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July  2019, 15(3): 1185-1211. doi: 10.3934/jimo.2018091

Maritime inventory routing problem with multiple time windows

1. 

Department of Industrial Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia

2. 

School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia

* Corresponding author: Nurhadi Siswanto

Received  May 2017 Revised  February 2018 Published  July 2018

Fund Project: The first author is supported by Ministry of Research, Technology and Higher Education, Republic of Indonesia through International Research Collaboration and Scientific Publication Research Grant No. 536/PKS/ITS/2017.

This paper considers a maritime inventory routing problem with multiple time windows. The typical time windows considered that certain ports permit ships entering and leaving during the daytime only due to various operational limitations. We have developed an exact algorithm to represent this problem. However, due to the excessive computational time required for solving the model, we have proposed a multi-heuristics based genetic algorithm. The multi-heuristics are composed of a set of strategies that correspond to four decision points: ship selection, ship routing, the product type and the quantity of loading and unloading products. The experimental results show that the multi-heuristics can obtain acceptable solutions within a reasonable computational time. Moreover, the flexibility to add or remove the strategies means that the proposed method would not be difficult to implement for other variants of the maritime inventory routing problem.

Citation: Nurhadi Siswanto, Stefanus Eko Wiratno, Ahmad Rusdiansyah, Ruhul Sarker. Maritime inventory routing problem with multiple time windows. Journal of Industrial & Management Optimization, 2019, 15 (3) : 1185-1211. doi: 10.3934/jimo.2018091
References:
[1]

A. Agra, M. Christiansen, et al., A maritime inventory routing problem with stochastic sailing and port times, Computers & Operations Research, 61 (2015), 18-30 doi: 10.1016/j.cor.2015.01.008.  Google Scholar

[2]

F. Al-Khayyal and S. J. Hwang, Discrete Optimization-Inventory constrained maritime routing and scheduling for multi-commodity liquid bulk, Part Ⅰ: Applications and model, European Journal of Operational Research, 176 (2007), 106-130.  doi: 10.1016/j.ejor.2005.06.047.  Google Scholar

[3]

K. Chakhlevitch and P. Cowling, Hyperheuristics: Recent developments, in Adaptive and Multilevel Metaheuristics (eds. C. Cotta, M. Sevaux, K. Sorensen), Springer-Verlag Berlin Heidelberg, (2008), 3-29 Google Scholar

[4]

M. Christiansen and B. Nygreen, A method for solving ship routing problems with inventory constraints, Annals of Operations Research, 81 (1998), 357-378.  doi: 10.1023/A:1018921527269.  Google Scholar

[5]

M. Christiansen and B. Nygreen, Modeling path flows for a combined ship routing and inventory management problem, Annals of Operations Research, 82 (1998), 391-412.  doi: 10.1023/A:1018979107222.  Google Scholar

[6]

M. Christiansen, Decomposition of a combined inventory and time constrained ship routing problem, Transportation Science, 33 (1999), 3-16.  doi: 10.1287/trsc.33.1.3.  Google Scholar

[7]

M. Christiansen and K. Fagerholt, Robust ship scheduling with multiple time windows, Naval Research Logistics, 49 (2002), 611-625.  doi: 10.1002/nav.10033.  Google Scholar

[8]

M. ChristiansenK. FagerholtB. Nygreen and D. Ronen, Chapter 4 maritime transportation in handbooks in operations research and management science, (eds, C. Barnhart, G. Laporte), North-Holland, Amsterdam, 14 (2007), 189-284.   Google Scholar

[9]

M. Christiansen and K. Fagerholt, Maritime inventory routing problems, in Encyclopedia of Optimization, Second edition (eds C. A. Floudas, P. M. Pardalos), Springer-Verlag, (2009), 1947-1955 Google Scholar

[10]

