In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving ... more In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.Applied Soft Computing Journalhttps://doi.org/10.1016/j.asoc.2019.01.03
Abstract Just-in-time production in large enterprises along with the factory’s limited space high... more Abstract Just-in-time production in large enterprises along with the factory’s limited space highlights the need for scheduling tools that consider blocking conditions. This study contributes to the scheduling literature by developing an effective metaheuristic to address the Blocking Flowshop Scheduling Problems with Sequence-Dependent Setup-Times (BFSP with SDSTs). Including a new constructive heuristic and a local search mechanism customized for the blocking and setup time features, the Extended Iterated Greedy (EIG) algorithm effectively solves this highly intractable scheduling extension. The performance of the EIG algorithm is compared with that of the best-performing algorithms in the literature developed to solve the BFSP with SDSTs. Extensive numerical tests and statistical analyses verify EIG’s superiority over the benchmark algorithms and show that EIG performs steadily over various operational situations. Applications of the improved Iterated Greedy in this study are worthwhile topics to solve other complex scheduling problems.
System-wide optimization of distributed manufacturing operations enables process improvement beyo... more System-wide optimization of distributed manufacturing operations enables process improvement beyond the standalone and individual optimality norms. This study addresses the production planning of a distributed manufacturing system consisting of three stages: production of parts (subcomponents), assembly of components in Original Equipment Manufacturer (OEM) factories, and final assembly of products at the product manufacturer’s factory. Distributed Three Stage Assembly Permutation Flowshop Scheduling Problems (DTrSAPFSP) models this operational situation; it is the most recent development in the literature of distributed scheduling problems, which has seen very limited development for possible industrial applications. This research introduces a highly efficient constructive heuristic to contribute to the literature on DTrSAPFSP. Numerical experiments considering a comprehensive set of operational parameters are undertaken to evaluate the performance of the benchmark algorithms. It i...
Abstract Advanced analytics benefits lean manufacturing by upgrading the scheduling problems into... more Abstract Advanced analytics benefits lean manufacturing by upgrading the scheduling problems into operational strategic tools that help minimize non-value-adding activities. Considering production environments with prevalent setup operations, this study develops an Unsupervised Learning-based Artificial Bee Colony (ULABC) algorithm to improve the effectiveness of minimizing idle times in unrelated parallel machine production settings. For this purpose, the k-means method is integrated into the approximation algorithm to address sequence-dependent setup operations. An exemplary case from the forging industry is provided to evaluate the performance of the ULABC algorithm. Reducing setup times through effective job clustering by the learning mechanism, it is shown that the solution quality is significantly improved in large-scale benchmark tests with 16 and 24 percentages of reduction in the makespan value of instances requiring short and long setup operations, respectively. The statistical analysis confirms the significance of the resulting improvements. This improvement is expected to be even more substantial when very-large industry-scale problems are solved. Overall, this study narrows the gap between scheduling theory and modern industrial applications through applications of advanced analytics in the production management context.
International Journal of Production Research, 2016
This paper studies the scheduling problem of minimising total weighted earliness and tardiness pe... more This paper studies the scheduling problem of minimising total weighted earliness and tardiness penalties on identical parallel machines against a restrictive common due date. This problem is NP-hard in the strong sense and arises in many just-in-time production environments. A fast ruin-and-recreate (FR&R) algorithm is proposed to obtain high-quality solutions to this complex problem. The proposed FR&R algorithm is tested on a well-known set of benchmark test problems that are taken from the literature. Computational results provide evidence of the efficiency of FR&R, which consistently outperform existing algorithms when applied to benchmark instances. This work provides a viable alternative approach for efficiently solving this practical but complex scheduling problem.
Abstract Setup operations and waiting time between production procedures are prime examples of no... more Abstract Setup operations and waiting time between production procedures are prime examples of non-value-adding activities that can be alleviated through well-informed production decisions. Application of the No-wait Flowshop Group Scheduling Problems with Sequence-Dependent Setup Times (NWFGSP_SDST) as an optimization tool helps maintain the setup and waiting times at their minimum when optimizing advanced production systems. This research contributes to the understudied literature of Group Scheduling Problems (GSP), developing two metaheuristics, a Revised Multi-start Simulated Annealing (RMSA) and a local search-based variant (RMSA L S ), to solve the NWFGSP_SDST problem. Yielding the best-found solution in more than 99.7 percent of the benchmark instances, it is shown that RMSA is superior to the existing state-of-the-art algorithms developed to solve the NWFGSP_SDST problem. Besides, about 80 percent of the best-found solutions were further improved by RMSA L S with the statistical analysis confirming that RMSA L S reduced the total completion time obtained by RMSA at the expense of longer computational time. Overall, this research explored scheduling as an operational strategic tool facilitating lean production.
