Resources for algorithm selection for large berth allocation problem

This dataset comprises test instances for the Algorithm Selection Problem (ASP) for large Berth Allocation Problem (BAP). BAP is a maritime logistic problem involving scheduling arrving ships on terminal berths. ASP is a problem of selecting best algorithms for a problem at hand, in this case, scheduling algorithms for BAP.Test InstancesValues of the following parameters were drawn (pseudo)randomly:• ship ready times, r_j,• ship processing times, p_j,• ship importance(or value, weight), w_j,• ship and berth lengths L_i,\lambda_j.Collections of the instances:• random instances N - in order to test impact of the number of ships n, the range of n has been swept by visiting n=2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000. For each test value of n, 100 test instances were generated. The remaining parameters r_j, p_j, w_j, \lambda_i, L_j, were generated pseudorandomly as follows: m~U[1,100], r_j~U[0,1000], p_j~U[1,24], w_j~U[1,1000], L_j~U{200; 215; 290; 305; 400}. By U[a,b] we denote that certain parameter is generated from discrete uniform distribution with integer values in range [a,b]. ~U{a,...,b} denotes that the parameter values are chosen with discrete uniform distribution from the set {a,...,b}.In the collection of 1200 random instances N, a subset of the instances with n=10000 has been singled out. The former set of all random instances N is called dataset 1, the latter set of n=1000 instances is called dataset 2.• random instances M - were generated to test the impact of the number of berths m. The range of m has been swept by assuming m=1,2,5,10,20,50,100 while the remaining parameters r_j, p_j, w_j, \lambda_i, L_j, were randomized as described above. For each test value of m, 100 test instances were generated.In the collection of 700 random instances M, a subset of the instances with m=2 has been singled out. The former set of all random instances M is called dataset 3, the latter set of m=2 instances is called dataset 4.• real instances - ship arrivals of Gdańsk, Long Beach, Los Angeles, Le Havre, Hamburg, Rotterdam, Shanghai, Singapore over 2016.Instance File FormatInstance files are text files with data in the following order:- 1st line: number of the ships (n)- lines 2 to n+1 - ship data (in the order): id (j), ready time (r_j), length (\lambda_j), processing time (p_j), weight (importance, w_j), ship owner (o_j - can be ignored)- line n+2: number of berths (m)- line n+3 (the last line): berth lengths from the first to the last (L_1,...,L_m)File Name Convention• Random instances N are named:ship#ninst#.txtwhere ship# is the number of ships (n), inst# is the number of the instance generated for the current value of n.• Random instances M are named:berth#minst#.txtwhere bert# is the number of berths (m), inst# is the number of the instance generated for the current value of m.• Real instances - filenames derived from port names.The instances are collected in the following filesinstances-Nall.zip - all random instances N - dataset 1, ~16.1Minstances-N10k.zip - random instances N, for n=2 - dataset 2, ~8.4Minstances-Mall.zip - allrandom instances M - dataset 3, ~3.1Minstances-M2.zip - random instances M, for m=2 - dataset 4, ~0.4Minstances-real.zip - 8 real port instances - dataset 5, 0.4MMore on the problem, test instance generation, algorithm portfolio construction and evaluation, can be found in an accompanying publication:Wawrzyniak J., M.Drozdowski, E.Sanlaville, Selecting Algorithms for Large Berth Allocation Problems, 2019, European Journal of Operational Research, Volume 283, Issue 3, 16 June 2020, Pages 844-862 https://doi.org/10.1016/j.ejor.2019.11.055