In job shop production the change towards synchronized job shop production, which is based on the concept of so-called taktlines, has been shown to enhance efficiency. In this dissertation an algorithm for the taktline layout is developed, following a multi-objective approach. The algorithm consists of two sequential discrete optimizations problems, namely a modified Substring Cover Problem and a partitioning Cluster Analysis, including a Multiple Sequence Alignment. For an overall validation, real-world data from tool manufacturers are subject to the proposed algorithm.

​Assembly Line Planning and Control describes the basic fundamentals of assembly lines for single model lines, mixed model make-to-stock lines, mixed model make-to-order lines and for one-station assembly. The book shows how to select the quantity of units to schedule for a shift duration, compute the number of operators needed on a line, set the conveyor speed, coordinate the main line with sub-assembly lines, assign the work elements to the operators on the line, sequence the models down the line, sequence the jobs down the line, calculate the part and component requirements for a line and for each station, determine the replenish needs of the parts and components from the suppliers, compute the similarity between the models being produced and show applications, use learning curves to estimate time and costs of assembly, and measure the efficiency of the line. The material is timeless and the book will never become obsolete. The author presents solutions with easy-to-understand numerical examples that can be applied to real-life applications.​

A Practical, Hands-on Guide to Lean Manufacturing This real-world resource offers proven solutions for implementing lean manufacturing in an enterprise environment, covering the engineering and production aspects as well as the business culture concerns. Filled with detailed examples, the book focuses on the rapid application of lean principles so that large, early financial gains can be made. How to Implement Lean Manufacturing explains Toyota Production System (TPS) practices and specifies the distinct order in which lean techniques should be applied to achieve maximum gains. Global case studies illustrate successes and pitfalls of lean manufacturing initiatives. Discover how to: Rigorously test and retest the state of your "leanness" with unique evaluators Develop and deploy plant-wide strategies and goals Improve speed and quality and dramatically reduce costs Reduce variation in the manufacturing system in order to reduce inventory Reduce lead times to enable improved responsiveness and flexibility Synchronize production and supply to the customer Create flow and establish pull-demand systems Perform system-wide and specific value-stream evaluations Generate a comprehensive list of highly focused Kaizen activities Sustain process gains Manage constraints and reduce bottlenecks Implement cellular manufacturing

This book attempts to treat line design and its related subjects in a cohesive manner, with an emphasis on design applications. It discusses general guidelines for setting up assumptions and determining line performance parameters, based on empirical data from literature reports.

The book deals with two main decision problems which arise when flow-line production systems are installed and operated. The assembly line balancing problem consists of partitioning the work, necessary to assemble the product(s), among different stations of an assembly line. If several models of a product are jointly processed on a line, this medium-term problem is connected with the short-term problem of determining an operating sequence of the models. In Part I balancing and sequencing problems are discussed, classified, and arranged within a hierarchical planning system. In the present second edition special emphasis is given to u-shaped assembly lines which are important components of modern just-in-time production systems. Part II is concerned with exact and heuristic procedures for solving those decision problems. For each problem type considered, a survey of existing procedures is given and new efficient solution methods are developed. Comprehensive numerical investigations showing the effectiveness of the new methods and their superiority over existing approaches are reported.

Incomplete information is an omnipresent issue when dealing with real-world optimization problems. Typically, such limitations concern the uncertainty of given data or the complete lack of knowledge about future parts of a problem instance. This thesis is devoted to investigations on how to cope with incomplete information when solving scheduling problems. These problems involve the temporal allocation of limited resources for executing activities so as to optimize some objective. Scheduling problems are apparent in many applications including, for example, manufacturing and service industries but also compiler optimization and parallel computing. There are two major frameworks for modeling limited information in the theory of optimization. One deals with "stochastic information", the other with "online information". We design algorithms for NP-hard scheduling problems in both, the online and the stochastic scheduling models. Thereby, we provide first constant performance guarantees orimprove previously best known results. Both frameworks have their legitimacy depending on the actual application. Nevertheless, problem settings are conceivable that comprise both, uncertain information about the data set and the complete lack of knowledge about the future. This rouses the need for a generalized model that integrates both traditional information environments. Such a general model is designed as a natural extension that combines stochastic and online information. But the challenging question is whether there exists any algorithm that can perform well in such a restricted information environment. More precisely, is there an algorithm that yields a constant performance guarantee? We successfully treat this intriguing question and give a positive answer by providing such algorithms for machine scheduling problems. In fact, our results are competitive with the performance guarantees best known in the traditional settings of stochastic and online scheduling. Thus, they do not only justify the generalized model but also imply - at least in the considered problem settings - that optimization in the general model with incomplete information does not necessarily mean to give up performance.