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    һרҵṩҵƵվ2002ΪڶͻṩҵơĶƵȷӮڶͻΪרערҵдʦ󲿷ȫ211/958ȸУIJʿ˶ʿƣִʣĿǰΪ5000λͻд⡣ ûΪģΪûֵվӵ޷Խӵۺϵҵɺרҵʦһһ޸ƣдͬѧһһĸ,Ϊ˳ҵݻ


    ʱ䣺2021/11/02 Դδ֪ ߣַ



    һ װߴͳԱ÷ʽΪʶӰ죬Ұ鳤Ա֪ޣ޷ʵ˸Ż⣬Զ༼ԱװеʱЧʡϸʺʵȼӹΪָꡣÿ͹۸ȨеĵֵȷָȨϵ༼Աʤָ¼㷽װʤֵָΪĿ꣬˸ùϵΪԼĶ༼ԱŻģͣһָĽ㷨ԱŻģͣʵװ߶༼Ա빤λ֮˸ŻM˾ͺMCVװ˸ΪʵĽ㷨Чװ߶༼Ա˸Ż⣬ЧԴ˷ѣЧ10.95%-12 86%.

    Թ̻еͺŲƷװԼӣ˹ŲޣԻŲ⣬ͨԻװߵŲйԶͺŲƷװѭ깤СΪŻĿ꣬װߵŲŻģͣŴ㷨ĿԺģ˻㷨Mtopois ж׼γֲŵԣһŴģ˻㷨װߵĽӲŻģͣʵֶͺŲƷװߵŲŻM˾̻еװ䳵жͺMCVװߵŲΪʵŴģ˻㷨ЧװŲŻ⣬װѭ3.min-43.min,Ч18.2%6-23%.


    ؼʣװߣʤָ༼ ԱȣŲȣ MESϵͳ


    With the increasingly fierce competition in the manufactural market, manufactural  companies with production orders as the core have gradually begun to develop in the drction of  multi-variety, variable batch size, cycle shortening, lean production. Due to the lack of a scientifie  evaluation system and sbjective avareness, the tadional way of manning job llocationo results  in people unable to make the most of their work and low production eficiency. Traditional  scheduling mcthods are dificult to obtain optimal scheduling plans under muliple constraints  due to limited manual computing apbilie. Therefore, this paper takes the mulisilled  personnel scheduling and scheduling method in the MES system of the construction machinery  assembly workshop as the main research objeet and conducts mathematical modeling and  example simulation to verify is benefit maximization. The main rescarch contents are as follows:

    Firstly, Aiming at the problem that the traditional stffing method of the assembly line is  casily ffccted by subjective consciousness and the team leader has limited awareness of  personnel capbilties, it is dificult to oplimize the deployment of personnel and posts. The  procssing data such as time eficiency, pass rate, and completion rate of mouli-skilled personnel  in the assembly process are used as evaluaion indicators, and the entropy method in the objective  weighing method is adopted to detemine the weigh cofficient of ceach evaluation index. This  paper proposes a calculaion method for the competence index of cach skill of muliskilled  personnel. To maximize the total competency index value of the assembly line, the opimizaion  model of muliskilled personnel scheduling is constructed with the constaint of the relationship  of prsonnel and post configuraion. An improved Hungarian algoritm to solve the opimization  model of personnel scheduling is proposed, which realizes the optimization of the deployment of  muli-killed personnel and workstations on the assembly line, Taking the man-post configuration  of the multi-model MCV mie-low assembly line of M company as an example, the simulation  resuls show that the improved Hungary algorithm can ellivcl solve the opimizaion problem of the mut-skilled personnel configurat ion of the assembly line, effectively avoid the waste of  resources, and increase the production fficiency by 10.95%~ 12.86%.

