Marketplace Location Decision Making and Tourism Route Planning
Abstract
:1. Introduction
2. Literature Review
3. Problem Description and Mathematical Formulation
3.1. Data Collection
3.2. Mathematical Model
4. Adaptive Large Neighborhood Search Algorithm
4.1. ALNS Algorithm
Algorithm 1: Adaptive Large Neighborhood Search (ALNS) algorithm of tourism route planning. |
1. Construct a feasible solution s; |
2. s*←s; |
3. Initialize weights; |
4. If the stopping criterion is not met, then |
4.1 Select q, r R, d D according to probabilities p |
4.2 s′ = r(d(s)) |
4.3 If the acceptance criterion is satisfied, then s←s′; |
If s is better than s*, then s*←s′; |
4.4 Adjust weights; |
5. Return s*. |
- (1)
- The feasible solution initially generated is s.
- (2)
- The initial solution s is the best solution s*.
- (3)
- Determine the random probability initial weight for the destruction and reconstruction operator.
- (4)
- Repeat this procedure until stopped:
- 4.1
- Both the number of destruction d and repair r are selected by a random probability with a dependent weight value.
- 4.2
- The destruction d and repair r of current solution s is a creative new solution s′.
- 4.3
- If the current solution s indicates a new solution s′, this conforms to the acceptance condition.
- 4.4
- The weight is adjusted for the new solution when s is better than the last solution s*.
- (5)
- New solution s* is returns to step 3–5 for destruction and proceeds until a new best solution is created and then stops.
Algorithm 2: Feasible solution construction. |
1. Lt <- {1, 2, …, n} travel places for construct travel routes |
1.1 Construct route r1 |
1.2 S = {r1} |
2. While L is not empty |
2.1 Randomly select travel place ct Lt |
2.2 Insert travel place ct at rk S; rk is the best feasible all route in the solution |
2.3 If there is no feasible solution, then create new route S |
2.4 Lt ← Lt − {ct} |
3. Lf <- {1, 2, …, m} farm places for construct suitable center |
4. While Lf is not empty |
4.1 Randomly select farm location cf Lf |
4.2 Assign farm cf to its best feasible depot in best route rk S |
4.3 Lf ← Lf − {cf} |
5. return S |
4.2. Destruction Methods
4.2.1. Random Removal
- Step 1
- The sort of TARR or FRR is selected randomly for sort destroying.
- Step 2
- The number of sort destroying is selected randomly for finding the number of point removals.
- Step 3
- If there is a random number of points, the operation removes the points from the route.
4.2.2. K-Route Removal
- Step 1
- A route is selected randomly from all the routes; the route randomly selected is one or more from route R S |R| = K.
- Step 2
- Determine A as an array of destination point R.
- Step 3
- Randomly select the removal method from all route removals.
- Step 4
- The route is selected from Step 1 is destroyed for finding L, where L is solution set to be removed.
4.2.3. Entire Route Removal
- Step 1
- A route is selected randomly from all the routes, where route random selection is one or more than route R S |R| ≥ 1.
- Step 2
- Determine if A is an array of destination point R.
- Step 3
- Route was selected for removal from all routes.
- Step 4
- A route is selected from Step 1 is destroyed for finding L, where L is the solution set to be removed.
4.2.4. Worst Removal
Algorithm 3: Worst removal algorithm. |
1. L ← {}; |
2. While |L| < q do |
2.1 Array: A = an array containing all tourism attractions or farms request from s not in L; |
2.2 Sort A such that (i < j) → cost(A[i]) < cost(A[j]); |
2.3 Choose a random number x from the interval(0,1); |
2.4 L ← L {A[xp |A|]}; |
3. remove the requests in L from s; |
4. return L |
- Step 1
- The initiation is a vacant set.
- Step 2
- Find L by repeating this procedure completely for the number of q:
- 2.1
- Array A is a created number of all tourism attractions or farms but set L is untraceable.
- 2.2
- Array A member is sorted by using cost function removal.
- 2.3
- Random x is an interval in the range 0–1.
- 2.4
- A tourism attraction or farm is a selected value in array A, in which an attraction point is random. Then, add a tourism attraction point or farm point to the set of L, where the value p is a random weight. A low value of p corresponds to greater randomness.
- Step 3
- The member completely removes point L from the solution.
- Step 4
- Return L is Step 2, where L is found in the next iteration.
4.2.5. Related Removal
Algorithm 4: Related removal algorithm. |
1. L ← {}; |
2. Random centroids p(lat,lng) |
3. While |L| < q, then |
3.1 Find ct is a nearly centroid tourism attraction point or farm point with Euclidean distance |
3.2 L ← L {ct}; |
3.3 Update centroids p(lat,lng) = centroids(L) = ; |
4. return L |
- Step 1
- A single tourism attraction position or single farm position is selected randomly from all the tourism attractions or farm locations, respectively, which is removed from the solution.
