A study of hybrid evolutionary algorithms for single machine scheduling problem with sequence-dependent setup times

We present a systematic comparison of hybrid evolutionary algorithms (HEAs), which independently use six combinations of three crossover operators and two population updating strategies, for solving the single machine scheduling problem with sequence-dependent setup times. Experiments show the competitive performance of the combination of the linear order crossover operator and the similarity-and-quality based population updating strategy. Applying the selected HEA to solve 120 public benchmark instances of the single machine scheduling problem with sequence-dependent setup times to minimize the total weighted tardiness widely used in the literature, we achieve highly competitive results compared with the exact algorithm and other state-of-the-art metaheuristic algorithms in the literature. Meanwhile, we apply the selected HEA in its original form to deal with the unweighted 64 public benchmark instances. Our HEA is able to improve the previous best known results for one instance and match the optimal or the best known results for the remaining 63 instances in a reasonable time.     

A hybrid genetic algorithm for the single machine scheduling problem with sequence-dependent setup times

This paper presents a hybrid approach based on the integration between a genetic algorithm (GA) and concepts from constraint programming, multi-objective evolutionary algorithms and ant colony optimization for solving a scheduling problem. The main contributions are the integration of these concepts in a GA crossover operator. The proposed methodology is applied to a single machine scheduling problem with sequence-dependent setup times for the objective of minimizing the total tardiness. A sensitivity analysis of the hybrid approach is carried out to compare the performance of the GA and the hybrid genetic algorithm (HGA) approaches on different benchmarks from the literature. The numerical experiments demonstrate the HGA efficiency and effectiveness which generates solutions that approach those of the known reference sets and improves several lower bounds.     

An iterated local search for the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times

Due to its simplicity yet powerful search ability, iterated local search (ILS) has been widely used to tackle a variety of single-objective combinatorial optimization problems. However, applying ILS to solve multi-objective combinatorial optimization problems is scanty. In this paper we design a multi-objective ILS (MOILS) to solve the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times to minimize the makespan and total weighted tardiness of all jobs. In the MOILS, we design a Pareto-based variable depth search in the multi-objective local search phase. The search depth is dynamically adjusted during the search process of the MOILS to strike a balance between exploration and exploitation. We incorporate an external archive into the MOILS to store the non-dominated solutions and provide initial search points for the MOILS to escape from local optima traps. We compare the MOILS with several multi-objective evolutionary algorithms (MOEAs) shown to be effective for treating the multi-objective permutation flowshop scheduling problem in the literature. The computational results show that the proposed MOILS outperforms the MOEAs.     

Beam search algorithms for the single machine total weighted tardiness scheduling problem with sequence-dependent setups

In this paper, we consider the single machine weighted tardiness scheduling problem with sequence-dependent setups. We present heuristic algorithms based on the beam search technique. These algorithms include classic beam search procedures, as well as the filtered and recovering variants. Previous beam search implementations use fixed beam and filter widths. We consider the usual fixed width algorithms, and develop new versions that use variable beam and filter widths.     

Total flow time minimization in a flowshop sequence-dependent group scheduling problem

We have developed a mathematical programming model for minimizing total flow time of the flow shop sequence dependent group scheduling (FSDGS) problem, typically classified as F_m|fmls, S_plk, prmu|∑C_j. As the problem is shown to be strongly NP-hard, a tabu search (TS) algorithm as well as a hybrid ant colony optimization (HACO) algorithm have been developed to heuristically solve the problem. A lower bounding (LB) method based on the Branch-and-Price algorithm is also developed to evaluate the quality of the metaheuristic algorithms. In order to compare the performance of metaheuristic algorithms, random test problems, ranging in size from small, medium, to large are created and solved by both the TS and the HACO algorithms. A comparison shows that the HACO algorithm has a better performance than the TS algorithm. The results of the heuristic algorithms are also compared with the results of the LB method to evaluate the quality of the solutions. The LB method presented in this paper can be generalized to solve the FSDGS problem with other objective functions.     

