Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain exten...Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain extent. However,the multi-objective sequencing problem of MMAL is an non-deterministic polynomial hard( NP-hard) problem and the shortcomings are slow convergence rate and low precision. To solve the shortcomings for optimization objectives of minimizing total utility time and keeping average consumption rate of parts, a chaos differential evolution SFLA( CDESFLA) is proposed in this study. Because SFLA is easy to fall into local optimum,the evolution operator of differential evolution algorithms is introduced in SFLA as a local search strategy,and differential mutation operator is introduced in chaotic sequence to prevent premature convergence. The examples show that the proposed CDESFLA is better for convergence accuracy than SFLA,genetic algorithm( GA) and particle swarm optimization( PSO)展开更多
The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem a...The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.展开更多
This paper presents an application of the simulated annealing algorithm to solve level schedules in mixed model assembly line. Solving production sequences with both number of setups and material usage rates to the mi...This paper presents an application of the simulated annealing algorithm to solve level schedules in mixed model assembly line. Solving production sequences with both number of setups and material usage rates to the minimum rate will optimize the level schedule. Miltenburg algorithm (1989) is first used to get seed sequence to optimize further. For this the utility time of the line and setup time requirement on each station is considered. This seed sequence is optimized by simulated annealing. This investigation helps to understand the importance of utility in the assembly line. Up to 15 product sequences are taken and constructed by using randomizing method and find the objective function value for this. For a sequence optimization, a meta-heuristic seems much more promising to guide the search into feasible regions of the solution space. Simulated annealing is a stochastic local search meta-heuristic, which bases the acceptance of a modified neighboring solution on a probabilistic scheme inspired by thermal processes for obtaining low-energy states in heat baths. Experimental results show that the simulated annealing approach is favorable and competitive compared to Miltenburg’s constructive algorithm for the problems set considered. It is proposed to found 16,985 solutions, the time taken for computation is 23.47 to 130.35, and the simulated annealing improves 49.33% than Miltenberg.展开更多
Mixed-model U-shaped assembly line balancing problems (MMUALBP) is known to be NP-hard resulting in it being nearly impossible to obtain an optimal solution for practical problems with deterministic algorithms. This p...Mixed-model U-shaped assembly line balancing problems (MMUALBP) is known to be NP-hard resulting in it being nearly impossible to obtain an optimal solution for practical problems with deterministic algorithms. This paper pre-sents a new evolutionary method called combinatorial optimisation with coincidence algorithm (COIN) being applied to Type I problems of MMUALBP in a just-in-time production system. Three objectives are simultaneously considered;minimum number workstations, minimum work relatedness, and minimum workload smoothness. The variances of COIN are also proposed, i.e. CNSGA II, and COIN-MA. COIN and its variances are tested against a well-known algo-rithm namely non-dominated sorting genetic algorithm II (NSGA II) and MNSGA II (a memetic version of NSGA II). Experimental results showed that COIN outperformed NSGA II. In addition, although COIN-MA uses a marginal CPU time than CNSGA II, its other performances are dominated.展开更多
The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancin...The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancing efficiency is maximized. If the organization assembles more than one model in the same line, then the objective is to maximize the average balancing efficiency of the models of the mixed model assembly line balancing problem. Maximization of average balancing efficiency of the models along with minimization of makespan of sequencing models forms a multi-objective function. This is a realistic objective function which combines the balancing efficiency and makespan. This assembly line balancing problem with multi-objective comes under combinatorial category. Hence, development of meta-heuristic is inevitable. In this paper, an attempt has been made to develop three genetic algorithms for the mixed model assembly line balancing problem such that the average balancing efficiency of the model is maximized and the makespan of sequencing the models is minimized. Finally, these three algorithms and another algorithm in literature modified to solve the mixed-model assembly line balancing problem are compared in terms of the stated multi-objective function using a randomly generated set of problems through a complete factorial experiment.展开更多
Mixed-Model assembly lines are often used in manufacturing based on just-in-time techniques. The effective utilization of these lines requires a schedule for assembling the different models be determined. The objectiv...Mixed-Model assembly lines are often used in manufacturing based on just-in-time techniques. The effective utilization of these lines requires a schedule for assembling the different models be determined. The objective is to minimize the total deviation of actual production rates from the desired production rates. Mathematical method with the optimization algorithm is proposed here to solve this problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved. The results show that the proposed algorithm is an efficient and effective algorithm which gives better results with the large problem sizes. This paper presents a practical procedure to minimize total product variation rates, and easy to use by practitioner.展开更多
In-house part supply affects the efficiency of mixed-model assembly lines considerably. Hence, we propose a reliable Just-In-Time part supply strategy with the use of decentralized supermarkets. For a given production...In-house part supply affects the efficiency of mixed-model assembly lines considerably. Hence, we propose a reliable Just-In-Time part supply strategy with the use of decentralized supermarkets. For a given production sequence and line layout, the proposed strategy schedules tow train routing and delivery problems jointly to minimize the number of employed town trains and the traveling time, while ensuring that stations never run out of parts. To solve this problem, a mathematical formulation is proposed for each sub-problem aiming at minimizing supply cost. Then, a dynamic programming algorithm for routing and a greedy algorithm for delivery are developed, both of which are of polynomial runtime. Finally, a computational study is implemented to validate the effectiveness of the strategy, and to investigate the effects of the delivery capacity of tow trains and storage capacity of stations on supply cost.展开更多
Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a d...Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a deterministic or stochastic/fuzzy model yields quite poor performance in reality. In real environments, assembly line balancing robustness is a more appropriate decision selection guide. A robust model based on the α worst case scenario is developed to compensate for the drawbacks of traditional robust criteria. A robust genetic algorithm is used to solve the problem. Comprehensive computational experiments to study the effect of the solution procedure show that the model generates more flexible robust solutions. Careful tuning the value of α allows the decision maker to balance robustness and conservativeness of as- sembly line task element assignments.展开更多
基金National Natural Science Foundation of China(o.61370037)
文摘Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain extent. However,the multi-objective sequencing problem of MMAL is an non-deterministic polynomial hard( NP-hard) problem and the shortcomings are slow convergence rate and low precision. To solve the shortcomings for optimization objectives of minimizing total utility time and keeping average consumption rate of parts, a chaos differential evolution SFLA( CDESFLA) is proposed in this study. Because SFLA is easy to fall into local optimum,the evolution operator of differential evolution algorithms is introduced in SFLA as a local search strategy,and differential mutation operator is introduced in chaotic sequence to prevent premature convergence. The examples show that the proposed CDESFLA is better for convergence accuracy than SFLA,genetic algorithm( GA) and particle swarm optimization( PSO)
文摘The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.
