CN103984985A - Method for optimizing customized furniture serial batch scheduling - Google Patents

Method for optimizing customized furniture serial batch scheduling Download PDF

Info

Publication number
CN103984985A
CN103984985A CN201410165203.6A CN201410165203A CN103984985A CN 103984985 A CN103984985 A CN 103984985A CN 201410165203 A CN201410165203 A CN 201410165203A CN 103984985 A CN103984985 A CN 103984985A
Authority
CN
China
Prior art keywords
order
batches
particle
scheduling
listed
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201410165203.6A
Other languages
Chinese (zh)
Inventor
李志�
陈新
孙盛
陈新度
刘强
王小军
叶东东
陈小奇
陈海燕
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong University of Technology
Original Assignee
Guangdong University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong University of Technology filed Critical Guangdong University of Technology
Priority to CN201410165203.6A priority Critical patent/CN103984985A/en
Publication of CN103984985A publication Critical patent/CN103984985A/en
Pending legal-status Critical Current

Links

Abstract

The invention discloses a method for optimizing customized furniture serial batch scheduling. The method comprises the steps of (1) sorting order sets according to the sequence of delivery time, (2) capturing the order set of the kth sequence, (3) obtaining the order and part information of the order set, and (4) conducting optimized batch setting on parts with the cellular particle swarm optimization algorithm to obtain an optimal part batch setting result. According to the method, the order sets are obtained, part machining time for the order sets is obtained at the same time, the parts in an order are sorted in a batch setting mode according to machining time, then a sorting result, namely a particle is obtained, the particle is initialized, different particles are obtained through variation and iterative operation, and particle batch setting with the highest adaptability is selected as the optimal batch scheduling method; batch setting can be conducted on edge sealing and drilling in large-scale customized furniture production, order accomplishing time is minimized, optimization of order delivery time is realized at the same time, and requirements of large-scale customized furniture enterprises for quick and efficient production can be met.

