Green Scheduling Problem Research Articles

Multi-objective fuzzy green scheduling optimization method of special vehicle body-in-white prototype shop considering equipment preventive maintenance

Based on the advantages of non-contact, flexibility, and small heat-affected zone, laser processing technology is widely used in material removal, addition, modification and other manufacturing fields. However, the emission of large amounts of smoke during laser processing of metal materials will result in environmental pollution in the job shop. Additionally, the processing time of laser equipment becomes uncertain as the power decays with prolonged use. Therefore, this paper is mainly focus on a multi-objective flexible job shop fuzzy green scheduling problem to minimize the fuzzy makespan, fuzzy total energy consumption and fuzzy total smoke emission. To solve it, an improved multi-objective particle swarm optimization algorithm has been designed. Firstly, a preventive maintenance strategy is proposed to reduce the makespan and equipment failure frequency by considering periodic power attenuation of laser equipment. Next, different particle updating strategies are employed to enhance particles’ exploration capabilities. Subsequently, five neighborhood structures are introduced to improve the performance of local search. Finally, comparison experiments are conducted with an expanded common benchmark, and a production case study in a special vehicle body-in-white prototype job shop. The results effectively demonstrate the feasibility and effectiveness of the developed model and improved multi-objective particle swarm optimization algorithm.

Multi-objective Evolutionary Algorithm with Variable Neighborhood Search for Optimizing Green Scheduling in a Re-Entrant Hybrid Flow Shop with Dynamic Events

In previous studies on re-entrant hybrid flow shops, the impact of dynamic events was often ignored despite being a common occurrence in practical production. To address this issue and simultaneously reduce energy consumption, a multi-objective evolutionary algorithm with variable neighborhood search (MOEA-VNS) has been proposed to optimize the green scheduling problem in a re-entrant hybrid flow shop with dynamic events (RHFS-GDS).The approach involves creating a green dynamic scheduling optimization model, which aims to minimize the makespan, total energy consumption, and stability of rescheduling solutions. A hybrid green scheduling decoding method is then employed to select machines for each operation and calculate fitness. Additionally, three neighborhood structures are designed to improve the population diversity and optimality of the MOEA-VNS algorithm. Two rescheduling strategies are also adopted to handle dynamic events that may occur during production. Experimental results demonstrate that these approaches are effective in solving the RHFS-GDS problem and can guide actual production. By incorporating dynamic events and rescheduling strategies into the optimization process, the proposed MOEA-VNS algorithm provides a comprehensive solution to the complex challenges faced by re-entrant hybrid flow shops in practical production environments.

Re-Entrant Green Scheduling Problem of Bearing Production Shops Considering Job Reworking

To solve various reworking and repair problems caused by unqualified bearing product quality inspections, this paper introduces a green re-entrant scheduling optimization method for bearing production shops considering job reworking. By taking into account quality inspection constraints, this paper establishes an integrated scheduling mathematical model based on the entire processing–transportation–assembly process of bearing production shops with the goals for minimizing the makespan, total carbon emissions, and waste emissions. To solve these problems, the concepts of the set of the longest common machine routes (SLCMR) and the set of the shortest recombination machine combinations (SSRMC) were used to propose the re-entrant scheduling optimization method, based on system reconfiguration, to enhance the system stability and production scheduling efficiency. Then, a multi-objective hybrid optimization algorithm, based on a neighborhood local search (MOOA-LS), is proposed to improve the search scope and optimization ability by constructing a multi-level neighborhood search structure. Finally, this paper takes a bearing production shop as an example to carry out the case study and designs a series of experimental analyses and comparative tests. The final results show that in the bearing production process, the proposed model and algorithm can effectively realize green and energy-saving re-entrant manufacturing scheduling.

Minimizing Carbon Emission in Hybrid Flow Shop Scheduling: A Comparative Analysis of Flow-based and Set Partitioning Formulations

This article addresses the Hybrid Flow Shop Green Scheduling Problem with Sequence and Machine Dependent Setup Times (HFSGSP-SMDST) in the context of Industry 4.0. The objective is to minimize carbon emission while optimizing scheduling. Two mathematical formulations, a flow-based positional formulation (FB) and a set partitioning positional formulation (HCG), are proposed to provide tighter lower bounds. The FB formulation is solved using a commercial solver, while the HCG algorithm incorporates an Iterated Local Search (ILS) to generate initial solutions and an upper bound for the problem. Computational experiments demonstrate that HCG achieves better lower bounds, although it requires more time compared to FB. The proposed algorithms offer valuable solutions for carbon emission optimization and scheduling in Industry 4.0, with a trade-off between lower bounds and convergence speed.

