampang	R(WDP←H1, 1)	0.247	0.316	0.174	0.308	0.064	0.266
Udon Thani	R(WDP←H1, 2)	0.130	0.120	0.177	0.048	0.309	0.131
Sing Buri	R(WDP←H2, 3)	0.122	0.100	0.298	0.018	0.180	0.168
Buri Ram	R(WDP←H6, 5)	0.089	0.114	0.033	0.027	0.015	0.235
Ratchaburi	R(WDP←H3, 1)	0.247	0.302	0.152	0.219	0.236	0.312
Chonburi	R(WDP←H4, 2)	0.278	0.297	0.224	0.265	0.294	0.305

Table 7. Local administrative organizations in each hub in charging hazardous waste transportation for scenario A.

Hub	Range of services (Set of LAOs)
Lampang	Chiang Mai, Lamphun, Uttaradit, Phrae, Nan, Phayao, Chiang Rai, Mae Hong Son, Tak, Sukhothai
Udon Thani	Bueng Kan, Nong Bua Lam Phu, Loei, Nong Khai, Kalasin, Sakon Nakhon, Nakhon Panom, Mukdahan
Sing Buri	Ang Thong, Lopburi, Chai Nat, Phichit, Phetchabun, Suphan Buri, Nakhon Sawan
Buri Ram	Nakhon Ratchasima, Surin, Si Sa Ket, Ubon Ratchathani, Yasothon, Chaiyaphum, Amnat Charoen, Khon Kaen, Maha Sarakham, Roi Et
Ratchaburi	Nonthaburi, Pathum Thani, Phra Nakhon Si Ayutthaya, Uthai Thani, Kamphaeng Phet, Nakhon Pathom, Samut Sakhon, Samut Songkhram, Phetchaburi, Prachuap Khiri Khan, Kanchanaburi
Chon Buri	Samut Prakan, Saraburi, Rayong, Chanthaburi,Trat, Chachoengsao, Prachin Buri, Nakhon Nayok, Sa Kaeo

Table 8. Local administrative organizations in each hub in charging hazardous waste transportation for scenario B.

Hub	Range of services (Set of LAOs)
Chiang Mai	Chiang Rai, Mae Hong Son
Nan	Phayao
Phitsanulok	Lamphun, Lampang, Uttaradit, Phrae, Tak, Sukhothai, Phichit, Nong Bua Lam Phu, Khon Kaen, Loei
Udon Thani	Bueng Kan, Nong Khai
Sakon Nakhon	Nakhon Panom, Mukdahan
Ubon Ratchathani	Amnat Charoen
Roi Et	Yasothon, Maha Sarakham, Kalasin
Nakhon Ratchasima	Buri Ram, Surin, Si Sa Ket, Chaiyaphum
Nakhon Sawan	Chai Nat, Kanchanaburi, Suphan Buri, Uthai Thani, Kamphaeng Phet
Lop Buri	Phetchabun
Saraburi	Prachin Buri, Nakhon Nayok, Sa Kaeo
Chonburi	Rayong, Chanthaburi, Trat, Chachoengsao
Samut Prakan	Nonthaburi, Pathum Thani, Phra Nakhon Si Ayutthaya, Sing Buri, Ang Thong
Samut Sakhon	Nakhon Pathom, Samut Songkhram
Ratchaburi	Phetchaburi, Prachuap Khiri Khan

Table 9. Comparative cost (USD) from ZOLD model based on both scenarios.

Scenario	Fixed cost	Transport cost		Penalty cost		Total cost
		LAO to Hub	Hub to WDP	LAO to Hub	Hub to WDP
A	205,714.29	841,204.29	435,935.00	383,657.86	70,333.57	1,936,845.00
B	514,285.71	171,840.00	259,253.00	512,300.71	139,087.14	1,596,766.57

Figure 3. Scatter diagram of the hazardous waste management solution set.

Samanlioglu, F. 2013. A multi-objective mathematical model for the industrial hazardous waste location-routing problem. Eur. J. Oper. Res. 226 (2):332–40. doi:10.1016/j.ejor.2012.11.019.

