Some implementation experiences with just-in-time manufacturing

A case study on the implementation of a just-in-time (JIT) production system by a Canadian manufacturer in the heavy machinery industry is presented. Many hurdles and difficulties were encountered during implementation and the way in which these problems were resolved is reported. While the successful implementation of a JIT system depends on a host of technical and human factors, this study suggests that the people involvement factor is the most crucial. It is seen that the key elements to securing people involvement are institution leadership, company-wide education and training and mutual understanding, trust and respect among employees. The moral of this study is that without employee involvement nothing will happen and no result will be achieved.     

具有超前或延误损失的单机排序问题

在变换条件下对于JIT准时生产制排序问题中的一种特殊情况──排序问题中只含超前损失和延误损失，得到最优序列的一些条件。     

基于机动时间的可分解平行工序顺序优化研究

平行工序的顺序优化是解决资源有限项目进度计划问题的最有效、最普遍的方法之一。对于该类问题的研究目前主要基于工序的不可分解性,而现实情况下有些工序是任意可分的。基于此,本文首先提出了最小路长定理,在其基础上,建立了任意可分的两个平行工序调整为顺序工序的亏值模型,并进行了理论证明,此外,针对从n个可分解平行工序中选取一个与指定工序调整为顺序工序的优化问题进行了研究,在已给亏值模型的基础上设计出了优化算法,越是大型网络,该方法的优越性越明显。     

A hybrid approach to sequencing jobs using heuristic rules and neural networks

A hybrid approach to solve job sequencing problems using heuristic rules and artificial neural networks is proposed. The problem is to find a job sequence for a single machine that minimizes the total weighted tardiness of the jobs. Two different cases are considered: (1) when there are no setups, and (2) when there are sequence-dependent setup times. So far, successful heuristic rules for these cases are: apparent tardiness cost (ATC) rule proposed by Vepsalainen and Morton for the former case, and an extended version of the ATC rule (ATCS) proposed by Lee, Bhaskaran, and Pinedo for the latter. Both approaches utilize some look-ahead parameters for calculating the priority index of each job. As reported by Bhaskaran and Pinedo, the proper value of the look-ahead parameter depends upon certain problem characteristics, such as due-date tightness and due-date range. Thus, an obvious extension of the ATC or the ATCS rule is to adjust the parameter values depending upon the problem characteristics: this is known to be a difficult task. In this paper, we propose an application of a neural network as a tool to ‘predict’ proper values of the look-ahead parameters. Our computational tests show that the proposed hybrid approach outperforms both the ATC rule with a fixed parameter value and the ATCS using the heuristic curve-fitting method.     

Minimizing the mean squared deviation from a common due date: unconstrained and constrained cases

The problem of minimizing the mean squared deviation (MSD) of completion times from a common due date in both unconstrained and constrained cases is addressed. It is shown that the unconstrained MDS function is unimodal for n ≤ 6, where n is the number of jobs. The constrained case is shown to be unimodular for n ≤ 3, The unconstrained case is shown, by counterexample, not to be unimodular for n = 8. The constrained case is shown not to be unimodular for n = 5. For the unimodular cases, a proposed search routine can find the optimum solution in less than three CPU seconds for n = 100. It provides an excellent heuristic solution otherwise. Computational results are shown in both cases.     

Minimizing tardiness on a single processor with sequence-dependent setup times: a simulated annealing approach

In today's fast-paced Just-In-Time and mass customization manufacturing in a sequence-dependent setup environment, the challenge of making production schedules to meet due-date requirements is becoming a more complex problem. Unfortunately, much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper considers the problem of minimizing tardiness, a common measure of due-date performance, in a sequence-dependent setup environment. Simulated annealing was used to solve the sequencing problem, and its performance was compared with random search. Our experimental results show that the algorithm can find a good solution fairly quickly, and thus can rework schedules frequently to react to variations in the schedule. The algorithm is invaluable for ‘on-line’ production scheduling and ‘last-minute’ changes to production schedule. The results of this research also suggest ways in which more complex and realistic job shop environments, such as multiple machines with a higher number of jobs in the sequence, and other scheduling objectives can be modeled. This research also investigates computational aspects of simulated annealing in solving complex scheduling problems.     

混合离散差分进化算法在单机批处理调度中的应用

本文研究单机批处理调度问题,批处理机有批次容量限制,批处理时间由每个批次所含作业中的最长作业处理时间决定。每个作业具有不同的大小、处理时间、提前拖期惩罚权重,所有作业具有公共交货期,且交货期无限晚。目标函数为最小化所有作业的加权提前拖期惩罚之和。该问题已被证明为NP难题,本研究找到了其最优解具有的一些性质,在此基础上利用它们提出了一种动态规划(DP)与差分进化(DE)算法相结合的混合离散差分进化(HDDE)算法来求解该问题,通过与传统的遗传算法、模拟退火算法和迭代贪婪算法进行对比,HDDE算法显示了更加强大的全局搜索能力。     

不确定条件下不同交货期窗口的Job Shop 调度

研究了具有不同交货期窗口的Job Shop 的提前/ 拖期调度问题,并考虑了处理时间的不确定 性,采用三角模糊数表示处理时间的不确定性,提出了基于遗传算法的求解算法. 仿真实验验证了 算法的有效性.     

Growing Up in A Single Mother Family and Student’s Tardiness: A Cross-National Study Exploring the Moderating Role of Family Resources

Research shows that growing up in a single-parent family has a negative effect on children’s educational level, whereas the relationship between family structure and test scores is less consistent or even nonexistent in some countries. Some authors suggested that something besides cognitive ability is responsible for the poorer school outcomes of children from nonintact families. In this study, we focus on a noncognitive outcome, in this case student tardiness, which is one of the components of problematic absenteeism. Using PISA 2003 data from Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Italy, New Zealand, Norway, Portugal, Spain, Sweden, the United Kingdom, and the United States, we find that children who grow up in a single-mother family have greater chances of arriving late for school in 16 of the 17 countries analyzed. Some studies have analyzed the extent to which the effect of growing up in a single-mother family is compensated by a high level of family resources. However, these yield mixed findings, which can be attributed to the differences between the countries studied. With the exception of Bernardi and Radl (2014), to our knowledge no study analyzes the heterogeneity of family structure effects using a cross-national approach. We find that in most of the 17 countries analyzed, a high level of family resources, such as home possessions, cultural resources, mother’s occupational and educational level, and mother’s type of work, do not compensate for the harmful effects of growing up in a single-mother family on children’s school tardiness.     

Evaluation of simple dispatching rules for dynamic single-machine earliness/tardiness problem

Abstract. This paper investigates the effects of four simple dispatching rules on just-in-time production related performance measures of mean and maximum absolute lateness. The rules used are modified due date (MDD), shortest processing time (SPT), earliest due date (EDD), and first in first out (FIFO). A single machine is used under three utilization levels. Due-dates are set according to total work content rule. The results indicate that each rule performs well under certain conditions. The MDD rule is the best one to minimize mean absolute lateness. The EDD and FIFO rules do well in minimizing the maximum absolute lateness. Economic interpretation of these performance measures are also discussed.