Worker deployment in dual resource constrained systems with a task-type factor

This paper investigates worker learning and forgetting phenomenon in a dual resource constrained system (DRC) setting. Worker learning and forgetting in two and three stage DRC systems are modeled according to the dual-phase learning and forgetting model (DPLFM), which is based on the theory that a task has separate cognitive and motor requirements. Results show that the task-type (with respect to its learning rate and proportion of cognitive and motor requirements) affects the performance of training and deployment policies in DRC systems, and as such it should be included in future DRC research.     

Incorporating human fatigue and recovery into the learning–forgetting process

The available learning and forgetting models do not consider the physical loading that performing a task requires. In some situations, physical loading results in workers’ fatigue on the job that is followed by rest breaks to alleviate it. The aim of this paper is to present the “learning–forgetting–fatigue–recovery model” (LFFRM) that addresses possible issues relating to workers’ capabilities and restrictions in manufacturing environments. Numerical examples are solved to address some research questions regarding the model optimization and its constraints. The main results show that incorporating learning into a production process decreases fatigue and improves the performance of the system. Worker fatigue, on the other hand, increases production time and decreases production output. A recovery break must be of enough length to alleviate some of the accumulated fatigue. However, longer recovery times extend the lead time and deteriorate the production performance due to forgetting.     

Learning and forgetting-based worker selection for tasks of varying complexity

This paper presents an approach for selecting workers for tasks of varying complexity based on individual learning and forgetting characteristics in order to improve system productivity. The performance of a learning and forgetting-based selection (LFBS) policy is examined using simulation and compared to a baseline policy representing criteria used in practice. The effects of factors including worker redundancy and task-tenure on productivity are also examined in the environment of continuously staffed independent tasks. Results demonstrate that the LFBs policy significantly improves productivity relative to common practice and suggests that lower levels of redundancy and shorter task-tenures tend to mitigate some of the negative effects of forgetting.     

A Simulation Study of Due-date Assignment Rules in a Dynamic Job Shop

This paper is a report of a simulation study that investigates a dynamic approach to scheduling jobs in a multi-machine job shop. The workload information of a job is used in different forms to evaluate the shop performance based on three measures: mean job lateness, percentage of tardy jobs and lateness variance. Different combinations of due-date assignment methods and sequencing rules are compared based on specific performance criteria. The results indicate that using the cumulative distribution function of workload information can yield a better performance than using a proportional function of workload information or ignoring shop congestion information. A few situations are identified in which workload information is not critical.     

Learning and forgetting effects on a group scheduling problem

This paper proposes to investigate learning and forgetting effects on the problem of scheduling families of jobs on a single machine to minimize total completion time of jobs. A setup time is incurred whenever the single machine transfers job processing from a family to another family. To analyze the impact of learning and forgetting on this group scheduling problem, we structure three basic models and make some comparisons through computational experiments. The three models, including no forgetting, total forgetting and partial forgetting, assume that the processing time of a job is dependent on its position in a schedule. Some scheduling rules and a lower bound are derived in order to constitute our branch-and-bound algorithm for searching an optimal sequence. In addition, an efficient and simply-structured heuristic is also built to find a near-optimal schedule.     

Evolutionary search for difficult problem instances to support the design of job shop dispatching rules

Dispatching rules are simple scheduling heuristics that are widely applied in industrial practice. Their popularity can be attributed to their ability to flexibly react to shop floor disruptions that are prevalent in many real-world manufacturing environments. However, it is a challenging and time-consuming task to design local, decentralised dispatching rules that result in a good global performance of a complex shop.An evolutionary algorithm is developed to generate job shop problem instances for which an examined dispatching rule fails to achieve a good solution due to a single suboptimal decision. These instances can be easily analysed to reveal limitations of that rule which helps with the design of better rules. The method is applied to a job shop problem from the literature, resulting in new best dispatching rules for the mean flow time measure.     

Trapezoidal type inequalities for n-time differentiable functions

In this paper we consider the flow shop scheduling problems with the effects of learning and deterioration. In this model the processing times of a job is defined as a function of its starting time and position in a sequence. The scheduling objective functions are makespan and total completion time. We prove that even with the introduction of learning effect and deteriorating jobs to job processing times, some special flow shop scheduling problems remain polynomially solvable.     

