Modelling manufacturing system effectiveness: an integration of analytical hierarchy process and linear programming

Development of a Flexible and Agile Multi-robot Manufacturing System

<p>This dissertation explores workforce planning in manufacturing and healthcare systems. In manufacturing systems, the existing workforce planning models often lack fidelity with respect to the mechanism of learning. Learning refers to that employees’ productivity increases as they gain more experience. Workforce scheduling in the short term has a longer term impact on organizations’ capacity. The mathematical representations of learning are usually nonlinear. This nonlinearity complicates the planning models and provides opportunities to develop solution methodologies for realistically-sized instances. This research formulates the workforce planning problem as a mixed-integer nonlinear program (MINLP) and overcomes the limitations of cur- rent solution methods. Specifically, this research develops a reformulation technique that converts the MINLP to a mixed integer linear program (MILP) and proposes several techniques to speed up the solution time of solving the MILP.</p><p>In organizations that use group work, workers learn not only by individual learning but also from knowledge transferred from team members. Managers face the decision of how to pair or team workers such that organizations benefit from this transfer of learning. Using a mathematical representation that incorporates both in- dividual learning and knowledge transfer between workers, this research considers the problem of grouping workers to teams and assigning teams to sets of jobs based on workers’ learning and knowledge transfer characteristics. This study builds a Mixed- integer nonlinear programs (MINP) for parallel systems with the objective of maximizing the system throughput and propose exact and heuristic solution approaches for solving the MINLP.</p><p>In healthcare systems, we focus on managing medical technicians in medical laboratories, in particular, the phlebotomists. Phlebotomists draw specimens from patients based on doctors’ orders, which arrive randomly in a day. According to the literature, optimizing scheduling and routing in hospital laboratories has not been regarded as a necessity for laboratory management. This study is motivated by a real case at University of Iowa Hospital and Clinics, where there is a team of phlebotomists that cannot fulfill doctors requests in the morning shift. The goal of this research is routing these phlebotomists to patient units such that as many orders as possible are fulfilled during the shift. The problem is a team orienteering problem with stochastic rewards and service times. This research develops an a priori approach which applies a variable neighborhood search heuristic algorithm that improves the daily performance compared to the hospital practice.</p>

PurposeA major factor to the success of flexible manufacturing systems (FMSs) is their ability to transport work pieces between different workstations. FMS have now become more advanced and material‐handling systems have become progressively more sophisticated, it is not exceptional to have automated steering of tools to workstations as well. Such system design will improve the tool‐handling capability and the system productivity while holding tool cost to a minimum. Tool cost could represent as much as 25 percent of the operating cost. The purpose of this paper is to propose a new colored Petri net (CPN)‐based approach to the design and development of a tool sharing control system that is intended to help use of the proper and minimal number of tools for a manufacturing system.Design/methodology/approachA new black token timed PN model is first developed, to reduce the complexity of the graphical representation a new CPN model is developed. The new CPN model also allows to find the optimal sequence. The optimal sequence has no effect on the work in process (WIP) but it influences the number of tools used in the system. The main input to the PN model for a manufacturing system is the process plan. Next, all the invariants and total number of possible elementary circuits are determined using the Integrated Net Analyzer (INA) software. Output from the INA software is exported to the Excel spreadsheet. The Excel spreadsheet can be designed to calculate the total number of tokens, processing time, cycle time, etc. of each elementary circuit. Subsequently, the constraints used in Lingo will be created according to critical circuit rules. Finally, linear programming (LP) technique is used to optimize the WIP and tool inventory. Lingo software is used for the LP, the constraints from the Excel sheet will be the input data to the Lingo program, and based on those constraints the Lingo will provide the optimal values for the desired parameters. The output from Lingo will be used to recalculate the cycle time of each elementary circuit in the Excel sheet. The system is then analyzed before and after the implementation of the CPN model.FindingsA new CPN model based on tool‐sharing philosophy for an FMS with N part types and M stations is proposed.Originality/valueThe paper presents a new CPN‐based approach to the design and development of a tool sharing control system, that is, intended to help use of the proper and minimal number of tools for a manufacturing system. The new CPN model also allows to find the optimal sequence. The idea is new and pure and has not been presented before using the methodology adopted in this paper.

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies.