    Secondly, Because of the complex constraints of the mixed-flow assembly line for multi-  model products of construction mac hinery, the limited calculation capac ity of manual production,  it is dificult to obtain the optimal scheduling plan, Through the general description of the  scheduling problem of the mixed-flow assembly line, the optimization goal is to minimize the  maximum cycle completion period of mixed-flow assembly of multi-model products, and the  production scheduling and scheduling optimization model of the mixed-flow assembly line is  constructed. Combining the rapid convergence characteristics of genetic algorithm and the  feature that the Metropolis criterion of simulated annealing algorithm overflows with local  optimality, a genetic simulated annealing algorithm is proposed to solve the scheduling  optimization model of the mixed-flow assembly line, and realize the optimization of production  scheduling of mixed-flow assembly line of multi-model products. Taking the scheduling  arrangement of the muti-model MCV mixe-low assembly line in the construction machinery  assembly workshop of M company as an example, the simulation results show that the genetic  simulated anneal ing algorithm can efetively solve the optimization problem of the mixsed-flow  assembly line scheduling arrangement, and the mixed-flow assembly cycle period is shortened  by 31. 1min~43.7min. The production eficiency is increased by 18.2%~-23%.

    Thirdly, This paper develops the MES system of construction machinery assembly  workshop. From bottom to top, the system is pided into database layer, server layer, data access  layer, business logic layer, user interface layer, and front-end Ul layer. The database layer mainly  conducts dynamic management and real-time update of various data of the system; the server  layer provides top-level support for the normal operation of the system and the interaction of  system software and hardware; the data access layer real izes the informat ion interaction between  the upper-layer business and the lower-layer database by concretizing the business; the business  logic layer mainly processes logic calculation examples for specific functions of the system, and  responds to front-end user instructions promptly; the user interface layer and the front-end UI  layer mainly include modules such as production management, quality management, inventory  management, equipment management, and basic data management. Users only need to select the  functions in the corresponding modules to operate according to their needs, and the business logic  layer will respond in time.

    Key words: Assembly line: competency index; multi-skilled personnel schedul ing; production  scheduling: MES system

    Ŀ ¼


    1.1 оĿļ

    ҵĿٷչͳģʽ쳵ϲƻ²ƲϵܵĴͳģʽ޷ҵٲϷչ󡣿ͻԽԽСơƷԽԽӻҪԽԽ̻ҵԽ滯ʹͳģʽ򼯳ɻܻתҵ4.0 ijֽݡƼ㡢˼빤ҵںϣΪҵǻ۹תӵһιҵĹҵ 4.0,¼۵ijֲƶҵ±ƽҵǻۻǻ۹ؽδҵչķĿ꣬Ҫպùҵ 4.0,ӿķչԹҵ MES ϵͳоͿԵΪҪ

    ҵǽ IOT Internet ںϵ켼¼㷺Ӧڹҵ[1],ṹͿɽֽɸ֪㡢Ӧò[2],ͼ 1-1 ʾ

    ֪Ƶʶ𡢴ȸ֪豸ʵʱض̬ȡĿݣҪƶͨʻʹȣͨ罫֪ĸϢʵʱ䣻ӦòͨԶյĸݡڽҵҵʵںϴܻʱֶһΪֳ㡢Ʋ㡢㡢ҵ[3],ͼ 1-2 ʾֳҵֳ豸ƲҪֿHMI ԼԴȣҪ DCS SCADAȣҪ MES ͹̬ҵҪָ ERP PLM,ͨҵ磨ֳߡҵ̫ȣɴϲײƵӣﵽҵֳ̡ҵɼҵֳݵҵ

