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Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (24 December 2022) | Viewed by 22650

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Special Issue Editor

Dr. Yaping Ren

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Guest Editor

School of Intelligent Systems Science and Engineering, Institute of Physical Internet, Jinan University (Zhuhai Campus), Zhuhai 519000, China
Interests: green manufacturing; production planning and scheduling; operations research and optimization

Special Issue Information

Dear Colleagues,

To address the increasingly prominent environmental pollution and energy shortage, many countries devote themselves to green manufacturing and logistics, in which some optimization problems are common and challenging, e.g., production planning and scheduling, supply chain management, location and allocation problems, vehicle routing problem, resource optimization, and pricing strategies. This Special Issue will focus on collecting recent mathematical modeling and intelligent optimization research in green manufacturing and logistics, including operations research, game theory, (meta)heuristics, machine learning, knowledge-driven, digital twin, and so on.

Topics include but are not limited to:

Operations research in green manufacturing and logistics
Game theory in green manufacturing and logistics
(Meta)heuristics in green manufacturing and logistics
Machine learning in green manufacturing and logistics
Knowledge-driven methods in green manufacturing and logistics
Digital twin in green manufacturing and logistics
Data-driven optimization in green manufacturing and logistics
Mechanism design in sustainable operations
Exacted algorithm for large-scale programming in complex logistics systems
Service design and disruptive innovation in green manufacturing and logistics

Dr. Yaping Ren
Guest Editor

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Keywords

mathematical modeling
intelligent optimization
operations research
green manufacturing
green logistics

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Published Papers (10 papers)

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Research

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17 pages, 2945 KiB

Open AccessArticle

Multi-Objective Optimization for Mixed-Model Two-Sided Disassembly Line Balancing Problem Considering Partial Destructive Mode

by Bao Chao, Peng Liang, Chaoyong Zhang and Hongfei Guo

Mathematics 2023, 11(6), 1299; https://doi.org/10.3390/math11061299 - 8 Mar 2023

Cited by 6 | Viewed by 1682

Abstract

Large-volume waste products, such as refrigerators and automobiles, not only consume resources but also pollute the environment easily. A two-sided disassembly line is the most effective method to deal with large-volume waste products. How to reduce disassembly costs while increasing profit has emerged as an important and challenging research topic. Existing studies ignore the diversity of waste products as well as uncertain factors such as corrosion and deformation of parts, which is inconsistent with the actual disassembly scenario. In this paper, a partial destructive mode is introduced into the mixed-model two-sided disassembly line balancing problem, and the mathematical model of the problem is established. The model seeks to comprehensively optimize the number of workstations, the smoothness index, and the profit. In order to obtain a high-quality disassembly scheme, an improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. The proposed model and algorithm are then applied to an automobile disassembly line as an engineering illustration. The disassembly scheme analysis demonstrates that the partial destructive mode can raise the profit of a mixed-model two-sided disassembly line. This research has significant application potential in the recycling of large-volume products. Full article

(This article belongs to the Special Issue Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics)

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32 pages, 8357 KiB

Open AccessArticle

Optimal Design of Reverse Logistics Recycling Network for Express Packaging Considering Carbon Emissions

by Jia Mao, Jinyuan Cheng, Xiangyu Li, Honggang Zhao and Ciyun Lin

Mathematics 2023, 11(4), 812; https://doi.org/10.3390/math11040812 - 5 Feb 2023

Cited by 5 | Viewed by 2380

Abstract

With the development of China’s express delivery industry, the number of express packaging has proliferated, leading to many problems such as environmental pollution and resource waste. In this paper, the process of reverse logistics network design for express packaging recycling is given as an example in the M region, and a four-level network containing primary recycling nodes, recycling centers, processing centers, and terminals is established. A candidate node selection model based on the K-means algorithm is constructed to cluster by distance from 535 courier outlets to select 15 candidate nodes of recycling centers and processing centers. A node selection model based on the NSGA-II algorithm is constructed to identify recycling centers and processing centers from 15 candidate nodes with minimizing total cost and carbon emission as the objective function, and a set of Pareto solution sets containing 43 solutions is obtained. According to the distribution of the solution set, the 43 solutions are classified into I, II, and III categories. The results indicate that the solutions corresponding to Class I and Class II solutions can be selected when the recycling system gives priority to cost, Class II and Class III solutions can be selected when the recycling system gives priority to environmental benefits, and Class III solutions can be selected when the society-wide recycling system has developed to a certain extent. In addition, this paper also randomly selects a sample solution from each of the three types of solution sets, conducts coding interpretation for site selection, vehicle selection, and treatment technology selection, and gives an example design scheme. Full article

