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Author: Christopher James Solo
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This research involves the development of a flexible, multi-objective optimization tool for use by supply chain managers in the design and operation of manufacturing-distribution networks under uncertain demand conditions. The problem under consideration consists of determining the supply chain infrastructure; raw material purchases, shipments, and inventories; and finished product production quantities, inventories, and shipments needed to achieve maximum profit while fulfilling demand and minimizing supply chain response time. The development of the two-phase mathematical model parallels the supply chain planning process through the formulation of a strategic submodel for infrastructure design followed by a tactical submodel for operational planning. The deterministic strategic submodel, formulated as a multi-period, mixed integer linear programming model, considers an aggregate production planning problem in which long-term decisions such as plant construction, production capacities, and critical raw material supplier selections are optimized. These decisions are then used as inputs in the operational planning portion of the problem. The deterministic tactical submodel, formulated as a multi-period, mixed integer linear goal programming model, uses higher resolution demand and cost data, newly acquired transit time information, and the previously developed infrastructure to determine optimal non-critical raw material supplier selections; revised purchasing, production, inventory, and shipment quantities; and an optimal profit figure. The supply chain scenario is then modified to consider uncertain, long-term demand forecasts in the form of discrete economic scenarios. In this case, a multi-period, mixed integer robust optimization formulation of the strategic submodel is presented to account for the probabilistic demand data. Once the stochastic strategic submodel is presented, short-term, uncertain demand data is assumed to be available in the form of continuous probability distributions. By modifying decision makers' objectives regarding demand satisfaction, the distribution-based demand data is accounted for through the development of a multi-period, mixed integer chance-constrained goal programming formulation of the tactical submodel. In order to demonstrate the flexibility of both the deterministic and stochastic versions of the overall two-phase model, numerical examples are presented and solved. The resulting work provides supply chain managers with a flexible tool that can aid in the design and operation of real-world production-distribution networks, where uncertain demand data is available at different times and in various forms.
Author: Christopher James Solo
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This research involves the development of a flexible, multi-objective optimization tool for use by supply chain managers in the design and operation of manufacturing-distribution networks under uncertain demand conditions. The problem under consideration consists of determining the supply chain infrastructure; raw material purchases, shipments, and inventories; and finished product production quantities, inventories, and shipments needed to achieve maximum profit while fulfilling demand and minimizing supply chain response time. The development of the two-phase mathematical model parallels the supply chain planning process through the formulation of a strategic submodel for infrastructure design followed by a tactical submodel for operational planning. The deterministic strategic submodel, formulated as a multi-period, mixed integer linear programming model, considers an aggregate production planning problem in which long-term decisions such as plant construction, production capacities, and critical raw material supplier selections are optimized. These decisions are then used as inputs in the operational planning portion of the problem. The deterministic tactical submodel, formulated as a multi-period, mixed integer linear goal programming model, uses higher resolution demand and cost data, newly acquired transit time information, and the previously developed infrastructure to determine optimal non-critical raw material supplier selections; revised purchasing, production, inventory, and shipment quantities; and an optimal profit figure. The supply chain scenario is then modified to consider uncertain, long-term demand forecasts in the form of discrete economic scenarios. In this case, a multi-period, mixed integer robust optimization formulation of the strategic submodel is presented to account for the probabilistic demand data. Once the stochastic strategic submodel is presented, short-term, uncertain demand data is assumed to be available in the form of continuous probability distributions. By modifying decision makers' objectives regarding demand satisfaction, the distribution-based demand data is accounted for through the development of a multi-period, mixed integer chance-constrained goal programming formulation of the tactical submodel. In order to demonstrate the flexibility of both the deterministic and stochastic versions of the overall two-phase model, numerical examples are presented and solved. The resulting work provides supply chain managers with a flexible tool that can aid in the design and operation of real-world production-distribution networks, where uncertain demand data is available at different times and in various forms.
Author: Barrie M. Cole
Publisher: Vernon Press
ISBN: 162273016X
Category : Business & Economics
Languages : en
Pages : 383
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Book Description
Drawing on cutting-edge research, this book proposes a new 'Supply Chain Optimization under Uncertainty’, technology. Its application can bring many proven benefits to supply chain entities, any associated service providers, and, of course, the customers. The technology can provide the best design and operating solution for a Supply Chain Network (SCN) that is subject to any prevailing conditions of Operational Uncertainty (OU). A SCN is defined as a network of production facilities, distribution centers and retail sales outlets. OU is defined as any relevant combination of i) multiple process objectives e.g. a business needs to maximize operating profits and to minimize inventory levels, ii) fuzziness (<, <=, >, or >=) e.g. sales <= 1500 t/mth and iii) probability e.g. sale of fertilizer is dependent on probabilistic rainfall. Following this method always enables the determination of realistic optimum supply chain solutions, since the effects of any operational uncertainties are always provided for. The book is arranged in two parts. The first part covers the theory and recent research into supply chain optimization under uncertainty. The second part documents the application of the newly proposed technology to an agricultural fertilizer’s (NPK, South Africa) supply chain.
