A STUDY ON GENERAL ASSEMBLY LINE BALANCING MODELING METHODS AND TECHNIQUES

The borders of the assembly line balancing problem, as classically drawn, are as clear as any other operations research topic in production planning, with well-defined sets of assumptions, parameters, and objective functions. In application, however, these borders are frequently transgressed. Many of these deviations are internal to the assembly line balancing problem itself, arising from any of a wide array of physical or technological features in modern assembly lines. Other issues are founded in the tight coupling of assembly line balancing with external production planning and management problems, as assembly lines are at the intersection of multiple related problems in job sequencing, part flow logistics, worker safety, and quality. The field of General Assembly Line Balancing is devoted to studying the class of adapted and extended solution techniques necessary in order to model these applied line balancing problems. In this dissertation a complex line balancing problem is presented based on the real production environment of our industrial partner, featuring several extensions for task-to-task relationships, station characteristics limiting assignment, and parallel worker zoning interactions. A constructive heuristic is developed along with two improvement heuristics, as well as an integer programming model for the same problem. An experiment is conducted testing each of these new solution methods upon a battery of testbed problems, measuring solution quality, runtime, and achievement of feasibility. Additionally, a new method for measuring a secondary horizontal line balancing objective is established, based on the options-mix paradigm rather than the customary model-mix paradigm

Best matching processes in distributed systems

The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individuals—from clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory. The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies

Assembly Line

An assembly line is a manufacturing process in which parts are added to a product in a sequential manner using optimally planned logistics to create a finished product in the fastest possible way. It is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The present edited book is a collection of 12 chapters written by experts and well-known professionals of the field. The volume is organized in three parts according to the last research works in assembly line subject. The first part of the book is devoted to the assembly line balancing problem. It includes chapters dealing with different problems of ALBP. In the second part of the book some optimization problems in assembly line structure are considered. In many situations there are several contradictory goals that have to be satisfied simultaneously. The third part of the book deals with testing problems in assembly line. This section gives an overview on new trends, techniques and methodologies for testing the quality of a product at the end of the assembling line

Progress in Material Handling Research: 2012

Table of Content

Line balancing using metaheuristic methods in BMW South Africa

This study documents a project to investigate the possibility of achieving savings in BMW South Africa’s Rosslyn assembly plant through the use of metaheuristics to optimise line balancing methods. Through this project, a customised Ant Colony Optimisation algorithm was developed for the optimisation of the frontend assembly line in this plant. This algorithm is one which was designed to take into account many of the constraints which are found in an automotive manufacturing environment such as work areas, shared processes and sequence constraints. Through the use of the algorithm, a solution was developed which shows improvements to the line balancing in the area. These improvements show a 17% reduction in labour costs in the area, an improvement of 13.12% in the area’s average work loading and an increase in the average work stability of 17.81%. Additionally, improvements were found which would allow this algorithm to be used in other lines in the assembly plant for further savings and improvements.MT 201

Design and Control of Warehouse Order Picking: a literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently for the whole supply chain. In order to operate efficiently, the orderpicking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.Order picking;Logistics;Warehouse Management