2018
DOI: 10.4236/ajor.2018.85023
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The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

Abstract: The 0/1 Multidimensional Knapsack Problem (0/1 MKP) is an interesting NP-hard combinatorial optimization problem that can model a number of challenging applications in logistics, finance, telecommunications and other fields. In the 0/1 MKP, a set of items is given, each with a size and value, which has to be placed into a knapsack that has a certain number of dimensions having each a limited capacity. The goal is to find a subset of items leading to the maximum total profit while respecting the capacity constr… Show more

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Cited by 20 publications
(16 citation statements)
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“…To clarify this point, in the case of small recruitment instances, we use the modified version of the standard genetic algorithm (SGA) to obtain the improved sequential version (SeqGA), the modifications, are made at the level of generation of the initial population, the crossover and 'insertion operators. The results obtained using SeqGA are satisfactory which allows to right decision recruitment [15,16]. In the case of large instances or when adding the constraints to recruitment problem as cost post constraints, we can use a parallel genetic algorithm PGA through a multiprocessor architectures.…”
Section: Introductionmentioning
confidence: 85%
“…To clarify this point, in the case of small recruitment instances, we use the modified version of the standard genetic algorithm (SGA) to obtain the improved sequential version (SeqGA), the modifications, are made at the level of generation of the initial population, the crossover and 'insertion operators. The results obtained using SeqGA are satisfactory which allows to right decision recruitment [15,16]. In the case of large instances or when adding the constraints to recruitment problem as cost post constraints, we can use a parallel genetic algorithm PGA through a multiprocessor architectures.…”
Section: Introductionmentioning
confidence: 85%
“…The second one determines the workload scheduling matrix such that a large enough workload is scheduled for execution on each server to generate heat to meet the demand. The latter problem can be solved using an adaptation of the Multiple Knapsack Problem [ 72 ].…”
Section: Thermal Aware Workload Schedulingmentioning
confidence: 99%
“…Constraints (11) and (12) are the equivalent forms of (2) and (3). The structure of (P 2 ) strictly follows the typical structure of MMKP [24] and hence, it is NP-hard. We propose the OIGA algorithm to solve (P 1 ) with a minimal optimality gap compared to branch-and-bound (BB) method.…”
Section: B Problem Formulationmentioning
confidence: 99%