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.

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem.

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.

A Grafted Genetic Algorithm for the Job-Shop Scheduling Problem

The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid genetic algorithm on the basis of the idea of graft in botany.Through the introduction of a grafted population and crossover probability matrix,this algorithm accelerates the convergence rate greatly and also increases the ability to fight premature convergence.Finally,the approach is tested on a set of standard instances taken from the literature and compared with other approaches.The computation results validate the effectiveness of the proposed algorithm.

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.

Research on Fuzzy Decision of Resources Selection in Job-sh op Scheduling for a One-of-a-Kind and Order-Oriented Production System

In a one-of-a-kind and order-orient ed production corporation, job shop scheduling plays an important role in the prod uction planning system and production process control. Since resource selection in job shop scheduling directly influences the qualities and due dates of produc ts and production cost, it is indispensable to take resource selection into acco unt during job shop scheduling. By analyzing the relative characteristics of res ources, an approach of fuzzy decision is proposed for resource selection. Finall y, issues in the application of the approach are discussed.

An adaptive multi-population genetic algorithm for job-shop scheduling problem

Job-shop scheduling problem （JSP） is a typical NP-hard combinatorial optimization problem and has a broad background for engineering application. Nowadays, the effective approach for JSP is a hot topic in related research area of manufacturing system. However, some JSPs, even for moderate size instances, are very difficult to find an optimal solution within a reasonable time because of the process constraints and the complex large solution space. In this paper, an adaptive multi-population genetic algorithm （AMGA） has been proposed to solve this prob- lem. Firstly, using multi-populations and adaptive cross- over probability can enlarge search scope and improve search performance. Secondly, using adaptive mutation probability and elite replacing mechanism can accelerate convergence speed. The approach is tested for some clas- sical benchmark JSPs taken from the literature and com- pared with some other approaches. The computational results show that the proposed AMGA can produce optimal or near-optimal values on almost all tested benchmark instances. Therefore, we can believe that AMGA can be considered as an effective method for solving JSP.

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.

Fuzzy Resource-Constrained Project Scheduling Problem for Software Development

This paper presents a new method to solve the resource-constrained project scheduling problem for software development. In this method,activity duration times are described as fuzzy variables and resource-constrained software project scheduling problems are described as fuzzy programming models. First,how to model the software project scheduling problem under the fuzzy environment conditions is proposed. Second,in order to satisfy the different requirements of decision-making,two novel fuzzy project scheduling models,expected cost model and credibility maximization model,are suggested. Third,a hybrid intelligent algorithm integrated by genetic algorithm and fuzzy simulation is designed to solve the above two fuzzy programming models. Numerical experiments illustrate the effectiveness of the hybrid intelligent algorithm.

Hybrid Genetic Algorithms with Fuzzy Logic Controller

In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we propose a hybrid genetic algorithms approach to deal with it. We adjust the crossover and mutation probabilities by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the GAs method proposed in the paper.

考虑模糊质检时间的柔性作业车间动态调度问题

为解决更符合现实情形的模糊质检时间柔性作业车间动态调度问题,以最小化完工时间为目标,立足紧急插单、机器在空载运行时发生故障和机器在加工工件时发生故障的三种故障情形,建立了带模糊质检时间的机器故障、紧急插单重调度模型。设计了基于元胞自动机邻域搜索和随机重启爬坡算法的改进遗传算法求解模型,即针对车间调度问题中存在的订单排序和机器选择双决策问题特征,设计包含工序码和机器码的双层编码方案,并基于遗传算法思想对工序码和机器码设计相应的交叉、变异等遗传操作。同时,将遗传操作应用于基于元胞自动机的邻域搜索算法框架中以增强算法全局搜索能力,整合基于关键工序的随机重启爬坡算法以提高算法局部开发能力。实验选取10个柔性车间调度算例验证了所提算法的有效性,同时,测试1个模糊质检时间柔性车间调度算例验证了模型的有效性。另外,实验也测试了不同故障场景,得出该动态调度方法优于实际场景中常使用的“工件后移”调度策略。

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.