Author: Boyi Wu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
A supply chain has a network structure and its design involves production-distributiondecisions on the location of production, distribution facilities, capacities andtransportation quantities. A multi-echelon supply chain involving multiple suppliers andmultiple retailers for multiple products among multiple periods is considered in this thesis,which is required to be modeled as a multi-objective problem coping with uncertaintiesduring the process. The uncertainties considered in this thesis include demanduncertainties and suppliers disruptions. The end retailers demand is assumed to beuncertain following a certain distribution. Suppliers disruptions are represented by a setof discrete scenarios with given probabilities of occurrence of shrink in storage capacity,which can cause insufficient supplies from suppliers. Hence, the model in this thesis isdifferent from traditional supply chain network models under a deterministic case or themodels with uncertainties in demand only.In order to study the effects of the various uncertainties involved in the chain on theoptimal decisions, multiple methods are tested using a multi-objective mixed-integernonlinear model. After comparison of computational performance, the min-max methodis applied to obtain detailed mathematical solution. Numerical examples are conducted toillustrate the developed model with scenario analysis and sensitivity analysis is alsoconducted.
Author: Juping Shao
Publisher: Springer
ISBN: 3662472503
Category : Business & Economics
Languages : en
Pages : 197
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Book Description
The subject of this book is supply chain logistics planning optimization under multiple uncertainties, the key issue in supply chain management. Focusing on the strategic-alliance three-level supply chain, the model of supply chain logistics planning was established in terms of the market prices and the market requirements as random variables of manufactured goods with random expected value programming theory, and the hybrid intelligence algorithm solution model was designed. Aiming at the decentralized control supply chain, in which the nodes were unlimited expansion, the chance-constrained stochastic programming model was created in order to obtain optimal decision-making at a certain confidence level. In addition, the hybrid intelligence algorithm model was designed to solve the problem of supply chain logistics planning with the prices of the raw-materials supply market of the upstream enterprises and the prices of market demand for products of the downstream enterprises as random variables in the supply chain unit. Aimed at the three-stage mixed control supply chain, a logistics planning model was designed using fuzzy random programming theory with customer demand as fuzzy random variables and a hybrid intelligence algorithm solution was created. The research has significance both in theory and practice. Its theoretical significance is that the research can complement and perfect existing supply chain planning in terms of quantification. Its practical significance is that the results will guide companies in supply chain logistics planning in the uncertain environment.
Author: Mir Saman Pishvaee
Publisher: Academic Press
ISBN: 0128209003
Category : Science
Languages : en
Pages : 284
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Book Description
Biomass to Biofuel Supply Chain Design and Planning under Uncertainty: Concepts and Quantitative Methods explores the design and optimization of biomass-to-biofuel supply chains for commercial-scale implementation of biofuel projects by considering the problems and challenges encountered in real supply chains. By offering a fresh approach and discussing a wide range of quantitative methods, the book enables researchers and practitioners to develop hybrid methods that integrate the advantages and features of two or more methods in one decision-making framework for the efficient optimization of biofuel supply chains, especially for complex supply chain models. Combining supply chain management and modeling techniques in a single volume, the book is beneficial for graduate students who no longer need to consult subject-specific books alongside mathematical modeling textbooks. The book consists of two main parts. The first part describes the key components of biofuel supply chains, including biomass production, harvesting, collection, storage, preprocessing, conversion, transportation, and distribution. It also provides a comprehensive review of the concepts, problems, and opportunities associated with biofuel supply chains, such as types and properties of the feedstocks and fuel products, decision-making levels, sustainability concepts, uncertainty analysis and risk management, as well as integration of biomass supply chain with other supply chains. The second part focuses on modeling and optimization of biomass-to-biofuel supply chains under uncertainty, using different quantitative methods to determine optimal design. Proposes a general multi-level framework for the optimal design and operation of biomass-to-biofuel supply chains through quantitative analysis and modeling, including different biomass and waste biomass feedstock, production pathways, technology options, transportation modes, and final products Explores how modeling and optimization tools can be utilized to address sustainability issues in biofuel supply chains by simultaneously assessing and identifying sustainable solutions Presents several case studies with different regional constraints to evaluate the practical applicability of different optimization methods and compares their performance in real-world situations Includes General Algebraic Modeling System (GAMS) codes for solving biomass supply chain optimization problems discussed in different chapters
Author: Turan Paksoy
Publisher: Springer
ISBN: 3319975110
Category : Business & Economics
Languages : en
Pages : 270
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Book Description
This book presents the latest developments in optimization and optimal control models; exact, approximate and hybrid methods; and their applications in lean and green supply chains. It examines supply chain network design and modeling, closed loop supply chains, and lean, green, resilient and agile or responsive networks, and also discusses corporate social responsibility and occupational health and safety. It particularly focuses on supply chain management under uncertainty – employing stochastic or nonlinear modeling, simulation based studies and optimization – multi-criteria decision-making and applications of fuzzy set theory, and covers various aspects of supply chain management such as risk management, supplier selection or the design of automated warehouses. Lastly, using experimental applications and practical case studies, it shows the impact of lean and green applications on vehicle/fleet management and operations management.
