Modelling and solving profit-oriented U-shaped partial disassembly line balancing problem

Disassembly lines are utilized frequently to disassemble the end-of-life products completely or partially to retain the valuable components for remanufacturing or recycling. This research introduces and solves the profit-oriented U-shaped partial disassembly line balancing problem (PUPDLBP) for the first time. A 0–1 integer linear programming model is formulated to tackle the PUPDLBP with AND/OR precedence, which is capable of solving the small-size instances optimally. As the considered problem is NP-hard, a novel discrete cuckoo search (DCS) algorithm is implemented and improved to solve the considered problem. The proposed DCS employs a two-phase decoding procedure to handle the precedence constraint, and new population update and new method to select and replace the abandoned individuals to achieve the proper balance between exploitation and exploration. Case studies demonstrate that the U-shaped line might obtain the larger total profit than a straight line. The comparative study shows that the improvements enhance the performance of DCS by a significant margin. The proposed algorithm outperforms CPLEX solver when solving large-sized instances and produce competing performance in comparison with 11 other algorithms

Hybrid multi-objective evolutionary algorithm for solving RALB-II problem

In this paper, we propose an MIP model for minimisation of cycle time and total assembly line cost simultaneously. Due to NP-hard nature of RALB (Rubinovitz and Bukchin, 1991), and to avoid local minima, a hybrid multi-objective evolutionary (H-MOE) algorithm developed based on the features of NSGA-II and simulated annealing algorithm is used to solve the RALB-II problem. Performance of the proposed algorithm is evaluated using datasets from Mukund et al. (2017b) and it was found that H-MOE algorithm outperformed the algorithm by Mukund et al. (2017b) in five out of seven cases on saving in cycle time and four out of seven in terms of total cost saving. In terms of average improvement, the proposed algorithm outperformed in terms total cost saving and underperformed in terms of time cycle compared with the performance of algorithm by Mukund et al. (2017b). Conclusions and future scope are highlighted

Model and migrating birds optimization algorithm for two-sided assembly line worker assignment and balancing problem

Worker assignment is a relatively new problem in assembly lines that typically is encountered in situations in which the workforce is heterogeneous. The optimal assignment of a heterogeneous workforce is known as the assembly line worker assignment and balancing problem (ALWABP). This problem is different from the well-known simple assembly line balancing problem concerning the task execution times, and it varies according to the assigned worker. Minimal work has been reported in worker assignment in two-sided assembly lines. This research studies worker assignment and line balancing in two-sided assembly lines with an objective of minimizing the cycle time (TALWABP). A mixed-integer programming model is developed, and CPLEX solver is used to solve the small-size problems. An improved migrating birds optimization algorithm is employed to deal with the large-size problems due to the NP-hard nature of the problem. The proposed algorithm utilizes a restart mechanism to avoid being trapped in the local optima. The solutions obtained using the proposed algorithms are compared with well-known metaheuristic algorithms such as artificial bee colony and simulated annealing. Comparative study and statistical analysis indicate that the proposed algorithm can achieve the optimal solutions for small-size problems, and it shows superior performance over benchmark algorithms for large-size problems

Mathematical models and migrating birds optimization for robotic U-shaped assembly line balancing problem

Modern assembly line systems utilize robotics to replace human resources to achieve higher level of automation and flexibility. This work studies the task assignment and robot allocation in a robotic U-shaped assembly line. Two new mixed-integer programming linear models are developed to minimize the cycle time when the number of workstations is fixed. Recently developed migrating birds optimization algorithm is employed and improved to solve large-sized problems. Problem-specific improvements are also developed to enhance the proposed algorithm including modified consecutive assignment procedure for robot allocation, iterative mechanism for cycle time update, new population update mechanism and diversity controlling mechanism. An extensive comparative study is carried out to test the performance of the proposed algorithm, where seven high-performing algorithms recently reported in the literature are re-implemented to tackle the considered problem. The computational results demonstrate that the developed models are capable to achieve the optimal solutions for small-sized problems, and the proposed algorithm with these proposed improvements achieves excellent performance and outperforms the compared ones

Flowshop scheduling with sequence dependent setup times and batch delivery in supply chain

With the emergence of advanced manufacturing and Industry 4.0 technologies, there is a growing interest in coordinating the production and distribution in supply chain management. This paper addresses the production and distribution problems with sequence dependent setup time for multiple customers in flow shop environments. In this complex decision-making problem, an efficient scheduling approach is required to optimize the trade-off between the total cost of tardiness and batch delivery. To achieve this, three new metaheuristic algorithms such as Differential Evolution with different mutation strategy variation and a Moth Flame Optimization, and Lévy-Flight Moth Flame Optimization algorithm are proposed and presented. In addition, a design-of-experiment method is used to identify the best possible parameters for the proposed approaches for the problem under study. The proposed algorithms are validated on a set of problem instances. The variants of differential evolution performed better than the other compared algorithms and this demonstrates the effectiveness of the proposed approach. The algorithms are also validated using an industrial case study

Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times

Industries are incorporating robots into assembly lines due to their greater flexibility and reduced costs. Most of the reported studies did not consider scheduling of tasks or the sequence-dependent setup times in an assembly line, which cannot be neglected in a real-world scenario. This paper presents a study on robotic assembly line balancing, with the aim of minimizing cycle time by considering sequence-dependent setup times. A mathematical model for the problem is formulated and CPLEX solver is utilized to solve small-sized problems. A recently developed metaheuristic Migrating Birds Optimization (MBO) algorithm and set of metaheuristics have been implemented to solve the problem. Three different scenarios are tested (with no setup time, and low and high setup times). The comparative experimental study demonstrates that the performance of the MBO algorithm is superior for the tested datasets. The outcomes of this study can help production managers improve their production system in order to perform the assembly tasks with high levels of efficiency and quality