Designing safe job rotation schedules using optimization and heuristic search

Job rotation is one method that is sometimes used to reduce exposure to strenuous materials handling; however, developing effective rotation schedules can be complex in even moderate sized facilities. The purpose of this research is to develop methods of incorporating safety criteria into scheduling algorithms to produce job rotation schedules that reduce the potential for injury. Integer programming and a genetic algorithm were used to construct job rotation schedules. Schedules were comprised of lifting tasks whose potential for causing injury was assessed with the Job Severity Index. Each method was used to design four job rotation schedules that met specified safety criteria in a working environment where the object weight, horizontal distance and repetition rate varied over time. Each rotation was assigned to a specific gender/lifting capacity group. Five versions of the integer programming search method were applied to this problem. Each version generated one job rotation schedule. The genetic algorithm model was able to create a population of 437 feasible solutions to the rotation problem. Utilizing cluster analysis, a rule set was derived from the genetic algorithm generated solutions. These rules provided guidelines for designing safe job rotation schedules without the use of a computer. The advantages and limitations of these approaches in developing administrative controls for the prevention of back injury are discussed.     

基于改进的基因表达式编程算法在作业车间调度问题中的应用

文章提出一种新颖的方法一改进的基因表达式编程算法来求解作业车间调度问题。作业车间调度问题是许多实际生产调度问题的简化模型,基因表达式编程算法结合了遗传算法和遗传编程的优点,具有更强的解决问题能力,对基因表达式编程算法进行改进使其在作业车间调度问题的应用上更加有效;最后应用一个实例来验证提出方法的有效性。     

GA-based discrete dynamic programming approach for scheduling inFMS environments

The paper presents a new genetic algorithm (GA)-based discrete dynamic programming (DDP) approach for generating static schedules in a flexible manufacturing system (FMS) environment. This GA-DDP approach adopts a sequence-dependent schedule generation strategy, where a GA is employed to generate feasible job sequences and a series of discrete dynamic programs are constructed to generate legal schedules for a given sequence of jobs. In formulating the GA, different performance criteria could be easily included. The developed DDF algorithm is capable of identifying locally optimized partial schedules and shares the computation efficiency of dynamic programming. The algorithm is designed In such a way that it does not suffer from the state explosion problem inherent in pure dynamic programming approaches in FMS scheduling. Numerical examples are reported to illustrate the approach.     

A two-stage genetic algorithm for multi-objective job shop scheduling problems

This paper presents a two-stage genetic algorithm (2S-GA) for multi-objective Job Shop scheduling problems. The 2S-GA is proposed with three criteria: Minimize makespan, Minimize total weighted earliness, and Minimize total weighted tardiness. The proposed algorithm is composed of two Stages: Stage 1 applies parallel GA to find the best solution of each individual objective function with migration among populations. In Stage 2 the populations are combined. The evolution process of Stage 2 is based on Steady-State GA using the weighted aggregating objective function. The algorithm developed can be used with one or two objectives without modification. The genetic algorithm is designed and implemented with the GALIB object library. The random keys representation is applied to the problem. The schedules are constructed using a permutation with m-repetitions of job numbers. Performance of the proposed algorithm is tested on published benchmark instances and compared with results from other published approaches for both the single objective and multi-objective cases. The experimental results show that 2S-GA is effective and efficient to solve job shop scheduling problem in term of solution quality.     

Dynamic scheduling of manufacturing job shops using genetic algorithms

Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date.A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.     

Chance-constrained model for uncertain job shop scheduling problem

Job shop scheduling problem is well-investigated and widely applied in the fields of operational research, system engineering and automatic management. This paper employs uncertain programming to deal with the job shop scheduling problem with uncertain processing time and cost. First, a chance-constrained model is proposed under the framework of uncertainty theory. The model is equivalent to a crisp one by inverse uncertainty distribution method. Furthermore, three heuristic algorithms (genetic algorithm, particle swarm optimization, firefly algorithm) are employed for solving the model. Finally, a numerical example is solved by these three algorithms, and the results are analyzed to show which algorithm is better for solving the established model.     

Generating robust and flexible job shop schedules using genetic algorithms

The problem of finding robust or flexible solutions for scheduling problems is of utmost importance for real-world applications as they operate in dynamic environments. In such environments, it is often necessary to reschedule an existing plan due to failures (e.g., machine breakdowns, sickness of employees, deliveries getting delayed, etc.). Thus, a robust or flexible solution may be more valuable than an optimal solution that does not allow easy modifications. This paper considers the issue of robust and flexible solutions for job shop scheduling problems. A robustness measure is defined and its properties are investigated. Through experiments, it is shown that using a genetic algorithm it is possible to find robust and flexible schedules with a low makespan. These schedules are demonstrated to perform significantly better in rescheduling after a breakdown than ordinary schedules. The rescheduling performance of the schedules generated by minimizing the robustness measure is compared with the performance of another robust scheduling method taken from literature, and found to outperform this method in many cases.     

