A Survey on Cost and Profit Oriented Assembly Line Balancing

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/0866.pdfInternational audienceProblems, approaches and analytical models on assembly line balancing that deal explicitly with cost and profit oriented objectives are analysed. This survey paper serves to identify and work on open problems that have wide practical applications. The conclusions derived might give insights in developing decision support systems (DSS) in planning profitable or cost efficient assembly lines

The applications of artificial intelligence in supply chain project management: a survey

This paper reviews the articles that address the use of artificial intelligence (AI) within the management of supply chain projects. The focus is on investigating AI's possible applications and benefits in project planning and execution. SCOPUS database is used to cover highly ranked scientific papers. The review concludes that AI connected supply chain projects were undertaken mainly in North America and Southeast Asia. AI offers excellent opportunities to increase the chance of achieving project targets. However, there is still a greater need for profound academic research on the use of AI in project management, emphasizing supply chain requirements within the serial production industry

An Exact Solution Algorithm For Balancing Simple U-Type Assembly

http://www.icpr21.de/images/downloads/icpr21_abstracts.pdfInternational audienceIn this research, we address U-type assembly line balancing and aim to solve large scale instances to optimality. Specifically, we consider simple line balancing problem with minimizing the number of workstations (UALBP-1) and cycle time objectives (UALBP-2). Optimal solution of UALBP-1 is important, since each additional station requires additional workers and equipment. On the other hand, for UALBP-2, a slight improvement in cycle time increases the production capacity. To be able solve large scale instances to optimality, a decomposition based algorithm is proposed and enhancement strategies are integrated. We perform computational experiments to test the efficiency of the algorithm and present the results. The main contribution of this paper is the proposed decomposition strategy and integrated acceleration mechanisms

Robust U-Type Assembly Line Balancing Problem: A Decomposition Based Approximate Solution Algorithm

http://www.medifas.net/IGLS/Papers2012/Paper220.pdfInternational audienceWe investigate balancing U-type assembly lines under uncertainty. We formulate the robust problem and develop an optimization model to design lines that hedge against disruptions. Offering more alternatives to group the operations, U-type assembly layouts are shown to be more efficient than conventional straight lines. As a result, they have been widely investigated in literature. However, a great majority of the studies assume deterministic environments and ignore various sources of uncertainty, like variability in operation times. We address this research gap. To hedge against variations in operation times, we employ robust optimization that considers worst case situations. However, this approach could result in pessimist solutions. In order to avoid that, we will assume that only a subset of operation times will be assigned to their worst case values. To solve the model, we will make use of Benders Decomposition, however it may converge slowly for large instances. To be able to solve real life problems, we will propose approximate solution algorithm. The efficiency of the algorithm will be evaluated with some computational tests

A decomposition based solution algorithm for U-type assembly line balancing with interval data

International audienceBalancing U-type assembly lines under uncertainty is addressed in this paper by formulating a robust problem and developing its optimization model and algorithm. U-type assembly layouts are shown to be more efficient than conventional straight lines. A great majority of studies on U-lines assume deterministic environments and ignore uncertainty in operation times. We aim to fill this research gap and, to the best of our knowledge, this study will be the first application of robust optimization to U-type assembly planning.We assume that the operation times are not fixed but they can vary. We employ robust optimization that considers worst case situations. To avoid over-pessimism, we consider that only a subset of operation times take their worst case values. To solve this problem, we suggest an iterative approximate solution algorithm. The efficiency of the algorithm is evaluated with some computational tests

U-Shaped Assembly Line Balancing under Uncertainty: A Robust Optimization Model

submission 328International audienceIn this research, we address U-line balancing under uncertainty and propose a robust optimization model to generate assembly lines that are protected against disruptions in operation times. U-shaped layouts have been widely investigated in literature because they are more efficient and flexible than traditional straight assembly lines. They offer more choices for grouping tasks since the same worker could work in different stations which are located at entrance and exit sides of the lines. Majority of the existing research on assembly line balancing makes a simplifying assumption and models deterministic environments. However, in real life we are subject to various sources of uncertainty, like variability in operation times. To avoid deviations from production targets, we will use robust optimization, which is a fundamental optimization method that hedge against uncertainty. The other approaches are stochastic programming, sensitivity analysis, parametric programming and fuzzy programming. Robust optimization addresses minmax and minmax regret objectives (see Kouvelis and Yu [1]). However, pessimistic solutions could be generated. To avoid over pessimism, Bertsimas and Sim [2] have proposed a restricted uncertainty model in which only a subset of coefficients are driven to their upper bounds. We use this approach to formulate the robust U-type assembly line problem with minimum number of workstations (UALBP-1). More specifically, we aim to design assembly lines that could are protected against variability in operations times. Cycle time is fixed and variability affect the number of stations installed

Simple assembly line balancing under uncertainty: a robust approach

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Robust Assembly Line Balancing: State of the Art and New Research Perspectives

International audienceIn many real-world applications, the problems with the data used for scheduling such as processing times, setup times, release dates or due dates is not exactly known before applying a specific solution algorithm which restricts practical aspects of scheduling theory. During the last decades, several approaches have been developed for sequencing and scheduling with inaccurate data, depending on whether the data is given as random numbers, fuzzy numbers or whether it is uncertain, i.e., it can take values from a given interval. This book considers the four major approaches for dealing with such problems: a stochastic approach, a fuzzy approach, a robust approach and a stability approach. Each of the four parts is devoted to one of these approaches. First, it contains a survey chapter on this subject, as well as between further chapters, presenting some recent research results in the particular area. The book provides the reader with a comprehensive and up-to-date introduction into scheduling with inaccurate data. The four survey chapters deal with scheduling with stochastic approaches, fuzzy job-shop scheduling, minmax regret scheduling problems and a stability approach to sequencing and scheduling under uncertainty. This book will be useful for applied mathematicians, students and PhD students dealing with scheduling theory, optimization and calendar planning

Assembly Line Balancing under Uncertainty: Robust Optimization Models and Exact Solution Method

International audienceThis research deals with line balancing under uncertainty and presents two robust optimization models. Interval uncertainty for operation times was assumed. The methods proposed generate line designs that are protected against this type of disruptions. A decomposition based algorithm was developed and combined with enhancement strategies to solve optimally large scale instances. The efficiency of this algorithm was tested and the experimental results were presented. The theoretical contribution of this paper lies in the novel models proposed and the decomposition based exact algorithm developed. Moreover, it is of practical interest since the production rate of the assembly lines designed with our algorithm will be more reliable as uncertainty is incorporated. Furthermore, this is a pioneering work on robust assembly line balancing and should serve as the basis for a decision support system on this subject