FLEXIBLE JOB-SHOP SCHEDULING WITH FUZZY GOAL THROUGH IOCDGA

The fuzzy goal flexible job-shop scheduling problem （FGFJSP） is the extension of FJSP. Compared with the convention JSP, it can solve the fuzzy goal problem and meet suit requirements of the key job. The multi-object problem, such as the fuzzy cost, the fuzzy due-date, and the fuzzy makespan, etc, can be solved by FGFJSP. To optimize FGFJSP, an individual optimization and colony diversity genetic algorithm （IOCDGA） is presented to accelerate the convergence speed and to avoid the earliness. In IOCDGA, the colony average distance and the colony entropy are defined after the definition of the encoding model. The colony diversity is expressed by the colony average distance and the colony entropy. The crossover probability and the mutation probability are controlled by the colony diversity. The evolution emphasizes that sigle individual or a few individuals evolve into the best in IOCDGA, but not the all in classical GA. Computational results show that the algorithm is applicable and the number of iterations is less.

INTEGRATED OPERATOR GENETIC ALGORITHM FOR SOLVING MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCHEDULING

In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.

Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms

The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain independent general purpose GA was used,which was an add-in to the spreadsheet software.An adaptation of the propritary GA software was demonstrated to the problem of minimizing the total completion time or makespan for simultaneous scheduling of machines and vehicles in flexible manufacturing systems.Computational results are presented for a benchmark with 82 test problems,which have been constructed by other researchers.The achieved results are comparable to the previous approaches.The proposed approach can be also applied to other problems or objective functions without changing the GA routine or the spreadsheet model.

A bi-objective model for job-shop scheduling problem to minimize both energy consumption and makespan

The issue of reducing energy consumption for the job-shop scheduling problem in machining systems is addressed, whose dual objectives are to minimize both the energy consumption and the makespan. First, the bi- objective model for the job-shop scheduling problem is proposed. The objective function value of the model represents synthesized optimization of energy consumption and makespan. Then, a heuristic algorithm is developed to locate the optimal or near optimal solutions of the model based on the Tabu search mechanism. Finally, the experimental case is presented to demonstrate the effectiveness of the proposed model and the algorithm.

Hybrid heuristic algorithm for multi-objective scheduling problem

This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.

An Improved Genetic Algorithm for Solving the Mixed⁃Flow Job⁃Shop Scheduling Problem with Combined Processing Constraints

The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.

基于改进生物迁徙算法的双资源柔性作业车间节能调度问题

节能调度是面向绿色制造的车间调度问题,已成为制造领域的研究热点。针对具有机器和工人双资源约束的柔性作业车间,综合考虑工人学习效应和工件运输时间的影响,以最小化车间能耗为目标,提出一种改进的生物迁徙算法(MBMA)。该算法采用基于工件-机器-工人的三段式编码方法表示调度解,并设计了一种种群初始化方法,以改善初始调度解的质量。考虑到基本生物迁徙算法无法直接应用于离散车间调度问题,提出一种基于交叉操作的离散迁徙算子,使算法能够直接在离散调度空间内进行搜索。此外,在迁徙算子中引入转换概率动态调整策略,以平衡算法探索与开发能力,另外增加了一种记忆池机制,避免算法过早收敛。对于个体更新算子,设计了一种局部搜索算法嵌入其中,以增强算法局部搜索能力。大量实验结果表明,MBMA算法的计算结果优于其他算法。

Distributed Flexible Job-Shop Scheduling Problem Based on Hybrid Chemical Reaction Optimization Algorithm

Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.

