Fundamentals of Computer Alori
Fundamentals of Computer Alori
Two-phases Method and Branch and Bound Procedures to Solve the Bi–objective Knapsack Problem
Journal of Global Optimization
Solving bicriteria 0-1 knapsack problems using a labeling algorithm
Computers and Operations Research
Where are the hard knapsack problems?
Computers and Operations Research
Multicriteria Optimization
A multi-objective model for environmental investment decision making
Computers and Operations Research
Solving efficiently the 0-1 multi-objective knapsack problem
Computers and Operations Research
Labeling algorithms for multiple objective integer knapsack problems
Computers and Operations Research
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Computers & Mathematics with Applications
Multicriteria 0-1 knapsack problems with k-min objectives
Computers and Operations Research
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In this paper, we present a dynamic programming (DP) algorithm for the multi-objective 0-1 knapsack problem (MKP) by combining two state reduction techniques. One generates a backward reduced-state DP space (BRDS) by discarding some states systematically and the other reduces further the number of states to be calculated in the BRDS using a property governing the objective relations between states. We derive the condition under which the BRDS is effective to the MKP based on the analysis of solution time and memory requirements. To the authors' knowledge, the BRDS is applied for the first time for developing a DP algorithm. The numerical results obtained with different types of bi-objective instances show that the algorithm can find the Pareto frontier faster than the benchmark algorithm for the large size instances, for three of the four types of instances conducted in the computational experiments. The larger the size of the problem, the larger improvement over the benchmark algorithm. Also, the algorithm is more efficient for the harder types of bi-objective instances as compared with the benchmark algorithm.