This paper presents an Axiomatic Design framework formanufacturing system design and illustrates how leancellular manufacturing can achieve volume flexibility. Axi-omatic Design creates a design framework by mappingthe functional requirements of a system to specific designparameters. Volume flexibility is often neglected as arequirement of manufacturing systems. Very few indus-tries are fortunate enough to experience stable or pre-dictable product demand. In reality, demand is oftenvolatile and uncertain. It is important that manufacturingsystem designers are aware of manufacturing systemtypes which can accommodate volume flexibility and fol-low a structured design methodology that assures that allrequirements are met by the system. INTRODUCTION Recent decades have led to the development and prolif-eration of many new paradigms in manufacturing. Withthe advent of Numerically Controlled (NC) and later Com-puter Numerically Controlled (CNC) machine tools androbotics, the complexity of manufacturing systems hasgrown considerably. In many instances, the advantagesand limitations of these new tools were not understood.This confusion led to many costly mistakes in manufac-turing system design. Many companies believed thatautomation was the wave of the future and invested inexpensive automated systems only to find that they werenot cost effective.There are two reasons why such mistakes have beenmade and continue to be made. First, the manufacturingfield as a whole does not fully understand the advantagesand disadvantages of different types of manufacturingsystems. Since the mid-1970's the variety of manufactur-ing system types has grown considerably. Since thattime, Flexible Manufacturing Systems, Agile ProductionSystems, and Lean Cellular Systems, have becomeaccepted types of manufacturing systems. Prior to that,the main types of systems were limited to Job Shops(jumbled flow systems), Disconnected Flow Lines(Batch Production), and Transfer Lines. The develop-ment of new system types complicates manufacturingsystem design considerably. Careful consideration of therequirements of the system is required to support aninformed system type selection.A major reason why highly automated manufacturing sys-tems may not be cost effective is that product demandcan be very unpredictable. Generally, the more auto-mated the system is, the less flexible it is in terms of vol-ume. An automated transfer line, for example may be themost cost effective type of system available at a particu-lar production rate. If demand increases or decreases,however, an automated system is either unable to meetthe demand or must take a variable cost increase (over-time, etc.) to do so. In many industries, demand is vola-tile and uncertain. As a result, the forecast of demand israrely accurate. Figure 1 illustrates that even with anaccurate forecast, demand will be normally distributedabout the forecast. If the forecast is skewed, the actualdistribution will be shifted either to the left or right.Figure 1. The uncertainty of the demand forecastThe problem of forecasting is magnified as the leadtimerequired to construct and validate a manufacturing sys-tem increases. As this leadtime increases, a longerrange forecast is required. The deviation ( ∆) between theforecast and the actual demand can be expected toincrease the farther into the future the forecast predicts.The second reason for ineffective manufacturing systemdesigns is that there has been no structured methodol-ogy to follow. Tools such as Quality Function Deployment(QFD) and Axiomatic Design have proven to be very

Impact of reconfiguration characteristics for capacity investment strategies in manufacturing systems

As a relatively young software company, PT. Hexavara Tech needs to select incoming projects since the number of requests exceeds the amount that the company can manage. Currently, there are no guidelines for selecting projects, so the project selection process is less structured and subjective. In fact, project selection is faced with several qualitative and quantitative criteria that tend to be conflictual in achieving objectives. So in this study, the integration of Analytical Hierarchy Process (AHP) and Goal Programming (GP) methods is used to select software projects that consider limitations and multi-objectivity. Based on the calculation of the level of importance of the criteria using AHP, the sequence of project selection criteria is obtained, namely profit for the company (0.287), potential development of the project scope (0.155), initial development capital (0.1), the opportunity to get relations from clients (0.097), conformity to team capabilities (0.093), risks contained (0.078), increasing the value of the company's portfolio (0.073), the opportunity to create a template for the next project (0.07), and the opportunity to learn new technology (0.047). After the optimization process using GP, the best final results are obtained, namely the e-wallet development project of savings and loan cooperatives, development of an android-based online motorcycle taxi application, development of e-commerce design works, development of overseas money transfer applications, and development of a website framework for cross border e-commerce

Self-Organising Electronic Institutions and Flexible Manufacturing Systems

The modern manufacturing systems recognize the importance of adopting lean and sustainable manufacturing principles. Especially, Indian automotive component manufacturing organizations are interested in adopting the integrated lean sustainable manufacturing system. An appropriate methodology is required for enabling the organizations to identify the dominant factors. In this context, this article presents the interpretive structural modelling approach for identifying the mutual relationship among factors influencing the integrated lean sustainable manufacturing system. From the literature study and survey conducted among Indian automotive component manufacturing organizations, 25 influencing factors have been identified. Based on the study, top-management commitment in adopting integrated lean sustainable manufacturing and environmental knowledge occupy the bottom level of model which shows that it has more driving power. Efforts are being taken to implement the factors in the surveyed organizations.