Multi-criteria sequence-dependent job shop scheduling using genetic algorithms

Real world job shops have to contend with jobs due on different days, material ready times that vary, reentrant workflows and sequence-dependent setup times. The problem is even more complex because businesses often judge solution goodness according to multiple competing criteria. Producing an optimal solution would be time consuming to the point of rendering the result meaningless. Commonly used heuristics such as shortest processing time (SPT) and earliest due date (EDD) can be used to calculate a feasible schedule quickly, but usually do not produce schedules that are close to optimal in these job shop environments. We demonstrate that genetic algorithms (GA) can be used to produce solutions in times comparable to common heuristics but closer to optimal. Changing criteria or their relative weights does not affect the running time, nor does it require programming changes. Therefore, a GA can be easily applied and modified for a variety of production optimization criteria in a job shop environment that includes sequence-dependent setup times.     

Pruning by dominance in best-first search for the job shop Scheduling problem with total flow time

Best-First search is a problem solving paradigm that allows to design exact or admissible algorithms. In this paper, we confront the Job Shop Scheduling problem with total flow time minimization by means of the A ^* algorithm. We devised a heuristic from a problem relaxation that relies on computing Jackson’s preemptive schedules. In order to reduce the effective search space, we formalized a method for pruning nodes based on dominance relations and established a rule to apply this method efficiently during the search. By means of experimental study, we show that the proposed method is more efficient than a genetic algorithm in solving instances with 10 jobs and 5 machines and that pruning by dominance allows A ^* to reach optimal schedules, while these instances are not solved by A ^* otherwise. These experiments have also made it clear that the Job Shop Scheduling problem with total flow time minimization is harder to solve than the same problem with makespan minimization.     

A genetic solution based on lexicographical goal programming for a multiobjective job shop with uncertainty

In this work we consider a multiobjective job shop problem with uncertain durations and crisp due dates. Ill-known durations are modelled as fuzzy numbers. We take a fuzzy goal programming approach to propose a generic multiobjective model based on lexicographical minimisation of expected values. To solve the resulting problem, we propose a genetic algorithm searching in the space of possibly active schedules. Experimental results are presented for several problem instances, solved by the GA according to the proposed model, considering three objectives: makespan, tardiness and idleness. The results illustrate the potential of the proposed multiobjective model and genetic algorithm.     

Characteristics of job rotation in the Midwest US manufacturing sector

Job rotation has been advocated as a suitable intervention to control work-related musculoskeletal disorders. However, little is known regarding the prevalence of job rotation, methods used to identify jobs for rotation or the benefits or limitations of job rotation. A web-based questionnaire was developed to survey job rotation practices from Midwest US manufacturing companies. Results indicated that 42.7% of the companies contacted used job rotation, where the median time for which they had used job rotation was 5 years. Job rotation was used mainly to reduce exposure to risk factors for work-related injuries and to reduce work related injuries, whereas supervisor decisions and ergonomic analyses were used to select jobs for the rotation scheme. Major limitations to successful implementation of job rotation included rotation of individuals with medical restrictions, decreased product quality and lack of jobs to rotate to. These findings suggest that further study is needed to determine if exposure to risk factors is reduced through current efforts.     

Characteristics of job rotation in the Midwest US manufacturing sector

Job rotation has been advocated as a suitable intervention to control work-related musculoskeletal disorders. However, little is known regarding the prevalence of job rotation, methods used to identify jobs for rotation or the benefits or limitations of job rotation. A web-based questionnaire was developed to survey job rotation practices from Midwest US manufacturing companies. Results indicated that 42.7% of the companies contacted used job rotation, where the median time for which they had used job rotation was 5 years. Job rotation was used mainly to reduce exposure to risk factors for work-related injuries and to reduce work related injuries, whereas supervisor decisions and ergonomic analyses were used to select jobs for the rotation scheme. Major limitations to successful implementation of job rotation included rotation of individuals with medical restrictions, decreased product quality and lack of jobs to rotate to. These findings suggest that further study is needed to determine if exposure to risk factors is reduced through current efforts.     

New model and heuristics for safety stock placement in general acyclic supply chain networks

We model the safety stock placement problem in general acyclic supply chain networks as a project scheduling problem, for which the constraint programming (CP) techniques are both effective and efficient in finding high quality solutions. We further integrate CP with a genetic algorithm (GA), which improves the CP solution quality significantly. The performance of our hybrid CP-GA algorithm is evaluated on randomly generated test instances. CP-GA is able to find optimal solutions to small problems in fractions of a second, and near optimal solutions of about 5% optimality gap to medium size problems in several minutes on average.     

求解模糊Job Shop调度的遗传算法与蚁群算法融合研究

提出一种算法融合策略,解决单一算法求解模糊Job Shop调度问题存在的不足,提高这类问题的求解质量.算法融合策略中,采用遗传算法和蚁群算法进行并行搜索;根据模糊Job Shop调度问题解的特征,提出基于关键工序的邻域选择方法,并将基于这种邻域选择方法的禁忌搜索算法作为局部搜索算法,加强了遗传算法和蚁群算法的局部搜索能力.采用算法融合策略的混合优化算法对以13个难的benchmarks问题经模糊化得到实例进行求解,在较短的时间内,得到的平均满意度较并行遗传算法(PGA)提高5.24%、较TSAB算法提高8.40% .采用算法融合策略构造的混合算法具有较强的搜索能力,说明提出的混合搜索策略是有效的.     