(This article belongs to the Special Issue Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics)

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31 pages, 14248 KiB

Open AccessArticle

A Multi-Objective Optimization Method for Flexible Job Shop Scheduling Considering Cutting-Tool Degradation with Energy-Saving Measures

by Ying Tian, Zhanxu Gao, Lei Zhang, Yujing Chen and Taiyong Wang

Mathematics 2023, 11(2), 324; https://doi.org/10.3390/math11020324 - 8 Jan 2023

Cited by 11 | Viewed by 2392

Abstract

Traditional energy-saving optimization of shop scheduling often separates the coupling relationship between a single machine and the shop system, which not only limits the potential of energy-saving but also leads to a large deviation between the optimized result and the actual application. In practice, cutting-tool degradation during operation is inevitable, which will not only lead to the increase in actual machining power but also the resulting tool change operation will disrupt the rhythm of production scheduling. Therefore, to make the energy consumption calculation in scheduling optimization more consistent with the actual machining conditions and reduce the impact of tool degradation on the manufacturing shop, this paper constructs an integrated optimization model including a flexible job shop scheduling problem (FJSP), machining power prediction, tool life prediction and energy-saving strategy. First, an exponential function is formulated using actual cutting experiment data under certain machining conditions to express cutting-tool degradation. Utilizing this function, a reasonable cutting-tool change schedule is obtained. A hybrid energy-saving strategy that combines a cutting-tool change with machine tool turn-on/off schedules to reduce the difference between the simulated and actual machining power while optimizing the energy savings is then proposed. Second, a multi-objective optimization model was established to reduce the makespan, total machine tool load, number of times machine tools are turned on/off and cutting tools are changed, and the total energy consumption of the workshop and the fast and elitist multi-objective genetic algorithm (NSGA-II) is used to solve the model. Finally, combined with the workshop production cost evaluation indicator, a practical FJSP example is presented to demonstrate the proposed optimization model. The prediction accuracy of the machining power is more than 93%. The hybrid energy-saving strategy can further reduce the energy consumption of the workshop by 4.44% and the production cost by 2.44% on the basis of saving 93.5% of non-processing energy consumption by the machine on/off energy-saving strategy. Full article

(This article belongs to the Special Issue Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics)

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27 pages, 5058 KiB

Open AccessArticle

Energy-Efficient Hybrid Flowshop Scheduling with Consistent Sublots Using an Improved Cooperative Coevolutionary Algorithm

by Chengshuai Li, Biao Zhang, Yuyan Han, Yuting Wang, Junqing Li and Kaizhou Gao

Mathematics 2023, 11(1), 77; https://doi.org/10.3390/math11010077 - 25 Dec 2022

Cited by 6 | Viewed by 1596

Abstract

Energy conservation, emission reduction, and green and low carbon are of great significance to sustainable development, and are also the theme of the transformation and upgrading of the manufacturing industry. This paper concentrates on studying the energy-efficient hybrid flowshop scheduling problem with consistent sublots (HFSP_ECS) with the objective of minimizing the energy consumption. To solve the problem, the HFSP_ECS is decomposed by the idea of “divide-and-conquer”, resulting in three coupled subproblems, i.e., lot sequence, machine assignment, and lot split, which can be solved by using a cooperative methodology. Thus, an improved cooperative coevolutionary algorithm (vCCEA) is proposed by integrating the variable neighborhood descent (VND) strategy. In the vCCEA, considering the problem-specific characteristics, a two-layer encoding strategy is designed to represent the essential information, and a novel collaborative model is proposed to realize the interaction between subproblems. In addition, special neighborhood structures are designed for different subproblems, and two kinds of enhanced neighborhood structures are proposed to search for potential promising solutions. A collaborative population restart mechanism is established to ensure the population diversity. The computational results show that vCCEA can coordinate and solve each subproblem of HFSP_ECS effectively, and outperform the mathematical programming and the other state-of-the-art algorithms. Full article

(This article belongs to the Special Issue Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics)

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16 pages, 2245 KiB

Open AccessArticle

Mathematical Formulations for Asynchronous Parallel Disassembly Planning of End-of-Life Products

by Leilei Meng, Biao Zhang, Yaping Ren, Hongyan Sang, Kaizhou Gao and Chaoyong Zhang

