Integrated Hierarchical Production Planning And Scheduling For Parallel Processors With Simultaneous Multiproduct Capability

This book presents a number of efficient techniques for solving large-scale production scheduling and planning problems in process industries. The main content is supplemented by a wealth of illustrations, while case studies on large-scale industrial applications, ranging from continuous to semicontinuous and batch processes, round out the coverage. The book examines a variety of complex, real-world problems, and demonstrates solutions that are applicable to scenarios and countries around the world. Specifically, these case studies include: • the production planning of the bottling stage of a major brewery at the Cervecería Cuauhtémoc Moctezuma (Heineken Int) in Mexico;• the production scheduling for multi-stage semicontinuous processes at an ice-cream production facility of Unilever in the Netherlands;• the resource-constrained production planning for the yogurt production line at the KRI KRI dairy production facility in Greece; and• the production scheduling for large-scale, multi-stage batch processes at a pharmaceutical batch plant in Germany. In addition, the book includes industrial-inspired case studies of: • the simultaneous planning of production and logistics operations considering multi-site facilities for semicontinuous processes; and• the integrated planning of production and utility systems in process industries under uncertainty. Solving Large-scale Production Scheduling and Planning in the Process Industries offers a valuable reference guide for researchers and decision-makers alike, as it shows readers how to evaluate and improve existing installations, and how to design new ones. It is also well suited as a textbook for advanced courses on production scheduling and planning in industry, as it addresses the optimization of production and logistics operations in real-world process industries.

A unified, systematic approach to applying mixed integer programming solutions to integrated scheduling in customer-driven supply chains Supply chain management is a rapidly developing field, and the recent improvements in modeling, preprocessing, solution algorithms, and mixed integer programming (MIP) software have made it possible to solve large-scale MIP models of scheduling problems, especially integrated scheduling in supply chains. Featuring a unified and systematic presentation, Scheduling in Supply Chains Using Mixed Integer Programming provides state-of-the-art MIP modeling and solutions approaches, equipping readers with the knowledge and tools to model and solve real-world supply chain scheduling problems in make-to-order manufacturing. Drawing upon the author's own research, the book explores MIP approaches and examples-which are modeled on actual supply chain scheduling problems in high-tech industries-in three comprehensive sections: Short-Term Scheduling in Supply Chains presents various MIP models and provides heuristic algorithms for scheduling flexible flow shops and surface mount technology lines, balancing and scheduling of Flexible Assembly Lines, and loading and scheduling of Flexible Assembly Systems Medium-Term Scheduling in Supply Chains outlines MIP models and MIP-based heuristic algorithms for supplier selection and order allocation, customer order acceptance and due date setting, material supply scheduling, and medium-term scheduling and rescheduling of customer orders in a make-to-order discrete manufacturing environment Coordinated Scheduling in Supply Chains explores coordinated scheduling of manufacturing and supply of parts as well as the assembly of products in supply chains with a single producer and single or multiple suppliers; MIP models for a single- or multiple-objective decision making are also provided Two main decision-making approaches are discussed and compared throughout. The integrated (simultaneous) approach, in which all required decisions are made simultaneously using complex, monolithic MIP models; and the hierarchical (sequential) approach, in which the required decisions are made successively using hierarchies of simpler and smaller-sized MIP models. Throughout the book, the author provides insight on the presented modeling tools using AMPL® modeling language and CPLEX solver. Scheduling in Supply Chains Using Mixed Integer Programming is a comprehensive resource for practitioners and researchers working in supply chain planning, scheduling, and management. The book is also appropriate for graduate- and PhD-level courses on supply chains for students majoring in management science, industrial engineering, operations research, applied mathematics, and computer science.

The goal of this work is to propose a multiperiod mixed-integer linear programming model for the design and production planning of multistage multiproduct batch plants integrating, simultaneously, scheduling decisions. The time horizon consists of several periods in which deterministic variations due to seasonal or market fluctuations like prices, products demand limits, costs, and raw materials availability, are taken into account. The plant operates using production campaigns along each time period. The objective function maximizes the net present value considering incomes from product sales and, investment, resources, inventories, operation, waste disposal and late delivery penalties costs. The proposed model allows not only finding the optimal plant structure (unit sizes and its duplication in parallel in each stage), but also the production planning in every time period as well as number and optimal sequence of batches in each campaign.

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.