Conceptual modeling of an object-oriented scheduling architecture based on the shifting bottleneck procedure

The importance of finite-capacity schedulers is increasing, with respect to the widespread MRP packages, due to their ability to model the shop floor more accurately. However, this very advantage may turn into a disadvantage, since it is quite difficult to devise a high-quality general purpose scheduler able to cope with the technological peculiarities of different production environments. Furthermore, a detailed schedule is prone to disruptions due to the uncertainty affecting the shop floor. Hence, we need both a modular approach to devise and assemble local schedulers, and a way to link predictive and real time scheduling. To cope with both requirements, we propose a scheduling architecture inspired by the well-known Shifting Bottleneck method. The knowledge needed to cope with technological peculiarities is pushed down the decision hierarchy, to the level of local schedulers. A material coordinator negotiates a reference trajectory with the local schedulers, by deriving local due dates which can be used as targets to drive real time dispatching. The modularity of the architecture is illustrated through an object-oriented conceptual model based on the Unified Modeling Language.     

Models for Determining Estimated Start Times and Case Orderings In Hospital Operating Rooms-

In this paper, we address a number of scheduling problems that are often faced in a hospital's operating room (OR). The operating room can be modeled as a one machine job shop where the surgical procedures are thought of as the jobs and the operating room itself is the machine. The procedure (job) times are stochastic and the operating room scheduler exerts control over the schedule of jobs. The situation can also be thought of as a D/GI/1 queueing system, where the arrival times of the customers are a decision variable.

Initially we address the problem where the scheduler is given the sequence of jobs and must determine the estimated starting times of the procedures in order that the surgeons may plan their personal schedules with respect to hospital rounds and office visits. The costs that must be balanced are (1) the idle time costs if the estimated starting time is later than the actual available start time, and (2) the surgeon's waiting time if the estimated starting time is before the actual ending time of the previous case. We also address the problem of jointly determining both the optimal order and the assigned starting times of a given group of procedures. The estimated starting times of the procedures are found to depend upon a single critical number that is easily determined from the relevant costs of the problem. Both analytic and simulation results are presented. The results of this paper can be used directly in hospital operating rooms as well as any system where a decision maker can exert control over the arrivals.     

A goal programming model for the operation of closed-loop supply chains

The implementation of closed-loop supply chains to handle the problem of increasing levels of electronic and electrical equipment waste can be beneficial both economically and ecologically. Three different problem analysis techniques—why–what's stopping analysis, fundamental objective hierarchy, and means objective network—are used to analyse the problem from various perspectives. A non-preemptive goal-programming model and solution approach have also been developed, with goals being assigned different weights according to the respective goal priorities. The model considers multiple products, as well as operations associated with the product, subassembly, part, and material levels. A major contribution of the research involves the fact that the objectives and related constraints for each member of the supply chain are explicitly modeled. The results of the analysis show both the effect of varying the priority/weight associated with a goal, and how the values of the deviational variables can aid a decision maker in model analysis.     

A Simple Algorithm for A Workforce Scheduling Model

This paper presents a simple solution algorithm to the problem of scheduling workforce for an organization operated seven days a week. For a given number of full-time workers, the objective is to maximize the total number of workers who get two consecutive days off. It can be shown that due to the special structure of the derived mathematical model, an optimal schedule may be found by a few arithmetic steps without the computer assistance which other existing methods require.     

A Computer-Aided Graphical Procedure for Scheduling Preventive Maintenance of Electric Power Generating Units

This article attempts to show how computer graphics can assist in computerized development of project-type schedules as exemplified by preventive maintenance scheduling in the electric power industry. The method is equally applicable to the scheduling of maintenance activities in general or, with modification, to devising shift schedules for personnel such as police, nurses, and toll collectors who must provide 24-hour service with changing demand levels. A hand procedure currently used by electric utilities for creating preventive maintenance shutdown schedules for generator units is first described. A mixed integer programming model of the problem is given which is computationally intractable for any realistic-size problems. The same problem using man-machine interaction via a graphics terminal to overcome the computational limitations of the mathematical formulation is then described and illustrated. A proposed extension to improve the speed of solution is also described.     