    ҵΪҵ 4.0 ĺģƶҪãΪҵϲƻײƲ֮"蹵"Լ"µ"˽[4],ڹҵ MES ϵͳĿ

    MES ϵͳҪְǶԳĸݽͳһϼƻ¼Ʋ֮䡣Ϊṩƻʵʩ׷ݣԴʵʱ״ݣǹͨϼƻ¼Ʋ֮мŦҵϲײ֮"հ"[5],ͼ 1-3 ʾ

    MES ֮ǰҵ൥һͬɹƩ豸桢ݲɼԱϢȹЭͬڵһ֮伯Բ㣬ݹʵ֣޷ʹ̴ﵽŻ[6, 7],ŹҵˮƽĸٲϵطչͻҪԽСƣԽӻͳijģʽ޷[8],磺ϲƻ޷ֻײƲÿҵֳײƲͻϢ޷ʱϲƻ㣻豸֮ʵЧļɺݹΪһ"µ";ҪϢݹҾӳԣ޷ԭʼݽмʱЧھ͹⣬Ϊ˽[9-12],ִϵͳMESӦ˶MES ijں̶ܴ϶ԳŻ̡

    MES һԴú״̬⣬Ĺ̡ȡ豸ĵƷٵȹֳݲɼܷȹģ[13],ͼ 1-4 ʾһҵʵѡȡеļģϲϹҵɴһܹܡ

    ڹҵ MES ϵͳij֣ҵܻ̣ʵӦʵ֣ҵʵʱȡֳϢҵֳĶ̬ܿءϢĿӻҪϢݵھãҵ̵ĿɻԣƻƲ㡢䡢豸֮ϵ빵ͨӿҵǻۻIJǿԲƷܿ 1.2 о״ִϵͳMESΪҵǻ۹֮һҪ̶ȲɺӣѱҵΪصзĿرڵ¹"ҵ 4.0""й 2025"˫սƶ£ֽ MES Ȼָʽ[14].ͬʱMES ϵͳһֱǹѧоĿ֮һڱͳΪҹϢ빤ҵںϣʼʵ"й 2025"սԣҵͿԺУƽ MES ķչ[15, 16].ȹģΪ MES ϵͳĺ֮һ䱾ԱȺŲΪĵŻ⣬ǺܻˮƽҪָ֮һ˶оΪҪ

    1.2.1 MES ϵͳо״

    ŴݡƼ㡢ͼھ򿪷רѧ߶MES ϵͳоԽ룬MES ϵͳҲԽԽܻϢ

    ףڡʦѵԴͳ ERP ޷"滯""ܻ"󣬲 DIS OPC ȼϢµ ERP ϵͳɣٽ˳"滯""ܻ"չ[17-21];»ҡȡŰ˵Դͳ MES ϵͳ޷㾺г仯ҵܻת͵ķչͨϢ˹ҵ˼ںϼ뵽̵ĹܿУʵִͳ MES ϵͳҵ̶ܻ[22-25];̸Ϣˮƽ⣬ʹ"+"ʽгIJ𣬲ִϢʵֳ MES ϢϢˮƽ[26];ŵڶгкǻ۹ʵ MES ܸϸͷΪ MES ʵṩ˿з[27, 28];Ӱ⡢ɢҵϢ̶ȵ£Чʵµ⣬ùҵ̫ʵݵĿٽԼ̿ƣڴ˻Ͽ MES IJֹģ飬Ϣˮƽ[29-32];Ф·Թҵݱ¹ҵϢȫͨ MES µĹҵݰȫƣҵȫ[33].

    Zwoliska B Դͳ MES ϵͳڸ߶ԶˮƽлԺԲ⣬ñҶ˹򣬿 MES 㷨 MES ϵͳԺ[34];Babak Shirazi Դͳ ERP MES ЧЧʵ£޷ҵ⣬һֻƼERPMESSPXɵϵṹҵЧ[35];AlmadaLobo FMithun Mukherjee ΪͻƻƷ󣬽Ƽ㡢ƶ豸ʹݵȼϣܻ MES ϵͳٽҵܻת[36, 37].

