A “reduce and solve” approach for the multiple-choice multidimensional knapsack problem

Chen, Yuning; Hao, Jin‐Kao

doi:10.1016/j.ejor.2014.05.025

Cited by 40 publications

(38 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To assess their proposal they used the instances proposed by Khan (1998). Chen and Hao (2014) propose a 'reduce and solve' heuristic that combines problem reduction techniques with integer linear programming. Their method recognizes variables which are highly likely to be part of the optimal solution and fixes them to one (group fixing).…”

Section: Literature Reviewmentioning

confidence: 99%

“…They assessed their approach using a 2.4 GHz Pentium computer having 4 GB of RAM and the CPLEX ® solver (v11.2). (5) The reduce-and-solve heuristic that combines problem reduction techniques with integer linear programming (Chen and Hao, 2014). In this regard, two different ways for performing the group and variable fixing are proposed.…”

Section: Comparison Of Sp-mmkp With the Best Literature Approachesmentioning

confidence: 99%

“…In this comparison, for setting an equal time limit to SP-MMKP, the time limit used in those approaches is taken into account. As described by Crévits et al (2012) the time limit was 500 seconds while in Mansi et al (2013) and Chen and Hao (2014) the time limit established was 3600. Furthermore, this comparison is divided into two tables in order to compare the algorithms with the models and highlight the best values among them.…”

Section: Comparison Of Sp-mmkp With the Best Literature Approachesmentioning

confidence: 99%

See 2 more Smart Citations

A set partitioning reformulation for the multiple-choice multidimensional knapsack problem

Voß

Lalla-Ruiz

2015

Engineering Optimization

View full text Add to dashboard Cite

The Multiple-choice Multidimensional Knapsack Problem (MMKP) is a well-known N P-hard combinatorial optimization problem that has received a lot of attention from the research community as it can be easily translated to several real-world problems arising in areas such as allocating resources, reliability engineering, cognitive radio networks, cloud computing, etc. In this regard, an exact model that is able to provide high-quality feasible solutions for solving it or being partially included in algorithmic schemes is desirable. The MMKP basically consists of finding a subset of objects that maximizes the total profit while observing some capacity restrictions. In this article a reformulation of the MMKP as a set partitioning problem is proposed to allow for new insights into modelling the MMKP. The computational experimentation provides new insights into the problem itself and shows that the new model is able to improve on the best of the known results for some of the most common benchmark instances.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Comparison Of Sp-mmkp With the Best Literature Approachesmentioning

confidence: 99%

Section: Comparison Of Sp-mmkp With the Best Literature Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A set partitioning reformulation for the multiple-choice multidimensional knapsack problem

Voß

Lalla-Ruiz

2015

Engineering Optimization

View full text Add to dashboard Cite

show abstract

“…Numerical results show that the performance of his approach is better than previously published results. In [36], Chen and Hao propose a 'reduce and solve' heuristic that combines problem reduction techniques with integer linear programming. Their method recognizes variables which are highly likely to be part of the optimal solution and fixes them to one (group fixing).…”

Section: Related Workmentioning

confidence: 99%

Smart Agents for the Multidimensional Multi-choice Knapsack Problem

Htiouech¹,

Alzaidi²

2017

IJCA

View full text Add to dashboard Cite

In this paper, we propose a multi-agent approach for solving the multidimensional multi-choice knapsack problem (called MMKP). The MMKP is an NP-Hard optimization problem in strong sense. It is considered as a combination of two other variants such as: the multi-choice knapsack problem (MCKP) and the multidimensional knapsack problem (MDKP). The MMKP can be applied in many problems in real world. It can model many industrial situations, such as capital budgeting, model of allocation resources and finance. The particular properties of the MMKP favor its decomposition into many MMKP sub-problems with small sizes. The assignment of sub-problems and the sharing of available resources are allocated to a first agent. Each subproblem is then solved by an agent. To work collaboratively, a strategic negotiation between agents has been defined. A coordinator agent (CA) will evaluate and merge the generated solutions to build a feasible solution to the initial problem. The choice rules of the CA is modeled as a multidimensional knapsack problem (MKP). The proposed method is able to solve several instances of literature effectively, in particular for large size instances. General TermsMetaheuristic, knapsack problem

show abstract

“…Several updates of these algorithms also were provided to optimize the mentioned algorithms for extended versions of 01KP such as the unbounded KP, multiple KP [10], quadratic 01KP and discounted 01KP [11][12].…”

Section: Related Work 21 Knapsack 01 (01kp) Solutionsmentioning

confidence: 99%

Grey Wolf Optimization Applied to the 0/1 Knapsack Problem

Yassien¹,

Masadeh²,

Alzaqebah³

et al. 2017

IJCA

View full text Add to dashboard Cite

The knapsack problem (01KP ) in networks is investigated in this paper. A novel algorithm is proposed in order to find the best solution that maximizes the total carried value without exceeding a known capacity using Grey Wolf Optimization (GWO) and K-means clustering algorithms. GWO is a recently established meta-heuristics for optimization, inspired by grey wolf's species. K-means clustering algorithm is used to group each 5-12 agents with each other at one cluster according to GWO constraint. The evaluated performance is satisfying. The simulation results show great compatibility between experimental and theoretical results. General TermsAlgorithms.

show abstract

A “reduce and solve” approach for the multiple-choice multidimensional knapsack problem

Cited by 40 publications

References 20 publications

A set partitioning reformulation for the multiple-choice multidimensional knapsack problem

A set partitioning reformulation for the multiple-choice multidimensional knapsack problem

Smart Agents for the Multidimensional Multi-choice Knapsack Problem

Grey Wolf Optimization Applied to the 0/1 Knapsack Problem

Contact Info

Product

Resources

About