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Liu, Z.; Xu, L.; Pan, C.; Gao, X.; Xiong, W.; Tang, H.; Lei, D. Low-Carbon Scheduling of Flexible Manufacturing and Crane Transportation. Encyclopedia. Available online: https://encyclopedia.pub/entry/49689 (accessed on 08 July 2024).
Liu Z, Xu L, Pan C, Gao X, Xiong W, Tang H, et al. Low-Carbon Scheduling of Flexible Manufacturing and Crane Transportation. Encyclopedia. Available at: https://encyclopedia.pub/entry/49689. Accessed July 08, 2024.
Liu, Zhengchao, Liuyang Xu, Chunrong Pan, Xiangdong Gao, Wenqing Xiong, Hongtao Tang, Deming Lei. "Low-Carbon Scheduling of Flexible Manufacturing and Crane Transportation" Encyclopedia, https://encyclopedia.pub/entry/49689 (accessed July 08, 2024).
Liu, Z., Xu, L., Pan, C., Gao, X., Xiong, W., Tang, H., & Lei, D. (2023, September 27). Low-Carbon Scheduling of Flexible Manufacturing and Crane Transportation. In Encyclopedia. https://encyclopedia.pub/entry/49689
Liu, Zhengchao, et al. "Low-Carbon Scheduling of Flexible Manufacturing and Crane Transportation." Encyclopedia. Web. 27 September, 2023.
Low-Carbon Scheduling of Flexible Manufacturing and Crane Transportation
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This entry is adapted from the peer-reviewed paper 10.3390/pr11092737

Manufacturing companies surveyed use multifunctional machining machines and variable speed cranes, as the lack of rational planning results in high energy wastage and low productivity.

low-carbon scheduling flexible manufacturing crane transportation multi-state collaborative configuration

1. Introduction

The increasing carbon emissions have caused a series of environmental pollution problems, and the global manufacturing industry urgently needs to transition to low carbon. According to relevant surveys, the energy efficiency of the manufacturing industry is low and pollution emissions are high in China, e.g., the proportion of industrial GDP, 33.2%, is obtained by consuming 70% of the national energy in 2022. With coal and oil accounting for 56% and 18.5%, respectively, of China’s energy mix in 2022, these two high-carbon emissions fuels remain the country’s main energy sources [1]. In this context, the government has introduced a series of strict countermeasures to accelerate the industrial transformation to low-carbon, which has greatly restricted the development of high-emission traditional heavy manufacturing industry [2]. It is well known that energy consumption is the primary source of carbon emissions. According to the National Bureau of Statistics, China’s power sector emitted about 9.64 billion tons of carbon dioxide in 2019, accounting for 45.8% of the country’s total emissions. The power sector has been the industry that generates the most carbon emissions in China, causing serious environmental problems [3]. So, improving energy efficiency can effectively reduce carbon emissions in manufacturing industries. In traditional heavy industry manufacturing enterprises, the machining process is the main contributor to carbon emissions. In addition, the energy consumption during the transportation of large workpieces cannot be ignored, and the proportion of transportation energy consumption in the actual manufacturing process of the surveyed enterprises can reach 38% [4]. Through the collaborative scheduling optimization of the machining process and the transportation process, the machining energy consumption and transportation energy consumption of the production process can be effectively reduced, thus reducing carbon emissions. Therefore, it is of great significance to study the problem of low-carbon coordination scheduling.
In the production environment of heavy manufacturing enterprises, the machining process and the transportation process in the workshop are often interrelated and affect each other. This makes the production process prone to problems such as idle machines and chaotic planning, making scheduling much more difficult. In addition, due to the increasing complexity of the production process, the use of variable-speed multi-functional machining machines and cranes is the development trend of the heavy manufacturing industry. This type of equipment has a variety of states, such as on/off and changing speed during operation. Changes in the various operating states of machines and cranes directly affect the entire production process, further increasing the flexibility and complexity of the manufacturing environment. However, various existing manufacturing systems in traditional heavy industries still rely heavily on manual scheduling. Limited by human decision-making, this mode cannot cope with such a complex production environment. It leads to the disorganized production process in heavy manufacturing enterprises, resulting in serious energy waste. Therefore, it is urgent to design an effective optimization method to optimize the multi-state collaborative configuration of the machining process and the transportation process to improve the energy efficiency of the manufacturing system and reduce the carbon emissions of the production process of heavy manufacturing enterprises.