Material supply scheduling in a ubiquitous manufacturing system

Recently, ubiquitous manufacturing has attracted wide attention in both academia and industry. To create a successful ubiquitous manufacturing system, an efficient material handling system is essential. In accordance with this reason, mobile robots have been used for transporting materials. This paper aims at developing a methodology for scheduling the material supply for a single mobile robot in a ubiquitous manufacturing environment. In this type of environment, the processing rate of the materials along with supply quantity corresponds to the cycle of material supply. The carrying capacity of the robots are limited and thus the problem of determining the material supply quantity and material supply schedule without lack of materials for production or service processes becomes complicated. In this work, a nonlinear program is formulated to schedule the supply of material and determine the required material quantity. A heuristic algorithm based on genetic algorithm is developed to solve the problem. From the numerical experiments conducted in this study, it is observed that the proposed algorithm shows good performance and can also be implemented to solve large scale problems.     

A two-stage heuristic for single machine capacitated lot-sizing and scheduling with sequence-dependent setup costs

This paper considers a single machine capacitated lot-sizing and scheduling problem. The problem is to determine the lot sizes and the sequence of lots while satisfying the demand requirements and the machine capacity in each period of a planning horizon. In particular, we consider sequence-dependent setup costs that depend on the type of the lot just completed and on the lot to be processed. The setup state preservation, i.e., the setup state at the end of a period is carried over to the next period, is also considered. The objective is to minimize the sum of setup and inventory holding costs over the planning horizon. Due to the complexity of the problem, we suggest a two-stage heuristic in which an initial solution is obtained and then it is improved using a backward and forward improvement method that incorporates various priority rules to select the items to be moved. Computational tests were done on randomly generated test instances and the results show that the two-stage heuristic outperforms the best existing algorithm significantly. Also, the heuristics with better priority rule combinations were used to solve case instances and much improvement is reported over the conventional method as well as the best existing algorithm.     

基于Petri网的柔性制造系统无死锁遗传调度算法

对以最小化加工时间为目标的柔性制造系统无死锁调度问题, 提出了一种遗传调度算法. 算法考虑到同类工件具有预先确定的相同加工路径, 而各工序的处理时间与工件有关. 用Petri网对工序和资源分配进行逻辑建模,利用遗传算法, 采用工序自然编码方式, 基于系统的最佳避免死锁Petri网控制器, 检测染色体的可行性, 修复不可行染色体使其对应的调度满足资源约束和无死锁控制约束, 从而保证算法所利用的所有染色体都对应系统的可行调度. 仿真结果表明了算法的可行性和有效性.     

A maximum principle based combined method for scheduling in a flexible manufacturing system

A continuous time dynamic model of discrete scheduling problems for a large class of manufacturing systems is considered in the present paper. The realistic manufacturing based on multi-level bills of materials, flexible machines, controllable buffers and deterministic demand profiles is modeled in the canonical form of optimal control. Carrying buffer costs are minimized by controlling production rates of all machines that can be set up instantly. The maximum principle for the model is studied and properties of the optimal production regimes are revealed. The solution method developed rests on the iterative approach generalizing the method of projected gradient, but takes advantage of the analytical properties of the optimal solution to reduce significantly computational efforts. Computational experiments presented demonstrate effectiveness of the approach in comparison with pure iterative method.     

基于两阶段调度的制造单元协同调度算法研究

针对单元制造问题,提出了一种基于两阶段的调度算法,通过过程分解和算法优化两方面实现问题求解。调度过程分为“预调度”和“整体调度”两个阶段,对大规模调度进行调度,不仅有效地降低了问题规模,同时制造单元调度结果对实际生产具有现实意义;调度算法采用了“精确”计算和“近似”求解相结合的方式,既提高计算效率又兼顾了全局优化目标。数值实验结果表明了的这一设计思路的有效性。     

A heuristic algorithm for group scheduling

Group technology is a rapidly developing productivity improvement tool that can have a significant impact on the development of totally integrated manufacturing facilities and flexible manufacturing systems. Production scheduling associated with group technology is called “Group Scheduling”. There are many heuristic algorithms developed for general job shop applications based on unrealistic hypothesis, complicated computations etc., which are not addressed to group scheduling. In this paper, from the existing algorithms for group scheduling, a heuristic algorithm has been developed and programmed for computer/microcomputer applications. The developed algorithm has been used to determine the optimal group and the optimal job sequence for a batch type production process with functional layout. The developed algorithm is far simpler and easier to compute, compared to the other similar heuristic algorithms and certainly in comparison to other optimization methods such as branch and bound method.     