    MES Ŀ C/S ΪϵͳܣϵͳԿͻǿάɱϸߣⴴʹ B/S ΪϵͳܣѶԿͻǿϵͳչԡ

    1.2.2 Ա㷨о״

    "Դ" 1954 Peter Drucker[38] ԴԴ߱ܶԣܹãͬʱз˸Żƥ 1980꣬ǶԴЧھ򡢺䡢˷ѵЧ;ҵвɻȱIJ[39],һֱܺѧע

    ·Աȹȱѧϵ⣬龰ַϣɳԱʤƳԱȹ[40];ƷõԱòЧʵµ⣬ WitnesseM-PlantԳԱŻнģͷһ̶ϸԱ[41-43];ʵ쳵˸ƥ䲻⣬˸÷ҵŻΪĿ꺯˸ģͣͨһֶĿ㷨һָĽŴ㷨ģͣҵɱЧ[44-46];÷㲿ӳ䣬ԱӹݲɼʱԱ׷ݲԱ׼ȷ⣬ RFID ʵʱɼӹݣͳƷԱˮƽ˳Աǿ˲ƷĹܿ[47, 48];ά깤СΪĿ꣬"һ˶"IJйģͣͨһָĽ˹ȺŻ㷨ģ̻ͣӳ깤ʱ䣬Լɱ[49, 50];ܵԱʷӹΪݣʱΪָԱֵԱֵΪ쳵ԱŻģͣû PSO 㷨ģͣʵ쳵ԱȺ[51];ֵװԱòװЧʵµ⣬ԱˮƽۼڸҵʱΪݣ˸Ӧܺ󻯡ƥӦȲСΪĿ꺯˸ģͣһֻڸλӦȾʽ㷨ģͣŻԱ빤λ֮ƥ⣬˲ߵЧ[52].

    X Cai ϼԱĹЧʵ⣬ԵԱɱСΪĿ꣬˸ŻģͣһָĽĶ׼Ŵ㷨[53]ģͣ˳ϼԱЧʣAlbert CorominasKoichi Nakade װ䳵Աò²װڹ⣬ֱװ̻ԱԱԱɱСΪĿ꺯˸ŻģֱͣöԹ滮[54] һڼԱŷŻ㷨[55]ģͣŻ˳˸⣬ĺɱCristobal Miralles[56]װˮԱ䲻ƽ⣬ ԱӦλװʱ䣬ЧԱʶȾΪĿ꣬˸Żģͣһֻڷ֧ͱ߽ʽ㷨ģͣЧԱʶȣYiyo Kuo[57]նװԱڶ֮ϵ⣬ԱȼߵͱţԱϵƵͻΪĿ꣬滮ģͣģģͽ⣬Ż༼Աڶ߼Ļϵ⡣


    1.2.3 Ų㷨о״

    Ųȸ 1954 ꣬Ӣѧ Johnson о̨֮Ų[58],˺ŲⱻоչӦõҵСҵŲⰴҪΪ࣬һǻӳŲȣһװ䳵Ųȡ


    ڶװ䳵ŲоУƻ½ѩ Patrick װ䳵Ĵͳ˹Ų⣬ֱ˻ԼŲģ[66]ԴȼŲģ[67, 68]ԼڶԵŲģ[69, 70],ģģͣŻװ䳵Ų⣻ܸM.Omkumar Զ༶װ䳵⣬³ִ⣬װΪĿ꺯ֱģͷԼԻ[71]һµĻȺ㷨ʽ㷨[72],Żװ䳵ŲʵֳRoberto Dominguez ԶͺŲƷװ䳵ŲŲڹ⣬Сװ깤ʱΪĿ꺯Ⱥ˹ۣһֻ͵Ⱥ㷨ڿܶȵѡԱӶԵʧֲŵ⣬Żװ䳵Ų[73];ͯСӢԶͺŲƷװ䳵Ʒӻ̻װѭʱ[74-77]װɱ[78]ΪĿ꺯ָĽŴ㷨ͺŲƷװˮŲģͣŻ˶ͺŲƷװˮŲ⡣


    1.3 Ҫо

    ĸݹ̻еװ䳵װߵص㣬ʵװԱЧߺװѭʱСΪĿ꣬ͨԱʤָۿϵĹԼԱȺŲģ͵ĽøĽĵ㷨ģͽ⣬ֲͳԱ÷ʹͳ˹ŲIJ㣬ʵֻװԱŲܵȣ󣬱ĽĽԱ㷨ԼŲ㷨Ӧõ̻еװ䳵 MES ϵͳĿСҪоУ



