Multi-Objective Material Logistics Planning with Discrete Split Deliveries Using a Hybrid NSGA-II Algorithm

Fang, Weikang; Guan, Zailin; Su, Peiyue; Luo, Dan; Ding, Linshan; Yue, Lei

doi:10.3390/math10162871

Cited by 4 publications

(1 citation statement)

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“…Aiming to minimize the inventory holding and total costs of parts handled, Satoglu and Sahin [15] developed a mathematical model and used heuristics to design an internal milk supply system that minimizes total material handling and inventory maintenance costs and solves scheduling and routing problems. Considering that multiple trains visit stations in batches, Weikang et al [16] proposed a nonlinear multi-objective mathematical model, which combines NSGA-II (Nondominated Sorting Genetic Algorithm-II) and a hybrid heuristic algorithm of variable neighborhood search to solve the scheduling. István [17] handled daily batch scaling and production scheduling activities by combining traditional manufacturing system simulations with advanced machine learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Design for Optimally Routing and Scheduling a Tow Train for Just-in-Time Material Supply of Mixed-Model Assembly Lines

Xia,

Zhang,

Gao

et al. 2023

Sustainability

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With the increase in product varieties, the combination of supermarkets and tow trains is being adopted by more automobile manufacturers for part feeding, especially in mixed-flow assembly lines. This paper focuses on the routing, scheduling, and loading problems of a single towed train that transports parts from one supermarket to the workstation buffer in a mixed-flow assembly line and aims to optimize the loading of the tow train, the optimal delivery schedule and route, and the appropriate departure time to minimize shipping and line inventory costs. To enable part feeding in line with the just-in-time (JIT) principle, a new mixed-integer mathematical model from nonlinearity to linearity and a novel artificial immune genetic algorithm-based heuristic are proposed. Both methods can provide reasonable solutions compared by minimizing the route length and inventory level in terms of speed, and the genetic algorithm shows better performance on a large scale.

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Section: Literature Reviewmentioning

confidence: 99%

Design for Optimally Routing and Scheduling a Tow Train for Just-in-Time Material Supply of Mixed-Model Assembly Lines

Xia,

Zhang,

Gao

et al. 2023

Sustainability

View full text Add to dashboard Cite

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Is NSGA-II Ready for Large-Scale Multi-Objective Optimization?

Nebro

Galeano-Brajones

Luna

et al. 2022

MCA

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NSGA-II is, by far, the most popular metaheuristic that has been adopted for solving multi-objective optimization problems. However, its most common usage, particularly when dealing with continuous problems, is circumscribed to a standard algorithmic configuration similar to the one described in its seminal paper. In this work, our aim is to show that the performance of NSGA-II, when properly configured, can be significantly improved in the context of large-scale optimization. It leverages a combination of tools for automated algorithmic tuning called irace, and a highly configurable version of NSGA-II available in the jMetal framework. Two scenarios are devised: first, by solving the Zitzler–Deb–Thiele (ZDT) test problems, and second, when dealing with a binary real-world problem of the telecommunications domain. Our experiments reveal that an auto-configured version of NSGA-II can properly address test problems ZDT1 and ZDT2 with up to 217=131,072 decision variables. The same methodology, when applied to the telecommunications problem, shows that significant improvements can be obtained with respect to the original NSGA-II algorithm when solving problems with thousands of bits.

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Green Distribution Route Optimization of Medical Relief Supplies Based on Improved NSGA-II Algorithm under Dual-Uncertainty

Peng

Liu

2023

Sustainability

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With growing concerns about environmental issues, sustainable transport schemes are receiving more attention than ever before. Reducing pollutant emissions during vehicle driving is an essential way of achieving sustainable transport plans. To achieve sustainable transport and reduce carbon emissions, on the premise of ensuring rescue timeliness, this research proposes a multi-objective distribution route optimization model considering the minimization of transportation cost and transportation risk under dual-uncertainty constraints, providing a practical framework for determining the optimal location of rescue centers and distribution routes in emergencies using fuzzy theory. First, this paper proposes objective functions that innovatively take into account the congestion risk and accident risk during the distribution of medical supplies while introducing the carbon emission cost into the transportation cost and using the fuzzy demand for supplies and the fuzzy traffic flow on the roads as uncertainty constraints. Then, this paper designs a multi-strategy hybrid nondominated sorting genetic algorithm (MHNSGA-II) based on the original form to solve the model. MHNSGA-II adapts a two-stage real number coding method for chromosomes and optimizes the population initialization, crowding distances selection, and crossover and mutation probability calculation methods. The relevant case analysis demonstrates that, compared with the original NSGA-II, MHNSGA-II can decrease the transportation cost and transportation risk by 42.55% and 5.73%, respectively. The sensitivity analysis verifies the validity and rationality of the proposed model. The proposed framework can assist decision makers in emergency logistics rescue.

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Multi-Objective Material Logistics Planning with Discrete Split Deliveries Using a Hybrid NSGA-II Algorithm

Cited by 4 publications

References 50 publications

Design for Optimally Routing and Scheduling a Tow Train for Just-in-Time Material Supply of Mixed-Model Assembly Lines

Design for Optimally Routing and Scheduling a Tow Train for Just-in-Time Material Supply of Mixed-Model Assembly Lines

Is NSGA-II Ready for Large-Scale Multi-Objective Optimization?

Green Distribution Route Optimization of Medical Relief Supplies Based on Improved NSGA-II Algorithm under Dual-Uncertainty

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