Community Agricultural Reservoir Construction and Water Supply Network Design in Ubon Ratchathani, Thailand, Using Adjusted Variable Neighborhood Strategy Adaptive Search
Abstract
:1. Introduction
2. Literature Review and Related Works
2.1. Mekong River Basin
2.2. Drought Management
2.3. VaNSAS and Metaheuristics
2.4. Most Recent Research in Reservoir Construction and Water Supply Network Design
3. Research Methodology
3.1. Model-Building Phase
- (1)
- The flow of water between two locations may not be possible due to a drainage divide or because the level of the area where the community’s reservoir is established is lower than the water demand points.
- (2)
- As the sizes of the reservoirs vary, the model must incorporate the size of the community reservoirs. Considering the appropriate land size or topography at a particular site, reservoir size can render some areas unsuitable for establishment. Therefore, it is imperative that this constraint be introduced into the proposed model.
- (3)
- Gravity flow is utilized to transport water from reservoirs to water demand nodes in the model. Consequently, the maximum distances of the pipes connecting to the reservoir must be enforced so that water can effectively flow from the reservoir to the demand node.
- (1)
- Step 2 of VaNSAS was updated to allow the current best tract to guide the search space.
- (2)
- Instead of employing three improvement approaches to enhance the solution quality of the tract solutions as in VaNSAS, we utilized five improvement strategies.
- (3)
- The newly constructed decoding method extracted the solution to the provided mathematical model. Pitakaso, R. et al. [7] proposed a decoding approach for solving the green 2-echelon location routing problem, whereas in this study, we solved the location–sizing–allocation issue. As mentioned in the section on mathematical model formulation, these two types of problems have different model attributes and characteristics; hence, the decoding method from real numbers was required to be the suggested model solution (this is explained in the following section).
3.2. Model Testing Phase
4. Results and Discussion
4.1. Model Building
4.1.1. Mathematical Model Formulation for Establishment of the Community Reservoir and Water Supply Network Design (CR–WSND)
Parameters | ||
${f}_{k}$ | Cost of constructing agricultural water resources with size $k$ (THB/cubic meters) | |
${U}_{k}$ | Volume of available water for CR of size $k\left({\mathrm{m}}^{3}\right)$ | |
$v$ | Cost of constructing irrigation system per distance from agricultural water resource $i$ to demand node $j$ (THB) | |
${w}_{j}$ | Water requirement at node $j$ (${\mathrm{m}}^{3}$) | |
${b}_{ij}$ | $\left\{\begin{array}{l}1\mathrm{if}\mathrm{water}\mathrm{from}\mathrm{node}i\mathrm{can}\mathrm{have}\mathrm{gravity}\mathrm{flow}\mathrm{to}j\left(\mathrm{drainage}\mathrm{divide}\mathrm{and}\mathrm{obstracle}\right)\\ 0\mathrm{otherwise}\end{array}\right.$ | |
