Simulated Job Shop Research Articles

Flow time analyses of a simulated flexible job shop by considering jockeying

It has been essential to include flexibility in manufacturing policy making since variability in demand and products are considerably increasing. However, it is important to know and to monitor the proper level and type of flexibility that is required to obtain full benefits from it. This paper analyses the effects of flexibility on flow time performance of a simulated job shop. For that purpose, several scenarios are developed under four flexibility levels with two different machine selection rule and three types of dispatching rules. Furthermore, effect of jockeying as a queuing policy on the flow time performance is also investigated through simulation modeling. Results indicated that full flexibility is a preferable state for most of the cases. However, in some cases, chain configurations perform similar results since it combines the benefits of pooling and specialization. In addition, it is observed that a queue control mechanism like jockeying is an effective way to improve performance even though it may increase complexity of controlling policy.

Genetic Programming Based Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop

In this paper, a genetic programming based data mining approach is proposed to select dispatching rules which will result in competitive shop performance under a given set of shop parameters (e.g. interarrival times, pre-shop pool length). The main purpose is to select the most appropriate conventional dispatching rule set according to the current shop parameters. In order to achieve this, full factorial experiments are carried out to determine the effect of input parameters on predetermined performance measures. Afterwards, a genetic programming based data mining tool that is known as MEPAR-miner (multi-expression programming for classification rule mining) is employed to extract knowledge on the selection of best possible conventional dispatching rule set according to the current shop status. The obtained results have shown that the selected dispatching rules are appropriate ones according to the current shop parameters. All of the results are illustrated via numerical examples and experiments on simulated data.

Gene expression programming based due date assignment in a simulated job shop

In this paper, a new approach for due date assignment in a multi-stage job shop is proposed and evaluated. The proposed approach is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is a relatively new member of the genetic programming family. The primary objective of this research is to compare the performance of the proposed due date assignment model with several previously proposed conventional due date assignment models. For this purpose, simulation models are developed and comparisons of the due date assignment models are made mainly in terms of the mean absolute percent error (MAPE), mean percent error (MPE) and mean tardiness (MT). Some additional performance measurements are also given. Simulation experiments revealed that for many test conditions the proposed due date assignment method dominates all other compared due date assignment methods.

Optimization and analysis aid via data-mining for simulated production systems

The optimization of a production system consists of determining a value for certain parameters which influence system performance. However, the majority of optimization methods deliver a solution without any form of explanation and this is no longer sufficient in a production context. Decision-makers would also like to have an analysis of their systems and especially of the high-performance behavior of those systems. In order to avoid the “black box” effect of many optimization methods, and to produce knowledge on system behavior (characterization of solutions that perform well, determination of critical parameters) and analyze efficient solutions, the author proposes a methodology which is based on the synergy between evolutionist optimization and an induction graph learning method. This approach is illustrated via the study of a simulated job-shop composed of five workstations, one entry station and one exit station; the numbers of machines at each station, the management method and the number of stock places in each station have to be optimized and analyzed.

Object oriented modelling and development of a dispatching algorithm for automated guided vehicles

The main problem for an automated guided vehicle (AGV) dispatching system is to assign vehicles to transport demands which optimise some predetermined objectives of a manufacturing shop. This paper presents a framework for an AGV dispatching system based on an object oriented approach using the unified modelling language (UML), and the development of a dispatching algorithm to facilitate a human controller to dispatch efficiently a fleet of AGVs in response to calls from any shop floor (or machine) operator. The main reason for this work is to model an AGV dispatching system as well as to develop a dispatching algorithm which can record details of the AGV position and movement and ensure their allocation of new orders. The provision of both immediate and pre-booked orders for an AGV is also incorporated in the proposed AGVs dispatching system. The underlying AGV dispatching system and algorithm are capable of dispatching a vehicle automatically to handle a call at the required time. In order to overcome difficulties associated with tackling immediate orders, pre-booked orders, and processing of information related to AGVs, a comprehensive dispatching algorithm is developed which aims to minimise lateness, traveling time and distance of empty vehicles in a simulated job-shop scenario. The effectiveness of the proposed framework for an AGV dispatching system is shown through a test problem.

