| Since professor Johnson proposed and optimized a two-stage and three-stage flow shop scheduling problems in 1954,scholars of all over the world have proposed a series of production shop scheduling models and corresponding optimization algorithms to solve them.For example,permutation flow shop scheduling problem,flexible flow shop scheduling problem,hybrid flow shop scheduling problem,job shop scheduling problem,flexible job shop scheduling problem,open shop scheduling problem,parallel machine scheduling problem and so on.These problems can provide effective theoretical support and practical guidance for the production scheduler.In recent years,with the globalization of economy and rapid development of manufacturing industry,some serious problems such as energy depletion,environmental pollution,global warming and increasing of manufacturing cost need to be resolved.Countries around the world have launched a series of policies,laws and regulations aimed at energy conservation,consumption reduction and emission reduction.Green energy-saving manufacturing,as a resource-saving and environmentfriendly manufacturing model,has been widely studied and applied in recent years.Existing researches on green energy-saving manufacturing focus on equipment level(developing more energy-efficient production equipment)and product process level(developing more energy-efficient manufacturing processes).However,researches on system level(developing more energy-efficient production scheduling methods),which actually has more energy-saving potential and need less investment,is relatively rare.Therefore,this paper has carried out a series of researches on the energy-saving production scheduling problems at the system level,aiming at constructing some energy-saving production scheduling models that is more in line with the real-world production and proposing some intelligent optimization a lgorithms to solve these models,which can provide theoretical support and method guidance for production managers.The main research contents and innovations are summarized as follows:1.In order to solve the two problems in the traditional method for energy-efficient job shop scheduling: 1)machine restart will produce considerable additional energy consumption;2)frequent restart will cause damage to the machine,this paper proposes a new energy-efficient job shop scheduling model.Then,three rules are proposed to solve the proposed model.Among them,rules 1-3 are used to set machine on/off criterion,move operations and operation blocks;the validity of these rules are mathematically proved.Finally,82 standard benchmark instanc es of job shop scheduling problems are solved.The efficiency of the proposed rules is verified by experimental comparison.2.Aiming at the problems of increased energy consumption and machine damage caused by machine restart in the existing energy-efficient flexible job shop scheduling,this paper proposes a new energy-efficient flexible job shop scheduling model.Then,an effective two-stage hybrid algorithm is proposed to solve the model,in which a variable neighborhood operator is proposed to improve its optimization ability;and a block movement operator is designed to reduce energy consumption and machine restart times without affecting the makespan.Next,85 standard benchmark instances of energy-efficient flexible job shop scheduling problems are constructed.The optimal combination of key parameters of the algorithm is obtained by using Taguchi experimental design method.Finally,the efficiency of the proposed algorithm is verified by experimental comparison.3.In order to solve the traditional flexible job shop scheduling problem without considering the flexibility of workers and without considering the processing time,worker cost and energy consumption simutaneously,this paper proposes an energyefficient double flexible job shop scheduling.Then,a new hybrid genetic algorithm is proposed to solve the proposed model.This algorithm uses a three-layer integer coding mechanism to encode the chromosomes;uses an active decoding method to decode the chromosomes;uses a job-based crossover operator for the operation vector and a random probability based crossover operator for the machine and worker vectors;and uses three effective mutation operators for the operation,machine and worker vectors.Next,10 standard benchmark instances of double flexible job shop energy-efficient scheduling problems are constructed.The optimal combination of key parameters of the algorithm is obtained by using Taguchi experimental design method.Finally,the effectiveness of the proposed algorithm is verified by expe rimental tests.4.An energy-efficient flexible flow shop scheduling problem model considering both machine flexibility and worker flexibility and its solving method are proposed.Firstly,due to the traditional flexible flow shop scheduling problem does n ot consider machine flexibility and worker flexibility simultaneously;and does not consider processing time,worker cost and green index simultaneously,a mathematical model of energy-efficient flexible flow shop scheduling problem with worker flexibility is constructed.Then,a new hybrid evolutionary algorithm is proposed to solve the proposed model,in which some encoding,decoding,crossover and mutation operators suiting for the model are used;and a variable neighborhood operator is designed to improve the search ability of the algorithm.Next,54 standard benchmark instances of energy-efficient flexible flow shop scheduling problem with worker flexibility problems are constructed.The optimal combination of key parameters of the algorithm is obtained by using Taguchi experimental design method.Finally,8 small-scale examples are solved by CPLEX to test the accuracy of the proposed mathematical model;54 standard benchmark instances are solved by the proposed algorithm and several other well-known algorithms;the effectiveness of the proposed algorithm is verified based on the comparison results.5.A distributed production scheduling model with different factories and workshops and its solving method are designed.Firstly,considering the traditional distributed production scheduling problem assumes that all factories are the same and does not consider the workshop,which is not consistent with the actual production,a mathematical model of distributed production scheduling problem with different factories and workshops is proposed.Then,a new memetic algorithm is proposed to solve the model,in which some encoding,decoding,crossover and mutation operators suiting for the model are used;and a local search algorithm is designed to improve the convergence speed and fully explore the solution space.Next,40 standard benchmark instances of distributed production scheduling problems with different factories and workshops are constructed.The optimal combination of key parameters of the algorithm is obtained by using Taguchi experimental design method.Finally,the effectiveness of the proposed algorithm is verified by experimental tests.6.By fully considering the internal relationship between machine on/off and machine maintenance,this paper first proposes an energy-efficient production scheduling model with machine maintenance.Then,four general rules are proposed to set machine on/off criteria and insert the maintenance time window etc.;and three heuristic rules are used to insert maintenance activities and move maintenanceoperation blocks aiming at reducing machine energy consumption and machine restart times.Finally,82 standard benchmark instances of job shop scheduling problems are solved.The efficiency of the proposed rules is verified by experimental comparison. |
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