Description

A kind ofly realize that having furniture made to order continues is listed as the method for optimizing scheduling in batches
Technical field
The invention belongs to the scheduling problem in advanced manufacturing running systems, being specifically related to utilize under a kind of personalized furniture large-scale customization production environment cellular particle cluster algorithm (PSO-CA) to realize parts continues and is listed as the method for optimizing scheduling in batches, particularly utilize particle cluster algorithm to realize to have furniture made to order to continue to be listed as a method for optimizing scheduling in batches, belong to that realizing has furniture made to order to continue is listed as the innovative technology of the method for optimizing scheduling in batches.
Background technology
At present, the research of producing for personalized furniture large-scale customization concentrates on product (family) design and product configuration more, and the production schedule and scheduling are as one of gordian technique of large-scale customization, directly affect the enforcement of large-scale customization success.Under large-scale customization environment, the variation of Customization demand is with personalized, the kind of order is increased, the quantity required of single kind sharply declines, cause aborning the reasonable Batch Scheduling difficulty of order to increase, on time completing of order caused to very large impact, increased the complicacy of production scheduling.
Meet each manufacturing procedure, time, in batches, under the constraint condition of machine environment, by order collection in batches, reach that same order parts completion date is basically identical, order completes on time these two objects of delivery are difficult tasks simultaneously.
Zeng Min is in " modeling of large-scale customization key issue and applied research ", and the Central China University of Science and Technology criticizes scheduling problem for the multiple-objection optimization of flexible production line in 2011, proposed a kind of parallel machine and continued and be listed as scheduling method Pm|s-batch|C in batches max.Designed self-adaptation cellular population multi-objective optimization algorithm, its innovative point is to use cellular automaton to carry out mutation search, has improved optimizing ability.In actual applications, utilize bottleneck decomposition method to effectively reduce the dimension of multi-objective optimization question, adopt scientific visualization technology to realize the visual demonstration of the evolutionary process of colony intelligence optimization, carried out the comparative studies that various combination parameters arrange, find out the preferably weights parameters based on golden section theory, reduced randomness that parameter arranges and empirical.Solve a scheduling difficult problem for the large-scale customization mode of production, realized the transformation of mass production method to Mass Customization Production Model.But the method, when adopting self-adaptation cellular PSO Algorithm parallel machine to continue to be listed as in batches multi-objective optimization question, can not solve the parts completion date significant difference problem of same order.Chen Yarong, Guan Lin, Peng Yunfang, Shao Xinyu is in " the bottleneck gang scheduling heuristic research of mass customization ", the 21st the 8th phase of volume of China Mechanical Engineering, in April, 2010, analyzed the production feature of Mass Customization Production Model, the module workpiece gang scheduling problem of mass customization has been described, propose take identification bottleneck operation and dragged phase gang scheduling heuristic as basic minimizing Weighted, realized production cost and the optimization at delivery date on wall scroll production line.The scheduling result of actual production data shows, criticizes scheduling and criticizes gang scheduling method completely and compare with module list, and the minimizing Weighted based on bottleneck drags phase module gang scheduling heuristic more feasible, more effective.But the method reckons without the problem in batches of product, can not be applicable to multiple production line, multiple batches of production environment.Gu Hengping, single Mi Yuan, Pu Lida is in " research of Mass Customization Enterprises production operation Problems of Optimal Dispatch ", in industrial-tech economy the 25th volume o. 11th on customization point separating thought basis, large-scale customization scheduling problem is studied, the same operation that has proposed job family in groups, the short run operation group based on JIT thought and the assembling product of part processing limits satisfied production scheduling strategy with the material of material, has solved part processing job shop scheduling problem and assembling product job shop scheduling problem.But, they do not criticize and analyze and research order optimization group, and the production feature of the described production feature of its method and personalized furniture large-scale customization is not exclusively identical, can not effectively be applied to the scheduling in batches that personalized furniture large-scale customization is produced.
Above method is all unilaterally Product in Mass Customization production efficiency to be optimized, under personalized furniture large-scale customization production environment, they can not solve the parts completion date significant difference problem of same order and the optimization problem at order delivery date simultaneously.Existing scheduling research in batches mainly concentrates on the fields such as the data packet dispatching, grid task scheduling, logistics distribution center scheduling of semiconductor heating furnace production line, steel and iron industry hot-rolling heating furnace production line, telecommunications delivery machine, but the research about scheduling in batches under personalized furniture large-scale customization production environment is also immature.For addressing the above problem, must be for the new dispatching method in batches of personalized furniture large-scale customization production scheduling invention.
Summary of the invention
The object of the invention is to consider the problems referred to above and a kind of same order parts completion date significant difference problem that solved is provided, realized the optimization at order delivery date simultaneously, contribute to furniture enterprise to be optimized group batch to custom order collection, the realization of enhancing productivity is had furniture made to order to continue and is listed as the method for optimizing scheduling in batches.
Technical scheme of the present invention is: realization of the present invention is had furniture made to order to continue and is listed as the method for optimizing scheduling in batches, includes following steps:
1) by the priority at delivery date, order collection is sorted;
2) the order collection of k sequence of intercepting;
3) obtain order and the components information of order collection;
4) use cellular particle cluster algorithm to be optimized in batches parts, the optimum of acquisition parts is result in batches.
