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Multi-objective optimization of greening scheduling problems of part feeding for mixed model assembly lines based on the robotic mobile fulfillment system

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Binghai Zhou
Binghai Zhou
Binghai Zhou
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Zhexin Zhu
Zhexin Zhu
Zhexin Zhu
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Abstract and Figures

Since greening scheduling problems are drawing increasing attention from researchers and modern manufacturing enterprises, and the energy consumption is a substantial problem regarding the greening and sustainability, the aim of this paper is to construct an energy-saving scheduling scheme to carry out the part feeding tasks of mobile robots in the automobile mixed model assembly lines. The objective of minimizing the total energy consumption of mobile robots is jointly incorporated with the operational criterions when implementing part feeding tasks. Due to the NP-hardness nature of the proposed greening problem, a multi-objective disturbance and repair strategy enhanced cohort intelligence (MDRCI) algorithm is established to deal with the multi-objective problem. Computational results indicate that the enhanced strategies are of great significance to the MDRCI algorithm and it outperforms the other benchmark algorithms on both global search capability and search depth. In addition, the energy-saving strategy and disturbance and repair strategy are validated by comparison experiments. Furthermore, managerial insights are illustrated to make trade-offs between the total line-side inventory level and the energy consumption, jointly making it helpful in the greening scheduling process of the practical production. The achievements acquired in this paper may be inspiring for further researches on the energy-related production scheduling problem.
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ORIGINAL ARTICLE
Multi-objective optimization of greening scheduling problems of part
feeding for mixed model assembly lines based on the robotic mobile
fulfillment system
Binghai Zhou
1
•Zhexin Zhu
1
Received: 18 August 2020 / Accepted: 20 January 2021 / Published online: 2 March 2021
ÓThe Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021
Abstract
Since greening scheduling problems are drawing increasing attention from researchers and modern manufacturing
enterprises, and the energy consumption is a substantial problem regarding the greening and sustainability, the aim of this
paper is to construct an energy-saving scheduling scheme to carry out the part feeding tasks of mobile robots in the
automobile mixed model assembly lines. The objective of minimizing the total energy consumption of mobile robots is
jointly incorporated with the operational criterions when implementing part feeding tasks. Due to the NP-hardness nature
of the proposed greening problem, a multi-objective disturbance and repair strategy enhanced cohort intelligence (MDRCI)
algorithm is established to deal with the multi-objective problem. Computational results indicate that the enhanced
strategies are of great significance to the MDRCI algorithm and it outperforms the other benchmark algorithms on both
global search capability and search depth. In addition, the energy-saving strategy and disturbance and repair strategy are
validated by comparison experiments. Furthermore, managerial insights are illustrated to make trade-offs between the total
line-side inventory level and the energy consumption, jointly making it helpful in the greening scheduling process of the
practical production. The achievements acquired in this paper may be inspiring for further researches on the energy-related
production scheduling problem.
Keywords Multi-objective optimization Greening scheduling Part feeding Mobile robot
1 Introduction
Due to the increasing global energy shortage, climate
deterioration and environmental issues, the greening
development of modern manufacturing enterprises has
become one of the toughest challenges faced with
humanity. Since production activities are responsible for
nearly 90% of greenhouse gas (GHG) emissions, reducing
the energy consumption and improving the energy effi-
ciency in the industrial sector inevitably led researchers
and manufacturers to pay serious attention to [1]. Under the
circumstance, practices on energy waste reduction through
energy-aware production scheduling methods are now
given great priority to among many enterprises’ cardinal
tasks [2], and the concept of ‘‘greening material handling
scheduling (GMHS)’’ and ‘‘energy-efficient part feeding
problem (EPFP)’’ has become attractive topics in both
practical applications and academic research. Therefore,
much advancement and increasing effort have been devo-
ted to exploit novel part feeding equipment and propulsion
technologies such as industrial robots, sensing devices and
new scheduling techniques [3].
However, though the utilization of industrial handling
robots can control GHG emissions, cut down production
costs and green the industry to a considerable degree [4,5],
it still consumes a large amount of electrical energy.
According to the International Federation of Robotics,
from 2013 to 2018, the global industrial robot market size
has exhibited a steady upward trend and has reached 168.2
billion dollars in 2018, twice as much as in 2013. These
&Binghai Zhou
bhzhou@tongji.edu.cn
Zhexin Zhu
zhuzhexin@163.com
1
School of Mechanical Engineering, Tongji University,
Shanghai 201804, People’s Republic of China
123
Neural Computing and Applications (2021) 33:9913–9937
https://doi.org/10.1007/s00521-021-05761-w(0123456789().,-volV)(0123456789().,-volV)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... The goal is to make them more transparent by discarding the need for metaphors. Some examples used in RMFS include Genetic Algorithms [34], Adaptive Large Neighborhood Search and Simulated Annealing [11], and Multi-Objective Disturbance and Repair Strategy Enhanced Cohort intelligence [35]. ...
... So, most authors have targeted less than five workstations. In contrast, the paper with the highest number of workstations is the one from Zhou et al. [35]. In said work, the authors considered up to 50 workstations, although this work represents an outlier. ...
