Zixiang Li

Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly process. Robots help to replace human labor and execute tasks efficiently at each workstation in the assembly line. From the literature, it is concluded that not much work has been conducted on two two-sided robotic assembly line balancing problems. This article addresses the two-sided robotic assembly line balancing problem with the objective of minimizing the cycle time. A mixed-integer programming model of the proposed problem is developed which is solved by the CPLEX solver for small-sized problems. Due to the problems in non-polynomial--hard nature, a co-evolutionary particle swarm optimization algorithm is developed to solve it. The co-evolutionary particle swarm optimization utilizes local search on the global best individual to enhance intensification, modification of global best to emphasize exploration, and restart mechanism to escape from local optima. The performances of the proposed co-evolutionary particle swarm optimization are evaluated on the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm is compared with five other well-known metaheuristics, and computational and statistical results demonstrate that the proposed co-evolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study.

An effective discrete artificial bee colony algorithm with idle time reduction techniques for two-sided assembly line balancing problem of type-II

Idle reduction techniques are induced to reduce search space.Task assignment rule reduces sequence-dependent idle time and balances the workload.Nine algorithms are extended to solve the two-sided assembly line balancing problem.The ANOVA method is utilized to demonstrate the reasonability of the improved operators.Statistically significant results indicate that effectiveness of the idle reduction techniques and the algorithms. Two-sided assembly line is broadly utilized to manufacture high-volume large-size products. Reconfiguration of this line is a major practice in industry, which is known as two-sided assembly line balancing problem of type-II. To solve this NP-hard problem, this paper proposes an improved discrete artificial bee colony (DABC) algorithm via blending idle time reduction techniques. The cycle time compression mechanism speeds up the search process by reducing total idle times to a larger extent on earlier iterations. Task assignment rule is specifically designed to eliminate sequence-dependent idle times. The overload and underload are taken into account as a secondary objective to reduce the remaining idle times. To achieve a fine balance between the diversification and intensification, the employed bees in the DABC algorithm generate new food sources by combining the features of two solutions, and the onlookers expand the search space with the variable neighborhood search. Nine recent meta-heuristics including a simulated annealing algorithm and an ant colony optimization algorithm are also extended for the TALBP-II to test the performance of the proposed DABC algorithm. Experimental results demonstrate that the proposed method outperforms the nine compared algorithms and find 22 brand-new results for large-size problems.

Comprehensive review and evaluation of heuristics and meta-heuristics for two-sided assembly line balancing problem

Heuristics and meta-heuristics proposed for TALBP-II are comprehensively reviewed.A set of encoding schemes and decoding procedures is summarized.New objective functions and an iterative search mechanism are developed.Eighteen meta-heuristics are evaluated on a set of benchmark problems.New best and optimum solutions of TALBP-II test problems are also achieved. This paper presents a comprehensive review and evaluation of heuristics and meta-heuristics for the two-sided assembly line balancing problem. Though a few reviews have been presented, some latest methods are not included and there is no comparison of the meta-heuristics in terms of their performances. Furthermore, since various kinds of encoding schemes, decoding procedures and objective functions have been applied, the results cannot be generalized and the published comparison might be unfair. This paper contributes to knowledge by comparing the published methods, ranging from well-known simulated annealing to recent published iterated local search, and evaluating the six encoding schemes, 30 decoding procedures and five objective functions on the performances of the meta-heuristics meanwhile. The experimental design approach is applied to obtain valid and convincing results by testing algorithms under four termination criteria. Computational results demonstrate that the proper selection of encoding scheme, decoding procedure and objective function improves the performance of the algorithms by a significant margin. Another unique contribution of this paper is that 15 new best solutions are obtained for the large-sized type-II two-sided assembly line balancing problem during the re-implementation and evaluation of the meta-heuristics tested.

Two-sided assembly line balancing problem of type I

Many meta-heuristic methods have been applied to solve the two-sided assembly line balancing problem of type I with the objective of minimizing the number of stations, but some of them are very complex or intricate to be extended. In addition, different decoding schemes and different objectives have been proposed, leading to the different performances of these algorithms and unfair comparison. In this paper, two new decoding schemes with reduced search space are developed to balance the workload within a mated-station and reduce sequence-depended idle time. Then, graded objectives are employed to preserve the minor improvements on the solutions. Finally, a simple iterated greedy algorithm is extended for the two-sided assembly line balancing problem and modified NEH-based heuristic is introduced to obtain a high quality initial solution. And an improved local search with referenced permutation and reduced insert operators is developed to accelerate the search process. Computational results on benchmark problems prove the efficiency of the proposed decoding schemes and the new graded objectives. A comprehensive computational comparison among 14 meta-heuristics is carried out to demonstrate the efficiency of the improved iterated greedy algorithm. Two new decoding schemes are developed and compared with existed ones.Graded objectives are developed to preserve the tiny improvementsA simple and effective iterated greedy algorithm is applied and evaluated.New local search is developed to reduce repeated insert operators.All optimal solutions are obtained for the first time.

Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm

Stochastic two-sided assembly line balancing with multiple constraints is considered.New priority-based decoding approach is developed to deal with multiple constraints.Hybrid TLBO algorithm is developed by combing the TLBO, crossover operator and VNS.Comparative evaluation of eleven algorithms indicates the superiority of hybrid HTLBO. Two-sided assembly lines are usually found in the factories which produce large-sized products. In most literatures, the task times are assumed to be deterministic while these tasks may have varying operation times in real application, causing the reduction of performance or even the infeasibility of the schedule. Moreover, the ignorance of some specific constraints including positional constraints, zoning constraints and synchronism constraints will result in the invalidation of the schedule. To solve this stochastic two-sided assembly line balancing problem with multiple constraints, we propose a hybrid teaching-learning-based optimization (HTLBO) approach which combines both a novel teaching-learning-based optimization algorithm for global search and a variable neighborhood search with seven neighborhood operators for local search. Especially, a new priority-based decoding approach is developed to ensure that the selected tasks satisfy most of the constraints identified by multiple thresholds of the priority value and to reduce the idle times related to sequence-dependence among tasks. Experimental results on benchmark problems demonstrate both remarkable efficiency and universality of the developed decoding approach, and the comparison among 11 algorithms shows the effectiveness of the proposed HTLBO.

Minimizing the Cycle Time in Two-Sided Assembly Lines with Assignment Restrictions: Improvements and a Simple Algorithm

The two-sided assembly line balancing problem type-II (TALBP-II) is of major importance for the reconfiguration of the two-sided assembly lines which are widely utilized to assemble large-size high-volume products. The TALBP-II is NP-hard, and some assignment restrictions in real applications make this problem much more complex. This paper provides an integer programming model for solving the TALBP-II with assignment restrictions optimally and utilizes a simple and effective iterated greedy (IG) algorithm to address large-size problems. This algorithm utilizes a new local search by considering precedence relationships between tasks in order to reduce the computational time. In particular, a priority-based decoding scheme is developed to handle these assignment restrictions and reduce sequence-dependent idle times by adjusting the priority values. Experimental comparison among the proposed decoding scheme and other published ones demonstrates the efficiency of the priority-based decoding. A comprehensive computational comparison among the IG algorithm and other eight recent algorithms proves effectiveness of the proposed IG algorithm.

Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints

Due to the NP-hardness of the two-sided assembly line balancing (TALB) problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints (TALB-MC). Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization (TLBO) algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost function. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search (VNS) is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm (LAHC) for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem

Robotics are extensively utilized in modern industry to replace human labor and achieve high automation and flexibility. In order to produce large-size products, two-sided assembly lines are widely applied, where robotics can be employed to operate tasks on workstations. Since the applied traditional optimization methods are limited, the current work presented a new discrete cuckoo search algorithm to solve the two-sided robotic assembly line balancing problem. The original cuckoo search algorithm was modified by employing neighbor operations. Furthermore, a new procedure to generate individuals to replace the abandoned nests was developed to enhance the intensification. Since the considered problem has two subproblems, namely the robot allocation and assembly line balancing, the present work extended the cuckoo search algorithm to cooperative coevolutionary paradigm by dividing the cuckoos into two sub-swarms, each addressing a subproblem. In order to emphasize the exploration, a restart mechanism was employed. The proposed discrete algorithm’s evolution process and convergence were compared with another two popular optimization algorithms, namely the genetic algorithm and particle swarm optimization algorithm. Computational study on the proposed algorithms and other five recent algorithms along with statistical analysis demonstrated that the proposed methods yielded promising results.

Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line

ABSTRACT This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently.

New MILP model and station-oriented ant colony optimization algorithm for balancing U-type assembly lines

Abstract U-type assembly lines are extensively applied in modern manufacturing systems for higher flexibility and productivity. This research presents a new mixed-integer linear programming model to minimize the number of stations, where one expression is used to represent the precedence relationship constraint rather than two expressions as in published researches. The proposed model is compared to three other models and the correctness or the incorrectness of these models are analyzed by enumerating all possible allocations between the two tasks. The comparison makes it clear that the proposed model iterates fast and achieves competing results. Additionally, a modified ant colony optimization approach, referred to as station-oriented ant colony optimization algorithm, is proposed to tackle large-size problems. This method generates a set of task assignments and selects the best one for the current station, rather than obtaining only one task assignment at a time. A set of benchmark problems is solved using the proposed method and the results are compared to those obtained by the state-of-the-art methods (including ULINO) and the variants of ant colony optimization approach. The computational study demonstrates the superiority of the proposed method over the compared ones as it achieves optimal solutions for 255 cases (out of 269) and outperforms the current best method, ULINO, for 21 cases. It is also worthy to mention that the station-oriented procedure improves the performance of original ant colony optimization by a significant margin.