M. ChristiansenK. FagerholtT. FlatbergØ. HaugenO. Kloster and E. H. Lund, Maritime inventory routing with multiple products: A case study from the cement industry, European Journal of Operational Research, 208 (2011), 86-94.  doi: 10.1016/j.ejor.2010.08.023.  Google Scholar

[11]

K. F. DoernerM. GronaltR. F. HartlG. Kiechle and M. Reimann, Exact and heuristic algorithms for the vehicle routing problem with multiple interdependent time windows, Computers and Operations Research, 35 (2008), 3034-3048.  doi: 10.1016/j.cor.2007.02.012.  Google Scholar

[12]

D. FavarettoE. Moretti and P. Pellegrini, Ant colony system for a VRP with multiple time windows and multiple visits, Journal of Interdisciplinary Mathematics, 10 (2007), 263-284.  doi: 10.1080/09720502.2007.10700491.  Google Scholar

[13]

K. C. FurmanJ. H. SongG. R. KocisM. K. McDonald and P. H. Warrick, Feedstock routing in the exxonmobil downstream sector, Interfaces, 41 (2011), 149-163.  doi: 10.1287/inte.1100.0508.  Google Scholar

[14]

A. Hemmati, L. M. Hvattum, et al., An iterative two-phase hybrid matheuristic for a multiproduct short sea inventory-routing problem, European Journal of Operational Research, 252 (2016), 775-788 doi: 10.1016/j.ejor.2016.01.060.  Google Scholar

[15]

S. J. Hwang, Inventory Constrained Maritime Routing and Scheduling for Multi-Commodity Liquid Bulk, Dissertation, School of Industrial and Systems Engineering, Georgia Institute of Technology, USA, 2005. Google Scholar

[16]

Y. Jiang and I. E. Grossmann, Alternative mixed-integer linear programming models of a maritime inventory routing problem, Computers & Chemical Engineering, 77 (2015), 147-161.  doi: 10.1016/j.compchemeng.2015.03.005.  Google Scholar

[17]

J. Lee and B. I. Kim, Industrial ship routing problem with split delivery and two types of vessels, Expert Systems with Applications, 42 (2015), 9012-9023.  doi: 10.1016/j.eswa.2015.07.059.  Google Scholar

[18]

D. J. PapageorgiouG. L. NemhauserJ. SokolM. S. Cheon and A. B. Keha, MIRPLib - A library of maritime inventory routing problem instances: Survey, core model, and benchmark results, European Journal of Operational Research, 235 (2014), 350-366.  doi: 10.1016/j.ejor.2013.12.013.  Google Scholar

[19]

D. J. Papageorgiou and A. B. Keha, et al., Two-stage decomposition algorithms for single product maritime inventory routing, INFORMS Journal on Computing, 26 (2014), 825-847 doi: 10.1287/ijoc.2014.0601.  Google Scholar

[20]

V. Rodrigues and R. Morabito, et al., Ship routing with pickup and delivery for a maritime oil transportation system: MIP model and heuristics, Systems, 4 (2016), p31. Google Scholar

[21]

B. Santosa and R. Damayanti, et al., Solving multi-product inventory ship routing with a heterogeneous fleet model using a hybrid cross entropy-genetic algorithm: a case study in Indonesia, Production & Manufacturing Research, 4 (2016), 90-113 doi: 10.1080/21693277.2016.1204961.  Google Scholar

[22]

M. Savelsbergh and J. H. Song, Inventory routing with continuous moves, Computers and Operations Research, 34 (2007), 1744-1763.  doi: 10.1016/j.cor.2005.05.036.  Google Scholar

[23]

N. SiswantoD. Essam and R. Sarker, Solving the ship inventory routing and scheduling problem with undedicated compartments, Computers and Industrial Engineering, 61 (2011), 289-299.  doi: 10.1109/ICCIE.2009.5223771.  Google Scholar

[24]

