The Experts below are selected from a list of 43053 Experts worldwide ranked by ideXlab platform
Andy Song - One of the best experts on this subject based on the ideXlab platform.
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A Bi-Level Optimization Model for Grouping Constrained Storage Location Assignment Problems
IEEE Transactions on Systems Man and Cybernetics, 2016Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the Storage Location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the Locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to Locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method.
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GECCO (Companion) - Evolving Self-Adaptive Tabu Search Algorithm for Storage Location Assignment Problems
Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15, 2015Co-Authors: Jing Xie, Yi Mei, Andy SongAbstract:This study proposes a novel grammar guided Genetic Programming method to solve a real world problem, the Storage Location Assignment Problem (SLAP) with Grouping Constraints. Self-adaptive Tabu Search algorithms are evolved by this approach and it can be used as solvers for SLAPs. A novel self-adaptive Tabu Search framework is proposed that key configurations of the algorithm are determined based on the problem-specific characters, and these configurations are changed dynamically during the search process. In addition, both the quality of the solutions and the execution speed are considered in the evaluation function. The experimental results show that more efficient Tabu Search algorithms can be found by this approach comparing to a manually-designed Tabu Search method.
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CEC - A Restricted Neighbourhood Tabu Search for Storage Location Assignment Problem
2015 IEEE Congress on Evolutionary Computation (CEC), 2015Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best Locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of picking the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the state-of-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.
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A Restricted Neighbourhood Tabu Search for Storage Location Assignment Problem
2015 IEEE Congress on Evolutionary Computation CEC 2015 - Proceedings, 2015Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best Locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of pick- ing the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the state- of-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.
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scaling up solutions to Storage Location assignment problems by genetic programming
Simulated Evolution and Learning, 2014Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem SLAP is to find an optimal stock arrangement in a warehouse. This study presents a scalable method for solving large-scale SLAPs utilizing Genetic Programming GP and two sampling strategies. Given a large scale problem, a sub-problem is sampled from the original problem for our GP method to learn an alLocation rule in the form of a matching function. Then this rule can be applied to the original problem to generate solutions. By this approach, the alLocation rule can be obtained in a much shorter time. When sampling the problem, the representativeness is a key factor that can largely affect the generalizability of the trained alLocation rule. To investigate the effect of representativeness, two sampling strategies, namely the random sampling and filtered sampling, are proposed and compared in this paper. The filtered sampling strategy adopts more information about the problem structure to increase the similarity of the sampled problem and the entire problem. The results show that the filtered sampling performs significantly better than the random sampling in terms of both solution quality and success rate i.e., the probability of generating feasible solutions for the large problem. The good performance of filtered strategy indicates the importance of sample representativeness on the scalability of the GP generated rules.
Jing Xie - One of the best experts on this subject based on the ideXlab platform.
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A Bi-Level Optimization Model for Grouping Constrained Storage Location Assignment Problems
IEEE Transactions on Systems Man and Cybernetics, 2016Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the Storage Location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the Locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to Locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method.
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GECCO (Companion) - Evolving Self-Adaptive Tabu Search Algorithm for Storage Location Assignment Problems
Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15, 2015Co-Authors: Jing Xie, Yi Mei, Andy SongAbstract:This study proposes a novel grammar guided Genetic Programming method to solve a real world problem, the Storage Location Assignment Problem (SLAP) with Grouping Constraints. Self-adaptive Tabu Search algorithms are evolved by this approach and it can be used as solvers for SLAPs. A novel self-adaptive Tabu Search framework is proposed that key configurations of the algorithm are determined based on the problem-specific characters, and these configurations are changed dynamically during the search process. In addition, both the quality of the solutions and the execution speed are considered in the evaluation function. The experimental results show that more efficient Tabu Search algorithms can be found by this approach comparing to a manually-designed Tabu Search method.
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CEC - A Restricted Neighbourhood Tabu Search for Storage Location Assignment Problem
2015 IEEE Congress on Evolutionary Computation (CEC), 2015Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best Locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of picking the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the state-of-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.
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A Restricted Neighbourhood Tabu Search for Storage Location Assignment Problem
2015 IEEE Congress on Evolutionary Computation CEC 2015 - Proceedings, 2015Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best Locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of pick- ing the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the state- of-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.