- Step 2
- The generated group set removal is an initial member ct.
- Step 3
- Finding L involves a repeated procedure for the number of q.
- 3.1
- One tourism attraction position or farm position is chosen at random from the set of L.
- 3.2
- Array A is a created member of all solutions but untraceable from set L.
- 3.3
- Array A member is sorted by using the function relation R(c1,c2) from less to more valuable. The defined relationship is a distance between c1 and c2 plus the opening time of destination c1,c2 finding from R(c1,c2) = α dist(c1,c2) + β|tac1 − tac2|.
- 3.4
- Random x is an interval ranging from 0–1.
- 3.5
- A point of tourism attraction or farm is a selected value in array A, of which the tourism attraction point or farm point is a random value. After that, the tourism attraction point or farm point is added to set L, where the value p is of random weight random, and a low value of p corresponds to greater randomness.
- Step 4
- When the member is completely removed, point L is the solution.
- Step 5
- Return L is Step 2, where L is found in the next iteration.
4.2.6. Cluster Removal
Algorithm 5: Cluster removal algorithm. |
1. Randomly select a tourism attraction or farm ct and remove it from the solution; |
2. L ← {ct}; |
3. while |L| < q then |
3.1 c ← randomly select a travel place in L; |
3.2 Array: A = an array containing all request from s not in L; |
3.3 Sort A such that (i < j) → R(c,A[i]) < R(c,A[j]); |
3.4 Choose a random number x from the interval [0,1); |
3.5 L ← L {A[xp |A|]}; |
4. remove the requests in L from s; |
5. return L |
- Step 1
- The initiation is a vacant set.
- Step 2
- Randomly choose centroids for removal.
- Step 3
- Finding L was repeated for the number of q.
- 3.1
- Find point ct as a nearly centroid point using the Euclidian distance method.
- 3.2
- A point is added in set L.
- 3.3
- The updated centroid is an improvement in the solution route.
- Step 4
- Remove L from solution S.
- Step 5
- Return L is Step 2 that found L in the next iteration.
4.3. Repairing Operation
4.3.1. Greedy Insertion
- Step 1
- Determine S as a solution set member {r1, r2, ..., rk}.
- Step 2
- Repeat each rk S to find the difference in the lowest cost with insertion c into i of rk Δfc,k.
- Step 3
- The destination position is inserted into different lowest-cost routes of all routes, as shown in Equation (37):
4.3.2. Regret-H Insertion
4.3.3. Greedy Insertion with New Route Opening
- Step 1
- Total number of routes is determined as the highest number of routes.
- Step 2
- When the number of routes is less than the prescribed route:
- 2.1
- If the creative new route is less expensive than the cost of greedy insertion, the new route is created.
- 2.2
- Vise versa: if the creative new route is not better than the cost of greedy insertion, the greedy insertion is selected for insertion.
- Step 3
- If the new route is over prescribed, the greedy insertion is selected instead of a new route.
4.3.4. Two-Option Route Repairing
4.3.5. Exchange Route Repairing
4.4. Acceptance Criterion
5. Results and Discussion
6. Conclusions
Case Study
Author Contributions
Funding
Conflicts of Interest
Appendix A
Order | Tourism Attraction | Latitude | Longitude | Open-Close Times |
---|---|---|---|---|
0 | Rest | 19.925738 | 99.82364 | 8:00 a.m.–6:00 p.m. |
1 | Wat Rong Khun | 19.824285 | 99.763159 | 7:00 a.m.–6:00 p.m. |
2 | Boonrod farm | 19.852997 | 99.743386 | 6:00 a.m.–8:00 p.m. |