The invention provides and a kind ofly utilize cellular particle cluster algorithm to realize personalized furniture large-scale customization to continue and be listed as the method for optimizing scheduling in batches, object is under personalized furniture large-scale customization production environment, order collection is optimized in batches, solve current furniture enterprise produce in the parts completion date significant difference problem of same order and the optimization problem at order delivery date, for the production of furniture enterprise tissue provides support.Technique effect of the present invention is embodied in: when current personalized furniture Mass Customization Enterprises scheduling order tissue is produced, each production line in batches, batch internal sort not science cause parts completion date significant difference, the order of same order can not delivery just-in-time.For above two problems, how at each processing link, to optimize in batches, solve the parts completion date significant difference problem of same order and the optimization problem at order delivery date has formed key point of the present invention simultaneously.The present invention is in edge sealing, boring link, utilize cellular particle cluster algorithm parts are optimized in batches and batch in processing sequence optimization, reached the basically identical and order of same order parts completion date two objects of delivery that on time complete.The present invention has important application meaning to the High-efficient Production of personalized furniture Mass Customization Enterprises.The present invention is that a kind of convenient and practical realization is had furniture made to order to continue and is listed as the method for optimizing scheduling in batches.
Accompanying drawing explanation
Fig. 1 is invention overview flow chart;
Fig. 2 is edge sealing, boring link parts process flow diagram in batches;
Fig. 3 is parts Batch Scheduling schematic diagram in batch device;
Fig. 4 is cellular particle cluster algorithm enforcement figure.
Embodiment
Embodiment:
Core content of the present invention is how to use cellular particle cluster algorithm to optimize personalized furniture large-scale customization in batches.The present invention, in conjunction with cellular particle cluster algorithm and personalized furniture large-scale customization production feature, regards cellular internal information as order parts position, and the order of first intercellular is regarded the processing sequence of order as.
The feature of cellular population: the speed of utilizing interlace operation to complete algorithm according to cellular population upgrades and position is upgraded and adopt effective neighbour structure to carry out Local Search to particle.If regard population as a kind of cellular model, each particle only with by the definite neighbours of neighbours' function, carry out information interchange, can make like this propagation of information in population slack-off, contribute to keep the diversity of population, explore search volume, and fully excavate the local message of each particle.
The inventive method implementation procedure is as follows:
Workshop has p bar streamline, the index of p is [1,2,3 ..., P], on every streamline, there is respectively one, the plate of sanction, edge sealing, boring machine, each parts must be successively through sanction plate, edge sealing, drilling operating processing, and the parts that are assigned on certain production line can only process on this production line, until machine through three process.Suppose to have I order, first press the delivery order sequence from small to large of order, and intercept order, give sequence 1～k, in each sequence, limit component number Q, and delivery order is roughly the same.Example: if the number of parts is M in order 1 1, in order 2, the number of parts is M 2, M 1+ M 2<=Q, 1,2 order composition sequences 1.After each sequence is cut out plate, global transfer is to edge sealing, drilling operating.Get k sequence.
(1) parts in k sequence are pressed to order classification, and generated at random order position data;
(2) process time of every procedure of each parts all definite, be (I min, I max);
(3) order opsition dependent data are sorted from small to large, the inner parts machining time t that presses of order pxdarrange from small to large.Parts are inserted in P batch by order from left to right, until parts complete, once finished in batches, obtain one and separate E 1;
(4) establishing global cycle number of times is H, and getting initial cycle is h=1, every (1)～(4) of repeating once, and h=h+1, until h=H finishes in batches, can obtain H and separate E 1～E h, i.e. H particle.
(5) by H particle E 1～E hbe defined as initial population, particle in initial population is carried out to initialization, and generate at random the position data of order;
(6) cellular variation iteration particle population, more new particle algebraically.
(7) iteration completes, and obtains optimum solution, and corresponding particle batches sort method is optimum batch.
The specific embodiment of the invention is as follows:
Step 1: order collection is sorted by the priority at delivery date;
Step 2: the order collection of k sequence of intercepting;
Step 3: the order and the components information that obtain order collection;
Step 4: use cellular particle cluster algorithm to be optimized in batches parts, the optimum of acquisition parts is result in batches.
4.1: order and the components information of input order collection;
4.2: give order position data, the position data of order is by sorting from big to small, and order inner body sorted from small to large according to the time simultaneously;
4.3: parts are carried out in batches;
1. set part number M.Total batch of P, be t the process time that p criticizes x part px, initiation parameter, part index m=1, column index x=0, line index p=1;
If 2. p≤P, t px=t m, m=m+1, p=p+1, otherwise x=x+1, p=1;
If 3. m≤M, goes to step 4. otherwise just export the result in batches of particle;
4.4: use cellular particle cluster algorithm to obtain optimum result in batches.
1. initialization population number H, h=1;
2. the result in batches of obtaining step 4.3;
3. calculate fitness function F ( E i ) = w 1 f ( x ) + w 2 g ( x ) , Local solution, give p i;
4. step 4～8 of every repetition, h=h+1, until h=H finishes in batches, can obtain H and separate E 1～E h, i.e. H particle;
5. initialization iterations K, k=1;
6. identify the state of the solution of each particle, and it is made a variation, concrete grammar is as follows:
According to formula t i t + 1 = w i t i t + c 1 r 1 ( P i t - S i t ) + c 2 r 2 ( P g t - S i t ) With S i t + 1 = S i t + t i t + 1 Calculate S i t + 1 ( P i ) = S i t ( P i ) With S i t + 1 ( P n ) = S i t ( P n ) ;
7. calculate fitness function F ( E i ) = w 1 f ( x ) + w 2 g ( x ) ;
8. upgrade locally optimal solution and globally optimal solution, concrete mode is as follows:
If F ( S i t + 1 ( P i ) ) < F ( E i + 1 ) = w 1 f ( x ) + w 2 g ( x ) , Upgrade local solution cellular particle candidate solution is around upgraded to the number of ambient particles; Otherwise, if upgrade
The represented implication of above-mentioned symbol used is as follows:
9. export globally optimal solution.

