The surprising effectiveness of earliest operation due-date sequencing

In make-to order production, schedule reliability is very important but still not sufficiently accomplished in industrial practice by the vast majority of companies. It has been known for long that processing the orders at a workstation in the order of their operation due-dates can compensate for lateness in the arrival at the workstation within certain boundaries. The paper analyses the effectiveness of earliest operation due date (EODD) sequencing by comparing it to an optimistic theoretical boundary. The surprising result is that EODD can nearly fully exploit the theoretical potential. It should therefore be used in practice whenever schedule reliability is important, with only few exceptions. Its effectiveness though is increasing with the workstation’s WIP level and thus is in conflict with the objective to reduce WIP levels and throughput times. A simple forecasting model allows to assess the extent to which lateness can be compensated by EODD sequencing and which schedule compliance can be achieved.     

A hybrid genetic algorithm/mathematical programming approach to the multi-family flowshop scheduling problem with lot streaming

This paper presents a hybrid genetic algorithm/mathematical programming heuristic for the n-job, m-machine flowshop problems with lot streaming. The number of sublots for each job and the size of sublots are directly addressed by the heuristic and setups may be sequence-dependent. A new aspect of the problem, the interleaving of sublots from different jobs in the processing sequence, is developed and addressed. Computational results from 12 randomly generated test sets of 24 problems each are presented.     

Time-based goal chasing method for mixed-model assembly line problem with multiple work stations

In sequencing problems for mixed-model assembly line in JIT production system, the Goal Chasing method (GC) is widely used for parts used leveling goal. The difference in assembly time of each product is not taken into consideration in the Goal Chasing method. Assembly time usually varies with product types. In recent years, the Time-Based Goal Chasing method (TBGC) has been proposed. The advantage of TBGC is to consider the influence of different assembly time of each product and idle time in production period. TBGC, however, has been only applied to single work station problems. In this paper, TBGC is applied to an assembly line problem with multiple work stations. Furthermore, the sequencing method and use of Simulated Annealing (SA) or Local Search (LS) for this problem are proposed.     

On the NEH heuristic for minimizing the makespan in permutation flow shops

Over the last 20 years the NEH heuristic of Nawaz, Enscore, and Ham published in this journal has been commonly regarded as the best heuristic for minimizing the makespan in permutation flow shops. In recent years some authors claimed to develop new heuristics that are competitive or outperform NEH. Our study reveals that these claims are not justified. We also address the issue of a fair comparison of the NEH results with those obtained by metaheuristics. Finally we conduct a thorough analysis of NEH leading to its modification which secures the optimality in the two-machine case and improves the general performance.     

A mathematical model for job shop scheduling with multiple process plan consideration per job

In this paper, a mixed integer programming model is formulated for scheduling a set of jobs through a shop when each job is supplied or provided with multiple process plans or process routings. Simultaneous selection of a process plan for each job and the sequencing of the jobs through the machines in the shop based on the set of selected process plans is addressed. The procedure developed seeks to integrate the selection of machines for each job and the sequencing of jobs on each machine based on the objective of minimizing production makespan. the application of the procedure is demonstrated with an example problem. The following conclusions were drawn as a result of the research: (1) the procedure developed produces optimal or near optimal solution; (2) the benefit from the developed approach is that it allows a shop to adaptively select process plans for jobs to optimize on production makespan. By combining solution quality with scheduling flexibility and efficiency, the productivity of a shop can be greatly enhanced.     

计算机模拟技术在制定月生产计划中的应用

本文设计了一个产品按月出产计划的计算机模拟方案,该方案具有计算机自动调整和人工调整的功能,大大加快了计划的编制速度。     

Analysis of antithetic sequences in flowshop scheduling to minimize makespan

The antithetic properties of flowshop sequences are investigated to improve the classical Monte Carlo method for solving the n -job, m -machine problem with minimization of makespan. The major issues considered are (1) establishing a negative correlation of the makespan values of forward and reverse sequences; (2) developing the Antithetical Monte Carlo (AMC) method, which can be used to quickly estimate the mean of the makespan distribution by exploiting the antithetic property of sequences; (3) using AMC to find low makespan values; (4) determining a threshold value of makespan beyond which it would be likely to find an optimal or near optimal makespan when reversing a sequence. Statistical tests indicate that the performance of AMC is superior to that of the classical Monte Carlo method. Possible applications of this concept are discussed including extensions to other mathematical problems with antithetic properties.     

Analysis of justification problems in CIMS review and projection

Attempts to study justification problems related to advanced manufacturing systems such as CIMS are made. Such systems conceptually promise industries many significant advantages, but many justification procedures that are used in appraising such system proposals remain ill-suited because of their failure to incorporate multifaceted strategic and tactical issues. The aim in this paper is to review some of these procedures and finally propose a total systems approach. The objective is to make a prognosis of the issues to prepare a projective base for organizational decision support. Two case studies from Indian industries where CIMS have been tried out are also described.     

经营者模糊需求在激励设计中的精确化研究

经营者报酬设计是一个非常复杂的系统性问题,复杂性表现为:委托人和经营者的需求是多方面的,各种需求与社会制度、文化环境、法律法规、政策、市场、价值观、道德、人格等因素之间存在非线性关系,而且有很多需求,尤其是心理情感方面的需求,是模糊不清的,任何一方要获得完全信息的成本非常高.因此,在现实生活中,委托人和经营者无一不是在人力资本价值无法估计、需求模糊、信息不对称的前提下,根据他们所掌握的有限信息进行协商或讨价还价的.本文从委托人、经营者都有模糊需求出发,利用模糊数刻画他们所有可能的需求,确定各自模糊需求的顺序,根据经营者、委托人模糊集的交集和需求顺序的比较,最终回答模糊环境下经营者激励设计中的解模糊思路和措施.     

Lot Splitting in Stochastic Flow Shop and Job Shop Environments*

In recent years many firms have been implementing small lot size production. Lot splitting breaks large orders into smaller transfer lots and offers the ability to move parts more quickly through the production process. This paper extends the deterministic studies by investigating various lot splitting policies in both stochastic job shop and stochastic flow shop settings using performance measures of mean flow time and the standard deviation of flow time. Using a computer simulation experiment, we found that in stochastic dynamic job shops, the number of lot splits is more important than the exact form of splitting. However, when optimal job sizes are determined for each scenario, we found a few circumstances where the implementation of a small initial split, called a “flag,” can provide measurable improvement in flow time performance. Interestingly, the vast majority of previous research indicates that methods other than equal lot splitting typically improves makespan performance. The earlier research, however, has been set in the static, deterministic flow shop environment. Thus, our results are of practical interest since they show that the specific method of lot splitting is important in only a small set of realistic environments while the choice of an appropriate number of splits is typically more important.