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scaling up solutions to Storage Location assignment problems by genetic programming
Simulated Evolution and Learning, 2014Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem SLAP is to find an optimal stock arrangement in a warehouse. This study presents a scalable method for solving large-scale SLAPs utilizing Genetic Programming GP and two sampling strategies. Given a large scale problem, a sub-problem is sampled from the original problem for our GP method to learn an alLocation rule in the form of a matching function. Then this rule can be applied to the original problem to generate solutions. By this approach, the alLocation rule can be obtained in a much shorter time. When sampling the problem, the representativeness is a key factor that can largely affect the generalizability of the trained alLocation rule. To investigate the effect of representativeness, two sampling strategies, namely the random sampling and filtered sampling, are proposed and compared in this paper. The filtered sampling strategy adopts more information about the problem structure to increase the similarity of the sampled problem and the entire problem. The results show that the filtered sampling performs significantly better than the random sampling in terms of both solution quality and success rate i.e., the probability of generating feasible solutions for the large problem. The good performance of filtered strategy indicates the importance of sample representativeness on the scalability of the GP generated rules.
Yi Mei - One of the best experts on this subject based on the ideXlab platform.
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A Bi-Level Optimization Model for Grouping Constrained Storage Location Assignment Problems
IEEE Transactions on Systems Man and Cybernetics, 2016Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the Storage Location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the Locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to Locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method.
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GECCO (Companion) - Evolving Self-Adaptive Tabu Search Algorithm for Storage Location Assignment Problems
Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15, 2015Co-Authors: Jing Xie, Yi Mei, Andy SongAbstract:This study proposes a novel grammar guided Genetic Programming method to solve a real world problem, the Storage Location Assignment Problem (SLAP) with Grouping Constraints. Self-adaptive Tabu Search algorithms are evolved by this approach and it can be used as solvers for SLAPs. A novel self-adaptive Tabu Search framework is proposed that key configurations of the algorithm are determined based on the problem-specific characters, and these configurations are changed dynamically during the search process. In addition, both the quality of the solutions and the execution speed are considered in the evaluation function. The experimental results show that more efficient Tabu Search algorithms can be found by this approach comparing to a manually-designed Tabu Search method.
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CEC - A Restricted Neighbourhood Tabu Search for Storage Location Assignment Problem
2015 IEEE Congress on Evolutionary Computation (CEC), 2015Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best Locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of picking the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the state-of-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.
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A Restricted Neighbourhood Tabu Search for Storage Location Assignment Problem
2015 IEEE Congress on Evolutionary Computation CEC 2015 - Proceedings, 2015Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best Locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of pick- ing the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the state- of-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.
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scaling up solutions to Storage Location assignment problems by genetic programming
Simulated Evolution and Learning, 2014Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem SLAP is to find an optimal stock arrangement in a warehouse. This study presents a scalable method for solving large-scale SLAPs utilizing Genetic Programming GP and two sampling strategies. Given a large scale problem, a sub-problem is sampled from the original problem for our GP method to learn an alLocation rule in the form of a matching function. Then this rule can be applied to the original problem to generate solutions. By this approach, the alLocation rule can be obtained in a much shorter time. When sampling the problem, the representativeness is a key factor that can largely affect the generalizability of the trained alLocation rule. To investigate the effect of representativeness, two sampling strategies, namely the random sampling and filtered sampling, are proposed and compared in this paper. The filtered sampling strategy adopts more information about the problem structure to increase the similarity of the sampled problem and the entire problem. The results show that the filtered sampling performs significantly better than the random sampling in terms of both solution quality and success rate i.e., the probability of generating feasible solutions for the large problem. The good performance of filtered strategy indicates the importance of sample representativeness on the scalability of the GP generated rules.
Gajendra K. Adil - One of the best experts on this subject based on the ideXlab platform.
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A review of methodologies for class-based Storage Location assignment in a warehouse
International Journal of Advanced Operations Management, 2010Co-Authors: Gajendra K. Adil, Venkata Reddy Muppani, Alakanada BandyopadhyayAbstract:The class-based Storage distributes the products among a number of classes and reserves a region for each class within the Storage area. This paper discusses three important issues in class-based Storage Location assignment: reduction in Storage space requirements under class-based policy compared to dedicated policy, cube-per-order index (COI) and general orderings of products in Storage class formation and combined inventory staggering and class-based Storage assignment. Accordingly, class-based Storage assignment problems are classified and methodologies for class-based Storage assignment available in the literature are reviewed. The paper concludes by suggesting some future research in the area of class-based Storage assignment.
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a branch and bound algorithm for class based Storage Location assignment
European Journal of Operational Research, 2008Co-Authors: Venkata Reddy Muppani, Gajendra K. AdilAbstract:Class-based Storage implementation decisions have significant impact on the required Storage space and the material handling cost in a warehouse. In this paper, a nonlinear integer programming model is proposed to capture the above. Effects of Storage area reduction on order picking and Storage space cost are incorporated. A branch and bound algorithm is developed to solve the model. Computational experience with randomly generated data sets and an industrial case shows that branch and bound algorithm is computationally more efficient than a baseline dynamic programming algorithm. It is further observed that the class based policy results in lower total cost of order picking and Storage space than the dedicated policy.