3 | Wat Phra Sing Chiang rai | 19.911672 | 99.830615 | 8.00 a.m.–6:00 p.m. |
4 | Wat Phra Kaew | 19.91171 | 99.827718 | 6:00 a.m.–6:00 p.m. |
5 | Wat Huai Pla Kha | 19.948406 | 99.806396 | 7:00 a.m.–6:00 p.m. |
. . . . | . . . . | . . . . | . . . . | . . . . |
113 | Bak International Port | 20.275078 | 100.40596 | 8.00 a.m.–7:00 p.m. |
114 | Saturday Night Market | 20.25453 | 100.410303 | 4.00 a.m.–9.00 p.m. |
115 | Wat Luang | 20.043683 | 100.379802 | 8.00 a.m.–6:00 p.m. |
Order | Farm | Latitude | Longitude |
---|---|---|---|
1 | Mae Kao Tom | 20.009389 | 99.912778 |
2 | Mae Kon | 19.849194 | 99.732778 |
3 | Ban Du | 19.977111 | 99.831444 |
4 | Rim Kok | 19.985417 | 99.935472 |
5 | San Sai | 19.856694 | 99.815528 |
. . . . | . . . . | . . . . | . . . . |
23 | San Klang | 19.592139 | 99.707333 |
24 | Pa Hung | 19.567889 | 99.702972 |
25 | Wiang Hao | 19.525389 | 99.85355 |
Order | Parameter | Set |
---|---|---|
1 | Attraction number | 1–115 |
2 | Farm number | 1–25 |
3 | Score | 1–10 |
4 | Level | 1–5 |
5 | Opening time | 180–510 min |
6 | Closing time | 1020–1380 min |
7 | Time spent at attraction | 60 min |
Normality Testing | Problem Size | |||||
---|---|---|---|---|---|---|
Small | Medium | Large | ||||
Lingo | ALNS | Lingo | ALNS | Lingo | ALNS | |
p-value | 0.119 | 0.119 | 0.419 | 0.388 | 0.430 | 0.100 |
Results | normal | normal | normal | normal | normal | normal |
Cost Per Unit (Bath/km) | Route | Distance (km) | Traveling Cost (Bath) |
---|---|---|---|
2.35 | 1 | 55.60 | 130.66 |
2 | 279.40 | 656.59 | |
3 | 379.80 | 892.53 | |
4 | 171.95 | 404.08 | |
5 | 119.63 | 281.13 | |
6 | 202.79 | 476.55 | |
7 | 209.70 | 492.79 | |
8 | 337.50 | 793.12 | |
9 | 184.40 | 433.34 | |
10 | 234.60 | 551.31 | |
11 | 201.85 | 474.34 | |
12 | 133.40 | 313.49 | |
13 | 27.40 | 64.39 | |
Total | 2538.02 | 5964.34 |
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Study | Topic | Approach | % Gap | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
TTN | GA | DE | TS | NNW | PTA | RA | CSCRA | LP | |||
Wang et al. (2018) | Electric vehicle tour planning | ✔ | 0 | ||||||||
Lim et al. (2018) | Tour recommendation and itinerary planning | ✔ | 2.5–15 | ||||||||
Liao and Zheng (2018) | Tourist trip design problem; TTDP | ✔ | ✔ | 1.98–13.10 | |||||||
Wu et al. (2017) | Tour route planning problem | ✔ | - | ||||||||
Nedjati et al. (2017) | Tour location routing problem | ✔ | - | ||||||||
Kotiloglu et al. (2017) | Multi-period tour | ✔ | 0.04 | ||||||||
Zheng et al. (2017) | Design personalized day tour route | ✔ | ✔ | 3.50–5.85 | |||||||
Xiao et al. (2017) | Tourism route planning | ✔ | - | ||||||||
Gavalas et al. (2014) | Time-dependent team orienteering problem | ✔ | 3.4–14 | ||||||||
Rodríguez et al. (2012) | Development of tool for individual tourists | ✔ | 2.17–4.28 | ||||||||
Zhu et al. (2012) | Tour planning problem | ✔ | 0.12–2.84 |
Problem Size | No. | Parameter | Lingo Program | ALNS | Difference | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Result | Result | |||||||||||
i, j | f | Status | Total Distance (km) | Processing Time (h) | Total Distance (km) | Processing Time (h) | Total Distance (km) | Processing Time (h) | Gap in Distance Traveled (%) | Processing Time Gap (%) | ||
Small | S-1 | 10 | 5 | Global Opt | 109.90 | 00:00:01 | 109.90 | 00:00:01 | 0 | 0 | 0.0 | 0.0 |
S-2 | 10 | 5 | Global Opt | 164.40 | 00:00:01 | 164.40 | 00:00:01 | 0 | 0 | 0.0 | 0.0 | |