    3Ա㷨Ų㷨ںϵ MES ϵͳУӦģ飬ʵֹ̻еװ䳵˸Ųŵܻʵʱ

    1.4 ĵ֯ṹ

    ķ¶Թ̻еװ䳵 MES ϵͳеԱȺŲоԼ MES ϵͳֹģĿ֯ṹͼ 1-5 ʾ


    һ£ȷָоĿĺ壬Ȼȫۺ MESϵͳԱȺŲȵоԱĵҪоݽиڶ£ͨװԱõص⣬װ߶༼ԱʤĶ;ģͽиԱʷӹеʱЧʡϸʺΪֵָ꣬ö༼Աʤָ㷽װ˸ƥŻģͣһָĽ㷨ģͣͨʵ֤


    £ȸݹ̻еװ䳵ʵ󣬶Թ̻еװ䳵 MES ϵͳܹƺͼѡͣȻϵͳݿģͽƣ IntelliJ IDEAMySQL Workbench ȿɹ̻еװ䳵 MES ϵͳĿ

    £оɹܽᣬҪԱȺŲȵоɹMES ϵͳĿɹодڵIJԼδչ˼

    2 ڸĽ㷨װԱŻо

    2.1 װԱȵص

    2.2 װ߶༼Աʤ

    2.2.1 ʤĶģ

    2.2.2 ༼Աʤָ

    2.3 װԱŻģ

    2.3.1 ԱŻģ͸

    2.3.2 ԱŻģ

    2.3.3 ԱŻģ͹

    2.4 Ľ㷨

    2.5 ʵ֤

    2.6 С

    3 ͺŻװŲŻо

    3.1 װŲ

    3.2 װŲŻģ

    3.3 Ŵģ˻㷨

    3.3.1 ȾɫʼȺ

    3.3.2 ӦȺ

    3.3.3 Ŵ

    3.3.4 ģ˻

    3.4 ʵ֤

    3.5 С

    4 ̻еװ䳵 MES ϵͳ

    4.1 ϵͳ

    4.2 ݿ

    4.2.1 ݿӦó

    4.2.2 ݿṹ

    4.2.3 ݱ

    4.3 MES ϵͳģ뼼ʵ

    4.3.1 ϵͳԭ

    4.3.2 ģ

    4.3.3 ģ

    4.3.4 ģ

    4.3.5 豸ģ

    4.3.6 ݹģ

    4.4 С

    5 չ


    빤̻еװ䳵ͻƷӻ̻󣬷װˮߵص⣬һָĽ㷨װԱŻȣһŴģ˻㷨װŲŻȣ B/S ܹƺͿ̻еװ䳵 MES ϵͳƽҵǻ۹Ľ̡Ҫоɹ£


    2ԴԼĶͺŻװˮߵ Flowshop ģʽͳ˹ŲʽΪʶӰ졢ԼⲻȫӰ죬»װˮЧʵºӳ⡣ķ˶ͺŻװˮFlowshop ģʽص㣬ƽƶʱ֯ʽҵϳȴ֣ԶͺŲƷװѭ깤ʱСΪĿ꺯˶ͺŻװŲŻģͣһŴģ˻㷨ŲģͣŻ˻װˮߵŲȣŲںڡ

    3 IntelliJ IDEA ƽ̨˹̻еװ䳵 MES ϵͳĿ Javaϵͳܴ CSSHTMLJavaScriptBootstrapJSPJSTL ȼϵͳǰҳĿ ServletJQueryJDBCTemplateDuirdBeanUtilsTomcatMySQL ȼϵͳ˹ܵĿ MySQL Workbench ϵͳݽж̬ϵͳҪ桢豸ͻݵ 5 ģ顣ʵ˹̻еװ䳵ԱȡƻŲءݹ豸״̬ȹܡ M ˾Ĺ̻еװ䳵ϵͳвԺ֤ͨԺ֤ĵ MES ϵͳ˳װԱȺͼƻŲ񣬲Чõ