N. Siswanto, D. Essam and R. Sarker, Multi-heuristics based genetic algorithm for solving maritime inventory routing problem, IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, (2011), 116-120 doi: 10.1109/IEEM.2011.6117890.  Google Scholar

[25]

J. Sokol, C. Zhang, G. Nemhauser, D. Papageorgiou and M. S. Cheon, Robust inventory routing with flexible time window allocation, Working paper, 2015. Google Scholar

[26]

J. H. Song and K. C. Furman, A maritime inventory routing problem: Practical approach, Computers and Operations Research, 40 (2011), 657-665.  doi: 10.1016/j.cor.2010.10.031.  Google Scholar

[27]

F. TricoireM. RomauchK. F. Doerner and R. F. Hartl, Heuristics for the multi-period orienteering problem with multiple time windows, Computers and Operations Research, 37 (2010), 351-367.  doi: 10.1016/j.cor.2009.05.012.  Google Scholar

show all references

References:
[1]

A. Agra, M. Christiansen, et al., A maritime inventory routing problem with stochastic sailing and port times, Computers & Operations Research, 61 (2015), 18-30 doi: 10.1016/j.cor.2015.01.008.  Google Scholar

[2]

F. Al-Khayyal and S. J. Hwang, Discrete Optimization-Inventory constrained maritime routing and scheduling for multi-commodity liquid bulk, Part Ⅰ: Applications and model, European Journal of Operational Research, 176 (2007), 106-130.  doi: 10.1016/j.ejor.2005.06.047.  Google Scholar

[3]

K. Chakhlevitch and P. Cowling, Hyperheuristics: Recent developments, in Adaptive and Multilevel Metaheuristics (eds. C. Cotta, M. Sevaux, K. Sorensen), Springer-Verlag Berlin Heidelberg, (2008), 3-29 Google Scholar

[4]

M. Christiansen and B. Nygreen, A method for solving ship routing problems with inventory constraints, Annals of Operations Research, 81 (1998), 357-378.  doi: 10.1023/A:1018921527269.  Google Scholar

[5]

M. Christiansen and B. Nygreen, Modeling path flows for a combined ship routing and inventory management problem, Annals of Operations Research, 82 (1998), 391-412.  doi: 10.1023/A:1018979107222.  Google Scholar

[6]

M. Christiansen, Decomposition of a combined inventory and time constrained ship routing problem, Transportation Science, 33 (1999), 3-16.  doi: 10.1287/trsc.33.1.3.  Google Scholar

[7]

M. Christiansen and K. Fagerholt, Robust ship scheduling with multiple time windows, Naval Research Logistics, 49 (2002), 611-625.  doi: 10.1002/nav.10033.  Google Scholar

[8]

M. ChristiansenK. FagerholtB. Nygreen and D. Ronen, Chapter 4 maritime transportation in handbooks in operations research and management science, (eds, C. Barnhart, G. Laporte), North-Holland, Amsterdam, 14 (2007), 189-284.   Google Scholar

[9]

M. Christiansen and K. Fagerholt, Maritime inventory routing problems, in Encyclopedia of Optimization, Second edition (eds C. A. Floudas, P. M. Pardalos), Springer-Verlag, (2009), 1947-1955 Google Scholar

[10]

M. ChristiansenK. FagerholtT. FlatbergØ. HaugenO. Kloster and E. H. Lund, Maritime inventory routing with multiple products: A case study from the cement industry, European Journal of Operational Research, 208 (2011), 86-94.  doi: 10.1016/j.ejor.2010.08.023.  Google Scholar

[11]

K. F. DoernerM. GronaltR. F. HartlG. Kiechle and M. Reimann, Exact and heuristic algorithms for the vehicle routing problem with multiple interdependent time windows, Computers and Operations Research, 35 (2008), 3034-3048.  doi: 10.1016/j.cor.2007.02.012.  Google Scholar

[12]

