Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
1,393
result(s) for
"Assembly-line methods."
Sort by:
Scheduling in supply chains using mixed integer programming
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.
eBook
America's Assembly Line
The assembly line was invented in 1913 and has been in continuous operation ever since. It is the most familiar form of mass production. Both praised as a boon to workers and condemned for exploiting them, it has been celebrated and satirized. (We can still picture Chaplin's little tramp trying to keep up with a factory conveyor belt.) In America's Assembly Line, David Nye examines the industrial innovation that made the United States productive and wealthy in the twentieth century.The assembly line -- developed at the Ford Motor Company in 1913 for the mass production of Model Ts -- first created and then served an expanding mass market. It inspired fiction, paintings, photographs, comedy, cafeteria layouts, and cookie-cutter suburban housing. It also transformed industrial labor and provoked strikes and union drives. During World War II and the Cold War, it was often seen as a bastion of liberty and capitalism. By 1980, Japan had reinvented the assembly line as a system of \"lean manufacturing\"; American industry reluctantly adopted this new approach. Nye describes this evolution and the new global landscape of increasingly automated factories, with fewer industrial jobs in America and questionable working conditions in developing countries. A century after Ford's pioneering innovation, the assembly line continues to evolve toward more sustainable manufacturing.
eBook
Chance-constrained stochastic assembly line balancing with branch, bound and remember algorithm
Assembly lines are widely used mass production techniques applied in various industries from electronics to automotive and aerospace. A branch, bound, and remember (BBR) algorithm is presented in this research to tackle the chance-constrained stochastic assembly line balancing problem (ALBP). In this problem variation, the processing times are stochastic, while the cycle time must be respected for a given probability. The proposed BBR method stores all the searched partial solutions in memory and utilizes the cyclic best-first search strategy to quickly achieve high-quality complete solutions. Meanwhile, this study also develops several new lower bounds and dominance rules by taking the stochastic task times into account. To evaluate the performance of the developed method, a large set of 1614 instances is generated and solved. The performance of the BBR algorithm is compared with two mixed-integer programming models and twenty re-implemented heuristics and metaheuristics, including the well-known genetic algorithm, ant colony optimization algorithm and simulated annealing algorithm. The comparative study demonstrates that the mathematical models cannot achieve high-quality solutions when solving large-size instances, for which the BBR algorithm shows clear superiority over the mathematical models. The developed BBR outperforms all the compared heuristic and metaheuristic methods and is the new state-of-the-art methodology for the stochastic ALBP.
Journal Article
Large neighborhood search for an aeronautical assembly line time-constrained scheduling problem with multiple modes and a resource leveling objective
This paper deals with a scheduling problem arising at the tactical decision level in aeronautical assembly line. It has the structure of a challenging multi-mode resource-constrained project scheduling problem with incompatibility constraints, a resource leveling objective and also a large number of tasks. We first present a new event-based mixed-integer linear programming formulation and a standard constraint programming formulation of the problem. A large-neighborhood search approach based on the constraint programming model and tailored to the resource leveling objective is proposed. The approaches are tested and compared using industrial instances, yielding significant improvement compared to the heuristic currently used by the company. Moreover, the large-neighborhood search method significantly improves the method proposed in the literature on a related multi-mode resource investment problem when short CPU times are required.
Journal Article
Comparison of MILP and CP models for balancing partially automated assembly lines
The objective of Assembly Line Balancing (ALB) is to find the proper assignment of tasks to workstations, taking into consideration various types of constraints and defined management goals. Early research in the field focused on solving the Simple Assembly Line Balancing problem, a basic simplified version of the general problem. As the production environment became more complex, several new ALB problem types appeared, and almost all ALB problems are NP-hard, meaning that finding a solution requires a lot of time, resources, and computational power. Methods with custom-made algorithms and generic approaches have been developed for solving these problems. While custom-made algorithms are generally more efficient, generic approaches can be more easily extended to cover other variations of the problem. Over the past few decades, automation has played an increasingly important role in various operations, although complete automation is often not possible. As a result, there is a growing need for partially automated assembly line balancing models. In these circumstances, the flexibility of a generic approach is essential. This paper compares two generic approaches: mixed integer linear programming (MILP) and constraint programming (CP), for two types of partially automated assembly line balancing problems. While CP is relatively slower in solving the simpler allocation problems, it is more efficient than MILP when an increased number of constraints is applied to the ALB and an allocation and scheduling problem needs to be solved.
Journal Article
A Digital Twin Case Study on Automotive Production Line
The manufacturing sector is one of the areas where the advantages of digital twin technology can benefit mostly. The product development, including its software, electronics, mechanics, and physical behavior, is included in the digital twin of the product. Furthermore, simultaneous data capturing from the sensors and data processing are also available in the digital twin. This enables each phase of the development cycle to be simulated, processed, and validated to discover the potential problems before the production of real components. In this study, the use of digital twin technology in the commercial production phase of the automotive production line with a case study is introduced. This study is one of the most comprehensive studies in the literature related to automotive production; therefore, it puts forth the power of using digital twin technology in that area. As the result of this case study, a 6.01% increase in the commercial production line efficiency and an 87.56% gain for downtime are achieved.
Journal Article