An economic approach for finding a time-scaled utilization profile for a bending press shop

Abstract

Finding the utilization of a set of machines in a shop floor of a production system by the ratio delay method is a well-known practice in conventional time and motion studies. In this method, the desired reliability of the study is preset and subsequently the data are collected to achieve that reliability. I do not continuously review the reliability in this method while conducting the study nor do I measure the benefit-cost ratio of the undergoing study. In this paper, I describe a method to determine the utilization of a set of production facilities and continuously review the reliability achieved at the end of each survey period. The random observations about the machine status generate a utilization profile of a set of machines on a continuous time scale. Based on the benefit-cost ratio, this method provides us with an information as to when the study has to be stopped in a real time situation. The study is substantiated with a case study and an indication of further research.     

Improving throughput for an electronic assembly line using a constraint analysis methodology

Electronic assembly operations are vital to industries such as telecommunications, computers and consumer electronics. This paper presents a constraint analysis methodology for planning and improving electronic assembly operations that draws on concepts from queueing theory, simulation and production planning. The proposed methodology identifies the operational bottleneck and predicts the utilization, throughput and lead time of the assembly line. It also quantifies the relationship between yields and utilization for the assembly operations. A case study is presented that applies the methodology at an Ericsson, Inc., telecommunications equipment assembly facility. The constraint analysis methodology provided valuable decision support as the managers of Ericsson evaluated the costs and benefits of additional production capacity. Although the focus of this paper is electronic assembly operations, the methodology can be applied to general flow line assembly systems with feedback loops for test and rework under dedicated high-volume production.     

An approximate summarization method of process data for acquiring knowledge to improve product quality

This paper describes a machine learning approach for a manufacturing database. The method is presented in the Nb-Ti superconducting wire domain. A Nb-Ti superconducting wire is produced by iterating the drawing and heat treatment operations. The purpose is to obtain approximate summarization of process data that describes how a production schedule can be improved for better product quality. The method consists of the following steps: First, define a ranking function for a production schedule. Then, generate 'positive' and 'negative' instances for improving a production schedule by comparing a pair of schedules and their ranking values in the database. Using a machine learning technique, called 'ID3', a 'modification patterns' are obtained that generalize the data for better production quality. The final step is to extract approximate information from the induced patterns, which is both desirable for easier understanding by human experts and necessary to avoid being too much influenced by excessive details or disturbances. Two criteria are proposed, correctness and applicability indices, for this approximation.     

Production lot sizing with process deterioration and machine breakdown under inspection schedule

The paper develops integrated production, inventory and maintenance models for a deteriorating production system in which the production facility may not only shift from an ‘in-control’ state to an ‘out-of-control’ state but also may break down at any random point in time during a production run. In case of machine breakdown, production of the interrupted lot is aborted and a new production lot is started when the on-hand inventory is depleted after corrective repair. The process is inspected during each production run to examine the state of the production process. If it is found in the ‘in-control’ state then either (a) no action is taken except at the time of last inspection where preventive maintenance is done (inspection policy-I) or (b) preventive maintenance is performed (inspection policy-II). If, however, the process is found to be in the ‘out-of-control’ state at any inspection then restoration is done. The proposed models are formulated under general shift, breakdown and repair time distributions. As it is, in general, difficult to find the optimal production policy under inspection policy-I, a suboptimal production policy is derived. Numerical examples are taken to determine numerically the optimal/suboptimal production policies of the proposed models, to examine the sensitivity of important model parameters and to compare the performance of inspection and no inspection policies.     

Scheduling algorithms for computer-aided line balancing in printed circuit board assembly

Generalized flexible flow line (GFFL) is a scheduling environment comprising several machine banks which the products visit in the same order but can skip some machine banks. The type of machines in a bank can differ but they are suitable for performing the same manufacturing tasks. To change one product to another demands a set-up operation of the machine. This paper describes several scheduling algorithms for the GFFL problem. The overall structure of these algorithms is similar, consisting of machine allocation and sequencing phases. The algorithms have been integrated into an interactive production scheduling system for electronics assembly. Sample cases are used to illustrate the operation of the system in practice.     

Development and implementation of a knowledge-based system for the dynamic scheduling of a two-stage production process

Abstract

This paper discusses the implementation of a knowledge-based system for the dynamic scheduling of a two-stage production process. It is an interactive, real-time, menu-driven system written in Prolog. The system architecture is delineated and the rules and problem-solving logic to be used under various dynamic situations are described. Results of a sample system test session are included to illustrate its use.     

