Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

Deep Reinforcement Learning Solves Job-shop Scheduling Problems

To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time.

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.

A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling

Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enables any machine from a certain set to handle an operation,and this is an NP-hard problem.Furthermore,due to the requirements in real-world cases,multi-objective FJS is increasingly widespread,thus increasing the challenge of solving the FJS problems.As a result,it is necessary to develop a novel method to address this challenge.To achieve this goal,a novel collaborative evolutionary algorithmwith two-population based on Pareto optimality is proposed for FJS,which improves the solutions of FJS by interacting in each generation.In addition,several experimental results have demonstrated that the proposed method is promising and effective for multi-objective FJS,which has discovered some new Pareto solutions in the well-known benchmark problems,and some solutions can dominate the solutions of some other methods.

An Effective Neighborhood Solution Clipping Method for Large-Scale Job Shop Scheduling Problem

The job shop scheduling problem(JSSP)is a classical combinatorial optimization problem that exists widely in diverse scenarios of manufacturing systems.It is a well-known NP-hard problem,when the number of jobs increases,the difficulty of solving the problem exponentially increases.Therefore,a major challenge is to increase the solving efficiency of current algorithms.Modifying the neighborhood structure of the solutions can effectively improve the local search ability and efficiency.In this paper,a genetic Tabu search algorithm with neighborhood clipping(GTS_NC)is proposed for solving JSSP.A neighborhood solution clipping method is developed and embedded into Tabu search to improve the efficiency of the local search by clipping the search actions of unimproved neighborhood solutions.Moreover,a feasible neighborhood solution determination method is put forward,which can accurately distinguish feasible neighborhood solutions from infeasible ones.Both of the methods are based on the domain knowledge of JSSP.The proposed algorithmis compared with several competitive algorithms on benchmark instances.The experimental results show that the proposed algorithm can achieve superior results compared to other competitive algorithms.According to the numerical results of the experiments,it is verified that the neighborhood solution clippingmethod can accurately identify the unimproved solutions and reduces the computational time by at least 28%.

基于POX交叉的遗传算法求解Job-Shop调度问题

通过改进传统的遗传算法求解Job -Shop调度问题。为基于工序的编码提出了一种新的POX交叉算子 ,并与其他交叉算子进行了比较以显示其高效性。为了保留父代的优良特征和减少遗传算子的破坏性 ,设计了一种子代交替模式的交叉方式。将提出的改进遗传算法应用于muthandthompson’s基准问题的实验运行 ,显示该算法的有效性。

可动态生成具有优先级工序集的动态Job-Shop调度算法

为了在满足约束条件的前提下使不同时刻加入到作业集合中的全部作业所用的加工总时间尽可能地少,提出算法:根据动态的作业集合构造虚拟加工树,虚拟加工树上的叶结点代表最先加工的工序,虚拟根节点代表最后一道工序,边代表偏序关系;以层优先为虚拟加工树上的工序设置优先级,工序的优先级可作为调度过程中考虑的一个因素;除了在工序需要动态调整的情况下,在调度过程中始终遵循着机器忙原则.在调度过程中,首先根据虚拟加工树动态地生成备选工序集合,然后根据工序的优先级并且结合其它的调度策略从备选集合中调度工序,直到备选集合为空,即所有作业加工完毕.这里提到的调度策略包括短用时策略、长路径策略和动态调整策略.通过实例验证,该算法对于动态Job-Shop问题可以获得比较好的解.

基于ACPM和BFSM的动态Job-Shop调度算法

通过对不同时刻开始加工的产品加工树的分解 ,可将产品加工工序分为具有惟一紧前、紧后的相关工序和独立工序 在对这两类工序研究分批综合应用拟关键路径法 (ACPM )和最佳适应调度方法 (BFSM)调度时 ,考虑了关键设备的工序紧凑性 通过分析与实例验证 ,所提出的调度方法对解决动态的Job

基于混合粒子群算法的多目标柔性Job-Shop调度方法

针对经典Job-Shop调度问题的局限性,提出了以时间、成本、质量三者综合为优化目标,具有柔性Job-Shop车间调度的优化模型。给出了优化目标的计算方法,并设计了混合粒子群算法,给出了使用此算法求解模型的具体实现过程。模型采用工序能力指数对质量目标进行量化,并采用综合评判线性加权模型解决柔性Job-Shop算法的权重选择问题,使决策者能够根据实际情况选择优化目标的偏好解。通过一个车间调度问题的实例验证了此调度模型和算法的有效性。

基于约束满足的Job-Shop调度算法研究

文章在分析Job-Shop调度问题的基础上,引入约束满足方法来研究Job-Shop的调度问题。首先建立基于CSP的JSS模型,然后针对该模型设计了调度算法框架,仿真结果证明该调度算法是可行和有效的。