... But within this great variety, few authors have pitted different techniques against each other. There were only seven papers in which authors compared different techniques [20], [21], [26], [35], [54], [61], [62]. In our opinion, this number should be higher and efforts should be allocated into comparing different types of techniques that have exhibited good results. ...
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The Robotic Mobile Fulfillment System (RMFS) is a method for handling products, in which a Line Follower Robot (LFR) transports products to a human workstation for packing. In this systematic review, we delve into the current state of RMFS research using data sourced from Scopus. After a comprehensive search, we found 264 manuscripts, which we filtered to 76 relevant articles. Our analysis covers several variables, from basic metadata to manuscript impact and specific conditions the authors consider. We discovered that there needs to be more focus on the pod allocation problem, despite its potential, with the majority of the emphasis on LFR displacement. We created a detailed diagram that outlines the essential elements and subproblems associated with RMFS. As the interest in RMFS continues growing, our study provides crucial insights and direction for future research efforts.
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... human-centred objectives) as a potential research direction for large-scale robotic picking systems, there is relatively little literature in this field. For example, Zhou and Zhu (2021) consider greening scheduling problems for part feeding at mixed-model assembly lines based on the RMFS. They propose a multi-objective method called MDRCI to solve the problem. ...
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... A practical multi-objective reliability optimisation model for integrating the scheduling of AGV and JSP is developed, and the problem is solved by a novel non-dominated sorted cuckoo search algorithm [32]. Zhou et al. [33] designed a cluster intelligence algorithm with multi-objective disturbance and repair strategies to minimise the TEC of a mobile robot combined with operational criteria. ...
Integrated Scheduling of Multi-Objective Job Shops and Material Handling Robots with Reinforcement Learning Guided Meta-Heuristics
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This study investigates the integrated multi-objective scheduling problems of job shops and material handling robots (MHR) with minimising the maximum completion time (makespan), earliness or tardiness, and total energy consumption. The collaborative scheduling of MHR and machines can enhance efficiency and reduce costs. First, a mathematical model is constructed to articulate the concerned problems. Second, three meta-heuristics, i.e., genetic algorithm (GA), differential evolution, and harmony search, are employed, and their variants with seven local search operators are devised to enhance solution quality. Then, reinforcement learning algorithms, i.e., Q-learning and state–action–reward–state–action (SARSA), are utilised to select suitable local search operators during iterations. Three reward setting strategies are designed for reinforcement learning algorithms. Finally, the proposed algorithms are examined by solving 82 benchmark instances. Based on the solutions and their analysis, we conclude that the proposed GA integrating SARSA with the first reward setting strategy is the most competitive one among 27 compared algorithms.
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... In view of the NP-hard nature of the BOGSP-FMC, exact methods including dynamic programming, branch & bound and column generation are difficult to find acceptable solutions in a short time, especially when the problem size is large (Zhou and He 2020;Zhou and Zhu 2021a). To solve such complex multi-objective optimization problems in a reasonable time, many novel intelligent optimization algorithms have been developed and successfully applied to production scheduling problems in the literature, such as multi-objective grey wolf optimizer (Mirjalili et al. 2016), structure enhanced discrete non-dominated sorting genetic algorithm-II (NSGA-II) (Tan et al. 2022) and ant colony optimization (ACO) behavior-based multi-objective evolutionary algorithm based on decomposition (MOEA/ D) (Shao et al. 2022). ...
An improved bi-objective salp swarm algorithm based on decomposition for green scheduling in flexible manufacturing cellular environments with multiple automated guided vehicles
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Energy-awareness in the industrial sectors has become a global consensus in recent decades. Green scheduling is acknowledged as an effective weapon to reduce energy consumption in the industrial sectors. Therefore, this paper is devoted to the green scheduling of flexible manufacturing cells (FMC) with auto-guided vehicle transportation, where conflict-free routing of the vehicles is considered. To deal with this problem, a bi-objective optimization model is proposed to achieve the minimization of the maximum completion time and the total energy consumption in an FMC. The studied problem is an extension of flexible job shop problem which is NP-hard. Thus, an improved bi-objective salp swarm algorithm based on decomposition (IMOSSA/D) is proposed and applied to the problem. The approach is based on the decomposition of the bi-objective problem. Salp swarm intelligence along with three stochastic-distribution-based operators are incorporated into the approach, to enhance and balance its exploring and exploiting ability. Computational experiments are performed to compare the proposed approach with two state-of-the-art algorithms. This study allows the decision makers to better trade-off between energy savings and production efficiency in flexible manufacturing cellular environment.