D. FavarettoE. Moretti and P. Pellegrini, Ant colony system for a VRP with multiple time windows and multiple visits, Journal of Interdisciplinary Mathematics, 10 (2007), 263-284.  doi: 10.1080/09720502.2007.10700491.  Google Scholar

[13]

K. C. FurmanJ. H. SongG. R. KocisM. K. McDonald and P. H. Warrick, Feedstock routing in the exxonmobil downstream sector, Interfaces, 41 (2011), 149-163.  doi: 10.1287/inte.1100.0508.  Google Scholar

[14]

A. Hemmati, L. M. Hvattum, et al., An iterative two-phase hybrid matheuristic for a multiproduct short sea inventory-routing problem, European Journal of Operational Research, 252 (2016), 775-788 doi: 10.1016/j.ejor.2016.01.060.  Google Scholar

[15]

S. J. Hwang, Inventory Constrained Maritime Routing and Scheduling for Multi-Commodity Liquid Bulk, Dissertation, School of Industrial and Systems Engineering, Georgia Institute of Technology, USA, 2005. Google Scholar

[16]

Y. Jiang and I. E. Grossmann, Alternative mixed-integer linear programming models of a maritime inventory routing problem, Computers & Chemical Engineering, 77 (2015), 147-161.  doi: 10.1016/j.compchemeng.2015.03.005.  Google Scholar

[17]

J. Lee and B. I. Kim, Industrial ship routing problem with split delivery and two types of vessels, Expert Systems with Applications, 42 (2015), 9012-9023.  doi: 10.1016/j.eswa.2015.07.059.  Google Scholar

[18]

D. J. PapageorgiouG. L. NemhauserJ. SokolM. S. Cheon and A. B. Keha, MIRPLib - A library of maritime inventory routing problem instances: Survey, core model, and benchmark results, European Journal of Operational Research, 235 (2014), 350-366.  doi: 10.1016/j.ejor.2013.12.013.  Google Scholar

[19]

D. J. Papageorgiou and A. B. Keha, et al., Two-stage decomposition algorithms for single product maritime inventory routing, INFORMS Journal on Computing, 26 (2014), 825-847 doi: 10.1287/ijoc.2014.0601.  Google Scholar

[20]

V. Rodrigues and R. Morabito, et al., Ship routing with pickup and delivery for a maritime oil transportation system: MIP model and heuristics, Systems, 4 (2016), p31. Google Scholar

[21]

B. Santosa and R. Damayanti, et al., Solving multi-product inventory ship routing with a heterogeneous fleet model using a hybrid cross entropy-genetic algorithm: a case study in Indonesia, Production & Manufacturing Research, 4 (2016), 90-113 doi: 10.1080/21693277.2016.1204961.  Google Scholar

[22]

M. Savelsbergh and J. H. Song, Inventory routing with continuous moves, Computers and Operations Research, 34 (2007), 1744-1763.  doi: 10.1016/j.cor.2005.05.036.  Google Scholar

[23]

N. SiswantoD. Essam and R. Sarker, Solving the ship inventory routing and scheduling problem with undedicated compartments, Computers and Industrial Engineering, 61 (2011), 289-299.  doi: 10.1109/ICCIE.2009.5223771.  Google Scholar

[24]

N. Siswanto, D. Essam and R. Sarker, Multi-heuristics based genetic algorithm for solving maritime inventory routing problem, IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, (2011), 116-120 doi: 10.1109/IEEM.2011.6117890.  Google Scholar

[25]

J. Sokol, C. Zhang, G. Nemhauser, D. Papageorgiou and M. S. Cheon, Robust inventory routing with flexible time window allocation, Working paper, 2015. Google Scholar

[26]

J. H. Song and K. C. Furman, A maritime inventory routing problem: Practical approach, Computers and Operations Research, 40 (2011), 657-665.  doi: 10.1016/j.cor.2010.10.031.  Google Scholar

[27]