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efficient formation of Storage classes for warehouse Storage Location assignment a simulated annealing approach
Omega-international Journal of Management Science, 2008Co-Authors: Venkata Reddy Muppani, Gajendra K. AdilAbstract:Class-based Storage policy distributes products among a number of classes and for each class it reserves a region within the Storage area. The procedures reported in the literature for formation of Storage classes primarily consider order-picking cost ignoring Storage-space cost. Moreover, in these procedures items are ordered on the basis of their cube per order index (COI), and items are then partitioned into classes maintaining this ordering. This excludes many possible product combinations in forming classes which may result in inferior solutions. In this paper, a simulated annealing algorithm (SAA) is developed to solve an integer programming model for class formation and Storage assignment that considers all possible product combinations, Storage-space cost and order-picking cost. Computational experience on randomly generated data sets and an industrial case shows that SAA gives superior results than the benchmark dynamic programming algorithm for class formation with COI ordering restriction.
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Class-based Storage-Location assignment to minimise pick travel distance
International Journal of Logistics-research and Applications, 2008Co-Authors: Venkata Reddy Muppani, Gajendra K. AdilAbstract:Storage-Location assignment in warehouses is an important task as it impacts productivity of other warehouse processes. The class-based Storage policy distributes the products, among a number of classes, and for each class it reserves a region within the Storage area. We propose a nonlinear integer-programming model to the problem of formation of classes and alLocation of Storage space, considering savings in required Storage space, due to random alLocation of products within a class. We develop a branch and bound algorithm (BBA) to solve the model and compare it with a benchmark dynamic programming algorithm (DPA). These algorithms are applied to randomly generated data sets and to an industrial case. Computational experience shows that class-based policy can result in shorter pick-travel distances than the dedicated policy. The proposed BBA is found to be computationally much more efficient than DPA.
Andreas T Ernst - One of the best experts on this subject based on the ideXlab platform.
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A Bi-Level Optimization Model for Grouping Constrained Storage Location Assignment Problems
IEEE Transactions on Systems Man and Cybernetics, 2016Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the Storage Location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the Locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to Locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method.
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CEC - A Restricted Neighbourhood Tabu Search for Storage Location Assignment Problem
2015 IEEE Congress on Evolutionary Computation (CEC), 2015Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best Locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of picking the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the state-of-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.
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A Restricted Neighbourhood Tabu Search for Storage Location Assignment Problem
2015 IEEE Congress on Evolutionary Computation CEC 2015 - Proceedings, 2015Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best Locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of pick- ing the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the state- of-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.
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scaling up solutions to Storage Location assignment problems by genetic programming
Simulated Evolution and Learning, 2014Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem SLAP is to find an optimal stock arrangement in a warehouse. This study presents a scalable method for solving large-scale SLAPs utilizing Genetic Programming GP and two sampling strategies. Given a large scale problem, a sub-problem is sampled from the original problem for our GP method to learn an alLocation rule in the form of a matching function. Then this rule can be applied to the original problem to generate solutions. By this approach, the alLocation rule can be obtained in a much shorter time. When sampling the problem, the representativeness is a key factor that can largely affect the generalizability of the trained alLocation rule. To investigate the effect of representativeness, two sampling strategies, namely the random sampling and filtered sampling, are proposed and compared in this paper. The filtered sampling strategy adopts more information about the problem structure to increase the similarity of the sampled problem and the entire problem. The results show that the filtered sampling performs significantly better than the random sampling in terms of both solution quality and success rate i.e., the probability of generating feasible solutions for the large problem. The good performance of filtered strategy indicates the importance of sample representativeness on the scalability of the GP generated rules.
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SEAL - Scaling Up Solutions to Storage Location Assignment Problems by Genetic Programming
Lecture Notes in Computer Science, 2014Co-Authors: Jing Xie, Andreas T Ernst, Yi Mei, Andy SongAbstract:The Storage Location Assignment Problem SLAP is to find an optimal stock arrangement in a warehouse. This study presents a scalable method for solving large-scale SLAPs utilizing Genetic Programming GP and two sampling strategies. Given a large scale problem, a sub-problem is sampled from the original problem for our GP method to learn an alLocation rule in the form of a matching function. Then this rule can be applied to the original problem to generate solutions. By this approach, the alLocation rule can be obtained in a much shorter time. When sampling the problem, the representativeness is a key factor that can largely affect the generalizability of the trained alLocation rule. To investigate the effect of representativeness, two sampling strategies, namely the random sampling and filtered sampling, are proposed and compared in this paper. The filtered sampling strategy adopts more information about the problem structure to increase the similarity of the sampled problem and the entire problem. The results show that the filtered sampling performs significantly better than the random sampling in terms of both solution quality and success rate i.e., the probability of generating feasible solutions for the large problem. The good performance of filtered strategy indicates the importance of sample representativeness on the scalability of the GP generated rules.
