Michael R. Johnson
Simon Fraser University
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Featured researches published by Michael R. Johnson.
International Journal of Production Research | 1995
Michael R. Johnson; M. H. Wang
SUMMARY This paper addresses an increasingly important aspect of product design and its relationship to life-cycle costing: disassembly for material recovery opportunities (MRO). MRO is defined as an opportunity to reclaim post-consumer products for recycling, remanufacturing and re-use. The authors have developed a methodology which can be used to identify and assess cost-effective characteristics of disassembly for the recovery of products. The central focus of this paper is aimed at improving the efficiency of the disassembly planning process and generating an optimal disassembly sequence. Four criteria are established to optimize the generation of the disassembly sequence: (1) material compatibility, (2) clustering for disposal, (3) concurrent disassembly operations, and (4( maximizing yield. In this paper we define the ‘disassemblability’ of a product as the ability to optimize the design and disassembly process for removal of specific pans or materials in a manner which will minimize costs. Steps ha...
International Journal of Production Research | 1998
Michael R. Johnson; Michael H. Wang
This paper introduced a procedure which integrates economical factors into the scheduling of disassembly operations for Material Recovery Opportunities (MRO). MRO are defined as opportunities to reclaim post-consumer products for recycling, remanufacturing and reuse. Traditionally, recyclers have resorted to using heuristics for analysing the breakdown of products and the associated costs. In this paper, a quantitative method of disassembly analysis is developed. Its aim is to improve the efficiency of the disassembly planning process and to generate an optimal disassembly sequence which maximizes profit. Three economic indices are used to evaluate the trade-off between reclamation and disposal of individual components. A systematic procedure of generating an optimal disassembly sequence based on maximizing the profits of material recovery is presented. Three criteria are established to reduce the search space and facilitate recovery opportunities: (1) material compatibility, (2) clustering for disposal, ...
Journal of Cleaner Production | 2004
A.J. Spicer; Michael R. Johnson
Abstract In this paper, three approaches to the implementation of Extended Producer Responsibility (EPR) are discussed and compared. They are OEM Takeback in which producers take direct responsibility, Pooled Takeback in which responsibility is shared through a consortium of producers, and Third-Party Takeback in which ‘Product Responsibility Providers’ are contracted to assume responsibility for end-of-life on behalf of producers. For many product categories, this latter approach may be the most effective way to meet the goals of EPR programs. This paper discusses the benefits and challenges of third-party demanufacturing in detail, and also introduces a particular example of the approach. The Renewit system aspires to create a structure for an industry of third-party recyclers, by providing a unified information system to enable efficient product recycling, and an OEM-driven financial mechanism to fund the recycling.
Journal of Manufacturing Systems | 2000
Hsin-Hao (Tom) Huang; Michael H. Wang; Michael R. Johnson
Abstract The problem of disassembly sequence generation is complex and has proven to be difficult to solve. Various methods have been used in attempting to solve the problem, including mathematical modeling and search techniques. This paper first presents the economic analysis method of the disassembly process and then describes the method of using an artificial neural network for disassembly sequence generation. A real-world disassembly example is described for material recovery considerations, and the result of implementing the neural network approach is demonstrated.
Concurrent Engineering | 1995
Michael H. Wang; Michael R. Johnson
In this research, we show an integrated concurrent design methodology which is environmentally conscious for the manufacturing sec tor By interactively examining various phases in manufacturing processes, materials used, waste generated and product quantity specified, we could then predict the amount of waste (recyclable or nonrecyclable) that will be generated, the kinds of toxic materials, the approximate energy consumed and the approximate recycling cost required Accompanied by other information such as yeld, the designer or decisionmaker can then go back and check if their product design or process selection is suitable and least harmful to the environment
Journal of Marketing Education | 2016
Emily Treen; Christina V. Atanasova; Leyland Pitt; Michael R. Johnson
Marketing instructors using simulation games as a way of inducing some realism into a marketing course are faced with many dilemmas. Two important quandaries are the optimal size of groups and how much of the students’ time should ideally be devoted to the game. Using evidence from a very large sample of teams playing a simulation game, the study described here seeks to answer two fundamental questions: What effects on performance does group size have? And, is it possible for groups to spend too much time on decision making? The results indicate that performance increases in line with group size until teams have five members, and then tapers off. Furthermore, performance is shown to rise as time spent on decision making increases, up to a point, after which additional time spent on the game is shown to detract from performance. Implications for marketing instructors are discussed.
Journal of Engineering and Technology | 2014
Michael R. Johnson; Ian P. McCarthy
Current Opinion in Environmental Sustainability | 2017
Ian P. McCarthy; Mark Collard; Michael R. Johnson
World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering | 2013
Michael R. Johnson; Ian P. McCarthy
hawaii international conference on system sciences | 2011
Ian P. McCarthy; Michael R. Johnson; Brian R. Gordon