Multi-objective green scheduling of integrated flexible job shop and automated guided vehicles

The traditional flexible job shop scheduling problem (FJSP) ignores transportation issues or merely introduces a time lag for transportation tasks while assuming an infinite number of transportation resources. With the development of intelligent manufacturing, automated guided vehicles (AGVs), which are the key transportation equipment for manufacturing enterprises, have been widely used for their high flexibility and stability. In addition, the increase in energy consumption and the trend of green manufacturing make it critical to take into account energy-related objectives in the decision-making of scheduling. Therefore, the multi-objective green scheduling problem of integrated flexible job shop and AGVs (MOGSP-IFJS&AGVs) is addressed in this paper. To solve this problem effectively, the multi-objective mixed-integer programming (MMIP) model is formulated to minimize total energy consumption and makespan simultaneously. An efficient heuristic algorithm (EHA) is designed to solve the MMIP model. In the EHA, one solution encoding scheme and corresponding greedy insertion decoding method considering the selection of AGVs are presented. To acquire a high-quality initial population, the population initialization method balancing the processing time and energy consumption is designed. Further, a local search strategy is presented to enhance the quality of solutions and accelerate the convergence speed of the EHA. Experiment results of 45 test instances indicate that the EHA can obtain better solutions than that of comparison algorithms, which confirms the effectiveness of the EHA for solving the MOGSP-IFJS&AGVs.

A digital-twin-based adaptive multi-objective Harris Hawks Optimizer for dynamic hybrid flow green scheduling problem with dynamic events

In many hybrid flow shop production environments, dynamic events (DEs) often occur and seriously impact production schedules. However, the traditional model rarely considers DEs, which makes the model seriously inconsistent with the actual situation. In addition, to reduce carbon emissions, there is an urgent need to carry out green optimization scheduling of production workshops, especially in emerging digital workshops. First, this paper proposes a dynamic digital-twin-based Multi-Objective Hybrid Flowshop Green Scheduling Model with DEs (MOHFGSM-DEs) to fill the above gaps. The MOHFGSM-DEs model’s bi-objectives are the makespan minimization and digital workshop total energy consumption minimization. In addition, the MOHFGSM-DEs model takes account of representative DEs in the actual dynamic production process, including controlled processing time, device dynamic reconfiguration events, and workpiece reworking events, which makes it more adaptive in practical production. Secondly, an Adaptive Multi-Objective Dynamic Harris Hawks Optimizer (AMODHHO) is presented with DEs parameters perceived by the digital twin of the workshop, adjusting the scheduling scheme adaptively in real time to address the MOHFGSM-DEs model efficiently. AMODHHO combines a nonlinear optimization strategy on balancing the exploration and exploitation of Harris Hawks Optimizer (HHO) and introduces the crossover operator of Genetic Algorithm (GA) to improve its optimal global ability. Moreover, a digital-twin-driven dynamic encoding methodology containing many optimization strategies is designed based on problem-specific characteristics. Finally, numerical experiments and application case comparisons are performed among AMODHHO, SPEA2, and NSGA-II. Results show that AMODHHO is superior to SPEA2 and NSGA-II regarding comprehensive performance. Therefore, the proposed model and AMODHHO are feasible to solve the real-world MOHFGSM-DEs adaptively. Furthermore, it performs dynamic adaptive adjustment for the actual scheduling schemes according to the real-time DEs the digital twin perceives.

Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

One of the most difficult challenges for modern manufacturing is reducing carbon emissions. This paper focuses on the green scheduling problem in a flexible job shop system, taking into account energy consumption and worker learning effects. With the objective of simultaneously minimizing the makespan and total carbon emissions, the green flexible job shop scheduling problem (GFJSP) is formulated as a mixed integer linear multiobjective optimization model. Then, the improved multiobjective sparrow search algorithm (IMOSSA) is developed to find the optimal solution. Finally, we conduct computational experiments, including a comparison between IMOSSA and the nondominated sorting genetic algorithm II (NSGA-II), Jaya and the mixed integer linear programming (MILP) solver of CPLEX. The results demonstrate that IMOSSA has high precision, good convergence and excellent performance in solving the GFJSP in low-carbon manufacturing systems.