Claims (6)

1. realization is had furniture made to order to continue and is listed as a method for optimizing scheduling in batches, it is characterized in that including following steps:
1) by the priority at delivery date, order collection is sorted;
2) the order collection of k sequence of intercepting;
3) obtain order and the components information of order collection;
4) use cellular particle cluster algorithm to be optimized in batches parts, the optimum of acquisition parts is result in batches.
2. realization according to claim 1 is had furniture made to order to continue and is listed as the method for optimizing scheduling in batches, it is characterized in that above-mentioned steps 4) method comprise the steps:
41) order and the components information of input order collection;
42) give order position data, the position data of order is by sorting from big to small, and order inner body sorted from small to large according to the time simultaneously;
43) parts are carried out in batches;
44) use cellular particle cluster algorithm to obtain optimum result in batches.
3. realization according to claim 2 is had furniture made to order to continue and is listed as the method for optimizing scheduling in batches, it is characterized in that above-mentioned steps 43) batch processes comprise the steps:
431) set M of part number.Total batch of P, be t the process time that p criticizes x part px, initiation parameter, part index m=1, column index x=0, line index p=1;
432) if p≤P, t px=t m, m=m+1, p=p+1, otherwise x=x+1, p=1;
433) if m≤M goes to step 44), otherwise just export the result in batches of particle.
4. realization according to claim 2 is had furniture made to order to continue and is listed as the method for optimizing scheduling in batches, it is characterized in that above-mentioned steps 44) use cellular particle cluster algorithm to obtain the optimum method of result in batches to comprise the steps:
441) initialization particle population number H;
442) result in batches of obtaining step 4.3;
443) calculate fitness function F ( E i ) = w 1 f ( x ) + w 2 g ( x ) , Local solution, give p i;
444) step 4～8 of every repetition, h=h+1, until h=H finishes in batches, can obtain H and separate E 1～E h, i.e. H particle;
445) initialization iterations K;
446) identify the state of the solution of each particle, and it is made a variation, concrete grammar is as follows:
According to formula t i t + 1 = w i t i t + c 1 r 1 ( P i t - S i t ) + c 2 r 2 ( P g t - S i t ) With S i t + 1 = S i t + t i t + 1 Calculate S i t + 1 ( P i ) = S i t ( P i ) With S i t + 1 ( P n ) = S i t ( P n ) ;
447) calculate fitness function F ( E i ) = w 1 f ( x ) + w 2 g ( x ) ;
448) upgrade locally optimal solution and globally optimal solution, concrete mode is as follows:
If F ( S i t + 1 ( P i ) ) < F ( E i + 1 ) = w 1 f ( x ) + w 2 g ( x ) , Upgrade local solution cellular particle candidate solution is around upgraded to the number of ambient particles; Otherwise, if upgrade
449) output globally optimal solution.
5. realization according to claim 4 is had furniture made to order to continue and is listed as the method for optimizing scheduling in batches, it is characterized in that above-mentioned steps 441) initialization population number H=1.
6. realization according to claim 4 is had furniture made to order to continue and is listed as the method for optimizing scheduling in batches, it is characterized in that above-mentioned steps 445) initialization iterations K=1.
CN201410165203.6A 2014-04-22 2014-04-22 Method for optimizing customized furniture serial batch scheduling Pending CN103984985A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201410165203.6A CN103984985A (en) 2014-04-22 2014-04-22 Method for optimizing customized furniture serial batch scheduling

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201410165203.6A CN103984985A (en) 2014-04-22 2014-04-22 Method for optimizing customized furniture serial batch scheduling

Publications (1)

Publication Number Publication Date
CN103984985A true CN103984985A (en) 2014-08-13

Family

ID=51276946

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201410165203.6A Pending CN103984985A (en) 2014-04-22 2014-04-22 Method for optimizing customized furniture serial batch scheduling

Country Status (1)

