An estimation of distribution algorithm for combinatorial optimization problems

Pérez-Rodríguez, Ricardo

doi:10.12928/ijio.v3i1.5862

Cited by 3 publications

(1 citation statement)

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“…Common metaheuristic techniques described in the literature include GAs [43], particle swarm optimization [44], firefly optimization [45], and more [46]- [48]. Among these, the GA has emerged as a popular intelligent search method owing to its straightforward implementation logic and robust capacity for exploring high-dimensional search spaces to find globally optimal solutions [49], [50], [59], [51]- [58]. By mimicking the natural selection processes, GAs can efficiently navigate complex search spaces and identify competitive solutions.…”

Section: Solution Approach-based Genetic Algorithmmentioning

confidence: 99%

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

Leuveano,

Asih,

Ridho

et al. 2023

Journal of Robotics and Control

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In Indonesia, the significant role of perishable products in food wastage has placed the country fourth globally in household food waste. Managing inventory for such products, with their short shelf life and stringent safety standards, emphasizes the need for efficient lot sizing planning. This study introduces a novel Dynamic Lot-Sizing (DLS) model, addressing perishable products and inventory constraints across multiple products, periods, and varying demands. The model aims to optimize production quantity and binary production, minimizing overall system costs. Employing a Genetic Algorithm (GA), this research solves the DLS model under constrained and unconstrained inventory capacities. Real-case data from a bread manufacturing company validates the model, while sensitivity analysis examines perishability's impact on the solution and model performance. The DLS-GA model not only reduces system costs but also effectively considers product perishability, offering optimal production plans.

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Section: Solution Approach-based Genetic Algorithmmentioning

confidence: 99%

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

Leuveano,

Asih,

Ridho

et al. 2023

Journal of Robotics and Control

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Optimizing lot sizing model for perishable bread products using genetic algorithm

Asih,

Leuveano,

Dharmawan

2023

j. sist. manaj. ind.

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This research addresses order planning challenges related to perishable products, using bread products as a case study. The problem is how to efficiently manage the various bread products ordered by diverse customers, which requires distributors to determine the optimal number of products to order from suppliers. This study aims to formulate the problem as a lot-sizing model, considering various factors, including customer demand, inventory constraints, ordering capacity, return rate, and defect rate, to achieve a near or optimal solution, Therefore determining the optimal order quantity to reduce the total ordering cost becomes a challenge in this study. However, most lot sizing problems are combinatorial and difficult to solve. Thus, this study uses the Genetic Algorithm (GA) as the main method to solve the lot sizing model and determine the optimal number of bread products to order. With GA, experiments have been conducted by combining the values of population, crossover, mutation, and generation parameters to maximize the feasibility value that represents the minimal total cost. The results obtained from the application of GA demonstrate its effectiveness in generating near or optimal solutions while also showing fast computational performance. By utilizing GA, distributors can effectively minimize wastage arising from expired or perishable products while simultaneously meeting customer demand more efficiently. As such, this research makes a significant contribution to the development of more effective and intelligent decision-making strategies in the domain of perishable products in bread distribution.

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Computing Offloading Decision Based on Adaptive Estimation of Distribution Algorithm in Internet of Vehicles

Yu¹,

Chen²,

2022

International Journal of Cognitive Informatics and Natural Intelligence

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Aimed to improve the efficiency of computing offloading in internet of vehicles (IoV), a collaborative multi-task computing offloading decision mechanism with adaptive estimation of distribution algorithm for MEC-IoV was proposed in this paper. The algorithm considered the energy and time consumption as well as priority among different tasks. It presented a local search strategy and an adaptive learning rate according to the characteristics of the problem to improve the estimation of distribution algorithm. Experimental results show that compared with other offloading strategies, the proposed offloading strategy has obvious effects on the total cost optimization; the solutions quality of AEDA is 86.6% of PSO and 67.3% of GA.

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An estimation of distribution algorithm for combinatorial optimization problems

Cited by 3 publications

References 0 publications

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

Optimizing lot sizing model for perishable bread products using genetic algorithm

Computing Offloading Decision Based on Adaptive Estimation of Distribution Algorithm in Internet of Vehicles

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