Deterministic/stochastic assumptions in job shops

The study investigates the effects which possibly unrealistic assumptions of accurately predicting operation times may have on relative performance of various job shop dispatching rules as compared with using an assumption of not being able to accurately predetermine such times. The experimental design includes factors dealing with the amount of accuracy in the estimated operation times, job dispatching heuristic rules, and shop loading categories. The stochastic operation times represent two different degrees of inaccuracy; one level reflects an estimated ‘normal’ amount of inaccuracy associated with an experienced predictor (shop foreman) while the other level doubles the amount of variance associated with the ‘normal’ predictor's error. These two stochastic levels are compared to a deterministic level where predetermined operation times are absolutely accurate. Five different heuristic rules are evaluated under six different shop loading levels. General conclusions indicate that an assumption of accurately predetermining actual operation times is not likely to weaken the analysis and impact of the research studies which have been performed using such an assumption. However, a specific conclusion indicates that, for at least one shop loading category, researchers should be careful when extending conclusions based on one operation time assumption to situations involving the other assumption.     

Minimizing makespan in a two-machine flow shop with effects of deterioration and learning

We consider a two-machine flow shop scheduling problem with effects of deterioration and learning. By the effects of deterioration and learning, we mean that the processing time of a job is a function of its execution starting time and its position in a sequence. The objective is to find a sequence that minimizes the makespan. Several dominance properties and two lower bounds are derived, which are used to speed up the elimination process of a branch-and-bound algorithm proposed to solve the problem. Two heuristic algorithms are also proposed to obtain near-optimal solutions. Computational results are presented to evaluate the performance of the proposed algorithms.     

Worst-case behavior of simple sequencing rules in flow shop scheduling with general position-dependent learning effects

A real industrial production phenomenon, referred to as learning effects, has drawn increasing attention. However, most research on this issue considers only single machine problems. Motivated by this limitation, this paper considers flow shop scheduling problems with a general position-dependent learning effects. By the general position-dependent learning effects, we mean that the actual processing time of a job is defined by a general non-increasing function of its scheduled position. The objective is to minimize one of the five regular performance criteria, namely, the total completion time, the makespan, the total weighted completion time, the total weighted discounted completion time, and the sum of the quadratic job completion times. We present heuristic algorithms by using the optimal permutations for the corresponding single machine scheduling problems. We also analyze the worst-case bound of our heuristic algorithms.     

Due dates in a manufacturing shop: An unweighted case

A dispatching rule is proposed for job shop operations where the performance criteria are due date related. The dispatching rule is constructed by combining the characteristics of the shortest process time rule and a dynamically determined earliest due date rule. The performance of the proposed rule is compared to currently well known rules across various shop environments using discrete simulation.This work was partially supported by: The National Science Foundation, Grant No. CDR-8300965; member companies of the Material Handling Research Center, the Georgia Institute of Technology and Texas A&M University.     

Unit Load Size Determination Based on Economic Move Quantity

Material handling in job shop environments is accomplished through the transfer of parts between work centres. Parts that are grouped together prior to transfer form a unit load. A new method is proposed for determining the unit load size, called the economic move quantity (EMQ), based on cost considerations during the design phase of the production system. EMQ takes into account system performance through the use of a queueing network model.     

Worst-case analysis for flow shop scheduling problems with an exponential learning effect

A real industrial production phenomenon, referred to as learning effects, has drawn increasing attention. However, most research on this issue considers only single machine problems. Motivated by this limitation, this paper considers flow shop scheduling problems with an exponential learning effect. By the exponential learning effect, we mean that the processing time of a job is defined by an exponent function of its position in a processing permutation. The objective is to minimize one of the four regular performance criteria, namely, the total completion time, the total weighted completion time, the discounted total weighted completion time, and the sum of the quadratic job completion times. We present heuristic algorithms by using the optimal permutations for the corresponding single-machine scheduling problems. We also analyse the worst-case bound of our heuristic algorithms.     