Country Link
CN (1) CN103984985A (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105149420A (en) * 2015-08-31 2015-12-16 索菲亚家居股份有限公司 Plate hole position generation method and device
CN105787678A (en) * 2016-05-10 2016-07-20 连云港美步家居有限公司 Disassembly method for furniture product design and detail disassembly
CN105976040A (en) * 2016-05-10 2016-09-28 连云港美步家居有限公司 Concentrated customization method based on small-amount multi-type household products
CN111311348A (en) * 2019-10-22 2020-06-19 佛山市南海利多邦卫浴有限公司 Plate cabinet customization method and device and computer readable storage medium
CN114936711A (en) * 2022-06-15 2022-08-23 广东工业大学 Order form kneading optimization method for large-scale plate-type customized furniture

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102968057A (en) * 2012-09-12 2013-03-13 浙江工业大学 Multi-species multi-process multi-unit manufacturing scheduling method based on improved cellular machine

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102968057A (en) * 2012-09-12 2013-03-13 浙江工业大学 Multi-species multi-process multi-unit manufacturing scheduling method based on improved cellular machine

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
周宏明等: "面向多品种变批量生产的制造单元生成方法", 《计算机集成制造系统》, vol. 16, no. 12, 31 December 2010 (2010-12-31), pages 2589 - 2595 *
曾敏等: "大规模定制板材排样的多种群蚁群优化算法", 《制造业自动化》, vol. 33, no. 5, 31 May 2011 (2011-05-31), pages 59 - 62 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105149420A (en) * 2015-08-31 2015-12-16 索菲亚家居股份有限公司 Plate hole position generation method and device
CN105149420B (en) * 2015-08-31 2017-04-26 索菲亚家居股份有限公司 Plate hole position generation method and device
CN105787678A (en) * 2016-05-10 2016-07-20 连云港美步家居有限公司 Disassembly method for furniture product design and detail disassembly
CN105976040A (en) * 2016-05-10 2016-09-28 连云港美步家居有限公司 Concentrated customization method based on small-amount multi-type household products
CN111311348A (en) * 2019-10-22 2020-06-19 佛山市南海利多邦卫浴有限公司 Plate cabinet customization method and device and computer readable storage medium
CN114936711A (en) * 2022-06-15 2022-08-23 广东工业大学 Order form kneading optimization method for large-scale plate-type customized furniture

Similar Documents

Publication Publication Date Title
Tang et al. Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization
Li et al. Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling
CN103984985A (en) Method for optimizing customized furniture serial batch scheduling
Yin et al. Mixed-integer programming model and hybrid driving algorithm for multi-product partial disassembly line balancing problem with multi-robot workstations
Echchakoui et al. Industry 4.0 and its impact in plastics industry: A literature review
CN105389402B (en) A kind of ETL method and apparatus towards big data
Zhang et al. Solving flexible job shop scheduling problems with transportation time based on improved genetic algorithm
Li et al. An effective hybrid algorithm for integrated process planning and scheduling
Yuan et al. Multi-objective optimal scheduling of reconfigurable assembly line for cloud manufacturing
Overcash et al. Unit Process Life Cycle Inventory (UPLCI)–a structured framework to complete product life cycle studies
Battaïa et al. Integrated process planning and system configuration for mixed-model machining on rotary transfer machine
CN103853938A (en) High-throughput sequencing data processing and analysis flow control method
Chakravorty et al. A heuristically directed immune algorithm to minimize makespan and total flow time in permutation flow shops
CN104573846A (en) Polymorphic job shop layout optimization method based on CA-PSO (Cellular Automata-Particle Swarm Optimization) hybrid optimization algorithm
CN102253861A (en) Method for executing stepwise plug-in computation
Wang et al. An estimation of distribution algorithm for the flexible job-shop scheduling problem
CN110705872A (en) Production logistics scheduling analysis method for composite parallel processing
Kheirfam A full-Newton step infeasible interior-point algorithm for linear complementarity problems based on a kernel function
Li et al. Production scheduling in intercell cooperative production mode
Yang et al. A green-feature based LCA backtracking mechanism
Wang et al. Study on modular design of trimming die structure for automotive panels
Lian et al. A cooperative simulated annealing algorithm for the optimization of process planning
Li et al. An efficient method for no-wait flow shop scheduling to minimize makespan
Osman et al. A linearization and decomposition based approach to minimize the non-productive time in transfer lines
Qi et al. A new optimized cutting method based on group technology in the structural member manufacturing

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20140813

RJ01 Rejection of invention patent application after publication