2. Low-Carbon Manufacturing Scheduling Optimization

Research on low-carbon manufacturing scheduling problems in various manufacturing environments can be divided into two main categories: flow shop scheduling problems and job shop scheduling problems.
For the optimization of low carbon flow shop scheduling, the construction of optimization models and algorithm innovation are the research focus of scholars. Tirkolaee et al. [5] proposed a novel dual-objective mixed-integer linear programming model with outsourcing options and just-in-time delivery to simultaneously minimize the total cost and total energy consumption of the production system. Yu and Han [6] examined machine scheduling problems inspired by the semiconductor manufacturing production environment and developed a flow shop scheduling model focusing on an important special case with proportional processing times. Fu et al. [7] proposed a dual-objective stochastic hybrid flow shop deteriorating scheduling problem to minimize makespan and total tardiness. For scheduling problems of energy-efficient block flow shop with setup time, Han et al. [8] constructed a multi-objective optimization model with makespan and energy consumption criteria. Shao et al. [9] developed a MIP model that considers time-of-use electricity tariffs for the distributed heterogeneous mixed-flow store scheduling problem under unequal time tariffs.
Furthermore, some scholars contributed algorithmic improvements and innovations to improve the optimization effect. For the distributed permutation flow shop with sequence-dependent setup times scheduling problem, Huang et al. [10] proposed three constructive heuristics and an effective discrete artificial bee colony algorithm. For the distributed heterogeneous hybrid flow shop scheduling problem with unrelated parallel machines and the sequence-dependent setup time, Li et al. [11] proposed an improved artificial bee colony algorithm. Wu et al. [12] focused on the robotic cell scheduling problem with batch-processing machines, and a green schedule algorithm and a multi-objective differential evolution algorithm are proposed to optimize the makespan and energy consumption of the batch-processing machines simultaneously. Pan et al. [13] executed five meta-heuristics are executed to solve the distributed batch flow alignment process shop scheduling problem. Qin et al. [14] considered the limited waiting time between batch and discrete processors to develop a learning-based scheduling method through custom genetic programming.
Research on low-carbon scheduling optimization in job shop scheduling is divided into two groups: machining process optimization and comprehensive method application.
For the machining process optimization group, Afsar et al. [15] established a multi-objective optimization model based on the green job shop scheduling problem with uncertain processing times, in which the dual goal is to minimize energy consumption and total manufacturing span during machine idle time. Wei et al. [16] proposed an energy-aware estimation model to compute different energy consumptions for different operating conditions of a machine. Duan et al. [17] developed a dynamic scheduling mathematical model considering machine idle time schedule and speed level selection during processing and proposed a method of calculating machine energy consumption and completion time under different states. Wu et al. [18] established a multi-objective mathematical model with the joint minimum of maximum completion time and total setup time, which effectively reduces the fixture loading and unloading time. Luo et al. [19] developed a hierarchical multi-intelligence deep reinforcement learning-based real-time scheduling approach to address the dynamic scheduling problem with new job insertions and machine breakdown. Jiang et al. [20] introduced a resilient scheduling model for the steel mill by considering the buffering times and machining speeds to enable the solution to absorb random disturbances and recover quickly.
For the comprehensive method application, Feng et al. [21] proposed an integrated method for intelligent green scheduling of the sustainable flexible workshop with edge computing considering uncertain machine state. He et al. [22] proposed a multi-objective optimization framework based on the fitness evaluation mechanism and an adaptive local search strategy. Wang et al. [23] presented a multi-period production planning-based real-time scheduling approach to carry out real-time scheduling based on real-time manufacturing data. Based on the processing energy characteristics in resource-constrained processing environments, Li et al. [24] proposed a comprehensive solution to minimize the energy consumption and completion time of resource-constrained. Kovalenko et al. [25] proposed a multi-intelligent control strategy to improve the flexibility of manufacturing systems. Kung and Liao [26] consider the optimization of joint predictive maintenance and job scheduling problems to minimize total shortage losses and develop a heuristic algorithm based on the Tabu search.
Extensive studies have been conducted on the scheduling problem of manufacturing systems with different influencing factors (including factors such as processing speed, setup, and time-sharing tariff). However, there is no further study on the multi-state collaborative configuration optimization problem. It is worth noting that machine and crane multi-states often interact with each other, further increasing the complexity of the optimization problems.