Abstract With many industrial applications in the production sector, the hybrid flowshop scheduli... more Abstract With many industrial applications in the production sector, the hybrid flowshop scheduling problem (HFSP) has received wide recognition in the scheduling literature. Given the NP-hard nature of the HFSP, which is characterized by highly intractable solution spaces, effective solution approaches are of particular interest to facilitate the real-world use cases. This study proposes a new benchmark metaheuristic, the Chaos-enhanced Simulated Annealing (CSA) algorithm to minimize makespan in the HFSP with identical machines. A recently published testbed is used to evaluate the performance of CSA against that of the upper bounds/best-known solutions in the literature. Besides improving the upper bounds, the computational results revealed that CSA performed very well in terms of computational efficiency and stability. The proposed CSA can serve as a strong benchmark algorithm for developing more algorithms to effectively and efficiently solve HFSPs and its extensions.
International Journal of Production Research, 2016
This study addresses the single-machine scheduling problem with a common due window (CDW) that ha... more This study addresses the single-machine scheduling problem with a common due window (CDW) that has a constant size and position. The objective is to minimise the total weighted earliness–tardiness penalties for jobs completed out of the CDW. To determine a schedule as close to optimum as possible, this study develops a dynamic dispatching rule and an effective constructive heuristic. The better performance of the proposed heuristic is demonstrated by comparing the results of it with those of a state-of-the-art greedy heuristic on a well-known benchmark problem set. In addition, we incorporate the constructive heuristic into a best-so-far meta-heuristic to examine the benefit of the proposed heuristic. The results show that the best known solutions in 144 out of the 250 benchmark instances are improved.
In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving ... more In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.Applied Soft Computing Journalhttps://doi.org/10.1016/j.asoc.2019.01.03
Abstract Just-in-time production in large enterprises along with the factory’s limited space high... more Abstract Just-in-time production in large enterprises along with the factory’s limited space highlights the need for scheduling tools that consider blocking conditions. This study contributes to the scheduling literature by developing an effective metaheuristic to address the Blocking Flowshop Scheduling Problems with Sequence-Dependent Setup-Times (BFSP with SDSTs). Including a new constructive heuristic and a local search mechanism customized for the blocking and setup time features, the Extended Iterated Greedy (EIG) algorithm effectively solves this highly intractable scheduling extension. The performance of the EIG algorithm is compared with that of the best-performing algorithms in the literature developed to solve the BFSP with SDSTs. Extensive numerical tests and statistical analyses verify EIG’s superiority over the benchmark algorithms and show that EIG performs steadily over various operational situations. Applications of the improved Iterated Greedy in this study are worthwhile topics to solve other complex scheduling problems.
System-wide optimization of distributed manufacturing operations enables process improvement beyo... more System-wide optimization of distributed manufacturing operations enables process improvement beyond the standalone and individual optimality norms. This study addresses the production planning of a distributed manufacturing system consisting of three stages: production of parts (subcomponents), assembly of components in Original Equipment Manufacturer (OEM) factories, and final assembly of products at the product manufacturer’s factory. Distributed Three Stage Assembly Permutation Flowshop Scheduling Problems (DTrSAPFSP) models this operational situation; it is the most recent development in the literature of distributed scheduling problems, which has seen very limited development for possible industrial applications. This research introduces a highly efficient constructive heuristic to contribute to the literature on DTrSAPFSP. Numerical experiments considering a comprehensive set of operational parameters are undertaken to evaluate the performance of the benchmark algorithms. It i...
