Due to a varying product demand (changing product mix) and different production speeds, bottlenecks may shift between the stages. In that case, a simultaneous lot-sizing and scheduling of these stages is recommendable. Hence, an improved version of the General Lot-Sizing and Scheduling Problem for Multiple production Stages (GLSPMS) was developed. Moreover, several reformulation techniques were applied to this model to solve it exactly. Besides, a new meta-heuristic which combines the principles of Variable Neighborhood Decomposition Search (VNDS) and Exchange was implemented to find good solutions, even for a real-world problem case. Finally, further model extensions, e.g., for scarce setup resources, were proposed.
This book is the outcome of my research in the field of multi levellot sizing and scheduling which started in May 1993 at the Christian-Albrechts-University of Kiel (Germany). During this time I discovered more and more interesting aspects ab out this subject and I had to learn that not every promising idea can be thoroughly evaluated by one person alone. Nevertheless, I am now in the position to present some results which are supposed to be useful for future endeavors. Since April 1995 the work was done with partial support from the research project no. Dr 170/4-1 from the "Deutsche For schungsgemeinschaft" (D FG). The remaining space in this preface shaH be dedicated to those who gave me valuable support: First, let me express my deep gratitude towards my thesis ad visor Prof. Dr. Andreas Drexl. He certainly is a very outstanding advisor. Without his steady suggestions, this work would not have come that far. Despite his scarce time capacities, he never rejected proof-reading draft versions of working papers, and he was always willing to discuss new ideas - the good as weH as the bad ones. He and Prof. Dr. Gerd Hansen refereed this thesis. I am in debted to both for their assessment. I am also owing something to Dr. Knut Haase. Since we al most never had the same opinion when discussing certain lot sizing aspects, his comments and criticism gave stimulating input.
This volume collects research papers addressing topical issues in economics and management with a particular focus on dynamic models which allow to analyze and foster the decision making of firms in dynamic complex environments. The scope of the contributions ranges from daily operational challenges firms face to strategic choices in dynamic industry environments and the analysis of optimal growth paths. The volume also highlights recent methodological developments in the areas of dynamic optimization, dynamic games and meta-heuristics, which help to improve our understanding of (optimal) decision making in a fast evolving economy.
Lotsizing problems are found in every company, which produces various items on resources with scarce capacities. In general, the objective is to find production plans, which meet demand with the resources` given capacities and at minimal costs. However, there is a great variety of company-specific lotsizing problems due to the complexity of the underlying production processes. Both standard MRP-systems and modern Advanced Planning Systems fail to provide adequate solutions. Instead, they most often compute production plans, which are not feasible in practice. In this book new lotsizing models are presented for multi-stage production systems with general product structures, dynamic demand, setup times and lead times. They are extended to cover production on parallel machines. An iterative solution procedure is presented, which generates feasible production plans within seconds of computation time. Potential readership includes scholars of supply chain management and production planning, interested students in these areas as well as practitioners involved in production planning.
Billions of dollars are tied up in the inventories of manufacturing companies which cause large (interest) costs. A small decrease of the inventory and/or production costs without reduction of the service level can increase the profit substantially. Especially in the case of scarce capacity, efficient production schedules are fundamental for short delivery time and on-time delivery which are important competitive priorities. To support decision makers by improving their manufacturing resource planning system with appropriate methods is one of the most of production planning. interesting challenges The following chapters contain new models and new solution strategies which may be helpful for decision makers and for further research in the areas of production planning and operations research. The main subject is on lotsizing and scheduling. The objectives and further characteristics of such problems can be inferred from practical need. Thus, before an outline is given, we consider the general objectives of lotsizing and scheduling and classify the most important characteristics of such problems in the following sections.
During the past decades scheduling has been among the most studied op- mization problemsanditisstillanactiveareaofresearch!Schedulingappears in many areas of science, engineering and industry and takes di?erent forms depending on the restrictions and optimization criteria of the operating en- ronments [8]. For instance, in optimization and computer science, scheduling has been de?ned as “the allocation of tasks to resources over time in order to achieve optimality in one or more objective criteria in an e?cient way” and in production as “production schedule, i. e. , the planning of the production or the sequence of operations according to which jobs pass through machines and is optimal with respect to certain optimization criteria. ” Although there is a standardized form of stating any scheduling problem, namely “e?cient allocation ofn jobs onm machines –which can process no more than one activity at a time– with the objective to optimize some - jective function of the job completion times”, scheduling is in fact a family of problems. Indeed, several parameters intervene in the problem de?nition: (a) job characteristics (preemptive or not, precedence constraints, release dates, etc. ); (b) resource environment (single vs. parallel machines, un- lated machines, identical or uniform machines, etc. ); (c) optimization criteria (minimize total tardiness, the number of late jobs, makespan, ?owtime, etc. ; maximize resource utilization, etc. ); and, (d) scheduling environment (static vs. dynamic,intheformerthenumberofjobstobeconsideredandtheirready times are available while in the later the number of jobs and their charact- istics change over time).
This book presents the proceedings of the XXII International Conference on Industrial Engineering and Operations Management, International IIE Conference 2016, and International AIM Conference 2016. This joint conference is a result of an agreement between ADINGOR (Asociación para el Desarrollo de la Ingeniería de Organización), ABEPRO (Associação Brasileira de Engenharia de Produção), AIM (European Academy for Industrial Management) and the IIE (Institute of Industrial Engineers), and took place at TECNUN-School of Engineering (San Sebastián, Spain) from July 13th to 15th, 2016. The book includes the latest research advances and cutting-edge analyses of real case studies in Industrial Engineering and Operations Management from diverse international contexts, while also identifying concrete business applications for the latest findings and innovations in operations management and the decisions sciences.
27th European Symposium on Computer Aided Process Engineering, Volume 40 contains the papers presented at the 27th European Society of Computer-Aided Process Engineering (ESCAPE) event held in Barcelona, October 1-5, 2017. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students, and consultants for chemical industries. Presents findings and discussions from the 27th European Society of Computer-Aided Process Engineering (ESCAPE) event