${d}_{ij}$ | Distance from agricultural water resource $i$ to demand node $j$ ($\mathrm{m})$ | |
${c}_{ij}$ | Construction cost of linking nodes i and j (THB/meters) | |
${m}_{k}$ | Maximum distance of water flow from water resource with size k $\left(\mathrm{m}\right)$ | |
${r}_{i}$ | Aridity risk in area of node $i$ | |
G | Amount of water supply required by the target area population | |
${O}_{ik}$ | $\left\{\begin{array}{l}1\mathrm{if}\mathrm{reservoir}\mathrm{type}k\mathrm{can}\mathrm{be}\mathrm{located}\mathrm{at}\mathrm{location}i\\ 0\mathrm{otherwise}\end{array}\right.$ | |
Decision variables | ||
${X}_{ik}$ | $\left\{\begin{array}{l}1,\mathrm{if}\mathrm{node}i\mathrm{is}\mathrm{selected}\mathrm{to}\mathrm{be}\mathrm{an}\mathrm{agricultural}\mathrm{water}\mathrm{resource}\mathrm{with}\mathrm{size}k\\ 0,\mathrm{otherwise}\end{array}\right.$ | |
${Y}_{ij}$ | $\left\{\begin{array}{l}1,\mathrm{if}\mathrm{water}\mathrm{from}\mathrm{node}i\mathrm{is}\mathrm{assigned}\mathrm{to}\mathrm{demand}\mathrm{node}j\\ 0,\mathrm{otherwise}\end{array}\right.$ | |
Constraints: | ||
${\displaystyle \sum}_{j=1}^{J}}{Y}_{ij}{w}_{j}\le {\displaystyle {\displaystyle \sum}_{k\in K}}{U}_{k}{X}_{ik$ | $i\in I$ | (6) |
${\displaystyle \sum}_{k\in K}}{X}_{ik}\le 1$ | $i\in I$ | (7) |
${d}_{ij}{Y}_{ij}\le {\displaystyle {\displaystyle \sum}_{k\in K}}{m}_{k}{X}_{ik}$ | $i\in I,j\in J$ | (8) |
${\displaystyle \sum}_{j\in J}}{Y}_{ij}\ge {\displaystyle {\displaystyle \sum}_{k=1}^{K}}{X}_{ik$ | $i\in I$ | (9) |
${Y}_{ij}{h}_{j}\le {h}_{i}$ | $i\in I,j\in J$ | (10) |
${Y}_{ij}\le {b}_{ij}$ | $i\in I,j\in J$ | (11) |
${\displaystyle \sum}_{i\in I}}{\displaystyle {\displaystyle \sum}_{k\in K}}{U}_{k}{X}_{ik}\ge G$ | (12) | |
${\displaystyle \sum}_{i\in I}}{Y}_{ij}\ge 1$ | $j\in J$ | (13) |
${X}_{ik}\le {O}_{ik}$ | $i\in I,k\in K$ | (14) |
4.1.2. Model Testing
Case Study Data for Community Reservoir and Water Supply Network Design
Adjusted Variable Neighborhood Strategy Adaptive Search Solving Community Reservoir and Water Supply Network Design
4.2. Discussion
5. Conclusions and Future Research Opportunities
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. Example of the Vector Used in the Proposed Method
Node Track | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
1 | 0.67 | 0.43 | 0.03 | 0.10 | 0.69 | 0.72 | 0.54 | 0.56 | 0.58 | 0.68 |
2 | 0.73 | 0.17 | 0.43 | 0.90 | 0.76 | 0.12 | 0.06 | 0.46 | 0.87 | 0.30 |
3 | 0.90 | 0.55 | 0.92 | 0.67 | 0.43 | 0.10 | 0.35 | 0.94 | 0.50 | 0.60 |
4 | 0.09 | 0.22 | 0.50 | 0.89 | 0.48 | 0.60 | 0.86 | 0.92 | 0.69 | 0.58 |
5 | 0.26 | 0.63 | 0.79 | 0.90 | 0.89 | 0.37 | 0.53 | 0.50 | 0.12 | 0.52 |
Appendix B. Details of the Candidate Nodes
Node no. | Water Requirements (for the Demand Node; Cubic Meters) | Drought Risk | Height above Sea Level (Meters) |
1 | 757.7 | 0.85 | 144 |
