RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.

A Genetic Algorithm-based Approach to Scheduling of Batch Production with Maximum Profit

The optimal scheduling of multi-product batch process is studied and a new mathematics model targeting the maximum profit is proposed, which can be solved by the modified genetic algorithm (MGA) with mixed coding (sequence coding and decimal coding) developed by us. In which, the partially matched cross over (PMX) and reverse mutation are used for the sequence coding, whereas the arithmetic crossover and heteropic mutation are used for the decimal coding. In axidition, the relationship between production scale and production cost is analyzed and the maximum profit is always a trade-off of the production scale and production cost. Two examples are solved to demonstrate the effectiveness of the method.

A Heuristic Genetic Algorithm for No-Wait Flowshop Scheduling Problem

No-wait flowshop scheduling problems with the objective to minimize the total flow time is an important se-quencing problem in the field of developing production plans and has a wide engineering background. Genetic algo-rithm (GA) has the capability of global convergence and has been proven effective to solve NP-hard combinatorial op-timization problems,while simple heuristics have the advantage of fast local convergence and can be easily imple-mented. In order to avoid the defect of slow convergence or premature,a heuristic genetic algorithm is proposed by in-corporating the simple heuristics and local search into the traditional genetic algorithm. In this hybridized algorithm,the structural information of no-wait flowshops and high-effective heuristics are incorporated to design a new method for generating initial generation and a new crossover operator. The computational results show the developed heuristic ge-netic algorithm is efficient and the quality of its solution has advantage over the best known algorithm. It is suitable for solving the large scale practical problems and lays a foundation for the application of meta-heuristic algorithms in in-dustrial production.

Improvement of Lagrangian relaxation performance for open pit mines constrained long-term production scheduling problem

Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it poses become an NP-hard problem.This problem has major practical significance because the effectiveness of the schedules obtained has strong economical impact for any mining project.Despite of the rapid theoretical and technical advances in this field,heuristics is still the only viable approach for large scale industrial applications.This work presents an approach combining genetic algorithms(GAs) and Lagrangian relaxation(LR) to optimally determine the CLTPSP of open pit mines.GAs are stochastic,parallel search algorithms based on the natural selection and the process of evolution.LR method is known for handling large-scale separable problems; however,the convergence to the optimal solution can be slow.The proposed Lagrangian relaxation and genetic algorithms(LR-GAs) combines genetic algorithms into Lagrangian relaxation method to update the Lagrangian multipliers.This approach leads to improve the performance of Lagrangian relaxation method in solving CLTPSP.Numerical results demonstrate that the LR method using GAs to improve its performance speeding up the convergence.Subsequently,highly near-optimal solution to the CLTPSP can be achieved by the LR-GAs.

Coordinated Scheduling of Two-Agent Production and Transportation Based on Non-Cooperative Game

A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers.The jobs of two agents compete for the processing position on a machine,and after the pro-cessed,they compete for the transport position on a transport vehicle to be trans-ported to two agents.The two agents have different objective functions.The objective function of the first agent is the sum of the makespan and the total trans-portation time,whereas the objective function of the second agent is the sum of the total completion time and the total transportation time.Given the competition between two agents for machine resources and transportation resources,a non-cooperative game model with agents as game players is established.The job pro-cessing position and transportation position corresponding to the two agents are mapped as strategies,and the corresponding objective function is the utility func-tion.To solve the game model,an approximate Nash equilibrium solution algo-rithm based on an improved genetic algorithm(NE-IGA)is proposed.The genetic operation based on processing sequence and transportation sequence,as well as the fitness function based on Nash equilibrium definition,are designed based on the features of the two-agent production and transportation coordination scheduling problem.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.When compared to heuristic rules such as the Longest Processing Time first(LPT)and the Shortest Processing Time first(SPT),the objective function values of the two agents are reduced by 4.3%and 2.6% on average.