具有平行批处理机的多目标FJSP问题研究

针对具有平行批处理机的多目标柔性作业车间调度问题,建立以最大完工时间、最大机器负荷和能耗为优化目标的数学模型,并提出了一种多种群MOEAD算法进行求解。算法结合了多种群策略的优势,三个子种群分别采用了WS、TE和BI三种聚合函数进行协同搜索,保持种群多样性的同时扩展了算法的搜索广度。设计了符合问题特性的进化算子和局部搜索策略,提高算法整体搜索性能。通过标准的FJSP算例和符合问题特性的实际数据,验证了所提算法的可行性与有效性。

考虑高耗能企业车间柔性排程的低碳工业园区双层优化调度

“双碳”目标下,工业园区作为工业系统最重要、最广泛的载体,其低碳转型势在必行。在此背景下,文中建立一种考虑高耗能企业车间柔性排程的低碳工业园区双层优化调度模型。模型充分挖掘高耗能企业的灵活调节能力,通过灵活调整高耗能企业的生产方案获取园区的最优低碳调度计划,实现工业园区和高耗能企业的协调运行。首先,工业园区作为上层计及碳交易和绿证交易成本,以总运行成本最小为目标建立优化调度模型。然后,下层针对园区中的高耗能企业流水车间柔性排程问题,以最小化最大加工时间和生产成本为目标进行优化。园区运行层将由企业排程节约的部分成本作为补贴,上下层不断协调调度方案从而达到最优目标。最后,通过算例验证了文中所提出模型的可行性和有效性。结果表明所提方法在降低企业的生产成本、提升生产效率的同时,可有效促进园区负荷削峰填谷,实现工业园区的低碳经济运行。

改进NSGA-III求解高维多目标绿色柔性作业车间调度问题

为解决NSGA-III在求解上述模型存在初始解质量差与局部搜索效率低的问题,提出一种改进的NSGA-III(NSGA-III-TV)。基于MSOS编码,分别对OS和MS染色体采取不同混合初始化策略以提高初始解质量;基于关键路径,采用改进N6邻域结构进行邻域搜索,有效缩短完工时间并降低搜索盲目性,同时采用3种有效变异算子扩大算法搜索空间,提高后期收敛能力。结果表明:NSGA-III-TV在求解高维多目标柔性作业车间调度问题上具有较好的性能以及实用性,为企业生产制造车间的智能绿色转型升级提供了有力支持。

A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time

Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper.

考虑运输与维护的柔性作业车间绿色调度

为研究具有运输与预防性维护的柔性作业车间绿色调度问题,建立以最小化最大完工时间和最小加工总能耗为目标的具有运输时间与预防性维护的柔性作业车间绿色调度(FJSP-TPM)模型,提出带有启发式初始化调整策略和可变邻域搜索的HVNSGA-Ⅱ算法进行求解。结合问题特点,设计3层染色体编码方式和基于不同编码层的交叉变异规则,同时加入启发式种群初始化策略和不同邻域结构的局部搜索策略,最后通过不同算例对比实验,证明算法在求解FJSP-TPM问题上的有效性。

分布式多柔性装配作业车间调度问题研究

针对分布式柔性装配作业车间环境,综合考虑了调度过程中的机器选择柔性、工人安排柔性和工序顺序柔性,构建了以最小化最大完工时间和最小化总能耗为优化目标的分布式多柔性装配作业车间调度问题(DMFAJSP)的数学模型。为求解DMFAJSP模型,提出了一种以分布估计算法为全局搜索组件、以邻域搜索算子为局部搜索组件的多维模因算法(MDMA)。最后,将所提出的算法与其他算法进行了对比试验,结果表明MDMA算法在求解DMFAJSP模型方面具有显著优势。

考虑设备预维护的特种车辆白车身试制车间绿色调度优化方法

综合考虑最长完工时间、设备总能耗以及总烟尘排放,构建了典型柔性作业车间多目标绿色调度优化模型,并设计了一种改进人工蜂群算法对其进行求解。首先,根据激光设备功率会发生周期性衰减的特点,提出了一种区分激光设备和普通机械设备的预维护策略,以减小最长完工时间并降低设备故障发生频率。其次,设计了一种基于设备分配和功率选择的变异方式以增强算法局部搜索能力,并在跟随蜂阶段引入基于拥挤距离的选择方法进行种群更新以获得优质个体。最后,扩展现有通用测试集并开展对比实验,同时以某汽车装备制造企业的特种车辆白车身试制车间为实例,进一步验证了模型与算法的有效性和可行性。