2 | 826.7 | 0.85 | 184 |
3 | 928.1 | 0.85 | 149 |
4 | 582.4 | 0.85 | 190 |
5 | 849 | 0.85 | 183 |
6 | 916.5 | 0.65 | 164 |
7 | 770.2 | 0.65 | 164 |
8 | 641.7 | 0.65 | 148 |
9 | 998.9 | 0.65 | 164 |
10 | 806.3 | 0.65 | 135 |
Appendix C. Details of the Water Reservoir
Type of Water Reservoir | Criteria Probability for Selection | Capacity (Cubic Meters) | Distance Limitation (Kilometer Limitation (Kilometers)) | Construction Cost (Baht) |
SC | 0–0.33 | 1260 | 30 | 43,470 |
LC | 0.34–0.66 | 3780 | 80 | 121,440 |
AW | 0.67–1.00 | 4520 | 100 | 255,000 |
Appendix D. Example of the K Transition Method
Node Track # | 1 | 2 | 3 | 4 | 5 |
Target track | 0.23 | 0.44 | 0.39 | 0.18 | 0.92 |
Candidate track | 0.64 | 0.73 | 0.07 | 0.27 | 0.57 |
New selected track | 0.23 | 0.73 | 0.39 | 0.27 | 0.92 |
Appendix E. Example of the K Cyclic Move Method
Node Track | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
Initial | 0.67 | 0.43 | 0.03 | 0.10 | 0.69 | 0.72 | 0.54 | 0.56 | 0.58 | 0.68 |
After KCM #1 | 0.67 | 0.10 | 0.03 | 0.54 | 0.69 | 0.72 | 0.58 | 0.56 | 0.43 | 0.68 |
After KCM #2 | 0.67 | 0.54 | 0.03 | 0.58 | 0.69 | 0.72 | 0.43 | 0.56 | 0.10 | 0.68 |
After KCM #3 | 0.67 | 0.58 | 0.03 | 0.43 | 0.69 | 0.72 | 0.10 | 0.56 | 0.54 | 0.68 |
Appendix F. Results of the GBT
Elements Track # | 1 | 2 | 3 | 4 | 5 |
Target track | 0.45 | 0.12 | 0.27 | 0.45 | 0.19 |
Best track | 0.59 | 0.02 | 0.57 | 0.88 | 0.60 |
New selected track | 0.45 | 0.02 | 0.57 | 0.45 | 0.19 |
Appendix G. Pseudocode of A-VaNSAS
Algorithm A1. Adjusted variable neighborhood strategy adaptive search (A-VaNSAS) |
Input: Number of tracks (NT), number of nodes (D), scaling factor (F), improvement factor (K), and number of improvement/black box (NBB) Output: Best_Track_Solution Begin Population = Initialize Population (NT, D) IBPop = Initialize Information BB (NBB) Encode Population to WP While the stopping criterion is not met carry out For i = 1: NT //selected improvement box by RouletteWheelSelection selected_BB = RouletteWheelSelection(IBPop) If (selected_BB = 1) Then new_u = SWAP(u) Perform SWAP Else if (selected_BB = 2) new_u = 2-Opt (u) Perform 2-Opt Else if (selected_BB = 3) new_u = K-Transition (u) Perform K-Transition Else if (selected_BB = 4) new_u = K-Cyclic (u) Perform K-Cyclic If (CostFunction(new_u) ≤ CostFunction(V_{i})) Then V_{i} = New_u //Loop update heuristic information of Black box For j = 1:NBB BBPopi = α*(ABBi) + (1 − α)*(GBBi) + β*(NBBi) End For Loop//end update heuristics information End For Loop i End While Loop Return Best_Track_Solution End |
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Parameters | Range of Value | Unit Name | Parameters | Range of Value | Unit Name |
---|---|---|---|---|---|
I | 218 | Locations | $s$ | 0.84 | Baht/m^{2} [300] |
K | 3 (SC, LC, AW) | Types | ${r}_{i}$ | [0.25, 0.95] | - |
${f}_{k}$ | [43.47, 12.14, 22.5] | Thousand Baht | ${m}_{k}$ | [30, 80, 100] | km |
