具有工序顺序柔性的车间调度问题研究综述

某些加工车间在生产过程中具有多种柔性,工序顺序柔性就是其中重要的一种。通过对具有工序顺序柔性的车间调度问题的优化求解,能够极大地提高生产效率,节约生产成本。本文对具有工序顺序柔性的车间调度问题的研究现状进行整理分析,主要包括混合车间调度问题(mixed shop scheduling problem, MSP)、分组车间调度问题(group shop scheduling problem, GSP)和部分车间调度问题(partial shop scheduling problem, PSP)。最后,指出了具有工序顺序柔性的车间调度问题的发展前景及发展方向。     

作业车间调度问题中的协同优化算法

本文采用了多种优化算法 ,针对作业车间问题的特点设计一个比较有效的协同算法 ,解决了算法实现中的几个关键技术问题 ,为提高解决这一问题的效率提供了比较新颖的思路 ,并通过实际计算验证了该方法的可靠性和有效性 ,这一方法可以用于实际车间的调度安排 ,能够提高车间的生产效率。     

基于改进遗传算法的导向辊生产调度方法研究

目的 研究导向辊生产车间中的调度优化问题,有利于缩短工件的完工时间,提高产线生产效率。方法 以某导向辊生产车间为研究对象,以最小化最大完工时间为目标建立数学模型。针对该导向辊生产车间的实际工况,提出一种改进的遗传算法进行求解。通过对10种不同尺寸的导向辊进行生产调度,分别采用改进的遗传算法和传统遗传算法进行试验分析。结果 改进的遗传算法相比传统遗传算法寻优能力更高,工件的完工时间从139 min缩短为113 min,缩短了18.7%左右,生成了完工时间为113 min的生产调度甘特图。结论 与传统遗传算法相比,改进的遗传算法在导向辊生产调度优化中具有更高的全局优化能力和寻优精度。     

考虑生产过程时间的柔性作业车间调度优化

通过研究生产过程时间,重新细分和定义等待时间,建立包括运输时间、调整时间、故障时间、等待时间、加工时间在内的柔性作业车间生产过程的时间模型,研究了柔性作业车间调度优化问题并设计了混合遗传算法的求解算法。最后,采用经典柔性作业车间调度用例,验证和对比了柔性作业车间调度的结果。结果表明,基于生产过程时间模型研究柔性作业车间调度问题,其优化性能有较好的改进,具有更好的实际应用价值。     

多资源作业车间的混合自适应GA优化调度

针对多资源约束的车间调度问题,将启发式算法和自适应GA优化方法结合起来,提出了混合自适应GA方法,建立了多资源约束的车间优化调度模型.根据启发式调度算法中优先规则对调度目标的影响,设计了新的编码规则.采用正弦函数作为自适应因子,使得交叉概率和变异概率随群体的适应度自动改变,提高了运算的效率,克服了启发式算法和普通GA的缺陷.通过实例仿真并与其他算法比较结果表明,混合自适应GA算法可以很好的解决作业车间在机床、刀具等多种生产资源约束下的优化调度,并在评价指标上较其他算法更优.     

基于遗传禁忌算法考虑转运约束的并行机批量调度问题研究

在多品种混流生产车间里，广泛存在着各种批量的任务在多台并行机上调度优化问题。这种并行机批量调度需要考虑批量大小设置、加工顺序优化、设备充分利用等多种要素，是一类典型NP-hard问题，且当任务加工完后还需要考虑转运过程时，问题将变得更加复杂。为了减少并行机生产过程中任务拖期和在制品积压，寻求更好的生产调度方案，针对典型并行机生产和转运场景，以最小化加权完工时间及拖期工件的惩罚费用、作业切换成本、库存成本之和为优化目标，设计了基于启发式规则的仿真程序与遗传禁忌算法相结合的优化算法，研究单工序不相关并行机调度环境下车间批量调度的最优调度方案，再通过案例验证了本文优化算法的有效性。结果表明，优化算法得出的并行机批量调度方案使得作业切换次数和拖期订单大大减少，减少在制品库存的同时提高了转运资源的利用率。     

多品种小批量订单型企业生产调度优化

目的研究多品种小批量订单型企业的生产调度优化问题,方法针对S公司的生产现状,应用遗传算法思想设计调度优化方案,采用不等长矩阵的编码方式实现订单的批量生产及车间排产的方案。结果通过仿真分析和S公司生产调度的实际应用,验证了该算法的可行性及有效性。结论基于遗传算法的调度优化算法实现了多品种小批量流程型生产企业生产调度优化,达到了缩短生产周期、有效利用生产资源的目的。     

多车间协同调度平台的研究与应用

针对传统企业在分布式制造模式下实现多个车间之间生产计划与调度的协作问题,从企业全局出发,研究多车间生产计划调度方法,提出采用多代理和规则引擎技术在制造执行系统(MES)中构建调度协同平台的解决方案.在扩充MES系统协同调度功能的基础上,实现不同车间生产制造信息共享,提高了制造企业各车间协同生产的效率.     