An improved intelligent water drops algorithm for solving multi-objective job shop scheduling

Multi-objective job shop scheduling (MOJSS) problems can be found in various application areas. The efficient solution of MOJSS problems has received continuous attention. In this research, a new meta-heuristic algorithm, namely the Intelligent Water Drops (IWD) algorithm is customized for solving the MOJSS problem. The optimization objective of MOJSS in this research is to find the best compromising solutions (Pareto non-dominance set) considering multiple criteria, namely makespan, tardiness and mean flow time of the schedules. MOJSS-IWD, which is a modified version of the original IWD algorithm, is proposed to solve the MOJSS problem. A scoring function which gives each schedule a score based on its multiple criteria values is embedded into the MOJSS-IWD’s local search process. Experimental evaluation shows that the customized IWD algorithm can identify the Pareto non-dominance schedules efficiently.     

Hadoop云平台的一种新的任务调度和监控机制

云平台任务监控与资源调度机制是云平台的核心功能之一。Hadoop云平台中任务监控和资源管理的任务是由JobTracker负责处理,并通过slave节点向其发送心跳消息来实现。这种方式导致JobTracker的负载过重,降低了Hadoop云平台的工作效率,限制了Hadoop云平台的规模。提出了一种新的任务监控方案,该方案将JobTracker的任务监控和资源管理功能分离,任务监控功能仍由JobTracker节点完成,资源管理功能由新增的资源管理节点完成,JobTracker通过增量更新的算法将任务调度所需的对象信息动态同步到资源管理节点上,资源管理节点根据心跳消息进行任务分配,并将分配结果返回给JobTracker节点。实验结果表明,本方案不仅通过监控节点实现了任务的监控,增加了监控的灵活性和鲁棒性,而且降低了Jobtracker节点的负担,可有效提高Hadoop云平台的工作效率和规模。     

混合遗传算法在Job-shop调度问题中的应用

首先建立了Ｊｏｂ－ｓｈｏｐ调度问题的神经网络模型，根据这种模型的特点，提出了求解复杂Ｊｏｂ－ｓｈｏｐ调度问题的混合遗传算法．仿真结果表明了本文方法的有效性，在运行时间和最优率方面具有较好的优势．     

Makespan minimization on single batch-processing machine via ant colony optimization

This paper investigates the problem of minimizing makespan on a single batch-processing machine, and the machine can process multiple jobs simultaneously. Each job is characterized by release time, processing time, and job size. We established a mixed integer programming model and proposed a valid lower bound for this problem. By introducing a definition of waste and idle space (WIS), this problem is proven to be equivalent to minimizing the WIS for the schedule. Since the problem is NP-hard, we proposed a heuristic and an ant colony optimization (ACO) algorithm based on the theorems presented. A candidate list strategy and a new method to construct heuristic information were introduced for the ACO approach to achieve a satisfactory solution in a reasonable computational time. Through extensive computational experiments, appropriate ACO parameter values were chosen and the effectiveness of the proposed algorithms was evaluated by solution quality and run time. The results showed that the ACO algorithm combined with the candidate list was more robust and consistently outperformed genetic algorithm (GA), CPLEX, and the other two heuristics, especially for large job instances.     

Development of a hybrid expert system for nurse shift scheduling

In this article, we discuss the effect of nurse shift job on circadian rhythm, work stress, and some important ergonomics criteria. We also review and compare different nurse shift scheduling methodologies via the criteria of flexibility, consideration of nurse preference, and consideration of ergonomics principles. A hybrid expert system, entitled NURSE-HELP, is developed to facilitate the nurse scheduling process with an emphasis on considering ergonomics criteria. Moreover, the combination of a linear zero-one goal programming and an expert system program reduces the program run time while maintaining the quality of the schedule. The evaluation of the system is done by comparing 18 sets of four-week schedules generated by the head nurses manually and by NURSE-HELP. Concerning the amount of time to generate the schedules, NURSE-HELP averages less than 20 minutes while the head nurses spend about two to four hours. The quality of the schedules is measured by the following four criteria; minimum staff level not satisfied, day off request not granted, backward rotation, and maximum consecutive work periods on the night shift. The results show that NURSE-HELP is superior than the head nurses in preparing schedules, both in terms of time and quality.     

基于多链拓展编码方案的量子遗传算法

为了提高量子遗传算法的性能,提出了一种基于多链拓展编码方案的量子遗传算法。根据编码方案,将每个量子位分解为多个并列的基因,有效地拓展了搜索空间;结合编码方案提出量子更新策略,并引入了动态调整旋转角机制对个体进行更新,使用量子非门变异策略实现量子变异。仿真实验中,分析了使用不同变异概率[0,0.1,…,0.9,1]时对算法性能的影响,对比了分别使用普通量子遗传算法、双链编码方案、三链编码方案以及四链编码方案的量子遗传算法在优化函数极值问题时算法的性能。实验结果证明,通过增加基因链可以显著提高算法的性能,多链拓展编码方案可以提高量子遗传算法的性能,是有效的。