    ĵоΪҵ MES ϵͳĿӦṩ˿еļҵǻ۹תҪ塣

    5.2 չ

    Թ̻еװ䳵 MES ϵͳеĵоһоɹԴһЩ֮費ϵĽѧϰʵ֤δչ£




    4Ĺ̻еװ䳵 MES ϵͳԼ򵥣ϵͳܽһ












    [1] ںɡ ҵӦüչ[J]. Ӽ̣ 2019, 08 20.

    [2] ΰ άҡ Ƚ컷Ĺҵؼо[J]. DZ2017, 03 4-7.

    [3] ã գ ܵȡ ȫʵֻͨı֮·--֮·ֻ[J]. йе̣ 2018, 2903 366-377.

    [4] Ө ҵչ[J]. ӲƷ磬 2018, 2503 9-14.

    [5] ľơ MESERPϵͳļоӦ[J]. 뷢չ 2009, 1903223-226.

    [6] ޷ ܹ MES ؼо[D]. ϿƼѧ 2017[7] Ϊ壬 MES ϵܹо[J]. г 2019, 260848-49.

    [8] ΰ £ ԲԲ ڹҵĹ̻еװؼ[J]. ƼӦã 2019, 06 139-140.

    [9] ˿ƴij MES [J]. ǻ۹ 2018, 10 35-36.

    [10] MES ϵͳҵĴ[J]. ǻ۹ 2018, 05 35.

    [11] ҵʵʩ MES ϵͳ[J]. ǻ۹ 2018, 01 38-39.

    [12] ΰȡ 켼--MES ϵͳװеӦü̽[J]. 켼 2016, 04 42-45.

    [13] ۣ ҵ MES ϵͳʵ·[J]. й繤ҵ 2016, 1296-97.

    [14] ѧգ MES ϵͳڿͳͿװеӦо[J]. ʵü 2020, 4520141-144.

    [15] οա й2025רҵȺʵѵѧϵعо[J].ʱ 2020, 19 45-46.

    [16] ᣬ ɼΡ ؼ[J]. ֤ 2020, 11 48-49.

    [17] ף ͤ ף dzʵʩ MES ϵͳҵչҪ[J]. Ӧã 2014, 1701 117-118.

    [18] ڣ ͮ ȡ MES ϵͳܳʵʩ[J]. ϢϢ2019, 12 61-64.

    [19] ʦѡ Դҵ ERP MES Ӧ[J]. Ӽ̣ 2019, 24 150-151.

    [20] Choi B.K., Kim B.H. MES manufacturing execution system architecture for FMScompatible to ERP enterprise planning system[J]. International Journal of ComputerIntegrated Manufacturing, 2002, 153 49-53.

    [21] D B., S H., et al. Development of the MES software and Integration with an existing ERPSoftware in Industrial Enterprise[J]. International Journal of Computer IntegratedManufacturing, 2020, 155 10-15.

    [22] » 滯 MES ϵͳֽҵеӦ[J]. ֽ 2018, 4602 13-17.

    [23] ҡ dz̸ MES ϵͳҵӦ[J]. ִϢ 2019, 10 117.

    [24] ȡ ͳ MES Ѿʱ[J]. 죬 2019, 06 20-28.

    [25] Űˡ װߵ MES ϵͳ[J]. ɽҵ 2020, 02 106-111.

    [26] ̸ա MES ϵͳϢ̽[J]. Ϣͨţ 2019, 09 138-140.

    [27] ŵ롣 MES ϵͳʵǻ۹Ľ[J]. 磬 2019, 4005 123-126.

    [28] ܡ ִйϵͳMESӦ̽[J]. ֵ̣ 2011, 3021 163-164.

