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In this thesis, we study a Stochastic Disassembly Line Balancing Problem (SDLBP) with hazardous tasks. We define success and failure events for each hazardous task and describe several scenarios over all hazardous tasks. Our aim is to maximize expected profit over all scenarios. We construct mathematical model with one, two and three hazardous tasks. Then, general formulation both in nonlinear and linear form is studied. We test the performance of the model on randomly generated data using the networks taken from the literature. Moreover, we study the Value of Stochastic Solution (VSS) which is the added value of considering uncertainty in a decision. The limits of the SDLBP models are observed by the increase of number of tasks in a network and by the increase in number of hazardous tasks. We propose some solution approaches and bounding procedures, and show their satisfactory performances-Ph.D. - Doctoral Progra

Profit-oriented disassembly-line balancing

As product and material recovery has gained importance, disassembly volumes have increased, justifying construction of disassembly lines similar to assembly lines. Recent research on disassembly lines has focused on complete disassembly. Unlike assembly, the current industry practice involves partial disassembly with profit-maximization or cost-minimization objectives. Another difference between assembly and disassembly is that disassembly involves additional precedence relations among tasks due to processing alternatives or physical restrictions. In this study, we define and solve the profit-oriented partial disassembly-line balancing problem. We first characterize different types of precedence relations in disassembly and propose a new representation scheme that encompasses all these types. We then develop the first mixed integer programming formulation for the partial disassembly-line balancing problem, which simultaneously determines (1) the parts whose demand is to be fulfilled to generate revenue, (2) the tasks that will release the selected parts under task and station costs, (3) the number of stations that will be opened, (4) the cycle time, and (5) the balance of the disassembly line, i.e. the feasible assignment of selected tasks to stations such that various types of precedence relations are satisfied. We propose a lower and upper-bounding scheme based on linear programming relaxation of the formulation. Computational results show that our approach provides near optimal solutions for small problems and is capable of solving larger problems with up to 320 disassembly tasks in reasonable time

Modeling and Optimization of Disassembly Systems with a High Variety of End of Life States.

Remanufacturing is a promising product recovery method that brings new life to cores that otherwise would be discarded thus losing all value. Disassembly is a sub-process of remanufacturing where components and modules are removed from the core, sorted and graded, and directly reused, refurbished, recycled, or disposed of. Disassembly is the backbone of the remanufacturing process because this is where the reuse value of components and modules is realized. Disassembly is a process that is also very difficult in most instances because it is a mostly manual process creating stochastic removal times of components. There is a high variety of EOL states a core can be in when disassembled and an economic downside due to not all components having reuse potential. This thesis focuses on addressing these difficulties of disassembly in the areas of sequence generation, line balancing, and throughput modeling. In Chapter 2, we develop a series of sequence generation models that considers the material properties, partial disassembly, and sequence dependent task times to determine the optimal order of disassembly in the presence of a high variety of EOL states. In Chapter 3, we develop a joint precedence graph method for disassembly that models all possible EOL states a core can be in that can be used with a wide variety of line balancing algorithms. We also develop a stochastic joint precedence graph method in the situation where some removal times of components are normal random variables. In Chapter 4, we further advance the analytical modeling framework to analyze transfer lines that perform routing logics that result from a high variety of EOL states, such as a restrictive split routing logic and the possibility that disassembly and split operations can be performed at the same workstation.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111570/1/robriggs_1.pd

Line Balancing Problem with Multi-Manned Workstations and Resource Constraints: The Case of Electronics Waste Disassembly

The increasing public awareness of environmental protection and the scarcity of rare earth elements have made closed-loop supply chains a necessity in many sectors. In particular, recycling components and parts from end-of-life consumer electronics have drawn the attention of both academics and practitioners. Disassembly line balancing improves the resource efficiency of recycling operations. This study proposes a new mathematical formulation and hybrid metaheuristics for solving the Disassembly Line Balancing Problem (DLBP) considering multi-manned workstations and resource constraints. The transformed AND/OR graph is used for prioritizing disassembly tasks in the modeling process. The method is applied for optimizing a real-world case of laptop disassembly to showcase the usefulness of the approach. The performance of the developed metaheuristics is compared to minimize the number of workstations, operators, and machines involved in the disassembly operations. Further, the results are analyzed through sensitivity analysis. This study concludes by providing practical insights and suggestions for the future development of DLBPs

Second order conic approximation for disassembly line design with joint probabilistic constraints

A problem of profit oriented disassembly line design and balancing with possible partial disassembly and presence of hazardous parts is studied. The objective is to design a production line providing a maximal revenue with balanced workload. Task times are assumed to be random variables with known normal probability distributions. The cycle time constraints are to be jointly satisfied with at least a predetermined probability level. An AND/OR graph is used to model the precedence relationships among tasks. Several lower and upper–bounding schemes are developed using second order cone programming and convex piecewise linear approximation. To show the relevance and applicability of the proposed approach, a set of instances from the literature are solved to optimality

A bibliographic review of production line design and balancing under uncertainty

This bibliography reviews the solution methods developed for the design and balancing problems of production lines such as assembly and disassembly lines. The line design problem aims in determining the number of workstations along with the corresponding assignment of tasks to each workstation, while the line balancing problem seeks an assignment of tasks, to the existing workstations of the line, which ensures that the workloads are as equal as possible among the workstations. These two optimisation problems can be also integrated and treated as a multi-objective optimisation problem. This review considers both deterministic and stochastic formulations for disassembly lines and is limited to assembly line design and balancing under uncertainty. This bibliography covers more than 90 publications since 1976 for assembly and 1999 for disassembly

Optimum assembly line balancing: A stochastic programming approach

Assembly line balancing problem is an approach of assigning a set of tasks to an ordered sequence of workstations. This assignment needs to be made in such a way that the underlying precedence constraints are not violated and efficiency measures are optimized subject to the restriction of the cycle time constraint. Research works, reported so far, mainly deal with the minimization of balancing loss, subject to precedence constraints. Lack of uniqueness in those optimum solutions and pressing demand to include system loss in the objective function have led to the present work of minimization of both balancing and system loss. As there is no standard measure for system loss, the current work proposes a measure for system loss and offers solution to this bi-objective problem

On the alignment of lot sizing decisions in a remanufacturing system in the presence of random yield

In the area of reverse logistics, remanufacturing has been proven to be a valu- able option for product recovery. In many industries, each step of the products’ recovery is carried out in lot sizes which leads to the assumption that for each of the different recovery steps some kind of fixed costs prevail. Furthermore, holding costs can be observed for all recovery states of the returned product. Although several authors study how the different lot sizes in a remanufacturing system shall be determined, they do not consider the specificity of the remanufacturing process itself. Thus, the disassembly operations which are always neglected in former analyses are included in this contribution as a specific recovery step. In addition, the assumption of deterministic yields (number of reworkable compo- nents obtained by disassembly) is extended in this work to study the system behavior in a stochastic environment. Three different heuristic approaches are presented for this environment that differ in their degree of sophistication. The least sophisticated method ignores yield randomness and uses the expected yield fraction as certainty equivalent. As a numerical experiment shows, this method already yields fairly good results in most of the investigated problem instances in comparison to the other heuristics which incorporate yield uncertainties. How- ever, there exist instances for which the performance loss between the least and the most sophisticated heuristic amounts to more than 6%.reverse logistics, remanufacturing, lot sizing, disassembly, random yield