Abstract Advanced analytics benefits lean manufacturing by upgrading the scheduling problems into... more Abstract Advanced analytics benefits lean manufacturing by upgrading the scheduling problems into operational strategic tools that help minimize non-value-adding activities. Considering production environments with prevalent setup operations, this study develops an Unsupervised Learning-based Artificial Bee Colony (ULABC) algorithm to improve the effectiveness of minimizing idle times in unrelated parallel machine production settings. For this purpose, the k-means method is integrated into the approximation algorithm to address sequence-dependent setup operations. An exemplary case from the forging industry is provided to evaluate the performance of the ULABC algorithm. Reducing setup times through effective job clustering by the learning mechanism, it is shown that the solution quality is significantly improved in large-scale benchmark tests with 16 and 24 percentages of reduction in the makespan value of instances requiring short and long setup operations, respectively. The statistical analysis confirms the significance of the resulting improvements. This improvement is expected to be even more substantial when very-large industry-scale problems are solved. Overall, this study narrows the gap between scheduling theory and modern industrial applications through applications of advanced analytics in the production management context.
International Journal of Production Research, 2016
This paper studies the scheduling problem of minimising total weighted earliness and tardiness pe... more This paper studies the scheduling problem of minimising total weighted earliness and tardiness penalties on identical parallel machines against a restrictive common due date. This problem is NP-hard in the strong sense and arises in many just-in-time production environments. A fast ruin-and-recreate (FR&R) algorithm is proposed to obtain high-quality solutions to this complex problem. The proposed FR&R algorithm is tested on a well-known set of benchmark test problems that are taken from the literature. Computational results provide evidence of the efficiency of FR&R, which consistently outperform existing algorithms when applied to benchmark instances. This work provides a viable alternative approach for efficiently solving this practical but complex scheduling problem.
Abstract Setup operations and waiting time between production procedures are prime examples of no... more Abstract Setup operations and waiting time between production procedures are prime examples of non-value-adding activities that can be alleviated through well-informed production decisions. Application of the No-wait Flowshop Group Scheduling Problems with Sequence-Dependent Setup Times (NWFGSP_SDST) as an optimization tool helps maintain the setup and waiting times at their minimum when optimizing advanced production systems. This research contributes to the understudied literature of Group Scheduling Problems (GSP), developing two metaheuristics, a Revised Multi-start Simulated Annealing (RMSA) and a local search-based variant (RMSA L S ), to solve the NWFGSP_SDST problem. Yielding the best-found solution in more than 99.7 percent of the benchmark instances, it is shown that RMSA is superior to the existing state-of-the-art algorithms developed to solve the NWFGSP_SDST problem. Besides, about 80 percent of the best-found solutions were further improved by RMSA L S with the statistical analysis confirming that RMSA L S reduced the total completion time obtained by RMSA at the expense of longer computational time. Overall, this research explored scheduling as an operational strategic tool facilitating lean production.
Abstract With many industrial applications in the production sector, the hybrid flowshop scheduli... more Abstract With many industrial applications in the production sector, the hybrid flowshop scheduling problem (HFSP) has received wide recognition in the scheduling literature. Given the NP-hard nature of the HFSP, which is characterized by highly intractable solution spaces, effective solution approaches are of particular interest to facilitate the real-world use cases. This study proposes a new benchmark metaheuristic, the Chaos-enhanced Simulated Annealing (CSA) algorithm to minimize makespan in the HFSP with identical machines. A recently published testbed is used to evaluate the performance of CSA against that of the upper bounds/best-known solutions in the literature. Besides improving the upper bounds, the computational results revealed that CSA performed very well in terms of computational efficiency and stability. The proposed CSA can serve as a strong benchmark algorithm for developing more algorithms to effectively and efficiently solve HFSPs and its extensions.
International Journal of Production Research, 2016
This study addresses the single-machine scheduling problem with a common due window (CDW) that ha... more This study addresses the single-machine scheduling problem with a common due window (CDW) that has a constant size and position. The objective is to minimise the total weighted earliness–tardiness penalties for jobs completed out of the CDW. To determine a schedule as close to optimum as possible, this study develops a dynamic dispatching rule and an effective constructive heuristic. The better performance of the proposed heuristic is demonstrated by comparing the results of it with those of a state-of-the-art greedy heuristic on a well-known benchmark problem set. In addition, we incorporate the constructive heuristic into a best-so-far meta-heuristic to examine the benefit of the proposed heuristic. The results show that the best known solutions in 144 out of the 250 benchmark instances are improved.
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