    [29] Ҷࡣ MESϵͳ[J]. ϻԶӹ 2019,10 154-155+160.

    [30] Ӱ Ѽѣ ȡ ɢҵ MES ݲɼ̽[J]. Ƽµ 2019, 1631 82+84.

    [31] ⡣ MES оӦ[J]. йͨţ 2020, 2204 118.

    [32] ȡ ɢҵ MES ϵͳʵʩӦ̽[J]. װ켼 2020, 05 273-275+278.

    [33] Ф·硣 MES ϵͳĹҵݰȫо[J]. Ϣԣ۰棩2020, 3213 195-197.

    [34] B Z., A T.A., N C.-G. Personalization of the MES System to the Needs of Highly VariableProduction[J]. Sensors Basel, Switzerland 2020, 2022 132-139.

    [35] Shirazi B. Cloud-based architecture of service-oriented MES for subcontracting andpartnership exchanges integration: A game theory approach[J]. Robotics and ComputerIntegrated Manufacturing, 2019, 59: 11-17.

    [36] F A.-L. The Industry 4.0 revolution and the future of manufacturing execution systemsMES[J]. Journal of innovation management, 2015, 34 16-21.

    [37] B C., J W., Mukherjee M. Smart factory of industry 4.0: Key technologies, application case,and challenges[J]. IEEE Access, 2017, 6: 6505-6519.

    [38] ˵õ³ˡ ʵ[J]. ѧԺ 2005, 12 F0001-F0002.

    [39] µȨ ԴսԷŻԴ[J]. ƼӦã 2012, 04221.

    [40] · 챳Աʤо[J]. о 2020,06 123-126.

    [41] Witness ijԱõϵͳ[J]. ƼѶ 2013, 2532-33.

    [42] Ʒá eM-Plant ijԱ÷о[J]. 㶫㲥Ӵѧѧ 2013,2203 110-112.

    [43] ź÷ Űƽ Աù滮о[J]. Ƽµ 2010, 05 235-236.

    [44] ֣ ΰˡ Աģͼ㷨[J]. ҵѧѧ 2012, 4405 144-148.

    [45] ʣ ܹ ֽƼԱԵij伯ɵ[J]. Ӧã2015, 5104 11-16+104.

    [46] ʣ ܹ ķȡ ԱԶȶҵȵӰо[J]. Ӧо 2016, 3310 3017-3020+3025.

    [47] ÷ ԡ ɢ쳵 MES Աϵͳ[J]. Ӽ̣2018, 11 206-207.

    [48] ȷ衣 MESɢͳԱϵͳ[J]. йϢ 2018,2101 67-69.

    [49] ά棬 ã ˧ Աװжʱҵ[J]. ҵԶ2020, 4208 87-94.

    [50] ά棬 ɡ ҵԱüҵо[J]. Ӧо2018, 3512 3722-3728.

    [51] ܣ ɣ ïȡ ʤָ쳵Żģ[J]. ϷʹҵѧѧȻѧ棩 2011, 3410 1466-1469.

    [52] ֣ 룬 ɵȡ װ߶༼ҵԱŻģ[J]. ѧѧ 2010,3312 21-26.

    [53] Cai X., Li K.N. A genetic algorithm for scheduling staff of mixed skills under multicriteria[J]. European Journal of Operational Research, 2000, 1252 65-69.

    [54] Corominas A., Pastor R., Plans J. Balancing assembly line with skilled and unskilledworkers[J]. Omega, 2006, 366 23-27.

    [55] Nakade K., Nishiwaki R. Optimal allocation of heterogeneous workers in a U-shapedproduction line[J]. Computers & Industrial Engineering, 2007, 543 6-10.

    [56] Miralles C., García-Sabater J.P., Andrés C. et al. Branch and bound procedures for solvingthe Assembly Line Worker Assignment and Balancing Problem: Application to ShelteredWork centres for Disabled[J]. Discrete Applied Mathematics, 2005, 1563 77-83.