Dynamic rescheduling that simultaneously considers efficiency and stability

Dynamic job shop scheduling is a frequently occurring and highly relevant problem in practice. Previous research suggests that periodic rescheduling improves classical measures of efficiency; however, this strategy has the undesirable effect of compromising stability and this lack of stability can render even the most efficient rescheduling strategy useless on the shop floor. In this research, a rescheduling methodology is proposed that uses a multiobjective performance measures that contain both efficiency and stability measures. Schedules are generated at each rescheduling point using a genetic local search algorithm that allows efficiency and stability to be balanced in a way that is appropriate for each situation. The methodology is tested on a simulated job shop to determine the impact of the key parameters on the performance measures.

Negotiation-based routing in job shops via collaborative agents

In recent years, the application of agent-based systems in manufacturing has received a lot of attention in several areas, particularly scheduling and shop-floor control. This paper explores two methods of enhancing the negotiation process employed by an agent-based system to support performance improvements in real-time routing of job orders within job-shop environments. The first method takes advantage of an extended negotiation period to provide a more complete picture of the shop's conditions in order to enhance the validity of the decisions made by individual agents. The second approach explores the possibility of process model data to increase the accuracy of time estimates used in the negotiation process. The resulting performance of the two methods is tested using a simulated job shop.

An Evaluation of Flexible Workday Policies in Job Shops*

ABSTRACTJob shops have long faced pressures for improvement in a challenging and volatile environment. Today's trends of global competition and shortening of product life cycles suggest that both the challenges and the intensity of market volatility will only increase. Consequently, the study of tactics for maximizing the flexibility and responsiveness of a job shop is important. Indeed, there is a significant body of literature that has produced guidelines on when and how to deploy tactics such as alternate routings for jobs and transfers of cross‐trained workers between machines.In this paper we consider a different tactic by adjusting the length of workdays. Hours in excess of a 40‐hour week are exchanged for compensatory time off at time and a half, and the total amount of accrued compensatory time is limited to no more than 160 hours in accordance with pending legislation. We propose several simple flexible workday policies that are based on an input/output control approach and investigate their performance in a simulated job shop. We find significant gains in performance over a fixed schedule of eight hours per day. Our results also provide insights into the selection of policy parameters.

Tooling constraints and shop floor scheduling: evaluating the impact of sequence dependency

SUMMARY In a previous study, we began the process of examining how finite tooling resources affect the operation of a simulated job shop. The paper extends this line of research by focusing on the problem of how to schedule a shop operating with both finite machine and tooling resources, and faced by varying levels of tooling-related sequence dependency in the setups. Four tool assignment rules and two dispatching procedures are evaluated. Findings show the importance of using tool assignment rules which consider not only tool-related information such as tool availability but also the priority of the jobs waiting to be processed. Specifically, as the level of sequence dependence increases and the availability of tooling decreases, the performance of the shop is greatly influenced by the tool assignment rule used to manage the flow of tooling to and from work centres

Man/Computer versus Automatic Scheduling

Automatic scheduling was compared with man/computer interactive scheduling for a simulated job shop in experimental sessions. Factors of the job stream were controlled and those factors which contributed to improved man/computer scheduling versus automatic scheduling were identified. Results show that man/computer scheduling generally was superior to automatic scheduling; very significantly in several job stream situations but in one situation the automatic method was best.

Studies in a Simulated Job Shop

The effectiveness of various scheduling or loading rules in a job shop has been widely studied through simulation, where a given shop can be subjected to alternative patterns of arrivals and processing. This study is particularly concerned with the case when arrivals are in batches, either of regular size or where the batch size is Poisson distributed, and when the batches arrive at regular time intervals, and certain empirical relationships between three loading rules are investigated. Two types of operational flexibility are examined, the first where more than one machine may perform a given operation and the second where changes in the sequence of operations are allowed, and the effect of such flexibilities on the expected job waiting time is determined. Finally, the effect of different procedures for determining due-dates for arriving jobs is investigated.

Interactive planning: a study of computer aiding in the execution of a simulated scheduling task

This paper examines some of the problems and possibilities of interactive decision-making within the experimental context of an industrial problem-production scheduling. An experiment is described in which subjects were required to produce items from, and generally control, a simulated job shop. A predictive facility was introduced into the simulation in order to study its effect on search strategies and, in particular, determine whether the decision horizon could be extended. One of the main themes underlying the experiment was an investigation of the extent to which subject problem-solving strategies could be formalized and incorporated into solution-seeking procedures. The experimental results show that the type of search was relatively unaffected by the predictive facility. An explanation of this result and a discussion of the subjects strategies is offered in the light of information obtained from the analysis of verbal protocol material.