Over the past three decades, practitioners and researchers in engineering and operations sciences have focused on disassembly planning as a way to increase profitability of re-manufacturing, recycling and disposal processes for end-of-life products. An important task in disassembly planning is the representation of feasible operations sequences for the products. Precedence constraints can be derived from geometrical and technical relations among a product’s parts and joints and used to narrow down the set of possible sequences. There are several studies which address the problem of finding minimal process trees or AND/OR graphs as a prerequisite to disassembly sequence planning. However, since most of the existing approaches focus on specific product examples or industrial case studies there is still a lack of generic data sets for the academic purpose of model testing and evaluation. In this study, a systematic approach is presented to establish feasible AND/OR graphs from scratch based on general product design assumptions. Artificial process data as generated using the proposed methodology can be applied to various problems in disassembly decision making. In this study, it is used to analyze the combined problem of operations sequence planning and machine scheduling for the disassembly of multiple heterogeneous products. For this matter, disassembly sequences for each product and the order of operations at the stations have to be determined simultaneously with the objective of minimizing makespan. In contrast to existing problem formulations, the presented model explicitly considers divergence of the product structure during disassembly by allowing for parallel processing of separate sub-assemblies that have been extracted from the same product. A mixed-integer-program is developed based on disassembly process graphs which are derived for each product to represent alternative and parallel operations. Model performance is evaluated using 360 random instances created by the proposed process data generator. In addition, an industrial case study is presented to demonstrate the application of the proposed MIP model in a real-world disassembly context.
Reverse logistics Disassembly Scheduling Data modeling
This is a preview of subscription content, log in to check access.
The author would like to thank Benedikt Zipfel who contributed to the case study as part of his diploma thesis as well as the executive staff at Gebrauchtgerätezentrum Dresden GmbH & Co. KG who kindly provided the industrial data. Also the author is grateful for the valuable comments of the reviewers who helped to improve the quality of the paper.
Agrawal S, Tiwari MK (2008) A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem. Int J Prod Res 46(6):1405–1429CrossRefzbMATHGoogle Scholar
Amin-Naseri MR, Afshari AJ (2012) A hybrid genetic algorithm for integrated process planning and scheduling problem with precedence constraints. Int J Adv Manuf Technol 59(1):273–287CrossRefGoogle Scholar
Andrés C, Lozano S, Adenso-Diaz B (2007) Disassembly sequence planning in a disassembly cell context. Robot Comput Integr Manuf 23(6):690–695CrossRefGoogle Scholar
Bourjault A (1984) Contribution à une approche méthodologique de l'assemblage automatisé: élaboration automatique des séquences opératoires (Doctoral dissertation)Google Scholar
Cheng TCE, Lin BMT, Tian Y (2013) A scheduling model for the refurbishing process in recycling management. Int J Prod Res 51(23–24):7120–7139CrossRefGoogle Scholar
De Fazio T, Whitney D (1987) Simplified generation of all mechanical assembly sequences. IEEE J Robot Autom 3(6):640–658CrossRefGoogle Scholar
De Mello LH, Sanderson AC (1990) AND/OR graph representation of assembly plans. IEEE Trans Robot Autom 6(2):188–199CrossRefGoogle Scholar
Ehm F (2017) Process data generation for integrated disassembly sequencing and machine scheduling. Proceedings of the 3rd international conference on remanufacturing (ICoR-17), 161–174Google Scholar
Ehm F (2018) Machine scheduling for multi-product disassembly. In: Operations research proceedings, vol 2016. Springer, Cham, pp 507–513Google Scholar
Fattahi P, Mehrabad MS, Jolai F (2007) Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. J Intell Manuf 18(3):331CrossRefGoogle Scholar
Gaudreault J, Frayret JM, Rousseau A, D’Amours S (2011) Combined planning and scheduling in a divergent production system with co-production: a case study in the lumber industry. Comput Oper Res 38(9):1238–1250CrossRefzbMATHGoogle Scholar
Lambert AJD, Gupta SM (2005) Disassembly Modelling for maintenance, reuse, recycling. CRC PressGoogle Scholar
Lambert AJD (2006) Exact methods in optimum disassembly sequence search for problems subject to sequence dependent costs. Omega 34(6):538–549CrossRefGoogle Scholar
Lambert AJD, Gupta SM (2005) Determining optimum and suboptimum disassembly sequences with an application to a cell phone. The 6th IEEE International Symposium on Assembly and Task Planning: From Nano to Macro Assembly and Manufacturing, 2005. (ISATP 2005):260–265Google Scholar
Luo H, Huang GQ, Shi Y, Qu T (2012) Divergent production scheduling with multi-process routes and common inventory. Int J Prod Res 50(20):5762–5782CrossRefGoogle Scholar
Ma YS, Jun HB, Kim HW, Lee DH (2011) Disassembly process planning algorithms for end-of-life product recovery and environmentally conscious disposal. Int J Prod Res 49(23):7007–7027CrossRefGoogle Scholar
Özgüven C, Özbakır L, Yavuz Y (2010) Mathematical models for job-shop scheduling problems with routing and process plan flexibility. Appl Math Model 34(6):1539–1548MathSciNetCrossRefzbMATHGoogle Scholar
Smith S, Smith G, Chen WH (2012) Disassembly sequence structure graphs: an optimal approach for multiple-target selective disassembly sequence planning. Adv Eng Inform 26(2):306–316CrossRefGoogle Scholar
Toffolo TA, Santos HG, Carvalho MA, Soares JA (2016) An integer programming approach to the multimode resource-constrained multiproject scheduling problem. J Sched 19(3):295–307MathSciNetCrossRefzbMATHGoogle Scholar
Tripathi M, Agrawal S, Pandey MK, Shankar R, Tiwari MK (2009) Real world disassembly modeling and sequencing problem: optimization by algorithm of self-guided ants (ASGA). Robot Comput Integr Manuf 25(3):483–496CrossRefGoogle Scholar