F. TricoireM. RomauchK. F. Doerner and R. F. Hartl, Heuristics for the multi-period orienteering problem with multiple time windows, Computers and Operations Research, 37 (2010), 351-367.  doi: 10.1016/j.cor.2009.05.012.  Google Scholar

Figure 1.  Loading and unloading activities at a port
Figure 2.  Daily multiple time windows at a port
Figure 3.  Detailed activities of a ship during its time in a port
Figure 4.  Several alternatives of a ship arriving and leaving a port when considering time windows
Figure 5.  An Example of Chromosome
Figure 6.  Chromosome in one and two steps
Figure 7.  Changing states during every assignment in a chromosome
Figure 8.  The values of the fitness functions for test problem 1 with the 15 day planning horizon from each of 40 runs
Table 1.  An example of strategies for each decision point
No Decision point Strategies
1 Ship selection Based on the least ships current time
2 Routing Visit two demand ports with the sequence based on the least CDik
3 Loading Compartment [1] for product[1], compartment[2] for product[2] with loading quantities up to the maximum of compartments capacities
4 Unloading Divide the same quantities for both ports
No Decision point Strategies
1 Ship selection Based on the least ships current time
2 Routing Visit two demand ports with the sequence based on the least CDik
3 Loading Compartment [1] for product[1], compartment[2] for product[2] with loading quantities up to the maximum of compartments capacities
4 Unloading Divide the same quantities for both ports
Table 2.  Data of port and their storages
No Description H1 H2 H3
S11 S12 S21 S22 S31 S32
1 Maximum capacity (unit) 160 180 55 41 68 51
2 Minimum level (unit) 0 0 0 0 0 0
3 Initial level (unit) 44 28 19 27 46 25
4 Daily supply/demand rate (unit/day) 8 9 6 4 2 5
5 Fixed setup loading time (day) 0.039 0.059 0.074 0.060 0.067 0.049
6 Variable loading time (day/unit) 0.025 0.010 0.003 0.026 0.028 0.014
7 Fixed setup loading cost ($) 10 8 6 9 8 10
8 Variable loading cost ($/unit) 0 0 0 0 0 0
9 Quantity penalty cost ($/day) 10 10 10 10 10 10
10 Fixed setup port time (day) 0 0 0
11 Fixed setup port cost ($) 0 0 0
12 Daily starting time windows 7.12am 7.12am 7.12am
13 Daily ending time windows 4.48pm 4.48am 4.48pm
No Description H1 H2 H3
S11 S12 S21 S22 S31 S32
1 Maximum capacity (unit) 160 180 55 41 68 51
2 Minimum level (unit) 0 0 0 0 0 0
3 Initial level (unit) 44 28 19 27 46 25
4 Daily supply/demand rate (unit/day) 8 9 6 4 2 5
5 Fixed setup loading time (day) 0.039 0.059 0.074 0.060 0.067 0.049
6 Variable loading time (day/unit) 0.025 0.010 0.003 0.026 0.028 0.014
7 Fixed setup loading cost ($) 10 8 6 9 8 10
8 Variable loading cost ($/unit) 0 0 0 0 0 0
9 Quantity penalty cost ($/day) 10 10 10 10 10 10
10 Fixed setup port time (day) 0 0 0
11 Fixed setup port cost ($) 0 0 0
12 Daily starting time windows 7.12am 7.12am 7.12am
13 Daily ending time windows 4.48pm 4.48am 4.48pm
