Disassembly Sequencing Using Tabu Search

Alshibli, Mohammad; Sayed, Ahmed El; Kongar, Elif; Sobh, Tarek M.; Gupta, Surendra M.

doi:10.1007/s10846-015-0289-9

Cited by 67 publications

(37 citation statements)

References 10 publications

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“…Equation 12indicates that any one of the disassembly tasks n must be assigned to a disassembly workstation k, and cannot be subdivided; Equation 13represents that the disassembly time of any one disassembly station cannot exceed the production tact of the disassembly line; Equation 14shows that there is a sequence between the disassembly tasks, and the priority constraints cannot be violated; Equation (15) indicates the value interval of the disassembly workstation; Equation (16) means that all disassembly tasks must be assigned to the disassembly workstation and cannot be omitted; Equation (17) represents the number of disassembly tasks contained in any one of the workstations k.…”

Section: Model Developmentmentioning

confidence: 99%

“…When solving the disassembly line balance problem by the exhaustive method, the difficulty of solving DLBP would increase geometrically due to the size of retired products and it is difficult to find the optimal solution within a limited time, which is an NP (Non-deterministic Polynomial) problem [12]. Different scholars respectively adopted the ant colony algorithm [13], immune algorithm [14], variable neighborhood search algorithm [15], tabu algorithm [16], simulated annealing algorithm [17] and other heuristic algorithms to solve NP problems, part of which have been improved to enhance the solving ability. For instance, adding the neighborhood mutation operator into the basic particle swarm optimization algorithm makes up the deficiency of particle swarm optimization in solving discrete problems, which not only strengthens the optimization ability of the algorithm, but also improves the efficiency and accuracy of the solution [18].…”

Section: Introductionmentioning

confidence: 99%

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A Balancing Method of Mixed-model Disassembly Line in Random Working Environment

et al. 2019

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Disassembly is a necessary link in reverse supply chain and plays a significant role in green manufacturing and sustainable development. However, the mixed-model disassembly of multiple types of retired mechanical products is hard to be implemented by random influence factors such as service time of retired products, degree of wear and tear, proficiency level of workers and structural differences between products in the actual production process. Therefore, this paper presented a balancing method of mixed-model disassembly line in a random working environment. The random influence of structure similarity of multiple products on the disassembly line balance was considered and the workstation number, load balancing index, prior disassembly of high demand parts and cost minimization of invalid operations were taken as targets for the balancing model establishment of the mixed-model disassembly line. An improved algorithm, adaptive simulated annealing genetic algorithm (ASAGA), was adopted to solve the balancing model and the local and global optimization ability were enhanced obviously. Finally, we took the mixed-model disassembly of multi-engine products as an example and verified the practicability and effectiveness of the proposed model and algorithm through comparison with genetic algorithm (GA) and simulated annealing algorithm (SA).

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Section: Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Balancing Method of Mixed-model Disassembly Line in Random Working Environment

et al. 2019

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“…However, these researchers do not take into account uncertainty in a explicit way, nor do they consider preparatory actions like switching tools or rotating the device. On the other hand, the work of Alshibli et al [23] is interesting because it seeks to address state uncertainty. Even so, they do not consider neither actions with multiple outcomes nor the possibility of reaching dead ends.…”

Section: Related Workmentioning

confidence: 99%

Practical Resolution Methods for MDPs in Robotics Exemplified With Disassembly Planning

Suárez-Hernández

Torras

Alenyà

2019

IEEE Robot. Autom. Lett.

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In this paper we focus on finding practical resolution methods for Markov Decision Processes (MDPs) in robotics. Some of the main difficulties of applying MDPs to real-world robotics problems are: (1) having to deal with huge state spaces; and (2) designing a method that is robust enough to dead ends. These complications restrict or make more difficult the application of methods such as Value Iteration, Policy Iteration or Labeled Real Time Dynamic Programming (LRTDP). We see in determinization and heuristic search a way to successfully work around these problems. In addition, we believe that many practical use cases offer the opportunity to identify hierarchies of subtasks and solve smaller, simplified problems. We propose a decision-making unit that operates in a probabilistic planning setting through Stochastic Shortest Path Problems (SSPPs), which generalize the most common types of MDPs. Our decisionmaking unit combines: (1) automatic hierarchical organization of subtasks; and (2) on-line resolution via determinization. We argue that several applications of planning benefit from these two strategies. We exemplify our approach with a robotized disassembly application. The disassembly problem is modeled in Probabilistic Planning Definition Language (PPDDL), and serves to define our experiments. Our results show many advantages of our method over LRTDP, like a better capability to handle problems with large state spaces and state definitions that change when new fluents are discovered.

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“…Buyang et al [50] presented a tabu search algorithm for a new problem class called cohesive clustering, which arises in a variety of business applications. Alshibli et al [51] used tabu search to obtain the optimal solution for disassembly sequencing.…”

Section: Introductionmentioning

confidence: 99%

Product Service Scheduling Problem with Service Matching Based on Tabu Search Method

Hao

Hong

Wei

et al. 2020

Journal of Advanced Transportation

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This paper considers the product service scheduling problem with service matching. This problem is a complex scheduling problem that integrates multiple traveling salesman problem (MTSP) and multiple service types. To solve the problem, a tabu search method was presented. With the objective of minimizing the total of engineers’ travel distances, total of the customer penalty values, and makespan, an optimization model of this problem is established. The historical search solutions are taken as tabu object, and limiting the quantity of search neighborhood solution is taken as the aspiration level. At last, the adaptability, validity, and stability of the model are verified by an example.

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Disassembly Sequencing Using Tabu Search

Cited by 67 publications

References 10 publications

A Balancing Method of Mixed-model Disassembly Line in Random Working Environment

A Balancing Method of Mixed-model Disassembly Line in Random Working Environment

Practical Resolution Methods for MDPs in Robotics Exemplified With Disassembly Planning

Product Service Scheduling Problem with Service Matching Based on Tabu Search Method

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