Robust scheduling on a single machine to minimize total flow time

In a real-world manufacturing environment featuring a variety of uncertainties, production schedules for manufacturing systems often cannot be executed exactly as they are developed. In these environments, schedule robustness that guarantees the best worst-case performance is a more appropriate criterion in developing schedules, although most existing studies have developed optimal schedules with respect to a deterministic or stochastic scheduling model. This study concerns robust single machine scheduling with uncertain job processing times and sequence-dependent family setup times explicitly represented by interval data. The objective is to obtain robust sequences of job families and jobs within each family that minimize the absolute deviation of total flow time from the optimal solution under the worst-case scenario. We prove that the robust single machine scheduling problem of interest is NP-hard. This problem is reformulated as a robust constrained shortest path problem and solved by a simulated annealing-based algorithmic framework that embeds a generalized label correcting method. The results of numerical experiments demonstrate that the proposed heuristic is effective and efficient for determining robust schedules. In addition, we explore the impact of degree of uncertainty on the performance measures and examine the tradeoff between robustness and optimality.     

New integration of preventive maintenance and production planning with cell formation and group scheduling for dynamic cellular manufacturing systems

This paper proposes a new integration method for cell formation, group scheduling, production, and preventive maintenance (PM) planning problems in a dynamic cellular manufacturing system (CMS). The cell formation sub-problem aims to form part families and machine groups, which minimizes the inter-cell material handling, under-utilization, and relocation costs. The production planning aspect is a multi-item capacitated lot-sizing problem accompanied by sub-contracting decisions, while the group scheduling problem deals with the decisions on the sequential order of the parts and their corresponding completion times. The purpose of the maintenance sub-problem is to determine the availability of the system and the time when the noncyclical perfect PM must be implemented to reduce the number of corrective actions. Numerical examples are generated and solved by Bender’s decomposition pack in GAMS to evaluate the interactions of the proposed model. Statistical analysis, based on a nonparametric method, is also used to study the behavior of the model’s cost components in two different situations. It is shown that by adding the PM planning decisions to the tactical decisions of the dynamic CMS, the optimal configuration and production plans of the system are heavily affected. The results indicate that omitting the PM actions increases the number of sudden failures, which leads to a higher total cost. Finally, it is concluded that the boost in the total availability of the dynamic CMS is one of the main advantages of the proposed integrated method.     

Maintenance scheduling in manufacturing systems based on predicted machine degradation

In this paper, we propose a new method for scheduling of maintenance operations in a manufacturing system using the continuous assessment and prediction of the level of performance degradation of manufacturing equipment, as well as the complex interaction between the production process and maintenance operations. Effects of any maintenance schedule are evaluated through a discrete-event simulation that utilizes predicted probabilities of machine failures in the manufacturing system, where predicted probabilities of failure are assumed to be available either from historical equipment reliability information or based on the newly available predictive algorithms. A Genetic Algorithm based optimization procedure is used to search for the most cost-effective maintenance schedule, considering both production gains and maintenance expenses. The algorithm is implemented in a simulated environment and benchmarked against several traditional maintenance strategies, such as corrective maintenance, scheduled maintenance and condition-based maintenance. In all cases that were studied, application of the newly proposed maintenance scheduling tool resulted in a noticeable increase in the cost-benefits, which indicates that the use of predictive information about equipment performance through the newly proposed maintenance scheduling method could result in significant gains obtained by optimal maintenance scheduling.     