A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots

Hybrid flowshop scheduling problems are encountered in many real-world manufacturing scenarios. With increasingly fierce market competition, the production mode of multiple varieties and small batches has gradually been accepted by enterprises, where the technology of lot streaming is widely used. Meanwhile, green criteria, such as energy consumption and carbon emissions, have attracted increasing attention to improving protection awareness. With these motivations, this paper studies a multiobjective hybrid flowshop green scheduling problem with consistent sublots (MOHFGSP_CS), aiming to minimize two objectives, i.e., makespan and total energy consumption, simultaneously. To solve this complex problem, we first formulate a novel multiobjective optimization model. However, due to the NP-hard nature of the problem, the model is computationally prohibitive as the problem scale increases. Thus, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is proposed. There are three phases in this algorithm: the VND-based employed bee phase, the adjustment weight onlooker bee phase, and the population interaction scout bee phase. In the experimental study, various small-scale and large-scale instances are collected to verify the effectiveness of the multiobjective optimization model and the MDABC. Comprehensive computational comparisons and statistical analysis show that the developed strategies and MDABC show superior performance.

An enhanced cross-entropy algorithm for the green scheduling problem of steelmaking and continuous casting with uncertain processing time

Under the mainstream trend of global carbon emission reduction, this paper studies an important steelmaking-continuous casting scheduling problem (SCCSP) considering the practical technological requirements in uncertain production environment. This problem can be modeled as the green SCCSP with uncertain processing time (GSCCSP_UPTHP), whose criteria are to minimize the maximum completion time, the average waiting time, and the carbon emissions simultaneously. An enhanced cross-entropy algorithm (ECEA) is proposed for addressing it. In terms of ECEA’s coding and decoding, a two-segment coding strategy and an adaptive decoding strategy are devised according to the problem’s characteristics. In ECEA’s global search, ECEA adopts two probability models with their own self-adjustment mechanisms to collaboratively learn the valuable information from excellent individuals and quickly guide the search to the promising regions. Moreover, the novel competition and assimilation mechanisms are devised to avoid ECEA falling into local optima early. In ECEA’s local search, a problem-dependent local search based on the key points (PDLS_KP) is proposed to sequentially execute the small neighborhood search (SNS) and the large neighborhood search (LNS) starting from the promising region of each selected key point. Meanwhile, a speed-up scanning method is developed to accelerate the search process of the LNS. Extensive experiments based on both the simulation and the real-world instances from an SCC process are constructed to show the effectiveness of TSOA in solving the GSCCSP_UPTHP.

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problem

As more and more enterprises have laid great emphasis on eco-friendly manufacturing processes while enhancing agility, this paper focuses on the green scheduling problem of the Flexible Manufacturing Cell with material handling robots (FMC-R). In the FMC, each job is characterized by several operations, processing machines, and machining time. Robots at one straight track transfer jobs to the related machines. Distinguished from most of the FMC scheduling problems, this paper considers not only the operation sequencing of jobs but also the robots’ transportation processes. A bi-objective mathematical model is established aiming to minimize the total makespan and the total energy consumption of the FMC system simultaneously. Due to the NP-hard nature of the problem, a Levy Flight and Weighted Distance-updated Multi-objective Grey Wolf Algorithm (LWMOGWO) is proposed. It combines the Levy flight with Multi-objective Grey Wolf Algorithm (MOGWO). A weighted distance updating mechanism is integrated to calculate the positions of individuals. A local neighbourhood search strategy is adopted, which makes it possible to take full advantage of the three leading wolves in population. The performance of the LWMOGWO algorithm is evaluated through groups of experiments by comparing with multiple hybrid meta-heuristic algorithms, hybrid particle swarm optimization (NGPSO) algorithm, an improved grey wolf optimization (IGWO) algorithm, MOGWO, NSGA-II (Non-dominated sorting genetic algorithm), and PLMEAPS. Results reveal the proposed LWMOGWO algorithm has a higher quality of solutions in solving the scheduling of the FMC-R problem.

Green scheduling optimization of ship plane block flow line considering carbon emission and noise

Recently, with the strategic requirements of sustainable development, more and more attention has been paid on the green scheduling problems. Different from the traditional flow-line scheduling problem, the ship plane block possesses the characteristics of larger volume and weight, so the problem of ship plane block flow-line can be regarded as a blocking flow-line scheduling problem (BFSP). In this paper, considering the complexity of ship building and the particularity of green scheduling problem (e.g., carbon emission and noise of equipment), the green blocking flow-line scheduling problem model of panel block (GBFSP) in shipbuilding was established to minimize the maximum completion time, carbon emission cost and noise cost. Moreover, the improved gray wolf optimization (IGWO) algorithm was proposed to solve this problem effectively. In the proposed IGWO algorithm, a ranked order value (ROV) method was performed to realize the transformation from continuous gray wolf individual position to discrete optimal solution. Secondly, a nonlinear convergence factor and PSO algorithm were introduced to balance the development and exploration ability of the IGWO algorithm. In addition, variable neighborhood search (VNS) was also used to improve the accuracy and effectiveness of local search. Furthermore, the validity of the proposed IGWO algorithm is verified by some famous benchmark examples. In addition, a real data of a shipyard was used for example verification and multiple numerical experiments. Our results suggested that the model and the IGWO algorithm can solve the problems existing in the ship plane block flow-line effectively. Meanwhile, reducing the impact of pipeline on the environment, and achieving the goal of green production.