A simulated annealing algorithm for designing cellular manufacturing systems with productivity consideration

In this paper, we propose a productivity model for solving the machine-part grouping problem in cellular manufacturing (CM) systems. First, a non-linear 0-1 integer programming model is developed to identify machine groups and part families simultaneously. This model aims to maximize the system productivity defined as the ratio of total output to the total material handling cost. Second, an efficient simulated annealing (SA) algorithm is developed to solve large-scale problems. This algorithm provides several advantages over the existing algorithms. It forms part families and machine cells simultaneously. It also considers production volume, sales price, and maximum number of machines in each cell and total material handling cost. The proposed SA also has the ability to determine the optimum number of manufacturing cells. The performance of the developed models is tested on eight problems of different size and complexity selected from the literature. The results show the superiority of the SA algorithm over the mathematical programming model in both productivity and computational time.     

Analysis of integrated procurement — production systems using mathematical and simulation modelling approaches

Abstract

Integrated procurement-production (IPP) systems offer interesting scope for unifying procurement and production policies in a batch environment. The available literature on IPP is restrictive in its scope for applying to real-life situations. In this paper, an attempt is made to widen the scope of application by judiciously combining an analytical model with a simulation model. The results presented apply to a number of experimental studies carried out with this hybrid approach. The usefulness of such a hybrid approach is highlighted through the results pertaining to a number of simulated problem sets generated.     

Flexible reasoning using sensible agent-based systems: A case study in job flow scheduling

Planning and control systems for highly dynamic and uncertain manufacturing environments require adaptive flexibility and decision-making capabilities. Modern distributed manufacturing systems assess the utility of planning and executing solutions for both system goals (e.g. minimize manufacturing production time for all parts or minimize WIP) and local goals (e.g. expedite part A production schedule or maximize machine X utilization). Sensible Agents have the ability to alter their autonomy levels to choose among a set of decision models in order to handle the differences between local and system goals. In this paper, Sensible Agents are applied to a production planning and control problem in the context of job shop scheduling and decision model theory. Sensible Agents provide for trade-off reasoning mechanisms among system and local utilities that are flexible and responsive to an agent's abilities, situational context and position in the organizational structure of the system.     

A SINGLE-MACHINE SCHEDULING MODEL WITH FIXED-INTERVAL DELIVERIES

We consider an environment where a production facility modeled as a single machine needs to assign delivery dates to several orders and find a feasible sequence. Tardy jobs are not allowed. The delivery dates are to be at prespecified fixed intervals. The objective is to minimize the due date penalty and the cost of earliness. We provide a dynamic programming-based solution procedure that runs in polynomial time. We develop several dominance results that reduced the computational requirement by an order of magnitude in our computational study.     

Analysis of a batch machine fed by an unreliable discrete machine

This paper analyses the behaviour of a manufacturing line consisting of two machines in series where the first machine processes lots one at a time, and is subject to failure and takes a random amount of time to repair when it fails, and the second machine is a perfectly reliable batch machine. A control limit policy is adopted to determine lot sizes for the batch machine. When the batch machine completes processing, if the number of lots in the buffer is greater than or equal to the critical number (Q?), all the lots in the buffer are loaded immediately, otherwise the batch machine waits until Q lots are accumulated. An embedded discrete time Markov-chain approach is proposed, and recursive approaches are developed to derive necessary performance measures. A numerical example explains how to obtain the optimal value of a critical number minimizing the cost functions.     

Production scheduling for apparel manufacturing systems

In this paper, we model an apparel manufacturing system characterized by the co-existence of the two production lines, i.e. traditional, long lead time production line and flexible, short lead time production line. Our goal is to find strategies which decide: (i) the fraction of the total production capacity to be allocated to each individual line, and (ii) the production schedules so as to maximize the overall profits. In this problem, searching for the best solution is prohibited in view of the tremendouscomputing budget involved. Using ordinal optimization ideas, we obtained very encouraging results not only have we achieved a high proportion of 'good enough' designs but also tight profit margins compared to a pre-calculated upper bound. There is also a saving of at least 1/2000 of the computation time.     

Simulation-based production schedule generation

Abstract

While simulation has been used in manufacturing for many years, predominantly for facility design, it is within the last few years, that simulation languages have been developed to a point where they can be used on a day-to-day basis to generate schedules and predict their performance. This paper describes our use of different modelling techniques to develop a production schedule generation system. An example describing a system for a small- to medium-sized order producing company using SIMAN/CINEMA is included. While addressing the shortcomings of existing scheduling systems it is shown how the approach taken is a feasible way of creating a dynamic goal-driven simulation-based production scheduler. The paper does not aim to describe an ‘off the shelf’ scheduling system product, but rather to give an overview to methods, techniques and experiences which enable us rapidly to tailor a simulation-based scheduling system to the specific needs of a company.     

