The Lot Scheduling Problem In The Hierarchy Of Decision Models

The economic lot scheduling problem (ELSP) surfaces from competition among products for a scarce resource, usually machine time. When scheduling production of batches in this environment, two issues must be resolved: the size of batches and the start times for production of each batch. Standard approaches to the ELSP focus on scheduling multiple products on a single machine. This thesis addresses three important issues that put the scheduling problem in the context of its physical setting and range of parameters: idle time, the zero switch rule, and stochastic input to a bottleneck machine. In most scheduling heuristics, the reason for idle time is to balance cyclic production patterns. Idle time is also optimal in solutions to problems with high setup costs. We show that the condition for inducing idle time, given zero setup costs, is when one product has dominant holding costs and the remaining products have low machine utilization. A common policy in scheduling is to start production only after the inventory reaches zero. This policy is called the zero switch rule (ZSR) and is regarded as a good scheduling policy. We show that the condition when ZSR is not optimal is when the ZSR solution yields lumpy production patterns for a product with dominant holding costs.

The purpose of this book is to provide readers with an introduction to the fields of decision making, location analysis, and project and machine scheduling. The combination of these topics is not an accident: decision analysis can be used to investigate decision seenarios in general, location analysis is one of the prime examples of decision making on the strategic Ievel, project scheduling is typically concemed with decision making on the tactical Ievel, and machine scheduling deals with decision making on the operational Ievel. Some of the chapters were originally contributed by different authors, and we have made every attempt to unify the notation, style, and, most importantly, the Ievel of the exposition. Similar to our book on Integer Programming and Network Models (Eiselt and Sandblom, 2000), the emphasis of this volume is on models rather than solution methods. This is particularly important in a book that purports to promote the science of decision making. As such, advanced undergraduate and graduate students, as weil as practitioners, will find this volume beneficial. While different authors prefer different degrees of mathematical sophistication, we have made every possible attempt to unify the approaches, provide clear explanations, and make this volume accessible to as many readers as possible.

Production Management is a large field concerned with all the aspects related to production, from the very bottom decisions at the machine level, to the top-level strategic decisicns. In this book, we are concerned with production planning and scheduling aspects. Traditional production planning methodologies are based on a now widely ac cepted hierarchical decom?osition into several planning decision levels. The higher in the hierarchy, the more aggregate are the models and the more important are the decisions. In this book, we only consider the last two decision levels in the hierarchy, namely, the mid-term (or tacticaQ planning level and the short-term (or operationaQ scheduling level. In the literature and in practice, the decisions are taken in sequence and in a top-down approach from the highest level in the hierarchy to the bottom level. The decisions taken at some level in the hierarchy are constrained by those already taken at upper levels and in turn, must translate into feasible objectives for the next lower levels in the hierarchy. It is a common sense remark to say that the whole hierarchical decision process is coherent if the interactions between different levels in the hierarchy are taken into account so that a decision taken at some level in the hierarchy translates into a feasible objective for the next decision level in the hierarchy. However, and surpris ingly enough, this crucial consistency issue is rarely investigated and few results are available in the literature.

Sustainable Food Supply Chains: Planning, Design, and Control through Interdisciplinary Methodologies provides integrated and practicable solutions that aid planners and entrepreneurs in the design and optimization of food production-distribution systems and operations and drives change toward sustainable food ecosystems. With synthesized coverage of the academic literature, this book integrates the quantitative models and tools that address each step of food supply chain operations to provide readers with easy access to support-decision quantitative and practicable methods. Broken into three parts, the book begins with an introduction and problem statement. The second part presents quantitative models and tools as an integrated framework for the food supply chain system and operations design. The book concludes with the presentation of case studies and applications focused on specific food chains. Sustainable Food Supply Chains: Planning, Design, and Control through Interdisciplinary Methodologies will be an indispensable resource for food scientists, practitioners and graduate students studying food systems and other related disciplines. Contains quantitative models and tools that address the interconnected areas of the food supply chain Synthesizes academic literature related to sustainable food supply chains Deals with interdisciplinary fields of research (Industrial Systems Engineering, Food Science, Packaging Science, Decision Science, Logistics and Facility Management, Supply Chain Management, Agriculture and Land-use Planning) that dominate food supply chain systems and operations Includes case studies and applications