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

基于改进遗传算法的PC构件生产调度优化

基于流水线车间生产调度理论,建立以生产成本为决策变量、以最大化生产利润为目标的数学模型;结合预制构件车间生产的实际情况,设计最优选择法、局部变异算子改进遗传算法,求解最优生产调度方案。某预制构件公司订单生产调度仿真研究表明,基于改进遗传算法的PC构件生产调度优化可以在较短的时间内获得更高的利润,为提高预制构件智能化生产效率提供了技术基础。

不确定环境下预制构件生产鲁棒性调度研究

在确定了预制构件方生产工艺和资源约束情况的基础上,分析了预制构件生产调度计划对鲁棒值的影响;基于关键链技术制定了考虑二次资源冲突的集中缓冲进度计划,构建了以预制构件生产完工时间最短和鲁棒值最大为目标函数的双层规划模型,并通过嵌套式遗传算法对该模型进行求解。实证结果表明:基于该模型得出的调度计划鲁棒值最大且在面对不确定环境时更为稳定。

基于改进遗传算法的装配式渠道生产调度优化

为有效提高装配式渠道生产效率,加快模具周转,降低生产成本,结合装配式渠道生产线养护室数量有限的特点,建立以最小生产周期为目标的装配式渠道生产调度优化模型,设计了求解该模型的改进遗传算法。以新疆石河子金土地水泥制品厂渠道生产线为例,验证生产模型的可行性,通过对比传统顺序生产调度与改进遗传算法得到的优化生产调度,验证改进遗传算法的稳定性与有效性。结果表明,基于改进遗传算法优化装配式渠道的生产调度,可有效降低装配式渠道的生产周期,提高生产效率,达到降低生产成本,提高企业核心竞争力的目的。

基于BOM生产的车间排产多约束条件问题研究

随着生产制造业的转型升级,企业对于物料的生产不再是预先生产,更多是根据实时订单生产,针对这一情况,就需要考虑物料的优先生产问题,该研究将“产品物料优于产品先加工生产”作为约束条件之一,在传统遗传算法的基础上,提出对染色体进行二次编码,设计基于BOM(物料清单)的变异算子。从订单完工时间、设备停滞运行时间、两者比值3个维度分析不同的可行方案,给出最优排产计划。实验表明,该改进遗传算法在满足“产品物料优于产品先加工生产”在内的所有约束条件的情况下从3个维度分析后产生了最优排产计划。该算法具有普适性,有较强的参考价值。

基于遗传算法的汽车底盘生产线调度优化模型研究

底盘作为汽车的重要组成部分,其生产线的调度对整个汽车生产流程的效率和质量至关重要。然而,汽车底盘生产线调度面临着复杂的制造难题,如生产工艺复杂、生产时间不确定以及订单变化频繁等。该文以汽车底盘生产线调度优化为研究对象,通过分析目前汽车底盘生产线制造的难点,提出基于遗传算法下汽车底盘生产线调度优化模型,通过将遗传算法引入底盘生产线调度优化中,可以在考虑生产线复杂约束条件的同时,寻找到最优调度方案。通过优化调度方案,实现生产线的柔性化和智能化,有望为汽车制造企业提高生产效率、适应市场需求提供有力的支持和指导。

多资源约束下的预制构件生产调度优化与资源再配置

预制构件作为装配式建筑的重要组成部分,其生产成本直接影响整个建筑物的造价,通过对生产进行调度可以有效降低预制构件的摊销成本。本文针对现有预制构件生产调度模型不完善的问题,建立多资源约束下的生产调度模型,所建立的模型包含预制构件生产的六道工序及六种资源约束,并分别以最小化完工时间和最小化拖期惩罚为优化目标,设计一种基于免疫遗传算法的优化方法对该调度模型进行优化求解。根据六种生产资源的可配置性将其划分为可再配置资源和不可再配置资源,并在优化结果基础上通过生产资源再配置实现完工时间和拖期惩罚的进一步优化。实验结果表明,通过两阶段的调度优化可以有效提高生产效率、避免拖期交货。

煤矿生产调度管理优化设计方案研究

针对煤矿物流运输,介绍了1种煤矿生产调度管理优化设计方案。根据煤矿物流运输的具体情况,构建了煤炭短倒运输多目标调度模型,并以此为基础,分别从多个方面设置了约束条件。最后,使用改进的多目标遗传算法对调度模型进行计算,以获得最优解,即煤炭短倒运输调度的最佳方案。通过应用该方案,提升了煤矿物流运输的效率,具有一定的应用价值。

精益管理下的车间调度优化研究

以A公司为例,生产车间传统的生产调度方式主观性大、调度方式合理性差,导致成为制约生产、浪费产能的问题之一,因此需要一个更加精益化的调度研究.研究分析A公司中现有的调度模式,并用一种改进的遗传算法调度进行重新分配.