J | 218 | Locations | G | 5760.9 | Thousand cubic meters |
${d}_{ij}$ | [0, 15,000] | Meters | ${U}_{k}$ | [1260, 3780, 4520] | Thousand cubic meters |
${C}_{ij}$ | [28, 56] | Baht/meters | ${w}_{j}$ | [0.04, 169.21] | Thousand cubic meters |
${n}_{i}$ | [8000–160,000] | m^{2} × 10^{3} | - |
Parameter | Small | Medium | Large |
---|---|---|---|
Capacity (cubic meters) | 1260 | 3780 | 4520 |
W1 (meters) | 19.0 | 19.0 | 19.0 |
W2 (meters) | 15.0 | 15.0 | 15.0 |
L1 (meters) | 39.0 | 39.0 | 39.0 |
L2 (meters) | 35.0 | 35.0 | 35.0 |
H (meters) | 2.0 | 6.0 | 7.2 |
Maximum distance of water | |||
Network pipe (meters) | 50,000 | 100,000 | 150,000 |
Construction cost (thousand baht) | 43,470 | 121,440 | 255,000 |
Water Reservoir | Size | Supply to Node | CR Construction Cost (baht) | Water Network Construction Cost (baht) | DRMI (baht) | Total Cost (baht) |
---|---|---|---|---|---|---|
3 | AW | 3,8,9,1,10 | 255,000 | 95,700 | 10,900 | 339,800 |
4 | SC | 4 | 43,470 | 0 | 7875 | 35,595 |
2 | SC | 2 | 43,470 | 0 | 8559 | 34,911 |
7 | LC | 7,5,6 | 121,440 | 52,890 | 10,060 | 164,270 |
Grand total cost | 463,380 | 148,590 | 37,394 | 574,576 |
KPI | Algorithm | |||
---|---|---|---|---|
GA | DE | VaNSAS | A-VaNSAS | |
NC (nodes) | 218 | 218 | 218 | 218 |
TDWS (cubic meters) | 419,248 | 419,248 | 419,248 | 419,248 |
TNCR (nodes) | 136 (s = 43 m = 70 l = 23) | 131 (s = 35 m = 81 l = 22) | 142 (s = 51 m = 62 l = 29) | 128 (s = 28 m = 90 l = 10) |
TVW (cubic meters) | 422,740 | 449,720 | 429,700 | 420,680 |
TVW/TDWS | 1.01 | 1.07 | 1.02 | 1.00 |
TCC (Baht) | 19,941,410 | 20,584,090 | 20,576,450 | 18,764,760 |
TDRC (Baht) | 5,266,007 | 5,501,719 | 5,626,589 | 5,769,953 |
% Budget Reduced | Available Budget (baht) | GA | DE | VaNSAS | A-VaNSAS | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RS | WS | LW | RS | WS | LW | RS | WS | LW | RS | WS | LW | ||
0% | 18,764,760 | 6.8 | 92.3 | 191 | 7.4 | 92.3 | 190 | 5.6 | 94.8 | 204 | 0.0 | 100.0 | 218 |
10% | 16,888,284 | 10.7 | 89.0 | 186 | 11.2 | 88.1 | 184 | 9.5 | 90.1 | 193 | 7.1 | 92.3 | 199 |
20% | 15,011,808 | 23.5 | 76.4 | 163 | 23.8 | 76.9 | 168 | 18.6 | 81.5 | 166 | 16.6 | 83.8 | 177 |
30% | 13,135,332 | 31.8 | 68.8 | 147 | 32.6 | 67.6 | 144 | 30.9 | 69.3 | 142 | 25.3 | 73.5 | 154 |
40% | 11,258,856 | 33.6 | 63.6 | 120 | 33.1 | 64.8 | 123 | 33.4 | 65.9 | 126 | 29.9 | 70.1 | 137 |
50% | 9,382,380 | 41.8 | 59.4 | 103 | 42.9 | 58.8 | 100 | 42.0 | 58.3 | 102 | 36.5 | 62.4 | 116 |
average | 14,073,570 | 24.7 | 74.9 | 151.7 | 25.2 | 74.8 | 151.5 | 23.3 | 76.7 | 155.5 | 19.2 | 80.4 | 166.8 |
%|diff| | 35.0 | 32.9 | 46.1 | 35.5 | 33.5 | 47.4 | 36.4 | 36.5 | 50.0 | 36.5 | 37.6 | 46.8 |
% Construction Cost Increased | GA | DE | VaNSAS | A-VaNSAS | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
RS | WS | LW | RS | WS | LW | RS | WS | LW | RS | WS | LW | |
