Comparing Various Genetic Algorithm Approaches for Multiple-choice Multi-dimensional Knapsack Problem (mm-KP)

Syarif, Admi; Anggraini, Dian; Muludi, Kurnia; Wamiliana, Wamiliana; Gen, Mitsuo

doi:10.22266/ijies2020.1031.40

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…The study had an objective determining the best reorder point of warehouses in supply chains. A genetic algorithm (GA) is well known as a robust optimization tool has been successfully used for complex combinatorial problems [10]. Thus, the algorithm has been used for solving various cases related to the transportation problems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This study is an extension of previous studies that also uses the genetic algorithm (GA) to find solutions of the problem [7]. The GA is chosen as it has been successfully applied for various complex combinatorial problems [10]. Several genetic operators are modified and tested to obtain the most suitable reproduction operator for the multistage distribution process.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Multi-Stage Distribution Process Using Improved Genetic Algorithm

Mahmudy¹,

Sarwani²,

Rahmi³

et al. 2021

IJIES

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A supply chain process in manufacturing industries involves a distribution process that ensures finished goods distribute to their customers properly. The process may involve several parties such as manufacturing plans, distributions centres, and retailers. To maximize profit, the companies need to optimize the distribution process by minimizing costs. The multi-stage distribution problem contains several constrains so obtaining optimum solution using exact methods may require excessive processing time. Genetic algorithm is proposed the solve the complex problem. The genetic algorithm is improved by modifying chromosome representation and related genetic reproduction operators. The new chromosome is designed to address different number of distribution stages in manufacturing industries. Thus, the proposed approach could be applied for various distribution problems. Several genetic operators are tested to obtain the most suitable reproduction operator for the multi-stage distribution process. The numerical experiments prove that the improved genetic algorithm is suitable for the optimization of the multistage distribution process and produce better result compare to those achieved by original genetic algorithm, simulated annealing, and random search as baseline method. The improved genetic algorithm produces solution with cost of Rp 20,167.8, lower than cost of Rp 21,860.4, Rp 23,354.30 and Rp 34,328.0 obtained by original genetic algorithm, simulated annealing, and random search, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Multi-Stage Distribution Process Using Improved Genetic Algorithm

Mahmudy¹,

Sarwani²,

Rahmi³

et al. 2021

IJIES

View full text Add to dashboard Cite

show abstract

“…Parra-Hernandez and Dimopoulos [7] extended the idea of their heuristic approach for the MKP to the MMKP. Over the last 10 years, studies of the MMKP have focused on iterative heuristics [16][17][18][19], branch-and-bound methods [20,21], Lagrangian relaxation [22][23][24], linear programming relaxation [25], reformulation/reduction [25][26][27][28], Pareto-algebraic heuristics [20,29], approximate core [30], core-based exact algorithm [31], two-phase kernel search [32], meta-heuristics such as genetic algorithm [33], swarm intelligence [23,[34][35][36][37], estimation of distribution algorithm [38], simulated annealing [39], tabu search [40], etc.…”

Section: Introductionmentioning

confidence: 99%

A Memetic Algorithm with a Novel Repair Heuristic for the Multiple-Choice Multidimensional Knapsack Problem

2022

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We propose a memetic algorithm for the multiple-choice multidimensional knapsack problem (MMKP). In this study, we focus on finding good solutions for the MMKP instances, for which feasible solutions rarely exist. To find good feasible solutions, we introduce a novel repair heuristic based on the tendency function and a genetic search for the function approximation. Even when the density of feasible solutions over the entire solution space is very low, the proposed repair heuristic could successfully change infeasible solutions into feasible ones. Based on the proposed repair heuristic and effective local search, we designed a memetic algorithm that performs well on problem instances with a low density of feasible solutions. By performing experiments, we could show the superiority of our method compared with previous genetic algorithms.

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“…Aided by GAs, researchers evolve solutions to complex combinatorial optimization problems easily and rapidly. Our past researches reported the excellent performance of GA in solving various combinatorial optimization problems [11,12], and [13]. In contrast to other heuristics methods, it utilizes a set population of solutions in its search.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Various Heuristic Algorithms to Solve Job Shop Scheduling Problem (JSSP)

Syarif¹,

Pamungkas²,

Kumar³

et al. 2021

IJIES

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Scheduling is a famous optimization problem that seeks the best strategy of allocating resources over time to perform jobs/tasks satisfying specific criteria. It exists everywhere in everyday life, particularly in manufacturing or industrial applications. An essential class of scheduling problems is a job shop scheduling problem (JSSP), an NPhard optimization problem. Several researchers have reported the use of heuristic methods to solve JSSP. This paper aims to investigate the performance of various heuristic algorithms to solve JSSP. Firstly, we developed a Genetic Algorithm (GA and compared the performance of some heuristic algorithms, including Particle Swarm Optimization (PSO), Upper-level algorithm (UPLA), Differential-based Harmony Search (DHS), Grey Wolf Optimization (GWO), Ant Colony Optimization (ACO), Bacterial Foraging Optimization (BFO), Parallel Bat Optimization (PBA), and Tabu Search (TS). The experimental results of the 28 benchmark test problems validated that the algorithms, except ACO, can provide the optimal solution of JSSP. PBA delivers the most impressive performance that solves 26 cases optimally, with the average error equal to 0.05%. Among those 28 test problems, TS, DHS, and PBA can solve 26 instances optimally, followed by GA that solves 21 cases.

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Comparing Various Genetic Algorithm Approaches for Multiple-choice Multi-dimensional Knapsack Problem (mm-KP)

Cited by 7 publications

References 17 publications

Optimization of Multi-Stage Distribution Process Using Improved Genetic Algorithm

Optimization of Multi-Stage Distribution Process Using Improved Genetic Algorithm

A Memetic Algorithm with a Novel Repair Heuristic for the Multiple-Choice Multidimensional Knapsack Problem

Performance Evaluation of Various Heuristic Algorithms to Solve Job Shop Scheduling Problem (JSSP)

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