 Web of Science ®Google Scholar

Berglund, P. G., and C. Kwon. 2014. Robust facility location problem for hazardous waste transportation. Network Spatial Econ. 14 (1):91–116. doi:10.1007/s11067-013-9208-4.

 Web of Science ®Google Scholar

Jiang, Y., X. Zhang, Y. Rong, and Z. Zhang. 2014. A multimodal location and routing model for hazardous materials transportation based on multi-commodity flow model. Procedia Soc. Behav. Sci. 138:791–99. doi:10.1016/j.sbspro.2014.07.262.

 Google Scholar

Li, L., S. Wang, Y. T. Lin, W. T. Liu, and T. Chi. 2015. A covering model application on Chinese industrial hazardous waste management based on an integer program method. Ecol. Indic. 51:237–43. doi:10.1016/j.ecolind.2014.05.001.

 Web of Science ®Google Scholar

Ghezavati, V., and S. Morakabatchian. 2015. Application of a fuzzy service level constraint for solving a multi-objective location-routing problem for the industrial hazardous wastes. J. Intell. Fuzzy Syst. 28 (5):2003–13. doi:10.3233/IFS-141341.

 Web of Science ®Google Scholar

Zhao, J., L. Huang, D. H. Lee, and Q. Peng. 2016. Improved approaches to the network design problem in regional hazardous waste management systems. Transp. Res. Part E Logist. Transp. Rev. 88:52–75. doi:10.1016/j.tre.2016.02.002.

 Web of Science ®Google Scholar

Yilmaz, O., B. Y. Kara, and U. Yetis. 2017. Hazardous waste management system design under population and environmental impact considerations. J. Environ. Manage. 203:720–31. doi:10.1016/j.jenvman.2016.06.015.

 PubMed Web of Science ®Google Scholar

Ma, C., W. Hao, R. He, and B. Moghimi. 2018. A multiobjective route robust optimization model and algorithm for hazmat transportation. Discrete Dyn. Nat. Soc. 2018:1–12. Article No. 2916391. doi:10.1155/2018/2916391.

 Web of Science ®Google Scholar

Rabbani, M., R. Heidari, and R. Yazdanparast. 2019. A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and Monte Carlo simulation. Eur. J. Oper. Res. 272 (3):945–61. doi:10.1016/j.ejor.2018.07.024.

 Web of Science ®Google Scholar

Araee, M., and B. Aghamohammadi. 2020. Designing a multi-objective model for a hazardous waste routing problem considering flexibility of routes and social effects. J. Ind. Prod. Eng. 37 (1):33–45. doi:10.1080/21681015.2020.1727970.

 Web of Science ®Google Scholar

Homayouni, Z., and M. S. Pishvaee. 2020. A bi-objective robust optimization model for hazardous hospital waste collection and disposal network design problem. J. Maternal Cycl. Waste Manage. 22 (6):1965–84. doi:10.1007/s10163-020-01081-8.

 Web of Science ®Google Scholar

Yu, H., X. Sun, W. D. Solvang, G. Laporte, and C. K. M. Lee. 2020. A stochastic network design problem for hazardous waste management. J. Clean. Prod. 277: Article No. 123566277. doi:10.1016/j.jclepro.2020.123566.

 PubMed Web of Science ®Google Scholar

Pourhejazy, P., D. Zhang, Q. Zhu, F. Wei, and S. Song. 2021. Integrated E-waste transportation using capacitated general routing problem with time-window. Transp. Res. Part E 145:Article No. 102169. doi:10.1016/j.tre.2020.102169.

 Google Scholar

Raeisi, D., and S. J. Ghoushchi. 2022. A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions. Appl. Intell. doi:10.1007/s10489-022-03334-5.

 PubMed Web of Science ®Google Scholar

Data availability statement

Data available on request from the authors: The data that support the findings of this study are available from the corresponding author, PL, upon reasonable request.