Author: Ho Yin Mak
Publisher:
ISBN:
Category :
Languages : en
Pages : 420
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Book Description
Author: Yamine Bouzembrak
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This thesis contributes to the debate on how uncertainty and concepts of sustainable development can be put into modern supply chain network and focuses on issues associated with the design of multi-criteria supply chain network under uncertainty. First, we study the literature review , which is a review of the current state of the art of Supply Chain Network Design approaches and resolution methods. Second, we propose a new methodology for multi-criteria Supply Chain Network Design (SCND) as well as its application to real Supply Chain Network (SCN), in order to satisfy the customers demand and respect the environmental, social, legislative, and economical requirements. The methodology consists of two different steps. In the first step, we use Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) to buildthe model. Then, in the second step, we establish the optimal supply chain network using Mixed Integer Linear Programming model (MILP). Third, we extend the MILP to a multi-objective optimization model that captures a compromisebetween the total cost and the environment influence. We use Goal Programming approach seeking to reach the goals placed by Decision Maker. After that, we develop a novel heuristic solution method based on decomposition technique, to solve large scale supply chain network design problems that we failed to solve using exact methods. The heuristic method is tested on real case instances and numerical comparisons show that our heuristic yield high quality solutions in very limited CPU time. Finally, again, we extend the MILP model presented before where we assume that the costumer demands are uncertain. We use two-stage stochastic programming approach to model the supply chain network under demand uncertainty. Then, we address uncertainty in all SC parameters: opening costs, production costs, storage costs and customers demands. We use possibilistic linear programming approach to model the problem and we validate both approaches in a large application case.
Author: Lihui Wang
Publisher: Springer Science & Business Media
ISBN: 0857296523
Category : Technology & Engineering
Languages : en
Pages : 502
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Book Description
With the increasing complexity and dynamism in today’s product design and manufacturing, more optimal, robust and practical approaches and systems are needed to support product design and manufacturing activities. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing presents a focused collection of quality chapters on state-of-the-art research efforts in multi-objective evolutionary optimisation, as well as their practical applications to integrated product design and manufacturing. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing consists of two major sections. The first presents a broad-based review of the key areas of research in multi-objective evolutionary optimisation. The second gives in-depth treatments of selected methodologies and systems in intelligent design and integrated manufacturing. Recent developments and innovations in multi-objective evolutionary optimisation make Multi-objective Evolutionary Optimisation for Product Design and Manufacturing a useful text for a broad readership, from academic researchers to practicing engineers.
Author: Lawrence V. Snyder
Publisher: John Wiley & Sons
ISBN: 1119024846
Category : Business & Economics
Languages : en
Pages : 784
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Book Description
Comprehensively teaches the fundamentals of supply chain theory This book presents the methodology and foundations of supply chain management and also demonstrates how recent developments build upon classic models. The authors focus on strategic, tactical, and operational aspects of supply chain management and cover a broad range of topics from forecasting, inventory management, and facility location to transportation, process flexibility, and auctions. Key mathematical models for optimizing the design, operation, and evaluation of supply chains are presented as well as models currently emerging from the research frontier. Fundamentals of Supply Chain Theory, Second Edition contains new chapters on transportation (traveling salesman and vehicle routing problems), integrated supply chain models, and applications of supply chain theory. New sections have also been added throughout, on topics including machine learning models for forecasting, conic optimization for facility location, a multi-supplier model for supply uncertainty, and a game-theoretic analysis of auctions. The second edition also contains case studies for each chapter that illustrate the real-world implementation of the models presented. This edition also contains nearly 200 new homework problems, over 60 new worked examples, and over 140 new illustrative figures. Plentiful teaching supplements are available, including an Instructor’s Manual and PowerPoint slides, as well as MATLAB programming assignments that require students to code algorithms in an effort to provide a deeper understanding of the material. Ideal as a textbook for upper-undergraduate and graduate-level courses in supply chain management in engineering and business schools, Fundamentals of Supply Chain Theory, Second Edition will also appeal to anyone interested in quantitative approaches for studying supply chains.