考虑批量装配的柔性作业车间调度问题研究

柔性作业车间调度是生产调度领域中的一个重要组合优化问题,由于取消了工序与加工设备的唯一性对应关系,因而相较于作业车间调度问题,具有更高的复杂度。针对该问题在批量装配方面的不足,考虑将批量因素与装配环节同时集成到柔性作业车间调度问题当中。以成品件的完工时间为优化目标,对该批量装配柔性作业车间调度问题进行了数学建模。针对该模型,提出一种多层编码结构的粒子群算法,并对该算法的各个模块进行了设计。最后,以实例验证了该数学模型的正确性及算法的有效性。

面向广义能耗的柔性作业车间调度优化模型

机械加工车间能耗不仅包括机床加工过程中消耗的直接电能,还包括因消耗刀具、切削液等辅助资源产生的间接能耗。柔性作业车间中加工任务的工艺路线和加工资源可选,其调度方案灵活性强,节能潜力大。为此,研究一种综合考虑直接电能和间接能耗的柔性作业车间广义能耗调度优化模型。首先分析柔性作业车间调度直接电能和间接能耗,然后建立以车间广义能耗最低和完工时间最小为优化目标的柔性作业车间调度多目标优化模型,并基于多目标模拟退火算法对模型进行求解。最后通过案例对比分析验证了所提模型的有效性。

基于改进人工蜂群算法的多目标绿色柔性作业车间调度研究

针对多目标绿色柔性作业车间调度问题(MGFJSP)的特点,提出从碳排放量、噪声和废弃物这3个指标来综合评定环境污染程度,建立了以最小化最大完成时间和环境污染程度为优化目标的MGFJSP模型,并提出了一种改进的人工蜂群算法来求解该模型。算法的具体改进包括:设计了一种三维向量的编码和对应解码方案,在跟随蜂搜索阶段引入一种有效的动态邻域搜索操作来提高算法的局部搜索能力,在侦查蜂阶段提出产生新食物源的策略用于增加种群的多样性。最后进行了实验研究与算法对比,以验证所建模型和所提算法的有效性。

超启发式遗传算法柔性作业车间绿色调度问题研究

针对启发式算法通用性较差的问题,建立了多目标柔性作业车间绿色调度模型,设计了一种超启发式遗传算法对问题进行求解。首先,建立了以最大完工时间和最小能耗为目标的柔性作业车间绿色调度模型,并设计了超启发式遗传算法对模型进行优化求解;然后,对于高层启发式策略采用遗传算法,随机生成初始种群,对种群进行了选择、交叉和变异操作,并且在常规算子基础上,结合柔性作业车间调度特点设计了9种适应该问题的算子,同时对于低层问题域种群采用了贪婪初始化方法生成;最后,通过基准算例验证了算法的运行效率,通过实例验证了算法的性能。研究结果表明:与参考算法相比,采用贪婪初始化生成初始种群的算法其收敛速度较快,运行效率较高,且不容易陷入局部最优;通过超启发式遗传算法获得的解中最大完工时间的最小值为64,最小能耗为647,解的质量不差于其它算法,算法的通用性较强。

机器多转速的柔性作业车间绿色调度问题

为了减少制造过程中的能耗,从生产运作的角度探讨了如何在制造业生产调度中实现绿色制造。以柔性作业车间调度问题为背景,考虑了机器多转速,在不影响生产进度的前提下通过调节机器转速来实现绿色制造。首先建立了考虑机器多转速的柔性作业车间调度优化模型,构建了能耗计算模型;然后依据带精英策略的非支配排序遗传算法的框架,提出了综合考虑完工时间和能源消耗的低碳调度算法,设计了多种变异方式,提出了精英保留与末位淘汰的选择方式;最后的数值实验结果表明,所提算法能够很好地求解此类问题。