0% | 41.8 | 59.4 | 103 | 42.9 | 58.8 | 100 | 42.0 | 58.3 | 102 | 36.5 | 62.4 | 116 |
5% | 44.3 | 56.6 | 94 | 45.4 | 56.1 | 93 | 44.2 | 57.8 | 96 | 38.1 | 60.8 | 109 |
10% | 47.5 | 53.8 | 87 | 46.9 | 54.8 | 90 | 46.8 | 55.1 | 92 | 39.8 | 59.3 | 105 |
15% | 48.9 | 52.6 | 82 | 47.8 | 54.2 | 88 | 47.3 | 53.7 | 87 | 41.4 | 57.1 | 94 |
20% | 51.8 | 50.4 | 79 | 50.1 | 51.3 | 81 | 49.9 | 51.5 | 82 | 43.7 | 56.3 | 89 |
25% | 52.5 | 48.2 | 74 | 52.3 | 48.5 | 74 | 51.5 | 49.2 | 76 | 45.6 | 54.9 | 82 |
Average | 47.8 | 53.5 | 86.5 | 47.6 | 54.0 | 87.7 | 47.0 | 54.3 | 89.2 | 40.9 | 58.5 | 99.2 |
%|diff| | 10.7 | 11.2 | 28.2 | 9.4 | 10.3 | 26.0 | 9.5 | 9.1 | 25.5 | 9.1 | 7.5 | 29.3 |
KPIs and Details | GA | DE | VaNSAS | A-VaNSAS |
---|---|---|---|---|
Number of candidate nodes: NC (nodes) | 218 | 218 | 218 | 218 |
Total area available: TA (m^{2}) | 1,217,600 | 1,217,600 | 1,217,600 | 1,217,600 |
Budget availability: BA (Baht) | 9,382,380 | 9,382,380 | 9,382,380 | 9,382,380 |
Total demand of water supply: TDWS (cubic meter) | 419,248 | 419,248 | 419,248 | 419,248 |
Number of locations which are in the water supply network (WSN): NLW (nodes) | 79 | 81 | 82 | 89 |
Number of locations that construct the community’s reservoir: NLC | 60 (s = 12 m = 32 l = 16) | 58 (s = 11 m = 31 l = 16) | 60 (s = 13 m = 31 l = 16) | 64 (s = 13 m = 40 l = 11) |
Full capacity of the constructed reservoirs (thousand cubic meter) (FCC) | 208,400 | 203,360 | 205,880 | 217,300 |
(FCC/TDWS) × 100 (%) | 49.7 | 48.5 | 49.1 | 51.8 |
Total area covered by WSN (m^{2}): TRC | 604,156 | 591,274 | 598,755 | 632,181 |
Total construction cost (baht): CC | 9,304,720 | 9,311,810 | 9,355,750 | 9,302,710 |
CC/BA | 99.2 | 99.2 | 99.7 | 99.2 |
Total drought risk mitigation incentive (baht): SC | 2,367,792.00 | 2,288,865.60 | 2,367,792.00 | 2,525,644.80 |
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Srivoramasa, R.; Nanthasamroeng, N.; Pitakaso, R.; Srichok, T.; Khonjun, S.; Sirirak, W.; Theeraviriya, C. Community Agricultural Reservoir Construction and Water Supply Network Design in Ubon Ratchathani, Thailand, Using Adjusted Variable Neighborhood Strategy Adaptive Search. Water 2023, 15, 591. https://doi.org/10.3390/w15030591
Srivoramasa R, Nanthasamroeng N, Pitakaso R, Srichok T, Khonjun S, Sirirak W, Theeraviriya C. Community Agricultural Reservoir Construction and Water Supply Network Design in Ubon Ratchathani, Thailand, Using Adjusted Variable Neighborhood Strategy Adaptive Search. Water. 2023; 15(3):591. https://doi.org/10.3390/w15030591
Chicago/Turabian StyleSrivoramasa, Rerkchai, Natthapong Nanthasamroeng, Rapeepan Pitakaso, Thanatkij Srichok, Surajet Khonjun, Worapot Sirirak, and Chalermchat Theeraviriya. 2023. "Community Agricultural Reservoir Construction and Water Supply Network Design in Ubon Ratchathani, Thailand, Using Adjusted Variable Neighborhood Strategy Adaptive Search" Water 15, no. 3: 591. https://doi.org/10.3390/w15030591