Optimization Of Fuzzy Logic Controllers With Genetic Algorithm For Two-Part-Type And Re-Entrant Production Systems

Improvement in the performance of production control systems is so important that many of past studies were dedicated to this problem. The applicability of fuzzy logic controllers (FLCs) in production control systems has been shown in the past literature. Furthermore, genetic algorithm (GA) has been used to optimize the FLCs performance. This is addressed as genetic fuzzy logic controller (GFLC). The GFLC methodology is used to develop two production control architectures named “genetic distributed fuzzy” (GDF), and “genetic supervisory fuzzy” (GSF) controllers. These control architectures have been applied to single-part-type production systems. In their new application, the GDF and GSF controllers are developed to control multipart- type and re-entrant production systems. In multi-part-type and re-entrant production systems the priority of production as well as the production rate for each part type is determined by production control systems. A genetic algorithm is developed to tune the membership functions (MFs) of input variables of GDF and GSF controllers. The objective function of the GSF controller is to minimize the overall production cost based on work-in-process (WIP) and backlog cost, while surplus minimization is considered in GDF controller. The GA module is programmed in MATLAB® software. The performance of each GDF or GSF controllers in controlling the production system model is evaluated using Simulink® software. The performance indices are used as chromosomes ranking criteria. The optimized GDF and GSF can be used in real implementations. GDF and GSF controllers are evaluated for two test cases namely “two-part-type production line” and “re-entrant production system”. The results have been compared with two heuristic controllers namely “heuristic distributed fuzzy” (HDF) and “heuristic supervisory fuzzy” (HSF) controllers. The results showed that GDF and GSF controllers can improve the performance of production system. In GSF control architecture, WIP level is 30% decreased rather than HSF controllers. Moreover the overall production cost is reduced in most of the test cases by 30%. GDF controllers show their abilities in reducing the backlog level but generally production cost for GDF controller is greater than GSF controller

Investigation into inspection system utilisation for advanced manufacturing systems.

Masters Degree. University of KwaZulu-Natal, Durban.Varied inspection is an aperiodic inspection utilisation methodology that was developed for advanced manufacturing systems. The inspection scheme was created as a solution to improve manufacturing performance where inspection hinders production, such as cases where inspection time is significantly larger than machining time. Frequent inspection impedes production cycles which result in undesirable blocking, starving, low machine utilisation, increased lead time and work-in-process. The aim of the inspection strategy was to aid manufacturing metrics by adjusting inspection utilisation through multiple control methods. The novelty of the research lies in using an inspection strategy for improved manufacturing performance. Quality control was traditionally viewed as an unintegrated aspect of production. As such, quality control was only used as a tool for ensuring certain standards of products, rather than being used as a tool to aid production. The problem was solved by using the amount of inspection performed as a variable, and changing that variable based on the needs of the manufacturing process. “Inspection intensity” was defined as the amount of inspection performed on a part stream and was based on inputs such as part quality, required production rates, work-in-process requirements among other factors. Varied inspection was executed using a two-level control architecture of fuzzy controllers. Lower level controllers performed varied inspection while an upper level supervisory controller measured overall system performance and made adjustments to lower level controllers to meet system requirements. The research was constrained to simulation results to test the effects of varied inspection on different manufacturing models. Simulation software was used to model advanced manufacturing systems to test the effects of varied inspection against traditional quality control schemes. Matlab’s SimEvents® was used for discrete-event simulation and Fuzzy Logic Toolbox® was used for the controller design. Through simulation, varied inspection was used to meet production needs such as reduced manufacturing lead time, reduced work-in-process, reduced starvation and blockage, and reduced appraisal costs. Machine utilisation was increased. The contribution of the research was that quality control could be used to aid manufacturing systems instead of slowing it down. Varied inspection can be used as a flexible form of inspection. The research can be used as a control methodology to improve the usage of inspection systems to enhance manufacturing performance

Evaluation of Pull Production Control Strategies Under Uncertainty: An Integrated Fuzzy Ahp-Topsis Approach

Purpose: Just-In-Time (JIT) production has continuously been considered by industrial practitioners and researchers as a leading strategy for the yet popular Lean production. Pull Production Control Policies (PPCPs) are the major enablers of JIT that locally control the level of inventory by authorizing the production in each station. Aiming to improve the PPCPs, three authorization mechanisms: Kanban, constant-work-in-process (ConWIP), and a hybrid system, are evaluated by considering uncertainty. Design/methodology/approach: Multi-Criteria Decision Making (MCDM) methods are successful in evaluating alternatives with respect to several objectives. The proposed approach of this study applies the fuzzy set theory together with an integrated Analytical Hierarchy Process (AHP) and a Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. Findings: The study finds that hybrid Kanban-ConWIP pull production control policies have a better performance in controlling the studied multi-layer multi-stage manufacturing and assembly system. Practical implications: To examine the approach a real case from automobile electro-mechanical part production industry is studied. The production system consists of multiple levels of manufacturing, feeding a multi-stage assembly line with stochastic processing times to satisfy the changing demand. Originality/value: This study proposes the integrated Kanban-ConWIP hybrid pull control policies and implements several alternatives on a multi-stage and multi-layer manufacturing and assembly production system. An integrated Fuzzy AHP TOPSIS method is developed to evaluate the alternatives with respect to several JIT criteriaPeer Reviewe

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Application of a continuous supervisory fuzzy control on a discrete scheduling of manufacturing systems.

10 pagesInternational audienceThis paper considers the modelling and simulation of a hierarchical production-flow control system. Particularly, the system capacity allocation has been addressed by a set of distributed and supervised fuzzy controllers. The objective is to adjust the machine's production rates in such a way that satisfies the demand while maintaining the overall performances within acceptable limits. Given the adjusted production rates, the problem of scheduling of jobs is considered at the shop-floor level. In this case, the actual dispatching times are determined from the continuous production rates through a sampling procedure. To deal with conflicts between jobs at a shared machine, a decision for the actual part to be processed is taken using some criteria which represent a measure of the job's priority. A case study demonstrates the efficiency of the proposed control approach

Hierarchical Control of Production Flow based on Capacity Allocation for Real-Time Scheduling of Manufacturing System

8International audienceThis paper considers the modelling and simulation of a hierarchical production-flow control system. It uses a continuous control approach for machine capacity allocation at the design level and real time scheduling at the shop-floor level. Particularly, at the design level, the control of machine throughput has been addressed by a set of distributed and supervised fuzzy controllers. The objective is to adjust the machine's production rates in such a way that satisfies the demand while maintaining the overall performances within acceptable limits. At the shop-floor level, the problem of scheduling of jobs is considered. In this case, the priority of jobs (actual dispatching times) is determined from the continuous production rates through a discretization procedure. A case study demonstrates the efficiency of the proposed methodology through a simulation case study

Designing pull production control systems:Customization and robustness

In this dissertation we address the issues of selecting and configuring pull production control systems for single-product flowlines. We start with a review of pull systems in the literature, yielding a new classification. Then we propose a novel selection procedure based on a generic system that we test on a case also studied in the literature. We further study our procedure for a variety of twelve production lines. We find new types of pull systems that perform well. Next, we raise the issue of designing pull systems under uncertainty. We propose a novel procedure to minimize the risk of poor performance. Results show that risk considerations strongly influence the selection of a specific pull system