考虑检修计划的铁水调度模型及算法

基于钢铁企业生产模式多样、多座高炉、混铁炉与转炉并行生产、出铁罐数不同的特点,同时考虑检修计划对铁水调度的影响,本文建立了以铁水罐次到达转炉前时间与目标冶炼时间总差值最小为目标的调度模型。使用基于均匀交叉和插入变异策略的遗传算法对此问题在混合生产模式下进行求解。实验结果表明,本文所提出的算法在能够在短时间求解高质量解,解决了混合生产模式下调度约束复杂、调度难度大的问题。

采用AGV分拣的型材下料车间成组调度问题研究

针对一种基于自动导引小车(AGV)分拣的型材下料车间分拣新方法,以最小化AGV损耗费用和AGV运行费用为目标,建立了混合整数线性规划模型,设计了一种改进遗传算法对模型进行求解。该算法使用带加工属性的多层编码方式,针对多层编码设计了分层式交叉变异的方式,在邻域搜索阶段采用基于禁忌表的双层协同优化策略。算例对比实验表明,所设计的改进遗传算法与基础遗传算法、基础蚁群算法、变邻域改进遗传算法,以及改进蜂群算法相比,在求解该问题上有显著优势和有良好的鲁棒性。

产品可合并的多炉灶出餐调度模型与算法

为了解决在用餐高峰期餐饮企业外卖出餐速度慢的问题,根据餐厅外卖出餐中“多炉灶同时工作,订单包含多种菜品,相同订单同时取货,相同菜品可合并”的特点,构建订单完成时间满足率为目标的双层调度模型。双层模型的下层是以平行机调度为基础思路的多炉灶菜品包调度模型,可得到炉灶加工计划;上层是以背包问题为基础思路的订单选择模型,可确定订单完成时间。为了保证模型的求解效率,将静态模型扩展应用于动态调度的现实场景,设计针对性的遗传算法。算例实验结果表明,相比于最紧急菜品优先、最长加工时间优先、最热门菜品优先3种启发式策略,本模型的调度方法订单完成率最高,超时时间最短。

预制构件生产-运输-装配一体化的建造调度优化

为研究预制构件生产-运输-装配一体化的建造调度优化问题,构建装配式构件全过程的多目标调度优化模型.优化模型将预制构件3个阶段的优化作为一个扩展的生产车间调度优化,并采用改进的双种群遗传算法对模型进行求解.结果表明:改进的双种群遗传算法能够显著提高遗传算法的搜索效率;优化模型能够有效地减少总完工时间和并行机器的总机器成本.

基于并行协同的多车间协同调度问题研究

传统企业在实际生产中,其多个关联车间之间的生产计划与调度存在难以协作的问题。为此,针对多车间协同调度问题建立了调度模型,提出了一种多车间协同调度的并行协同进化遗传算法(PCE-GA),并且采用该算法对上述模型进行了求解。首先,以最小化订单完工时间为目标,建立了单目标调度模型;然后,采用了并行协同进化遗传算法,对上述单目标调度模型进行了求解,基于工件、机器、装配关系的三层整数编码的染色体编码方案,提出了一种协同适应度值计算的方法;最后,以某液压缸生产企业为例,针对单目标调度问题,采用该算法与单车间遗传算法(JSP-GA)、并行协同模拟退火算法(PCE-SA)分别进行了求解,并对其结果进行了比较,以验证PCE-GA算法的优越性。研究结果表明:采用PCE-GA算法得到的优化率为13.3%,比单车间作业调度遗传算法求解的数据优化11.5%,该结果证明了PCE-GA算法在解决多车间协同优化问题时的优越性。