A simulated annealing algorithm for single machine scheduling problems with family setups

Motivated by the real-life scheduling problem in a steel-wire factory in China, this paper studies the problem of minimizing the maximum lateness on a single machine with family setups. In view of the NP-hard nature of the problem, neighborhood properties of the problem are investigated. It is found that the traditional move-based neighborhood is inefficient to search. Then a new neighborhood, which is based on batch destruction and construction, is developed. A simulated annealing algorithm with the new neighborhood is proposed. Experiments are carried out on the randomly generated problems and the real-life instances from a factory in China. Computational results show that the proposed algorithm can obtain better near optimal solutions than the existing algorithm.     

基于区块链的云制造系统内可信资源调度方案

针对目前云制造系统中存在的各参与主体间信任问题以及资源调度效率问题,研究了将区块链技术应用于云制造系统中.首先,阐述了区块链技术应用于云制造系统的意义,提出了一种基于区块链技术的云制造系统;其次,设计了基于智能合约的制造资源调度方式,构建制造成本最小、时间最短、合格率最高的资源调度模型并用差分进化算法进行求解;最后,进行实验仿真.结果表明,基于区块链技术的智能合约内进行资源调度方法在保证了系统内各参与主体间相互信任的同时,有效地提高了云制造系统的资源调度效率和资源调度方案的优越性.     

制造网格资源调度及安全性管理

根据制造网格发展的现状和方向,分析了网格资源调度的主要方法,并对Globus环境下的GSI体系结构做了深入研究,分析讨论了其任务提交执行过程和架构特点,对现有的GSI架构基础做了相关改进,构建了一个满足制造网格下部分特性的安全体系结构M-GSI,实现了实时认证和强授权功能,满足了制造网格部分安全需求。     

Criteria selection and analysis for single machine dynamic on-line scheduling with multiple objectives and sequence-dependent setups

Fertile opportunities exist for research involving dynamic and stochastic scheduling with multiple conflicting objectives and sequence-dependent setups as little has been reported in the literature to date. This research focuses on understanding and identifying the criteria that could be combined into a single rule using the linear weighted aggregation approach to consider the contradicting needs of cycle time and delivery accuracy. Eight dispatching criteria are compared and evaluated using discrete event simulation. In most studies, the basic concept is to combine different dispatching criterion that performs the best in each objective into a single rule but this may be insufficient. Simulation results show that it is necessary to take into account not only the criterion’s strength in optimizing a performance objective but also the degree of trade-off it has on the other conflicting performance objectives of interest. A correlation analysis of the objectives used is also presented.     

A rule-based robot scheduling system for flexible manufacturing cells

In this research we examine a class of flexible manufacturing cells (FMC) containing a robot. The role of the robot is to load parts onto machines, to unload parts from machines, and to transport parts between machines. Since the productivity of an FMC is directly proportional to the level of productive work performed by the robot, the manner in which robots move between machines affects productivity. The problem of finding efficient robot schedules/tours is therefore one of substantial economic significance in the operation of a FMC. Unfortunately, in many practical situations it is difficult to develop efficient robot schedules given the dynamic environments in which they exist. We devise a rule-based system to assist the cell supervisor in making good decisions by dynamically coordinating the available information during the production process. The rule-based system combines an algorithmic procedure to deal with a well-structured environment and a flexible heuristic approach employed to deal with less well-structured environments. Both the algorithmic and heuristic procedures are applied separately, then together, to control the robot's movement in a simulation experiment. We show that there is a predictable tradeoff between the quality of the resulting schedule and the information contents of heuristic used.     

考虑柔性机器检修的钢管热轧批量调度方法

本文从无缝钢管生产管理中提取并定义了周期性机器柔性检修环境下的钢管热轧批量调度问题,针对无缝钢管热轧阶段的生产特点,将其抽象为一类考虑序列相关设置成本和机器柔性检修的单机调度问题,建立了以最小化机器闲置时间和机器调整时间为优化目标的数学模型。分析闲置时间和检修时点的关系,证明了闲置时间最小化性质,结合问题特征设计了两阶段启发式算法。算法第一阶段采用最小轧机调整时间规则获取具有最小机器调整时间的初始批量轧制序列,第二阶段对初始轧制序列进行全局寻优搜索。基于实际生产数据设计了多种问题规模的对比实验,实验结果表明模型和算法对求解该类问题具有较好效果。