This book presents the mathematical models applicable to manufacturing systems management, covering problems from production to real time control. It explores manufacturing systems from the viewpoints of both physical structure and performance measures. Two broad classes of mathematical models are covered in detail: Generative models, which yield a set of decision variables optimizing a performance measure, based on mathematical optimization Evaluative models, which evaluate some performance measures as a function of some predefined decision strategy. Within this class Petri Nets and Queueing Networks are discussed. Advanced Models for Manufacturing Systems Management describes dynamic systems modeling by state equations, a unifying framework for a wide variety of models. The text/reference stresses model building, but it examines model solving as well. Computational techniques are illustrated, such as linear programming, branch and bound methods, and dynamic programming. Particular emphasis is given to the development of heuristic methods from mathematical models. The book provides readers with valuable tools for management and design. The use of descriptive models within an optimization algorithm is considered. Numerous examples illustrate theoretical concepts throughout text. Appendices are given at the end of the book in order to recall fundamentals, such as linear programming and graph theory. Appendices also appear within each chapter. In this way, readers can follow the main reading path without getting involved with details; these appendices can be read at a later time. This textual structure makes this book particularly well suited for self-study. Advanced Models for Manufacturing Systems Management is beneficial reading for both students and practitioners.

Manufacturing systems rarely perform exactly as expected and predicted. Unexpected events, such as order changes, equipment failures and product defects, affect the performance of the system and complicate decision-making. This volume is devoted to the development of analytical methods aiming at responding to variability in a way that limits its corrupting effects on system performance. The book includes fifteen novel chapters that mostly focus on the development and analysis of performance evaluation models of manufacturing systems using decomposition-based methods, Markovian and queuing analysis, simulation, and inventory control approaches. They are organized into four distinct sections to reflect their shared viewpoints: factory design, unreliable production lines, queuing network models, production planning and assembly.

Nahmias and Olsen skillfully blend comprehensive coverage of topics with careful integration of mathematics. The authors’ decades of experience in the field contributed to the success of previous editions; the eighth edition continues the long tradition of excellence. Clearly written, reasonably priced, with an abundance of expertly formulated practice problems and updated examples, this textbook is essential reading for analyzing and improving all facets of operations. Some of the material in the newest edition has been reorganized. For example, the first chapter introduces service strategy, the product/process matrix and flexible manufacturing systems, benchmarking, the productivity frontier, the innovation curve, and lean production as a strategy. The focus is slightly more international. The analysis of capacity growth planning now appears in the chapter on supply chain analytics. Aggregate planning details were added to chapter 3, including chase and level strategies in an appendix to the chapter. There is an expanded discussion on risk pooling in the chapter on supply chain strategy. The mechanics behind lean production are included in the chapter on push and pull production systems. The chapter on quality and assurance downplays sampling in favor of discussions of quality management, process capability, and the waste elimination side of lean. The separate chapter on facilities layout and location was eliminated and the information redistributed throughout the text. The authors reinforce the learning process through key points at the beginning of each chapter to guide the reader, snapshots that provide useful examples of applications to businesses, and historical notes that provide a context for the topics discussed. Production and Operations Analytics, 8/e provides the tools for adapting to the dynamic global marketplace.

The Seventh Edition of Production and Operations Analysis builds a solid foundation for beginning students of production and operations management. Continuing a long tradition of excellence, Nahmias and Olsen bring decades of combined experience to craft the most clear and up-to-date resource available. The authors’ thorough updates include incorporation of current technology that improves the effectiveness of production processes, additional qualitative sections, and new material on service operations management and servicization. Bolstered by copious examples and problems, each chapter stands alone, allowing instructors to tailor the material to their specific needs. The text is essential reading for learning how to better analyze and improve on all facets of operations.