Table 3.  Data of ship and their compartments
No Description V1 V2
C11 C12 C21 C22
1 Maximum compartment capacity 68 31 44 50
2 Initial level 0 0 40 4
3 Current product in the compartment - - P2 P1
No Description V1 V2
C11 C12 C21 C22
1 Maximum compartment capacity 68 31 44 50
2 Initial level 0 0 40 4
3 Current product in the compartment - - P2 P1
Table 4.  Data of travelling cost and time between ports
Table 5.  The result of exact algorithm solved using Lingo
Test Problem (TP) Planning Horizon (PH) Scenario 1 (Mp=3;Mc=2) Scenario 2 (Mp=3;Mc=2) Gap (%)
Optimal Solution Run Time (in Second) Optimal Solution Run Time (in Second)
1 10 55.8 1,329 - ﹥﹥8.64E + 4(*) -
15 91.4 21,423 - ﹥﹥8.64E + 4(*) -
2 10 66.8 1,012 66.8 582 0
15 103, 0 25,451 103.0 74,166 0
3 10 99.0 46 - ﹥﹥8.64E + 4(*) -
15 216.0 34,827 - ﹥﹥8.64E + 4(*) -
4 10 137.0 640 137.0 211 0
15 265.0 47,210 265.0 ﹥﹥8.64E + 4(n) 0
(n)the solution did not terminate before the time limit of 8.64E+4 seconds (24 hours)
(*)a feasible solution was not obtained before the time limit of 8.64E+4 seconds (24 hours)
Test Problem (TP) Planning Horizon (PH) Scenario 1 (Mp=3;Mc=2) Scenario 2 (Mp=3;Mc=2) Gap (%)
Optimal Solution Run Time (in Second) Optimal Solution Run Time (in Second)
1 10 55.8 1,329 - ﹥﹥8.64E + 4(*) -
15 91.4 21,423 - ﹥﹥8.64E + 4(*) -
2 10 66.8 1,012 66.8 582 0
15 103, 0 25,451 103.0 74,166 0
3 10 99.0 46 - ﹥﹥8.64E + 4(*) -
15 216.0 34,827 - ﹥﹥8.64E + 4(*) -
4 10 137.0 640 137.0 211 0
15 265.0 47,210 265.0 ﹥﹥8.64E + 4(n) 0
(n)the solution did not terminate before the time limit of 8.64E+4 seconds (24 hours)
(*)a feasible solution was not obtained before the time limit of 8.64E+4 seconds (24 hours)
Table 6.  The sequence of visiting demand ports
Gene4 Gene3 The first visiting port The second visiting port
0 0 CDP[0]
1 CDP[0] CDP[1]
1 0 CDP[1]
1 CDP[1] CDP[0]
Gene4 Gene3 The first visiting port The second visiting port
0 0 CDP[0]
1 CDP[0] CDP[1]
1 0 CDP[1]
1 CDP[1] CDP[0]
Table 7.  An example of one ship's assignment output
(A) Determining loading and unloading quantities
Product # Quantity in supply port at the time a ship arrive(*) Remaining demands(*) Compartment Capacity(*) Loading Quantity(*) Unload for the first visited port(*) Unload for the second visited port(*)
P1 40.8 28.6 44.0 28.6 0.0 28.6
P1 50.4 43.8 50.0 43.8 16.0 27.8
(*) in product units
(B) Routing and schedule of the selected ship
Source Port Departure Destination Port Port's last visit Arrival Waiting time to enter(n) Lay time
Date Time Date Time Date Time Date Time
H3 May 11 2.24pm H1 May 7 7.12am May 12 9.36am 0:00 May 12 9.36am
H1 May 13 3.37pm H3 May 11 2.24am May 14 10.49am 0:00 May 14 10.49am
H3 May 15 7.12am H2 May 11 7.12am May 16 7.12am 0:00 May 16 7.12am
(n) in (hour:minutes)
(C) Routing and schedule of the selected ship (continue)
Destination Port Loading Time(n) Waiting time for supply/space(n) Ready to Leave Waiting time to leave(n) Leaving time
Date Time Date Time
