Research On Distributed Blocking Flow Shop Scheduling Problem And Its Algorithm Under The Background Of Carbon Peaking And Carbon Neutrality

The concepts of low carbon,environmental protection,and sustainability have become an important direction of global manufacturing development with the increasingly serious problems of global climate change and environmental pollution.In this context,“double carbon”(i.e.carbon peaking and carbon neutrality)has become one of the main goals of manufacturing development.There is more and more cooperation among enterprises,and distributed manufacturing mode has widely existed in manufacturing enterprises due to global integration and increasing market competition.The manufacturing industry has also gradually paid attention to the production scheduling problem in the context of “double carbon” with the increasingly prominent problem of carbon emissions and enterprises have increased their awareness of energy conservation and environmental protection.Enterprises have increased their awareness of energy conservation and environmental protection with the increasingly prominent issue of carbon emissions,and the manufacturing industry has gradually focused on production scheduling issues in the context of "double carbon".Green job shop scheduling is an important part of green manufacturing.Through reasonable resource allocation,operation sequencing,and operation mode optimization,which improve production efficiency,reduce energy consumption,reduce carbon emissions,and reduce resource waste,thus effectively improving economic efficiency.Typical job shop scheduling problems include the flow shop scheduling problem,which usually only is considered the economic indicator.The green job shop scheduling problem is an extension of the traditional job shop scheduling problem,which needs to consider the green production index and economic index together,mainly minimizing the consumption of resources and minimizing the impact on the environment.The distributed blocking flow shop scheduling problem is an extension of the traditional flow shop scheduling problem and is widely utilized in many production processes such as chemical,pharmaceutical,plastics,steel,and electronics.In the distributed blocked flow shop scheduling problem,some machines may be blocked in the production process due to machine maintenance,breakdowns,or other reasons.These blocked machines cause delays in the production process,affecting production cycles and overall productivity.It has been demonstrated that flow shop scheduling is a non-deterministic polynomial(NP)hard problem,which is usually large-scale,non-linear,strongly constrained,and multi-objective.Compared with the traditional flow shop scheduling problem,the distributed blocking flow shop scheduling problem is considered the scheduling problem in the no-buffer scenario,which is closer to the actual production scenario and has more important theoretical significance and practical application value.Therefore,the study of the distributed blocking flow shop scheduling problem in the context of "double carbon" is of great significance to the sustainability and low carbon development of the manufacturing industry.The distributed blocking flow shop scheduling problem and its extension problem are studied in depth based on several constraints faced in the actual production process in this paper.The main research work in this dissertation is as follows.(1)A multi-objective discrete differential evolution(MODE)algorithm is proposed in this dissertation to solve the energy-efficient distributed blocking flow shop scheduling problem(EEDBFSP)to minimize the makespan and total energy consumption(TEC).First,a cooperative initialization strategy is proposed to generate the initial population of the EEDBFSP.Secondly,the mutation,crossover,and selection operators are redesigned so that the MODE algorithm is applied to discrete space.A local search strategy based on five knowledge operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP.Finally,a non-critical path energy saving strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP.The effectiveness of each strategy in the MODE algorithm is verified in the benchmark test suite and compared with the state-of-the-art algorithms in 720 instances.The numerical results show that the MODE algorithm is an effective optimizer for solving the EEDBFSP.(2)A Pareto-based discrete Jaya algorithm(PDJaya)is designed in this dissertation to solve the carbon-efficient distributed blocking flow shop scheduling problem(CEDBFSP)based on the minimum total tardiness(TTD)and total carbon emissions(TCE).A mixedinteger linear programming model of the CEDBFSP is presented in this dissertation.An effective constructive heuristic is designed to generate the initial population.The new individual is generated by the update mechanism of the PDJaya.The self-adaptive operator local search strategy is proposed to improve the exploitation capability of the PDJaya.A carbon saving strategy based on the critical path is introduced to further reduce carbon emissions.The effectiveness of each strategy in the PDJaya is verified in the benchmark test suite and compared with the state-of-the-art algorithms.The numerical results prove that the PDJaya is an effective algorithm for solving the CEDBFSP.(3)A learning-based discrete Jaya algorithm(LDJaya)is designed in this dissertation to solve the multi-objective sustainable distributed blocking flow shop scheduling problem with heterogeneous factories(HFMS-DBFSP)to minimize TTD,TCE,and negative social impact(NSI).Firstly,the mixed-integer linear programming model of the HFMS-DBFSP is proposed and the calculation criteria of TTD,TCE,and NSI are proposed.A cooperative initialization strategy for population initialization is designed based on the characteristics of the HFMS-DBFSP.Secondly,the self-learning operation selection strategy is introduced to guide the operation selection,and the local search operator is proposed to improve the diversity of the population and the exploitation capability of the LDJaya.Finally,the carbon saving speed adjustment strategy is proposed to further reduce carbon emissions.The effectiveness of each strategy in the LDJaya is verified in the benchmark test suite and compared with the state-of-the-art algorithms.The simulation results demonstrate that the proposed LDJaya is superior to other comparison algorithms in terms of significance and effectiveness in solving the HFMS-DBFSP.