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Cost-efficient design and optimization of robotic assembly lines using a non-dominated sorting genetic algorithm framework
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The total cost of assembly is a critical factor in robotic assembly line balancing, as it encompasses all the costs associated with the assembly line, including initial costs, setup, maintenance, and energy cost. This study introduces a different approach to the robotic assembly line balancing problem, with a dual focus on minimizing both cycle time and overall assembly costs. The effectiveness of the proposed approach is validated through three case study problems taken from the literature and results are compared to traditional assembly allocation methods. For case study 1, 89.4% (42 out of 47) of the solutions achieved a lower total cost, and 34% (16 out of 47) of the solutions utilized fewer workstations; and for case study 2, 96.4% (108 out of 112) of the solutions achieved a lower total cost, and 58.9% (66 out of 112) of the solutions utilized fewer workstations for the same cycle time. These results demonstrate a significant savings in cost and a notable improvement in workstation efficiency for a substantial portion of the solutions. This comprehensive approach allows an effective resource allocation, reduces inefficiencies, and enhances the overall cost-effectiveness and performance of the robotic assembly line. It also supports decision-makers in selecting more sustainable and economically viable assembly line solutions that optimize both productivity and energy efficiency.
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A multipopulation grasshopper algorithm for multiobjective material distribution scheduling problem of assembly line with flexible line inventory
Article
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Purpose Owing to the finite nature of the boundary of the line (BOL), the conventional method, involving the strong matching of single-variety parts with storage locations at the periphery of the line, proves insufficient for mixed-model assembly lines (MMAL). Consequently, this paper aims to introduce a material distribution scheduling problem considering the shared storage area (MDSPSSA). To address the inherent trade-off requirement of achieving both just-in-time efficiency and energy savings, a mathematical model is developed with the bi-objectives of minimizing line-side inventory and energy consumption. Design/methodology/approach A nondominated and multipopulation multiobjective grasshopper optimization algorithm (NM-MOGOA) is proposed to address the medium-to-large-scale problem associated with MDSPSSA. This algorithm combines elements from the grasshopper optimization algorithm and the nondominated sorting genetic algorithm-II. The multipopulation and coevolutionary strategy, chaotic mapping and two further optimization operators are used to enhance the overall solution quality. Findings Finally, the algorithm performance is evaluated by comparing NM-MOGOA with multi-objective grey wolf optimizer, multiobjective equilibrium optimizer and multi-objective atomic orbital search. The experimental findings substantiate the efficacy of NM-MOGOA, demonstrating its promise as a robust solution when confronted with the challenges posed by the MDSPSSA in MMALs. Originality/value The material distribution system devised in this paper takes into account the establishment of shared material storage areas between adjacent workstations. It permits the undifferentiated storage of various part types in fixed BOL areas. Concurrently, the innovative NM-MOGOA algorithm serves as the core of the system, supporting the formulation of scheduling plans.
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Iterated-local-search-based chaotic differential evolution algorithm for hybrid-load part feeding scheduling optimization in mixed-model assembly lines
Article
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  • ENG COMPUTATION
Purpose Driven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted. Design/methodology/approach A novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy. Findings The CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod. Originality/value This study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production.
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Optimally scheduling and loading tow trains of in-plant milk-run delivery for mixed-model assembly lines
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The paradigms of sustainability and circular economy have represented separate streams of academic literature but there is a growing realization that these two paradigms may have a mutually-constitutive relationship. Particularly, the relationship among the two notions in the context of supply chains remains unexplored and this is the focus of this research. We draw on the organizational sense making literature to identify the organizational enablers of circular supply chains and their relationships with the environmental performance of supply chains. Data was collected from various supply chains within the United Arab Emirates (UAE). A structural equation modelling (SEM) approach was adopted to test the hypothesis derived from a systematic review of the literature. Originality of this work stems from the conceptualisation and validation of a framework for circular supply chains and sustainable performance as a combination of process facilitators and a persuasive organizational narrative that enables organizations to embrace the circular economy practices. The validated framework has implications for both academia and the industry because of its emphasis on the joint effects of a) the discursive ability of organizational actors to articulate a paradigm shift towards circular supply chains b) the process facilitators comprising of actions and practices that enable circular supply chains.
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An integrated approach for line balancing and AGV scheduling towards smart assembly systems
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  • Dec 2019
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Purpose- Optimizing material handling within the factory is one of the key problems of modern assembly line systems. This paper focuses on simultaneously balancing a robotic assembly line and scheduling of material handling required for the operation of such a system, a topic that has received limited attention in academia. Manufacturing industries focuses on full autonomy due to the rapid advancements in different elements of Industry 4.0 such as internet of things, big data and cloud computing. In smart assembly systems, this autonomy aims at the integration of automated material handling equipment like automated guided vehicles (AGVs) to robotic assembly line systems to ensure a reliable and flexible production system. Design/methodology/approach- This paper tackles the problem of designing a balanced robotic assembly lines and scheduling of automated guided vehicles to feed materials to these lines such that the cycle time and total tardiness of the assembly system are minimized. Due to the combination of two well-known complex problems: line balancing and material handling, a heuristic and metaheuristic-based integrated decision approach is proposed. Findings- Detailed computational study demonstrates how integrated decision approach can serve as an efficient managerial tool in designing/redesigning assembly line systems and support automated transportation infrastructure. Originality/value- This work is beneficial for the production managers in understanding the main decisional steps involved in the designing/redesigning of smart assembly systems and providing guidelines in decision making. Moreover, this study explores the material distribution scheduling problems in assembly systems, which is not yet largely explored in the literature.
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