H1 30:01 0:00 May 13 3.37pm 0:00 May 13 3.37pm
H3 6:33 0:00 May 14 7.12pm 12:00 May 15 7.12am
H2 22:38 0:00 May 17 5.50am 1:22 May 17 7.12am
(n) in (hour:minutes)
(D) Information of each visiting port's storage
Destination Port Product# CDik Remaining Demand(*) Level of Storages(*) at ship's lay time Available space in storage(*) Loading/Unloading quantity(*)
H1 P1 27.30 0.0 40.8 - 28.6
H1 P2 26.80 0.0 50.4 - 43.8
H3 P1 25.00 0.0 21.1 46.9 0.0
H3 P2 21.80 16.0 36.7 14.3 16.0
H2 P1 20.23 28.6 23.6 31.4 28.6
H2 P2 18.05 27.8 7.0 34.0 27.8
(*) in product units
(A) Determining loading and unloading quantities
Product # Quantity in supply port at the time a ship arrive(*) Remaining demands(*) Compartment Capacity(*) Loading Quantity(*) Unload for the first visited port(*) Unload for the second visited port(*)
P1 40.8 28.6 44.0 28.6 0.0 28.6
P1 50.4 43.8 50.0 43.8 16.0 27.8
(*) in product units
(B) Routing and schedule of the selected ship
Source Port Departure Destination Port Port's last visit Arrival Waiting time to enter(n) Lay time
Date Time Date Time Date Time Date Time
H3 May 11 2.24pm H1 May 7 7.12am May 12 9.36am 0:00 May 12 9.36am
H1 May 13 3.37pm H3 May 11 2.24am May 14 10.49am 0:00 May 14 10.49am
H3 May 15 7.12am H2 May 11 7.12am May 16 7.12am 0:00 May 16 7.12am
(n) in (hour:minutes)
(C) Routing and schedule of the selected ship (continue)
Destination Port Loading Time(n) Waiting time for supply/space(n) Ready to Leave Waiting time to leave(n) Leaving time
Date Time Date Time
H1 30:01 0:00 May 13 3.37pm 0:00 May 13 3.37pm
H3 6:33 0:00 May 14 7.12pm 12:00 May 15 7.12am
H2 22:38 0:00 May 17 5.50am 1:22 May 17 7.12am
(n) in (hour:minutes)
(D) Information of each visiting port's storage
Destination Port Product# CDik Remaining Demand(*) Level of Storages(*) at ship's lay time Available space in storage(*) Loading/Unloading quantity(*)
H1 P1 27.30 0.0 40.8 - 28.6
H1 P2 26.80 0.0 50.4 - 43.8
H3 P1 25.00 0.0 21.1 46.9 0.0
H3 P2 21.80 16.0 36.7 14.3 16.0
H2 P1 20.23 28.6 23.6 31.4 28.6
H2 P2 18.05 27.8 7.0 34.0 27.8
(*) in product units
Table Appendix A.  The results of the multi-heuristics based GA in comparison to the results of the exact algorithm
Test Problem (TP) Planning Horizon (PH) Exact Algorithm Solution Multi-Heuristics based GA (40 runs repetition)
No. of Individuals in a population Best Solution (Min) Gap (%) Max. Solution Average Standart Deviation No. of infeasible solutions Average Running Time (in 2nd)
11055.82055.8055.855.80050.3
5055.8055.855.800105.6
10055.8055.855.800222.7
1591.42091.40108.794.35.40166.0
5091.40102.091.92.00401.2
10091.4091.491.400879.0
20$(*)$20107.7-148.0126.99.30248.5
50107.7-135.0122.27.10626.0
100107.7-122.4116.03.701,312.0
25$(*)$20146.3-196.9175.414.75234.9
50140.8-195.4165.315.92774.2
100143.4-188.7154.410.501,824.1
21066.82066.8076.868.43.6093.08
5066.8068.666.90.20212.9
10066.8066.866.800497.1
15103.020109.36.12140.2125.66.40197.3
50103.00130.5120.26.50535.2