Mathematics 2022, 10(20), 3854; https://doi.org/10.3390/math10203854 - 18 Oct 2022

Cited by 2 | Viewed by 1555

Abstract

Disassembly is one of the most time-consuming and labor-intensive activities during the value recovery of end-of-life (EOL) products. The completion time (makespan) of disassembling EOL products is highly associated with the allocation of operators, especially in parallel disassembly. In this paper, asynchronous parallel disassembly planning (APDP), which avoids the necessity to synchronize disassembly tasks of manipulators during the parallel disassembly process, is studied to optimize the task assignment of manipulators for minimal makespan. We utilize four mixed integer linear programming (MILP) formulations to identify the optimal solutions. A set of different-sized instances are used to test and compare the performance of the proposed models, including some real-world cases. Finally, the proposed exact algorithm is further compared with the existing approach to solving APDP. Results indicate that a significant difference exists in terms of the computational efficiency of the MILP models, while three of four MILP formulations can efficiently achieve better solutions than that of the existing approach. Full article

(This article belongs to the Special Issue Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics)

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20 pages, 4843 KiB

Open AccessArticle

Cloud-Edge-Terminal-Based Synchronized Decision-Making and Control System for Municipal Solid Waste Collection and Transportation

by Ming Wan, Ting Qu, Manna Huang, Xiaohua Qiu, George Q. Huang, Jinfu Zhu and Junrong Chen

Mathematics 2022, 10(19), 3558; https://doi.org/10.3390/math10193558 - 29 Sep 2022

Cited by 3 | Viewed by 1903

Abstract

Due to dynamics caused by factors such as random collection and transportation requirements, vehicle failures, and traffic jams, it is difficult to implement regular waste collection and transportation schemes effectively. A challenge for the stable operation of the municipal solid waste collection and transportation (MSWCT) system is how to obtain the whole process data in real time, dynamically judge the process control requirements, and effectively promote the synchronization operation between multiple systems. Based on this situation, this study proposes a cloud-edge-terminal-based synchronization decision-making and control system for MSWCT. First, smart terminals and edge computing devices are deployed at key nodes of MSWCT for real-time collection and edge computing analysis of the whole process data. Second, we propose a collaborative analysis and distributed decision-making method based on the cloud-edge-terminal multi-level computing architecture. Finally, a “three-level and two-stage” synchronization decision-making mechanism for the MSWCT system is established, which enables the synchronization operation between various subsystems. With a real-world application case, the efficiency and effectiveness of the proposed decision-making and control system are evaluated based on real data of changes in fleet capacity and transportation costs. Full article

(This article belongs to the Special Issue Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics)

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15 pages, 1852 KiB

Open AccessArticle

Multi-AGV Flexible Manufacturing Cell Scheduling Considering Charging

by Jianxun Li, Wenjie Cheng, Kin Keung Lai and Bhagwat Ram

Mathematics 2022, 10(19), 3417; https://doi.org/10.3390/math10193417 - 20 Sep 2022

Cited by 18 | Viewed by 2746

Abstract

Because of their flexibility, controllability and convenience, Automated Guided Vehicles (AGV) have gradually gained popularity in intelligent manufacturing because to their adaptability, controllability, and simplicity. We examine the relationship between AGV scheduling tasks, charging thresholds, and power consumption, in order to address the issue of how AGV charging affects the scheduling of flexible manufacturing units with multiple AGVs. Aiming to promote AGVs load balance and reduce AGV charging times while meeting customer demands, we establish a scheduling model with the objective of minimizing the maximum completion time based on process sequence limitations, processing time restrictions, and workpiece transportation constraints. In accordance with the model’s characteristics, we code the machine, workpiece, and AGV independently, solve the model using a genetic algorithm, adjust the crossover mutation operator, and incorporate an elite retention strategy to the population initialization process to improve genetic diversity. Calculation examples are used to examine the marginal utility of the number of AGVs and electricity and validate the efficiency and viability of the scheduling model. The results show that the AVGs are effectively scheduled to complete transportation tasks and reduce the charging wait time. The multi-AGV flexible manufacturing cell scheduling can also help decision makers to seek AGVs load balance by simulation, reduce the charging times, and decrease the final completion time of manufacturing unit. In addition, AGV utilization can be maximized when the fleet size of AGV is 20%-40% of the number of workpieces. Full article

(This article belongs to the Special Issue Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics)