S-3 | 10 | 5 | Global Opt | 302.60 | 00:00:33 | 302.60 | 00:00:33 | 0 | 0 | 0.0 | 0.0 | |
S-4 | 10 | 5 | Global Opt | 308.73 | 00:00:58 | 308.73 | 00:00:58 | 0 | 0 | 0.0 | 0.0 | |
S-5 | 10 | 5 | Global Opt | 286.60 | 00:00:37 | 286.60 | 00:00:37 | 0 | 0 | 0.0 | 0.0 | |
Average | 234.45 | 00:00:24 | 234.45 | 00:00:24 | 234.45 | 0 | 0.0 | 0.0 | ||||
Medium | M-1 | 40 | 15 | Feasible | 898.65 | 40:59:27 | 901.50 | 00:05:05 | −2.85 | 40:54:22 | −0.32 | 99.88 |
M-2 | 40 | 15 | Feasible | 1157.84 | 60:17:21 | 1161.01 | 00:05:05 | −3.17 | 60:12:16 | −0.27 | 99.92 | |
M-3 | 40 | 15 | Feasible | 997.78 | 41:49:13 | 1002.37 | 00:05:55 | −4.59 | 41:43:46 | −0.46 | 99.88 | |
M-4 | 40 | 15 | Feasible | 1131.47 | 55:56:29 | 1153.50 | 00:05:25 | −22.03 | 55:51:02 | −1.95 | 99.91 | |
M-5 | 40 | 15 | Feasible | 992.78 | 50:49:25 | 1018.52 | 00:05:45 | −25.74 | 50:44:20 | −2.59 | 99.90 | |
Average | 1035.70 | 49:45:24 | 1047.38 | 00:05:25 | −11.68 | 49:40:28 | −1.12 | 99.90 | ||||
Large | L-1 | 80 | 25 | Lower bound | 2058.56 | >120 | 2077.58 | 00:14:48 | −19.02 | 119:51:52 | −0.92 | 99.88 |
L-2 | 80 | 25 | Lower bound | 1938.81 | >120 | 1987.10 | 00:13:46 | −48.29 | 119:50:54 | −2.49 | 99.89 | |
L-3 | 80 | 25 | Lower bound | 2076.52 | >120 | 2099.13 | 00:15:54 | −22.61 | 119:51:46 | −1.09 | 99.87 | |
L-4 | 80 | 25 | Lower bound | 1965.64 | >120 | 1973.07 | 00:14:51 | −7.43 | 119:50:49 | −0.38 | 99.88 | |
L-5 | 80 | 25 | Lower bound | 2087.31 | >120 | 2092.70 | 00:15:43 | −5.39 | 119:50:57 | −0.26 | 99.87 | |
Average | 2025.37 | 120 | 2045.92 | 00:14:54 | −20.55 | 119:50:48 | −1.03 | 99.88 |
Problem Size | p-Value | |
---|---|---|
Total Traveling Distance | Processing Time | |
Small | 1.000 | 1.000 |
Medium | 0.081 | 0.000 * |
Large | 0.055 | 0.000 * |
Problem | Route | Destinations | Outlet Locations | Farms | Distance (km) | Distant Total (km) |
---|---|---|---|---|---|---|
Case study | 1 | 0-7-15-9-21-0 | 9 | 4,1 | 55.60 | 2538.02 |
2 | 0-81-71-72-77-75-74-91-94-80-3-0 | 71 | 11 | 279.40 | ||
3 | 0-8-28-29-83-103-114-115-113-76-73-79-82-0 | 114 | 22,10 | 379.80 | ||
4 | 0-49-50-52-51-48-106-68-66-0 | 106 | 16,14 | 171.95 | ||
5 | 0-1-27-14-18-0 | 1 | 2,6,7,5 | 119.63 | ||
6 | 0-67-102-99-105-111-112-100-109-107-108-101-104-110-70-32-0 | 108 | 13,15 | 202.79 | ||
7 | 0-11-45-42-41-2-23-0 | 41 | 21 | 209.70 | ||
8 | 0-16-44-46-47-43-88-90-87-85-84-86-89-40-0 | 47 | 20,18 | 337.50 | ||
9 | 0-25-26-22-60-61-58-62-59-69-64-20-0 | 20 | 19,3 | 184.40 | ||
10 | 0-19-95-93-96-97-98-92-78-30-4-0 | 92 | 17,9 | 234.60 | ||
11 | 0-10-31-5-37-34-35-36-39-38-33-24-0 | 34 | 25,24,23 | 201.85 | ||
12 | 0-63-65-55-57-54-56-53-0 | 54 | 8 | 133.40 | ||
13 | 0-13-17-12-6-0 | 12 | 12 | 27.40 |
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Share and Cite
Sirirak, W.; Pitakaso, R. Marketplace Location Decision Making and Tourism Route Planning. Adm. Sci. 2018, 8, 72. https://doi.org/10.3390/admsci8040072
Sirirak W, Pitakaso R. Marketplace Location Decision Making and Tourism Route Planning. Administrative Sciences. 2018; 8(4):72. https://doi.org/10.3390/admsci8040072
Chicago/Turabian StyleSirirak, Worapot, and Rapeepan Pitakaso. 2018. "Marketplace Location Decision Making and Tourism Route Planning" Administrative Sciences 8, no. 4: 72. https://doi.org/10.3390/admsci8040072
APA StyleSirirak, W., & Pitakaso, R. (2018). Marketplace Location Decision Making and Tourism Route Planning. Administrative Sciences, 8(4), 72. https://doi.org/10.3390/admsci8040072