基于改进遗传算法的偏柔性作业车间调度

偏柔性作业车间调度是生产管理中的重要问题。由于模型和计算的复杂性,传统优化方法往往难以得到最优解。采用改进遗传算法求解偏柔性作业车间的调度问题,设计相应的编码方法,利用所生成的染色体以及通过遗传操作得到的染色体生成可行的调度方案。基于工序串和机器串的编码方法,采用精英解保留策略、轮盘赌选择策略和基于划分集的交叉策略,提出基于均匀分布试验的变异法则,引入贪婪式解码方法对偏柔性作业车间调度进行求解。实例仿真表明,该算法在求解偏柔性作业车间调度方面具有良好的效率和优越性。     

多层级装配作业车间分批调度算法设计与分析

多层级装配作业车间调度是一类包含加工与装配的双阶段调度问题，装配产品具有不同的树状结构，且各层级的装配工序需要直属零部件完工方可执行。分批调度可以提高车间生产流动性，故而被运用在作业车间调度等领域。装配作业车间分批调度需要解决关联零部件及其下属子批的进度协同性问题，为此建立了多层级装配作业车间的分批优化调度模型，以最小化拖期成本与库存持有成本为优化目标。出于求解效率考虑，构建基于遗传算法与优先分派规则的混合求解算法以应对批量划分与排序两个子问题。最后，设计仿真实验验证分批调度算法的有效性，并分析评估在8种作业分派规则、3类分批策略下混合算法对于差异化产品结构的适应性。通过分析实验结果发现，等量分批策略可以在给定条件下有效提升混合算法的调度性能。     

A tabu search algorithm for simultaneous machine/AGV scheduling problem

Machines and automated guided vehicles (AGVs) scheduling problems are two essential issues that need to be addressed for the efficiency of the overall production system. The purpose of this paper is to study the simultaneous scheduling problem of machines and AGVs in a flexible manufacturing system (FMS) since the global optimum cannot be reached by considering each of them individually. In this paper, a mixed integer linear programming (MILP) model is developed with the objective of makespan minimisation. The MILP model consists of the following two constraint sets: machines and AGVs scheduling sub-problems. As both sub-problems are known to be NP-hard, a heuristic algorithm based on tabu search (TS) is proposed to get optimal or near to optimal solution for large-size problems within reasonable computation time. The proposed algorithm includes a novel two-dimensional solution representation and the generation of two neighbour solutions, which are alternately and iteratively applied to improve solutions. Moreover, an improved lower bound calculation method is introduced for the large-size problems. Computational results show the superior performance of the TS algorithm for the simultaneous scheduling problem.     

Integrated optimisation on flow-shop production with cutting stock

This paper addresses the integrated scheduling optimisation on flow-shop production with one-dimensional cutting stock in make-to-order environments. The upstream cutting-stock process provides the items for the downstream production, while the flow-shop production can be executed only when the required items are released. The efficient schedules on cutting plan and the production sequence should be determined in a coordinated manner to improve the overall efficiency. This study aims to find an integrated schedule to minimise the makespan of the entire manufacturing process. We develop a hybrid algorithm by integrating a local search method and some efficient strategies under the nested partitions framework. Numerical experiments show that the proposed approach is capable of achieving high-quality solutions within a reasonable time.     

A genetic algorithm-based approach to machine assignment problem

Over the last few decades, production scheduling problems have received much attention. Due to global competition, it is important to have a vigorous control on production costs while keeping a reasonable level of production capability and customer satisfaction. One of the most important factors that continuously impacts on production performance is machining flexibility, which can reduce the overall production lead-time, work-in-progress inventories, overall job lateness, etc. It is also vital to balance various quantitative aspects of this flexibility which is commonly regarded as a major strategic objective of many firms. However, this aspect has not been studied in a practical way related to the present manufacturing environment.

In this paper, an assignment and scheduling model is developed to study the impact of machining flexibility on production issues such as job lateness and machine utilisation. A genetic algorithm-based approach is developed to solve a generic machine assignment problem using standard benchmark problems and real industrial problems in China. Computational results suggest that machining flexibility can improve the overall production performance if the equilibrium state can be quantified between scheduling performance and capital investment. Then production planners can determine the investment plan in order to achieve a desired level of scheduling performance.     

A Constraint Programming model for food processing industry: a case for an ice cream processing facility

This paper presents a Constraint Programming (CP) scheduling model for an ice cream processing facility. CP is a mathematical optimisation tool for solving problems either for optimality (for small-size problems) or good quality solutions (for large-size problems). For practical scheduling problems, a single CP solution model can be used to optimise daily production or production horizon extending for months. The proposed model minimises a makespan objective and consists of various processing interval and sequence variables and a number of production constraints for a case from a food processing industry. Its performance was compared to a Mixed Integer Linear Programming (MILP) model from the literature for optimality, speed, and competence using the partial capacity of the production facility of the case study. Furthermore, the model was tested using different product demand sizes for the full capacity of the facility. The results demonstrate both the effectiveness, flexibility, and speed of the CP models, especially for large-scale models. As an alternative to MILP, CP models can provide a reasonable balance between optimality and computation speed for large problems.     

Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems

Multi-factory production networks have increased in recent years. With the factories located in different geographic areas, companies can benefit from various advantages, such as closeness to their customers, and can respond faster to market changes. Products (jobs) in the network can usually be produced in more than one factory. However, each factory has its operations efficiency, capacity, and utilization level. Allocation of jobs inappropriately in a factory will produce high cost, long lead time, overloading or idling resources, etc. This makes distributed scheduling more complicated than classical production scheduling problems because it has to determine how to allocate the jobs into suitable factories, and simultaneously determine the production scheduling in each factory as well. The problem is even more complicated when alternative production routing is allowed in the factories. This paper proposed a genetic algorithm with dominant genes to deal with distributed scheduling problems, especially in a flexible manufacturing system (FMS) environment. The idea of dominant genes is to identify and record the critical genes in the chromosome and to enhance the performance of genetic search. To testify and benchmark the optimization reliability, the proposed algorithm has been compared with other approaches on several distributed scheduling problems. These comparisons demonstrate the importance of distributed scheduling and indicate the optimization reliability of the proposed algorithm.     

Mixed Self-adapting GA Optimal Scheduling Algorithm for a Multiple Resource Job-shop

With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.     

A capacity-oriented hierarchical approach to single-item and small-batch production planning using project-scheduling methods

Most production planning and control (PPC) systems used in practice have an essential weakness in that they do not support hierarchical planning with feedback and do not observe resource constraints at all production levels. Also, PPC systems often do not deal with particular types of production, for example, low-volume production. We propose a capacity-oriented hierarchical approach to single-item and small-batch-production planning for make-to-order production. In particular, the planning stages of capacitated master production scheduling, multi-level lot sizing, temporal and capacity planning, and shop floor scheduling are discussed, where the degree of aggregation of products and resources decreases from stage to stage. It turns out that the optimization problems arising at most stages can be modelled as resourceconstrained project scheduling problems.     

Integrated process planning and scheduling – multi-agent system with two-stage ant colony optimisation algorithm

In this paper, a two-stage ant colony optimisation (ACO) algorithm is implemented in a multi-agent system (MAS) to accomplish integrated process planning and scheduling (IPPS) in the job shop type flexible manufacturing environments. Traditionally, process planning and scheduling functions are performed sequentially and the actual status of the production facilities is not considered in either process planning or scheduling. IPPS is to combine both the process planning and scheduling problems in the consideration, that is, the actual process plan and the schedule are determined dynamically in accordance with the order details and the status of the manufacturing system. The ACO algorithm can be applied to solve IPPS problems. An innovative two-stage ACO algorithm is introduced in this paper. In the first stage of the algorithm, instead of depositing pheromones on graph edges as in common ant algorithms, ants are directed to deposit pheromones at the nodes to select a set of more favourable processes. In the second stage, the set of nodes not selected in the first stage will be ignored, and pheromones will be deposited along the graph edges while the ants traverse the paths connecting the selected set of nodes.     

Applied column generation-based approach to solve supply chain scheduling problems

More and more enterprises have chosen to adopt a made-to-order business model in order to satisfy diverse and rapidly changing customer demand. In such a business model, enterprises are devoted to reducing inventory levels in order to upgrade the competitiveness of the products. However, reductions in inventory levels and short lead times force the operation between production and distribution to cooperate closely, thus increasing the practicability of integrating the production and distribution stages. The complexity of supply chain scheduling problems (integrated production and distribution scheduling) is known to be NP-hard. To address the issues above, an efficient algorithm to solve the supply chain scheduling problem is needed. This paper studies a supply chain scheduling problem in which the production stage is modelled by an identical parallel machine scheduling problem and the distribution stage is modelled by a capacitated vehicle routing problem. Given a set of customer orders (jobs), the problem is to find a supply chain schedule such that the weighted summation of total job weighted completion time and total job delivering cost are minimised. The studied problem was first formulated as an integer programme and then solved by using column generation techniques in conjunction with a branch-and-bound approach to optimality. The results of the computational experiments indicate that the proposed approach can solve the test problems to optimality. Moreover, the average gap between the optimal solutions and the lower bounds is no more than 1.32% for these test problems.     

Cross-trained workers scheduling for field service using improved NSGA-II

The proper balancing of geographically distributed task schedules and the associated workforce distributions are critical determinants of productivity in any people-centric production environment. The paper has investigated the cross-trained workers scheduling problem considering the qualified personal allocation and temporally cooperation of engineers simultaneously. A 0–1 programming model is developed and the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to deal with the NP-hard problem. In order to enforce the NSGA-II, significant improvements are made to function the approach in a more efficient way. It is observed that the improved NSGA-II outperforms the original NSGA-II in the experimental test. The promising outcomes of the formulation in the experiment make its implementation easily customisable and transferable for solving other intricate problems in the context of skilled workforce scheduling. Furthermore, the modified NSGA II can be used as an efficient and effective tool for other multiobjective optimisation problems.