原油处理过程短期生产计划优化

在原油处理过程短期生产计划的递阶求解方法中,原油处理短期生产计划问题分为上下两层,上层根据市场需求产生一个目标炼油计划;在此基础上,下层得到一个详细生产计划以实现目标炼油计划。研究了在上层目标炼油计划已知的情况下,下层详细生产计划的求解问题。为该问题建立了基于离散时间表示的混合整数线性规划模型,分析了问题的特点并将其进行转化,给出了基于启发式的求解方法,在保证目标炼油计划实现的前提下,对原油转运过程中油品切换及不同油品的罐底混合进行了优化,取得了一定的成果。用一个工业实例验证了启发式规则的可行性和有效性。     

ROBOT SCHEDULING IN A CIRCUIT BOARD PRODUCTION LINE:A HYBRID OR/ANN APPROACH

A hybrid method that combines human intelligence, an optimization technique (semi-Markov decision model) and an artificial neural network to solve real-time scheduling problems is proposed. The proposed method consists of three phases: data collection, optimization, and generalization. The testbed of this approach is the robot scheduling problem in a circuit board production line where one overhead robot is used to transport jobs through a line of sequential chemical process tanks. Because chemical processes are involved in this production system, any mistiming or misplacing will result in defective jobs. The proposed hybrid system performs better than the human scheduler from whom the models were formulated, both in terms of productivity and quality.     

Development of a real-time multi-objective scheduler for a semiconductor fabrication system

Semiconductor wafer fabrication involves possibly one of the most complex manufacturing processes ever used. This causes a number of decision problems. A successful system control strategy would assign appropriate decision rules for decision variables. Therefore, the goal of this study is to develop a scheduler for the selection of decision rules for decision variables in order to obtain the desired performance measures given by a user at the end of a certain production interval. In this proposed methodology, a system control strategy based on a simulation technique and a competitive neural network is suggested. A simulation experiment was conducted to collect the data containing the relationship between the change of decision rule set and current system status and the performance measures in the dynamic nature of semiconductor manufacturing fabrication. Then, a competitive neural network was applied to obtain the scheduling knowledge from the collected data. The results of the study indicate that applying this methodology to obtaining a control strategy is an effective method considering the complexity of semiconductor wafer fabrication systems.     

Due-date determination with resequencing

Traditional scheduling and due-date determination models assume that the production system is operating in a static and deterministic environment and that the system carries no workload at each scheduling epoch. In this research we consider a due-date determination model where the scheduler wishes to update the existing schedule when some new jobs have: arrived into the system. In this model, jobs are categorized as either "old" or "new" jobs, where the due-dates of the old jobs are treated as given parameters and those of the new jobs are decision variables. The objective is to minimize the maximum weighted tardiness penalty and the due-date assignment cost. The computational complexity of this model is analyzed, and an efficient algorithm is developed for an important special case.     

Performance-based dynamic scheduling model for flexible manufacturing systems

A performance-based dynamic scheduling model for random flexible manufacturing systems (FMSs) is presented. The model is built on the mathematical background of supervisory control theory of discrete event systems. The dynamic FMS scheduling is based on the optimization of desired performance measures. A control theory-based system representation is coupled with a goal programming-based multi-criteria dynamic scheduling algorithm. An effectiveness function, representing a performance index, is formulated to enumerate the possible outputs of future schedules. Short-term job scheduling and dispatching decisions are made based on the values obtained by optimizing the effectiveness function. Preventive actions are taken to reduce the difference between actual and desired target values. To analyse the real-time performance of the proposed model, a software environment that included various Visual Basic Application® modules, simulation package Arena®, and Microsoft Access® database was developed. The experimentation was conducted (a) to determine the optimum look-ahead horizons for the proposed model and (b) to compare the model with conventional scheduling decision rules. The results showed that the proposed model outperformed well-known priority rules for most of the common performance measures.     

A multi-level heuristic search algorithm for production scheduling

This paper introduces a multi-level heuristic search algorithm for identifying the optimal production schedule considering different levels of manufacturing requirements and constraints. The multi-level heuristic search algorithm generates search nodes at different levels. An upper level search node is composed of lower level search nodes, and evaluated based upon the evaluation of these lower level search nodes using a heuristic function. A production scheduling system was developed based upon the multi-level heuristic search algorithm. In this scheduling system, production requirements and constraints are represented at three different levels: task level, process level, and resource level. A task describes a manufacturing requirement. A process defines a method to achieve the goal of a task. A resource, such as a machine or a person, is a facility for accomplishing a required process. The multi-level heuristic search-based scheduling system was implemented using Smalltalk, an object-oriented programming language. Discussions on scheduling quality and efficiency are addressed at the end of this paper.     

Heuristic solution to the scheduling problems in flexible manufacturing system

The scheduling problems in flexible manufacturing systems deal with (1) tool allocation (2)parts scheduling (3) pallets scheduling (4) machines scheduling and (5) material handling equipment scheduling. This paper presents an approach to determine an optimal schedule of parts integrating all the above scheduling criteria. The problem is formulated as a hierarchical process and solved through eigenvector analysis of priority ordering. Effectiveness of the heuristic is illustrated with an example.     