3. Manufacturing Scheduling Optimization Considering Transportation

For the flow shop scheduling optimization problem considering transportation, Wang et al. [27] considered constraints such as transportation capacity and transportation time and proposed a heuristic optimization algorithm. Lei et al. [28] studied the flexible flow shop scheduling problem with dynamic transportation waiting times and developed a memetic algorithm integrated with the waiting time calculation approach. For the permutation flow shop scheduling problem with sequence-dependent setup time, Xin et al. [29] designed an improved discrete whale swarm optimization algorithm that combines differential evolution, augmented search, and job-swapped mutation to enhance performance; Yuan et al. [30] considered both sequence-dependent setup time between groups and the transportation time between machines and proposed a novel discrete differential evolution mechanism with a cooperative-oriented optimization strategy to evolve both the sequence of jobs in each group and the sequence of groups synergistically.
For the job shop scheduling optimization problems considering transportation, Goli et al. [31] investigated the role of AGVs and human factors in cell formation and scheduling of parts under fuzzy processing time and developed a hybrid genetic algorithm and a whale optimization algorithm. Zhou et al. [32] focused on the green scheduling problem of the flexible manufacturing cell with material handling robots and proposed a levy flight and weighted distance-updated multi-objective grey wolf algorithm. Ren et al. [33] considered the constraints of transportation resources and transportation time, and a novel particle swarm optimization algorithm integrated with genetic operators is developed to respond to dynamic events and generate the rescheduled plan in time. Li et al. [34] proposed an efficient hybrid of iterated greedy and simulated annealing algorithms, taking into account the two objectives of makespan and total energy consumption. Li and Lei [35] studied the energy-efficient flexible job shop integrated scheduling problem considering transportation and process-dependent setup times and developed an imperialist competitive algorithm with feedback to minimize the makespan, total tardiness, and total energy consumption, simultaneously.
For other forms of job shop scheduling optimization considering transportation, Zhao et al. [36] proposed a digital twin-driven energy-efficient multi-crane scheduling and crane number selection method for multi-crane systems. Sun et al. [37] proposed two novel robotic job-shop scheduling models with robot movement and deadlock considerations to avoid transportation conflicts for the deadlock problem of robot-driven production lines. Numerical examples illustrate that models can completely avoid transportation conflicts. Zou et al. [38] studied a novel automatic guided vehicle (AGV) energy-efficient scheduling problem with release time and established a multi-objective mathematical model with energy consumption, number of AGVs used, and customer satisfaction as optimization objectives, and proposed an efficient multi-objective greedy algorithm. Li et al. [39] used deep reinforcement learning to address the dynamic flexible job shop scheduling problem with insufficient transportation resources. Zhao et al. [40] developed a model for the scheduling problem of considering multiple cranes and their dual-load capacity and proposed a heuristic method based on the two-stage model.

References

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Subjects: Engineering, Industrial
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Zhengchao Liu
,
Liuyang Xu
,
Chunrong Pan
,
Xiangdong Gao
,
Wenqing Xiong
,
Hongtao Tang
,
Deming Lei
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Update Date: 27 Sep 2023
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