Experimental earliness/tardiness production planning with the due window system

In order to use the philosophy of JIT to improve the production planning method of MRP-II, we propose the experimental software system of the earliness/tardiness produc tion planning problem with due window. By means of the approaches and model reported in this paper, the optimal production planning can be achieved. The recommended model extends the problem of due window from the shop scheduling level into the aggregated planning level of mass manufacturing systems. Simulation results have demonstrated that the experimental software is a useful tool for the production management of repetitive manufacturing enterprises.     

A two-phase robot selection procedure

In this paper, a two-phase methodology is proposed for robot selection. In phase 1, data envelopment analysis is used as a means to determine the technically efficient robot alternatives, considering cost and technical performance parameters. Using data envelopment analysis permits us to consider the fact that the performance parameters specified by the vendors are generally unattainable in practice. In the second phase, a fuzzy robot selection algorithm is utilized to rank the technically efficient robots according to both predetermined objective criteria and additional vendor-related subjective criteria. The algorithm is based on calculating fuzzy suitability indices for the technically efficient robot alternatives, and then, ranking the fuzzy indices to select the best robot alternative. A comprehensive example is provided to illustrate the decision procedure. The algorithm proposed in here is also applicable to a broader area of decision problems, e.g. facility site selection, determination of the best CNC machine or flexible manufacturing system among a set of mutually exclusive alternatives.     

An icon-based data image construction method for production data visualization

Visualization takes advantage of human's rapid and flexible pattern recognition capacity to provide a powerful information-processing environment. It is especially suitable for identifying patterns that are difficult, or impossible, to be defined by a mathematical or computer model. This paper presents an icon-based method for constructing images to visualize the abstract, multidimensional production data stored in tables. The method employs a simple, topology-preserving mapping to convert numbers in a table to icons. This mapping strategy not only makes the meaning of the resulted data image easy to understand, but also makes the maximum number of dimensions and data items that could be displayed be virtually unlimited. Visual patterns in the data image are formed by adjacent icons that have similar grey scales. The area sampling techniques could be applied to reduce the size of the data image while still retaining its visual patterns. The proposed visualization method has been applied to several production planning and control activities, including analysing WIP patterns, production indices, and the machine idle cost performance of production plans. These industrial case studies demonstrate that the ability to quickly and easily examine large amount of manufacturing data through visualization is critical for manufacturing companies to understand their performance, discover possible problems and take necessary actions so that they could remain competitive in the market.     

A simulation study comparing performance of CONWIP and bottleneck-based release rules

Many firms seeking production economies and quicker throughput times have adopted cellular manufacturing techniques. New product introductions, increased product variety and process changes all add elements of uncertainty to the cell, and dynamic management techniques are required to achieve the benefits expected. This paper considers the problem of managing the flow of material into a manufacturing cell in order to maximize the output rate for a given level of work-inprocess inventory. The manufacturing cell is patterned after an Intel facility that assembles and tests microprocessors for the computer industry. The cell is studied under conditions in which a single bottleneck arises and those under which there are multiple bottlenecks. Alternative procedures for releasing material into the cell are evaluated in order to determine which offer the greatest advantage to managers.     

Classification of Listeria monocytogenes Persistence in Retail Delicatessen Environments Using Expert Elicitation and Machine Learning

Increasing evidence suggests that persistence of Listeria monocytogenes in food processing plants has been the underlying cause of a number of human listeriosis outbreaks. This study extracts criteria used by food safety experts in determining bacterial persistence in the environment, using retail delicatessen operations as a model. Using the Delphi method, we conducted an expert elicitation with 10 food safety experts from academia, industry, and government to classify L. monocytogenes persistence based on environmental sampling results collected over six months for 30 retail delicatessen stores. The results were modeled using variations of random forest, support vector machine, logistic regression, and linear regression; variable importance values of random forest and support vector machine models were consolidated to rank important variables in the experts’ classifications. The duration of subtype isolation ranked most important across all expert categories. Sampling site category also ranked high in importance and validation errors doubled when this covariate was removed. Support vector machine and random forest models successfully classified the data with average validation errors of 3.1% and 2.2% (n = 144), respectively. Our findings indicate that (i) the frequency of isolations over time and sampling site information are critical factors for experts determining subtype persistence, (ii) food safety experts from different sectors may not use the same criteria in determining persistence, and (iii) machine learning models have potential for future use in environmental surveillance and risk management programs. Future work is necessary to validate the accuracy of expert and machine classification against biological measurement of L. monocytogenes persistence.