Optimal specialization of a maintenance workforce

This article develops an analytical method for determining an optimal specialization strategy for a maintenance workforce. The method assumes that maintenance tasks are generated by a system of statistically identical machines that experience random malfunctions and require periodic service. The impact of alternative workforce structures on system performance is evaluated with a queueing network model. Markov decision analysis is employed to determine an optimal assignment of maintenance personnel to pending tasks as the network status varies over time. A linear programming algorithm is derived to enable simultaneous optimization of specific assignment decisions and the overall workforce structure. A manufacturing example demonstrates the applicability of the method to many industrial contexts. The method is also applied to the problem of maximizing military aircraft sortie generation subject to a constraint on maintenance personnel expenditure.     

An interactive scheduler for a wafer probe centre in semiconductor manufacturing

This paper analyses production operations and scheduling constraints on a wafer probe centre in semiconductor manufacturing. Such actions as lot split and hot lot preemption are discussed. Due to the combinatorial difficulties of scheduling problems with sequence-dependent setup costs and multiple criteria, a humancomputer interactive scheduler named the 'interactive computer aided scheduling system' (ICASS) is proposed. This ICASS can search for schedules to achieve a specific performance level set by a human. An experiment was conducted on a wafer probe centre to compare the performance of ICASS with that of a manual approach and six priority rules. The results indicate that ICASS spent less scheduling time and provided better schedules than the manual approach. All the six priority rules yielded only poor schedules with unbalanced performance in three kinds of criteria, although they were beneficial in computation time.     

A Multifactor Priority Rule for Jobshop Scheduling Using Computer Search

Priority rules are widely used in jobshop scheduling to determine the sequence in which jobs are to be processed. The research in this area has been directed at developing generally applicable priority rules. This paper presents a method for determining an effective priority rule specific to the jobshop scheduling problem to be solved. First, a generalized objective function is formulated which is the sum of costs of tardiness, carrying in-process inventory and machine idleness. Second, a multifactor priority rule is developed which is a weighted average of four factors used in simple priority rules. Third, a method is presented for using a computer search technique to determine the best weights to use in the priority rule. Finally, a computer simulation for testing this approach versus using other priority rules is described and the experimental results are reported.     

Goal Programming Model Subsystem of a Flexible Nurse Scheduling Support System

A model for nurse scheduling via the goal programming versions of the traditional “set covering” model is discussed in detail. The demand profile and nurses preferences are fed into an expert like capability designed to formulate a goal programming representation of the problem. Solutions to the goal programmed model are made available for various goal priority options. Each solution can be disaggregated into specific assignments for specific units and nurses.     

物料需求计划不稳定性的模拟研究

介绍物料需求计划不稳定性的基本概念和模拟研究方法。在不确定性需求的流动式计划环境下,研究冻结参数和计划算法在不同生产条件下对物料需求计划不稳定性的影响。通过设计模拟实验和大量模拟计算及统计分析表现：费用结构、预测模式、冻结比例、计划周期和计划算法对物料需求计划不稳定性有较大影响,且交互作用显著。研究结果对减小物料需求计划的不稳定性有一定指导意义。     

A Multiple Objective Nurse Scheduling Model

The authors present a model for the nurse scheduling problem which works in two phases. In the first phase, the nurses are assigned their day-on/day-off pattern for the two-week scheduling horizon by a goal programming model which allows for consideration of the multiple conflicting objectives inherent in scheduling a nursing staff. The second phase makes specific shift assignments through the use of a heuristic procedure. The two-phase approach results in considerable reductions in problem size, thus reducing the solution effort. Extensions to the basic model are also examined.     

A problem of aggregate scheduling An application of goal programming

The paper introduces mi overall scheduling problem. One crucial aspect is identified; this is the production of an aggregate schedule which is to be fed into the detailed scheduling package currently employed. The problem is seen to be that of achieving a balance between a smooth work-load on the factory floor and matching production with promised delivery dates.

A goal programming model for this coordination is developed and discussed. Numerical examples illustrating the approach are presented, and computational experience is discussed.     

Shift scheduling for steppers in the semiconductor wafer fabrication process

In this paper, an approach is proposed for scheduling stepper machines that are acting as bottleneck machines in the semiconductor wafer fabrication process. We consider the problem of scheduling the steppers for an 8 hour shift, determining which types of wafer lots to work on each machine. The scheduling objective is to find the optimal stepper allocations such that the schedule meets target production quantities that have been derived from the given target Work-In-Process (WIP) levels. A Mixed Integer Programming (MIP) model is formulated, and three heuristic approaches are proposed and tested to approximately solve the M1P model. Numerical tests show that one of the proposed heuristics using linear programming relaxation of MIP generates, on average, schedules within 5° of the optimum values.