    [57] Kuo Y., Yang T. Optimization of mixed-skill multi-line operator allocation problem[J].

    Computers & Industrial Engineering, 2007, 533 91-97.

    [58] Johnson S.M. Optimal two-and three-stage production schedules with setup timesincluded[J]. Naval research logistics quarterly, 1954, 11 43-45.

    [59] JW ˾Ųо[J]. ȼ 2019, 17 39-41.

    [60] 溣 ΰ ڷֲŴ㷨ѹŲŻ[J]. Ӧüѧ 2019,1901 65-71.

    [61] һ һУ ʷȡ ڸĽŴ㷨ˮŲŻо[J]. ϵͳʵ 2016, 3606 1616-1624.

    [62] ԰԰ ޣ ɵȡ ȺŴ㷨ɢҵŲео[J]. ϵͳӦã 2016, 2505 94-100.

    [63]  Ŵ㷨ɲƬƻŲеӦʵ[J]. ϵͳӦã2019, 2804 225-230.

    [64] ڣ 񶫵ȡ Լ۵ֳ֯ŲŻ[J]. Ṥе 2020, 3803 100-104.

    [65] ɣ Ԭϼȡ ̳˿ԶŲо[J]. 2020,3902 98-101+135.

    [66] ƻ ʷ ۡ Ϳװװ䳵ƻŲģ[J]. Ϣƣ 2013,4205 652-656.

    [67] ½ѩ ʯɣ  װ䳵ԶŲϵͳо[J]. ԪϢ2019, 305 77-80.

    [68] Paul M., Sridharan R., Ramanan T.R. A multi-objective decision-making framework usingpreference selection index for assembly job shop scheduling problem[J]. Int. J. ofManagement Concepts and Philosophy, 2016, 94 66-68.

    [69] Philipoom P.R., Russell R.S., Fry T.D. A preliminary investigation of multi-attribute basedsequencing rules for assembly shops[J]. International Journal of Production Research, 1991,294 75-79.

    [70] Ernst A.T., Jiang H., Krishnamoorthy M. et al. Staff scheduling and rostering: A review ofapplications, methods and models[J]. European Journal of Operational Research, 2004,1531 66-69.

    [71] ܸ Ϻɭ ༶װ䳵ļŲŻģ[J]. ϵͳʵ2010, 3010 1891-1900.

    [72] Omkumar M., Shahabudeen P. Ant Colony Optimisation for multi-level assembly job shopscheduling[J]. Int. J. of Manufacturing Research, 2009, 44 89-93.

    [73] Du H., Liu D., Zhang M.-h. et al. A Hybrid Algorithm Based on Particle Swarm Optimizationand Artificial Immune for an Assembly Job Shop Scheduling Problem[J]. MathematicalProblems in Engineering, 2016, 2016: 77-81.

    [74] ͯСӢ ɣ 滷»Ŵ㷨ĻװŲŻо[J]. 켼 2018, 11 29-35.

    [75] ǣ  ȡ Ŵ㷨ĶƷС͵װŲŻ[J]. ֵ̣ 2020, 3904 293-295.

    [76] ־ף ˺Ͽ ƽ Ŵ㷨ŲŻ[J]. ̫ȿѧϢѧ 2014, 1204 595-599.

    [77] Shi F., Zhao S., Meng Y. Hybrid algorithm based on improved extended shifting bottleneckprocedure and GA for assembly job shop scheduling problem[J]. International Journal ofProduction Research, 2020, 589 93-96.

    [78] Wong T.C., Chan F.T.S., Chan L.Y. A resource-constrained assembly job shop schedulingproblem with Lot Streaming technique[J]. Computers & Industrial Engineering, 2009,57377-79.

    [79] 硣 ָĸĽ㷨[J]. Ƽӽ磬 2012, 14 106-108.

    [80] ͢ Ǯ룬 ڸĽ㷨Ķ༼Աȷ[J]. Ƽѧѧ 2016, 3802 144-149.


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