文摘This paper presents an application of the simulated annealing algorithm to solve level schedules in mixed model assembly line. Solving production sequences with both number of setups and material usage rates to the minimum rate will optimize the level schedule. Miltenburg algorithm (1989) is first used to get seed sequence to optimize further. For this the utility time of the line and setup time requirement on each station is considered. This seed sequence is optimized by simulated annealing. This investigation helps to understand the importance of utility in the assembly line. Up to 15 product sequences are taken and constructed by using randomizing method and find the objective function value for this. For a sequence optimization, a meta-heuristic seems much more promising to guide the search into feasible regions of the solution space. Simulated annealing is a stochastic local search meta-heuristic, which bases the acceptance of a modified neighboring solution on a probabilistic scheme inspired by thermal processes for obtaining low-energy states in heat baths. Experimental results show that the simulated annealing approach is favorable and competitive compared to Miltenburg’s constructive algorithm for the problems set considered. It is proposed to found 16,985 solutions, the time taken for computation is 23.47 to 130.35, and the simulated annealing improves 49.33% than Miltenberg.
文摘Mixed-model U-shaped assembly line balancing problems (MMUALBP) is known to be NP-hard resulting in it being nearly impossible to obtain an optimal solution for practical problems with deterministic algorithms. This paper pre-sents a new evolutionary method called combinatorial optimisation with coincidence algorithm (COIN) being applied to Type I problems of MMUALBP in a just-in-time production system. Three objectives are simultaneously considered;minimum number workstations, minimum work relatedness, and minimum workload smoothness. The variances of COIN are also proposed, i.e. CNSGA II, and COIN-MA. COIN and its variances are tested against a well-known algo-rithm namely non-dominated sorting genetic algorithm II (NSGA II) and MNSGA II (a memetic version of NSGA II). Experimental results showed that COIN outperformed NSGA II. In addition, although COIN-MA uses a marginal CPU time than CNSGA II, its other performances are dominated.
文摘The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancing efficiency is maximized. If the organization assembles more than one model in the same line, then the objective is to maximize the average balancing efficiency of the models of the mixed model assembly line balancing problem. Maximization of average balancing efficiency of the models along with minimization of makespan of sequencing models forms a multi-objective function. This is a realistic objective function which combines the balancing efficiency and makespan. This assembly line balancing problem with multi-objective comes under combinatorial category. Hence, development of meta-heuristic is inevitable. In this paper, an attempt has been made to develop three genetic algorithms for the mixed model assembly line balancing problem such that the average balancing efficiency of the model is maximized and the makespan of sequencing the models is minimized. Finally, these three algorithms and another algorithm in literature modified to solve the mixed-model assembly line balancing problem are compared in terms of the stated multi-objective function using a randomly generated set of problems through a complete factorial experiment.
文摘Mixed-Model assembly lines are often used in manufacturing based on just-in-time techniques. The effective utilization of these lines requires a schedule for assembling the different models be determined. The objective is to minimize the total deviation of actual production rates from the desired production rates. Mathematical method with the optimization algorithm is proposed here to solve this problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved. The results show that the proposed algorithm is an efficient and effective algorithm which gives better results with the large problem sizes. This paper presents a practical procedure to minimize total product variation rates, and easy to use by practitioner.
基金supported in part by the National Key Technology Research and Development Program(No.2012BAF15G01)
文摘In-house part supply affects the efficiency of mixed-model assembly lines considerably. Hence, we propose a reliable Just-In-Time part supply strategy with the use of decentralized supermarkets. For a given production sequence and line layout, the proposed strategy schedules tow train routing and delivery problems jointly to minimize the number of employed town trains and the traveling time, while ensuring that stations never run out of parts. To solve this problem, a mathematical formulation is proposed for each sub-problem aiming at minimizing supply cost. Then, a dynamic programming algorithm for routing and a greedy algorithm for delivery are developed, both of which are of polynomial runtime. Finally, a computational study is implemented to validate the effectiveness of the strategy, and to investigate the effects of the delivery capacity of tow trains and storage capacity of stations on supply cost.
基金Supported by the National High-Tech Research Development (863) Program of China (No.2006AA04Z160)
文摘Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a deterministic or stochastic/fuzzy model yields quite poor performance in reality. In real environments, assembly line balancing robustness is a more appropriate decision selection guide. A robust model based on the α worst case scenario is developed to compensate for the drawbacks of traditional robust criteria. A robust genetic algorithm is used to solve the problem. Comprehensive computational experiments to study the effect of the solution procedure show that the model generates more flexible robust solutions. Careful tuning the value of α allows the decision maker to balance robustness and conservativeness of as- sembly line task element assignments.