Project Scheduling Using Hybrid Genetic Algorithm with Fuzzy Logic Controller in SCM Environment

In supply chain management (SCM) environment, we consider a resource-constrained project scheduling problem (rcPSP) model as one of advanced scheduling problems considered by a constraint programming technique. We develop a hybrid genetic algorithm (hGA) with a fuzzy logic controller (FLC) to solve the rcPSP which is the well known NP-hard problem. This new approach is based on the design of genetic operators with FLC through initializing the serial method which is superior for a large rcPSP scale. For solving these rcPSP problems, we first demonstrate that our hGA with FLC (flc-hGA) yields better results than several heuristic procedures presented in the literature. We have revealed a fact that flc-hGA has the evolutionary behaviors of average fitness better than hGA without FLC.

基于改进MOEA/D的模糊柔性作业车间调度算法

针对实际生产车间中加工时间的不确定性,将加工时间以模糊数的形式表示,建立以最小化模糊最大完工时间和模糊总材料消耗为优化目标的多目标模糊柔性作业车间调度问题数学模型,提出一种改进基于分解的多目标进化算法(IMOEA/D)进行求解。该算法基于机器和工序两层编码并采用混合的初始化策略提高初始种群的质量,利用插入式贪婪解码策略对机器的选择进行解码,缩短总加工时间;采用基于邻域和外部存档的选择操作结合改进的交叉变异算子进行种群更新,提高搜索效率;设置邻域搜索的启动条件,并基于4种邻域动作进行变邻域搜索,提高局部搜索能力;通过田口实验设计方法研究关键参数对算法性能的影响,同时得到算法的最优性能参数。在Xu 1~Xu 2、Lei 1~Lei 4和Remanu 1~Remanu 4测试集上将所提算法与其他算法进行对比,结果表明,IMOEA/D算法的解集数量和目标函数值均较优,在Lei 2算例获得的解集个数为对比算法的2倍以上。

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。

模糊加工时间调度问题的研究

提出模糊加工时间调度问题，隶属函数建立在工件的模糊加工时间上，隶属度表示工件在一段加工时间下属于完工集合的程度．在假设工件的隶属函数是单调递增的情况下，分析了多个工件所迭加的联合隶属函数所对应的性质．根据这些性质研究了一种单机模糊加工时间的调度模型．

一种具有模糊费用系数的VSP的修正C-W节约算法

将传统的确定性车辆调度问题扩展为具有模糊特征的模糊车辆调度问题.在对具有模糊费用系数的车辆调度问题进行简单描述的基础上,构建了模糊车辆调度的数学模型;通过Gaufmann Gupta模糊数排序方法与传统车辆调度问题的C W节约算法的有效结合,提出了解决该问题的一种改进C W节约算法.最后,给出了一个算例.

基于模糊规划的处理时间不确定条件下的Job shop问题

研究了处理时间不确定条件下的 Job shop生产调度问题 ,建立了基于模糊规划理论的不确定 Job shop调度模型。在采用两种模糊运算的基础上 ,结合模糊优化和遗传算法给出了一个解决非线性模糊优化问题的可行算法 ,通过仿真数据说明了所建模型及算法的有效性 。

自适应遗传算法求解模糊作业车间调度问题

研究模糊作业车间调度问题(FJSSP),用三角模糊数表示模糊加工时间,用半梯形模糊数表示模糊交货期,以最大化最小客户满意度为调度目标,建立了模糊环境下Job-shop调度问题的模型。提出了一种自适应遗传算法,该算法采用基于优先列表的编码方式,提高了编码效率;在进化过程中对种群采用精英保留策略,确保最优个体不被破坏;并对自适应交叉变异算子进行了改进,使种群最优个体参与进化。仿真结果证明所提算法在寻优能力及收敛性能方面均有所改善。

模糊环境下的多目标非满载车辆调度问题

针对现实物流配送过程中存在的时间参数模糊化问题,采用梯形模糊数表征时间参数,给出了一种具有模糊时间窗和模糊配送时间,以最小化配送车辆数、提前/滞后惩罚以及配送里程为目标的多目标非满载车辆调度问题模型。在问题求解方面,针对基本粒子群算法容易陷入局部最优的问题,引入利用混沌局部搜索策略,给出了一种基于混沌优化技术的混合粒子群算法。该求解算法的可行性和有效性最后通过仿真试验进行了验证。