Improved African buffalo optimization algorithm for the green flexible job shop scheduling problem considering energy consumption

The conventional production scheduling problem has mainly emphasized the time-related metrics, such as makespan, machine workload and tardiness/earliness, and so on. With the advent of the sustainable manufacturing, the green scheduling problem has b

Estimation of distribution evolution memetic algorithm for the unrelated parallel-machine green scheduling problem

With the increasing concern on greenhouse gas emissions, green scheduling decision in the manufacturing factory is gaining more and more attention. This paper addresses the unrelated parallel machine green scheduling problem (UPMGSP) with criteria of minimizing the makespan and the total carbon emission. To solve the problem, the estimation of distribution evolution memetic algorithm (EDEMA) is proposed. Firstly, based on the minimum machine load first principle, the initialization of the population is proposed. Second, a multi-objective non-dominated sorting approach and the crowding distance are adopted to improve the diversity of individual. Third, to estimate the probability distribution of the solution space, a probability model is presented to enhance the searching ability. Third, five neighbourhood searching operators are designed to handle the job-to-machine assignment. Moreover, the population catastrophe is used to maintain the sustainable diversity of the population. Finally, based on the randomly generated instances of the UPMGSP, extensive computational tests are carried out. The obtained computational results show that the EDEMA has the better searching capability and the better objective value than those of the non-dominated sorting genetic algorithm II and the estimation of distribution evolution algorithm (EDEA) in solving the UPMGSP.

A multiobjective evolutionary algorithm based on decomposition for hybrid flowshop green scheduling problem

Energy saving has attracted growing attention due to the advent of sustainable manufacturing. By this motivation, this paper studies a hybrid flowshop green scheduling problem (HFGSP) with variable machine processing speeds. A multi-objective optimization model with the objectives of minimizing the makespan and total energy consumption is developed. To solve this complex problem, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is suggested. In VND-based employed bee phase, the variable neighborhood descent (VND) with five designed neighborhood is employed to each subproblem to realize their self-evolution. In the collaborative onlooker bee phase, the promising subproblems selected by the order preference technique according to their similarity to an ideal solution (TOPSIS) is evolved by collaborating with the other neighboring subproblems. Particularly, a dynamic neighborhood strategy is developed to define the neighborhood relationship to retain the population diversity. In the solution exchange-based scout bee phase, a solution exchange strategy is developed to enhance the algorithm efficiency and enable the solutions to be exploited in different directions. Moreover, according to the problem-specific characteristics, encoding and decoding methodologies are developed to represent the solution space, and several definitions are proposed to implement objective normalization, and an energy saving procedure is designed to reduce the energy consumption. Through comprehensive computational comparisons and statistical analysis, the developed strategies and MDABC shows highly effective performance.

Green scheduling model of shuttle tanker fleet considering carbon tax and variable speed factor

Green scheduling is an important means to achieve sustainable industrial development and enhance the green efficiency of enterprises. Given the characteristics of a modern crude oil supply system, the shuttle tanker fleet green scheduling problem (FGSP) is discussed considering a carbon tax and variable tanker speed factor. To minimize the green operating cost (i.e., sum of general operating cost and carbon tax) of the tanker fleet, an integer programming model for shuttle tanker fleet green scheduling (FGSM) is established. The FGSM optimizes the number and sizes of tankers, the number and positions of floating production storage and offloading units (FPSO) at which to berth and the scheduling plan (i.e., berthing order and sailing speed) of each tanker in the fleet. Based on the column generation algorithm, a shuttle tanker fleet green scheduling algorithm is designed to solve the above model accurately. The experimental results show that considering the speed factor, the green operating cost of an example fleet decreases. For different carbon tax rates, speed optimization is an effective way to reduce the green operating cost of the fleet. The above results also show that the FGSM and algorithm can effectively solve the FGSP, improve the operation level and efficiency, and reduce the green operating cost of oil companies.