Agile manufacturing system with autonomously operated square arrayed machining cells

Integration of analytic hierarchy process and data envelopment analysis for assessment and optimization of personnel productivity in a large industrial bank

Framework for The Development of a New Reconfigurable Guillotine Shear and Bending Press Machine

Flexible manufacturing systems (FMS), transitional or absolute customer to encounter the various requirements and preferences, helping to protect organizations which in the circumstances of competitive environment in eachother exactly known always to created, are constructed as a structure that could really keep producing various products and services with minor modifications. Computers are used to help manufacturing lines. This research provides the literature review of manufacturing-production systems, categorisation of manufacturing techniques, advanced manufacturing technologies, manufacturing process modeling, modeling definitions, and database organizational structures, data regulations and procurement for Flexible Manufacturing Systems-Techniques, and eventually the research conclusion and recommendation. Mostly with significant developments in the manufacturing sector, the database idea has become an essential and fundamental element of every type of production system. Its most fundamental tasks are the preservation and accessibility of services required to regulate industrial operations. This study also utilizes SWOT (Strengths, Weaknesses, Opportunities, and Threats) evaluation to explore the appropriateness of manufacturing techniques, FMS in industrial manufacturing automated processes. The highlighted elements indicate manufacturing systems' considerable potential to address advanced manufacturing modernization challenges.

To simulate and debug or diagnose manufacturing systems is always challenging. This paper presents a hybrid PLC-controlled and PC Visual Basic (VB)-based Human-Machine Interface (HMI) platform based on Petri net (PN) theory for Manufacturing Systems. An augmented PN model was adopted to design and simulate the operational sequence of the Flexible Manufacturing System (FMS) at the National Kaohsiung First University of Science and Technology (NKFUST). With the simulated PN model, the control sequences were quickly translated into PLC programs. A PC-based HMI monitoring system based on the PN model was developed to provide an intuitive offline simulation and online diagnostic tools for the FMS. This hybrid system shows many of advantages by using the economical discrete control unit with PLC, and PC-based HMI using PN model as a base. The discrete event control sequence of an FMS can be easily modeled and evaluated before its build-up. More importantly, it can be diagnosed online when the system is malfunctioning. That is, the presented system provides an adequate means for offline system design, simulation, performance evaluation, and quick online system diagnostic.

Fixtures are used to locate, hold and support workpieces in manufacturing operations such as machining, inspection, and assembly. Designing and fabricating fixtures can take up to 10-20% of the total cost of a manufacturing system. Fixture planning is a complex activity restricted by the extreme diversity of workpiece, product batch size, product geometry, part accessibility, and working force. Computer-Aided Fixture Planning (CAFP) has been used to improve the fixture design for over 20 years. CAFP contributes to the reduction of lead-time and human interaction in fixture planning. It helps verification of the fixture quality and integration of fixture design with CAD/CAM systems. This paper summarizes constraints of fixture planning and four phases of CAFP. Various approaches to CAFP are surveyed. Application systems of CAFP including some recent patents are reviewed. The paper concludes with the research trend of CAFP. The global competition and customer demand change lead to greater product variety, smaller product batch size, shorter production time and higher product quality. Industrial organizations have to pursue flexibility and automation in manufacturing systems to remain competitive and profitable. Flexible Manufacturing System (FMS) developed in the past decades is to meet such challenge. FMS usually consists of one or more CNC machines which are capable of machining a variety of workpieces. With the development of Computer- Aided Design and Computer-Aided Manufacturing (CAD/ CAM) technology, product design and machining program- ming have been greatly computerized. However, one of the difficulties in FMS is the fixture design for locating and securing a workpiece during manufacturing operations. Fixture planning is time-consuming as it is customized for workpieces. Computer-aided fixture planning (CAFP) aims to reduce the lead-time and human interaction in fixture planning. It can verify fixture quality to integrate fixture design with CAD/CAM systems by automating the reasoning process. The integration of CAFP with CAD/CAM has greatly reduced the production lead-time and cost for an overall optimal solution in product design and manufacturing (1). Fixtures are used to locate, hold and support workpieces in manufacturing operations such as machining, inspection, and assembly. Fixture design and manufacturing is costly due to the tight tolerance and restricted machining opera- tions. Designing and fabricating fixtures can take up to 10- 20% of the total cost of a manufacturing system. Dedicated fixtures are mostly applied in mass production to gain high efficiency. For smaller batch size, the fixture flexibility becomes more important than efficiency due to the workpiece variety and machining requirements. Flexible and