100105.22.14124.2116.64.401,207.4
20$(*)$20149.7-191.8170.39.83208.3
50142.3-184.0166.27.55694.3
100149.7-191.0163.710.431,520.2
25$(*)$20177.5-231.2202.812.512295.8
50171.6-207.3190.910.46938.4
100173.6-208.7187.08.641,771.2
31099.02099.0099.099.00043.9
5099.0099.099.000109.0
10099.0099.099.000239.9
15216.020216.00241.0222.45.00147.0
50216.00241.0220.14.80377.2
100216.00221.0217.42.30796.1
20$(*)$20306.0-423.0343.625.10184.0
50304.0-344.0316.511.350585.2
100304.0-401.0312.016.001,222.6
25$(*)$20401.0-508.0466.824.41236.7
50346.0-522.0422.546.22753.3
100346.0-483.0404.841.601,476.9
410137.020137.00147.0140.04.6084.7
50137.00137.0137.000180.4
100137.00137.0137.000406.6
15265.020277.04.53363.0291.614.60196.3
50275.03.77294.0284.65.40530.0
100265.00287.0281.44.801,294.4
20$(*)$20354.0-479.0423.724.71217.5
50407.0-454.0423.215.25676.3
100350.0-454.0396.329.501,498.9
25$(*)$20484.0-632.0543.231.914304.6
50431.0-567.0517.934.37894.2
100431.0-558.0505.331.861,818.1
Note: (*) a feasible solution was not found before the time limit of 8.64E+4 seconds (24 hours)
Test Problem (TP) Planning Horizon (PH) Exact Algorithm Solution Multi-Heuristics based GA (40 runs repetition)
No. of Individuals in a population Best Solution (Min) Gap (%) Max. Solution Average Standart Deviation No. of infeasible solutions Average Running Time (in 2nd)
11055.82055.8055.855.80050.3
5055.8055.855.800105.6
10055.8055.855.800222.7
1591.42091.40108.794.35.40166.0
5091.40102.091.92.00401.2
10091.4091.491.400879.0
20$(*)$20107.7-148.0126.99.30248.5
50107.7-135.0122.27.10626.0
100107.7-122.4116.03.701,312.0
25$(*)$20146.3-196.9175.414.75234.9
50140.8-195.4165.315.92774.2
100143.4-188.7154.410.501,824.1
21066.82066.8076.868.43.6093.08
5066.8068.666.90.20212.9
10066.8066.866.800497.1
15103.020109.36.12140.2125.66.40197.3
50103.00130.5120.26.50535.2
100105.22.14124.2116.64.401,207.4
20$(*)$20149.7-191.8170.39.83208.3
50142.3-184.0166.27.55694.3
100149.7-191.0163.710.431,520.2
25$(*)$20177.5-231.2202.812.512295.8
50171.6-207.3190.910.46938.4
100173.6-208.7187.08.641,771.2
31099.02099.0099.099.00043.9
5099.0099.099.000109.0
10099.0099.099.000239.9
15216.020216.00241.0222.45.00147.0
50216.00241.0220.14.80377.2
100216.00221.0217.42.30796.1
20$(*)$20306.0-423.0343.625.10184.0
50304.0-344.0316.511.350585.2
100304.0-401.0312.016.001,222.6
25$(*)$20401.0-508.0466.824.41236.7
50346.0-522.0422.546.22753.3
100346.0-483.0404.841.601,476.9
410137.020137.00147.0140.04.6084.7
50137.00137.0137.000180.4
100137.00137.0137.000406.6
15265.020277.04.53363.0291.614.60196.3
50275.03.77294.0284.65.40530.0
100265.00287.0281.44.801,294.4
20$(*)$20354.0-479.0423.724.71217.5
50407.0-454.0423.215.25676.3
100350.0-454.0396.329.501,498.9
25$(*)$20484.0-632.0543.231.914304.6
50431.0-567.0517.934.37894.2
100431.0-558.0505.331.861,818.1
Note: (*) a feasible solution was not found before the time limit of 8.64E+4 seconds (24 hours)
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