Machine Criticality Measures and Subproblem Solution Procedures in Shifting Bottleneck Methods: A Computational Study

We examine the effects of different machine criticality measures (MCMs) and subproblem solution procedures (SSPs) on the performance of Shifting Bottleneck (SB) methods for the problem of minimizing maximum lateness in a job shop. Extensive computational experiments show that for problems with balanced workloads and random routeings simple MCMs and SSPs can be used without affecting solution quality. Routeing structure affects the performance of the SB method significantly. We also find that, contrary to some previous studies, the problem of maintaining feasibility in the SB method is significant.     

Steady-state skill levels of workers in learning and forgetting environments: A dynamical system analysis

This article presents a study on the long-term (i.e., steady-state, convergence) characteristics of workers’ skill levels under learning and forgetting in processing units in a manufacturing environment, in which products are produced in batches. Assuming that all workers already have the basic knowledge to execute the jobs, workers learn (accumulate their skill) while producing units within a batch, forget during interruptions in production, and relearn when production resumes. The convergence properties in the paper are examined under assumptions of an infinite time horizon, a constant demand rate, and a fixed lot size. Our work extends the steady-state results of Teyarachakul, Chand, and Ward (2008) to the learning and forgetting functions that belong to a large class of functions possessing some differentiability conditions. We also discuss circumstances of manufacturing environments where our results would provide useful managerial information and other potential applications.     

农民工的中等职业教育、在职培训与工资——基于校正多元样本选择偏差的分析

将雇主提供一年期及以上合同的工作作为稳定就业,将农民工的就业状态分为稳定就业、非稳定就业、未就业三种,在校正多元样本选择偏差的基础上估计了农民工稳定就业的工资方程和非稳定就业的工资方程,重点分析了中等职业教育和在职培训对农民工工资的影响.结果表明,中等职业教育对农民工获得稳定就业具有显著的正向影响,中等职业教育和在职培训对农民工稳定就业和非稳定就业的工资均具有正向影响,但对稳定就业工资的影响要显著大于对非稳定就业工资的影响.因此,政府应当继续加大农村地区职业教育的投入,同时鼓励用工企业对农民工开展与工作技能相关的在职培训,从而使农民工获得更为稳定的就业和更高的工资水平,顺利实现市民身份的转换.     

Minimizing maximum completion time in a proportionate flow shop with one machine of different speed

A flow shop with identical machines is called a proportionate flow shop. In this paper, we consider the variant of the n-job, m-machine proportionate flow shop scheduling problem in which only one machine is different and job processing times are inversely proportional to machine speeds. The objective is to minimize maximum completion time. We describe some optimality conditions and show that the problem is NP-complete. We provide two heuristic procedures whose worst-case performance ratio is less than two. Extensive experiments with various sizes are conducted to show the performance of the proposed heuristics.     

基于启发式Q学习的汽车涂装车间作业排序优化

针对汽车涂装车间中的作业优化排序问题,提出一种基于启发式Q学习的优化算法。首先,建立包括满足总装车间生产顺序和最小化喷枪颜色切换次数的多目标整数规划模型。将涂装作业优化排序问题抽象为马尔可夫过程,建立基于启发式Q算法的求解方法。通过具体案例,对比分析了启发式Q学习、Q学习、遗传算法三种方案的优劣。结果表明：在大规模问题域中,启发式Q学习算法具有寻优效率更高、效果更好的优势。本研究为机器学习算法在汽车涂装作业优化排序问题的应用提出了新思路。     

重入排序问题综述

重入排序问题打破传统假设：工件在加工过程中不止一次地访问某台机器,是一种新型的排序问题. 重入的特点源于半导体生产, 并广泛存在于其他领域. 对重入排序问题已有文献中的成果进行梳理和分析,按问题所处机器环境的不同, 对内容和方法进行分类介绍和总结：包括单机问题、流水作业问题、混合流水作业问题及其他机器环境下的重入排序问题. 最后展望未来的趋势和研究方向.     

Research on permutation flow shop scheduling problems with general position-dependent learning effects

Machine learning exists in many realistic scheduling situations. This study focuses on permutation flow shop scheduling problems, where the actual processing time of a job is defined by a general non-increasing function of its scheduled position, i.e., general position-dependent learning effects. The objective functions are to minimize the total completion time, the makespan, the total weighted completion time, and the total weighted discounted completion time, respectively. To solve these problems, we present approximation algorithms based on the optimal permutations for the corresponding single machine scheduling problems and analyze their worst-case error bound.