用遗传算法解决生产线调度Job-Shop问题

在分析国内外各种解决生产线作业调度问题方法的基础上，引出生产线调度Ｊｏｂ－Ｓｈｏｐ问题的数学模型。针对该模型，用遗传算法对其进行优化调度。首先针对优化目标进行染色体编码，定义群体规模，然后设计适应度函数，最后通过选择、交叉、变异等操作，得到目标的全局较优解。本文针对某一工程实例，给出了此调度算法的仿真结果。

基于遗传算法的Job-Shop调度问题求解方法

调度问题是许多计算机应用领域的重要问题，Ｊｏｂ－Ｓｈｏｐ调度是其中的一类典型的困难问题，它通常包含多个可并行实现的目标以及实现这些目标的多种方法与资源．本文以一类实用的Ｊｏｂ－Ｓｈｏｐ问题模型为基础，给出了用遗传算法求解调度问题应采用的染色体表示方法，并针对问题的特点，给出了面向资源空间与面向规划空间的遗传操作的设计思想与方法．实验结果表明，基于遗传算法的Ｊｏｂ－Ｓｈｏｐ调度问题求解方法具有较好的性能，同时也表明，对于求解过程中可能出现的提前收敛问题可通过改变遗传操作概率及调节适应度等方法予以解决．

基于遗传算法的Job-shop调度问题研究

Job -shop调度问题是一个强NP -难问题 .为此提出了一种简单、易操作的遗传算法对该问题进行求解 .采用一种插入式贪婪解码算法保证生成的调度是活动调度 (activeschedule) .通过几个著名的MuthandThomp son测试例子及与目前较好的遗传算法的比较实验 ,以验证这一算法的有效性 .

动态Job-Shop调度问题的一种自适应遗传算法

提出了基于序列号的、独特的混合编码与级联编码相结合的遗传编码构造方法 ,给出了序列海明距离的定义和具体的计算方法 ,并引入到自适应遗传算法中。实例仿真计算的结果表明 ,提出的自适应遗传算法提高了全局求解能力 ,在解决动态Job -Shop调度问题时具有明显的优越性。

不确定信息条件下Job-shop调度的混合智能算法

目前大多数生产调度的研究往往聚焦于经典调度问题的优化算法而忽略了车间中大量存在的不确定性,因而难以应用于实际车间调度。采用随机变量来描述真实车间中存在的一些不确定信息,在基于不确定规划理论的基础上建立了相应的不确定性调度模型,并研究了解决此类问题的混合智能算法。开发了混合智能优化原型系统,并结合仿真工具对该调度模型和混合智能算法进行了验证。

一种求解Job-shop调度问题的遗传局部搜索算法

利用遗传局部搜索算法求解了作业车间调度问题,遗传算法中的染色体编码采用基于工序的编码,并用插入式贪婪解码机制将染色体解码至主动调度。为了克服传统遗传算法易于早熟收敛的缺点,设计了一种改进的优先操作交叉IPOX操作和子代产生模式的遗传算法。对于遗传算法每个染色体个体,使用基于N6邻域结构的局部搜索进一步使它们得到改善。利用所提出的混合遗传算法求解基准问题,验证了算法的有效性。

基于免疫算法的多目标柔性job-shop调度研究

建立了多目标柔性job-shop调度模型;然后提出了带有保优机制免疫算法,利用免疫记忆、接种疫苗等机制,在算法中保留并充分利用每代最优抗体和局部最优基因,使算法加快收敛;针对这类调度的柔性,提出基于工序设备双层抗体编码方案和基于设备能力空间的解码方案;采用多目标分级评价方法同时对时间、设备和成本等多目标进行评价和优化.最后,用Benchm ark标准问题的仿真和西安航空发动机(集团)有限公司的调度实例验证了算法、策略和调度模型的有效性和优越性.

基于免疫蚁群算法的Job-shop调度问题

描述了作业调度问题,借鉴生物免疫机理提出了求解车间调度问题的免疫蚁群算法,该方法在蚂蚁搜索程中,运用免疫机理提取疫苗,并对进化种群进行免疫操作,从而有效地抑制了蚁群算法的“早熟”和搜索效率低下的问题,显著地提高了蚁群算法对全局最优解的搜索能力和收敛速度,给出了免疫蚁群算法的具体步骤,并对算法进行了实例验证。

计及机器调整和物料运送时间的Job-Shop调度算法

对考虑机器调整时间和物料运送时间的Job-Shop调度问题进行了研究。优化计算时考虑了物料运送时间的作用及其对车间调度的影响,并给出了其求解方法;利用GA、SA与PSO相结合的混合PSO算法解决了Job-Shop调度问题。仿真实例模拟实际工况,对工件的运送、加工和机器调整时间及其相互间的影响进行了分析和计算,并与传统的忽略机器调整时间、物料运送时间以及直接计入工件加工时间的调度方案进行了对比,结果表明提出的方法可行,更加符合实际生产工况。