A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational process, considering this characteristic, a mathematical model was developed and integrated with evolutionary algorithms (EAs), which includes a sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO). In the model, the encoding of the algorithms was divided into three segments for different optimization dimensions with the objective of minimizing the makespan and energy consumption of machines and the number of AGVs. The sectional encoding described the sequence of operations of related jobs, the matching relation between transfer tasks and AGVs (AGV-task), and the matching relation between operations and machines (operation-machine) respectively for multi-dimensional optimization scheduling. The effectiveness of the proposed three EAs was verified by a typical experiment. Besides, in the experiment, a comparison among SE-GA, SE-DPSO, H-SE-GA-DPSO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO) and a tabu search algorithm (TSA) was performed. In H-GA-PSO and TSA, the former just takes the sequence of operations into account, and the latter takes both the sequence of operations and the AGV-task into account. According to the result of the comparison, the superiority of H-SE-GA-DPSO over the other algorithms was proved.

Bi-objective green scheduling in uniform parallel machine environments

Green and sustainability issues are being considered extensively in industry and in the literature. Most of the green-related effects in the manufacturing sector can be modeled by the costs associated with the production machines due to their resource consumption and/or pollutant emission, as a part of the operation cost. Accordingly, taking into account the machine processing costs alongside the other production criteria is one of the main approaches to consider green issues in the production management problems. In the same way, scheduling as one of the major operational problems in the factories can benefit this point of view to involve sustainability aspects. In this paper, the aforementioned modeling approach is taken one more time to establish a formulation for a green scheduling problem in uniform parallel machine environments. Here, it is assumed that the machines have different processing cost rates, i.e. they are different with regard to sustainability. The considered problem objectives are the total green cost and the makespan which are minimized simultaneously with the aim of earning Pareto optimal solutions. To this end, the ε-constraint method is first used to convert the problem into single objective problems. Then an existing heuristic is improved to solve these problems and estimate the Pareto solutions that show the trade-offs between the green cost objective and the ordinary time objective. Moreover, having a rigorous analyze in a theorem, some useful upper bounds for the gap of the green cost of the heuristic solution from optimality is derived. Finally, conducting numerical experiments at the end of the paper, the superiority of the improved heuristic to the previous one and its effectiveness in approximating the Pareto optimal solutions is demonstrated.

Green Job Shop Scheduling Problem With Discrete Whale Optimization Algorithm

In the last few decades, production scheduling problems have been studied for optimizing production efficiency involving the time-related indicators, such as completion time, earliness/tardiness time, or flow time. Currently, with the consideration of sustainable development, the green scheduling problem has been paid more and more attention. Here, a green job shop scheduling problem is considered to minimize the sum of energy-consumption cost and completion-time cost in the workshop. In this paper, a mathematical model is first established with the consideration of multi-speed machines. A discrete whale optimization algorithm (DWOA) is then proposed for solving the model. In the proposed algorithm, a two-string encoding is adopted to represent the two sub-problems: job permutation and speed selection. Then, a heuristic method is used to initialize the population to enhance the quality of initial solutions. By considering the discrete characteristics of the problem, the individual updating operators are redesigned to ensure the algorithm work directly in a discrete scheduling domain. In addition, a variable neighborhood search strategy is embedded to further improve the search ability. The extensive experiments have been performed to test the DWOA. The computational data reveal the promising advantages of the DWOA on the considered problem.

Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption

Since the energy waste of manufacturing industries exacerbates pollution emissions and directly affects the environment, the energy efficiency optimization of the production process is an effective way to promote cleaner production. The transportation process of a workpiece is non-negligible during the entire workshop production process. Particularly in traditional heavy-duty industrial manufacturing enterprises, the energy consumption of the transportation process accounts for a significant proportion of the total workshop energy consumption. To reduce the comprehensive energy consumption of the machining process and transportation process in an actual manufacturing environment, this paper addresses a novel integrated green scheduling problem of flexible job shop and crane transportation (IGSP-FJS&CT). First, a mixed integer programming (MIP) model is formulated to minimize the total cost of the comprehensive energy consumption and makespan in the IGSP-FJS&CT. In the MIP model, a comprehensive energy consumption model is built to optimize the comprehensive energy efficiency of the crane's complete operating cycle and machining process. Then, an integrated GA-GSO-GTHS algorithm is presented to solve the proposed MIP model. In GA-GSO-GTHS, a genetic algorithm (GA) is employed to perform the global search and a glowworm swarm optimization (GSO) algorithm is applied to perform the local search. The green transport heuristic strategy (GTHS) is proposed to guide the search direction of the GA-GSO algorithm. Finally, the performance and effectiveness of the proposed method are demonstrated in a case study. In China's traditional heavy industry manufacturing enterprises, the workshop production is mainly scheduled by manual dispatcher. Consequently, the proposed method has a wide application background in improving energy waste of workshop production, which provides a guiding significance on promoting cleaner production of traditional heavy industry manufacturing.