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17 pages, 2010 KiB

Open AccessArticle

A Multi-Period Vehicle Routing Problem for Emergency Perishable Materials under Uncertain Demand Based on an Improved Whale Optimization Algorithm

by Xiaodong Li, Yang Xu, Kin Keung Lai, Hao Ji, Yaning Xu and Jia Li

Mathematics 2022, 10(17), 3124; https://doi.org/10.3390/math10173124 - 31 Aug 2022

Cited by 6 | Viewed by 1982

Abstract

The distribution of emergency perishable materials after a disaster, such as an earthquake, is an essential part of emergency resource dispatching. However, the traditional single-period distribution model can hardly solve this problem because of incomplete demand information for emergency perishable materials in affected sites. Therefore, for such problems we firstly construct a multi-period vehicle path distribution optimization model with the dual objectives of minimizing the cost penalty of distribution delay and the total corruption during delivery, and minimizing the total amount of demand that is not met, by applying the interval boundary and most likely value weighting method to make uncertain demand clear. Then, we formulate the differential evolutionary whale optimization algorithm (DE-WOA) combing the differential evolutionary algorithm with the whale algorithm to solve the constructed model, which is an up-and-coming algorithm for solving this type of problem. Finally, to validate the feasibility and practicality of the proposed model and the novel algorithm, a comparison between the proposed model and the standard whale optimization algorithm is performed on a numerical instance. The result indicates the proposed model converges faster and the overall optimization effect is improved by 23%, which further verifies that the improved whale optimization algorithm has better performance. Full article

(This article belongs to the Special Issue Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics)

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22 pages, 5250 KiB

Open AccessArticle

Multi-Objective Robust Optimization for the Sustainable Location-Inventory-Routing Problem of Auto Parts Supply Logistics

by Ao Lv and Baofeng Sun

Mathematics 2022, 10(16), 2942; https://doi.org/10.3390/math10162942 - 15 Aug 2022

Cited by 9 | Viewed by 2431

Abstract

A great loss of transportation capacity has been caused in auto parts supply logistics due to the independent transportation from auto parts suppliers (APSs) to the automobile production line (APL). It is believed that establishing distribution centers (DCs) for centralized collection and unified distribution is one effective way to address this problem. This paper proposes a unified framework simultaneously considering the location-inventory-routing problem (LIRP) in auto parts supply logistics. Integrating the idea of sustainable development, a multi-objective MIP model is developed to determine the location and inventory capacity of DCs and routing decisions to minimize the total system cost and carbon emissions while concerning multi-period production demand. In addition, a robust optimization model is developed further in the context of uncertain demand. Numerical experiments and sensitivity analyses are conducted to verify the effectiveness of our proposed deterministic and robust models. The results show that synergistically optimizing the location and capacity of DCs and routing decisions are beneficial in reducing total system cost and carbon emissions. The analysis can provide guidelines to decision-makers for the effective management of auto parts supply logistics. Full article

(This article belongs to the Special Issue Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics)

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Review

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24 pages, 2283 KiB

Open AccessReview

A Review of Combinatorial Optimization Problems in Reverse Logistics and Remanufacturing for End-of-Life Products

by Yaping Ren, Xinyu Lu, Hongfei Guo, Zhaokang Xie, Haoyang Zhang and Chaoyong Zhang

Mathematics 2023, 11(2), 298; https://doi.org/10.3390/math11020298 - 6 Jan 2023

Cited by 8 | Viewed by 3063

Abstract

During the end-of-life (EOL) product recovery process, there are a series of combinatorial optimization problems (COPs) that should be efficiently solved. These COPs generally result from reverse logistics (RL) and remanufacturing, such as facility location and vehicle routing in RL, and scheduling, planning, and line balancing in remanufacturing. Each of the COPs in RL and remanufacturing has been reviewed; however, no review comprehensively discusses and summarizes the COPs in both. To fill the gap, a comprehensive review of the COPs in both RL and remanufacturing is given in this paper, in which typical COPs arising at the end of the product life cycle are discussed and analyzed for the first time. To better summarize these COPs, 160 papers published since 1992 are selected and categorized into three modules: facility location and vehicle routing in RL, scheduling in remanufacturing, and disassembly in remanufacturing. Finally, the existing research gaps are identified and some possible directions are described. Full article

(This article belongs to the Special Issue Mathematical Modeling and Intelligent Optimization in Green Manufacturing & Logistics)

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