The Experts below are selected from a list of 90393 Experts worldwide ranked by ideXlab platform
S. C. Liu - One of the best experts on this subject based on the ideXlab platform.
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A hybrid Heuristic Method for the periodic inventory routing problem
The International Journal of Advanced Manufacturing Technology, 2015Co-Authors: S. C. Liu, Chih-hung ChungAbstract:The periodic inventory routing problem (PIRP) determines the delivery routing and the inventory policies for retailers from a supplier in a periodic time based on the minimal cost criterion. Since it is a non-deterministic polynomial-time (NP)-hard problem, a Heuristic Method is needed for this problem. In the past, different global Heuristic Methods, such as tabu search (TS) and simulated annealing (SA), have been proposed; however, they seem ineffective. Particle swarm optimization (PSO) is known as resolving multidimensional combinatorial problems such as PIRP; however, it is easily trapped in local optimality. The authors of this paper propose a hybrid Heuristic Method for the PIRP. The hybrid Method integrates a large neighborhood search (LNS) into PSO to overcome the drawbacks of PSO and LNS. The PSO is adopted first. A local search is applied to each particle in different iterations. Then, a local optimal solution (particle) for each particle is obtained. Last, the LNS is applied to the global best solution to avoid becoming trapped in local optimality. The results show that the proposed hybrid Heuristic Method is 10.93 % better than the existing Method and 1.86 % better than the pure Heuristic Method in terms of average cost.
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A hybrid Heuristic Method for the fleet size and mix vehicle routing problem
Journal of Industrial and Production Engineering, 2013Co-Authors: S. C. LiuAbstract:Numerous Heuristic Methods have been proposed for the fleet size and mix vehicle routing problem (FSMVRP), since the FSMVRP is an NP-hard problem. However, the vehicle type information is always ignored in the local improvement approaches of these Heuristic Methods. Hence, the solutions found by these Heuristic Methods without the vehicle type information are worse than those with the vehicle type information. In this paper, a hybrid Heuristic Method, incorporating the vehicle type information into variable neighborhood search (VNS), is proposed. The experimental results indicate that the proposed Method is better than other reported Methods and VNS without the vehicle type information in terms of average percentage deviation.
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A Heuristic Method for the inventory routing problem with time windows
Expert Systems with Applications, 2011Co-Authors: S. C. Liu, Wei-ting LeeAbstract:This paper is to resolve the VRPTW and the inventory control decision problem simultaneously since both the vehicle routing decision with time windows and the inventory control decision affect each other and must be considered together. A mathematical model of inventory routing problem with time windows (IRPTW) is proposed. Since finding the optimal solution(s) for IRPTW is a NP-hard problem, this paper proposes a two-phase Heuristic Method. The first phase is to find the initial solution. The second phase is to improve the solution adopting the variable neighborhood tabu search (VNTS) selecting better neighborhood solutions, to obtain the optimal solution. Moreover, the proposed Method was compared with three other Heuristic Methods. The experimental results indicate that the proposed Method is better than the three other Methods in terms of average supply chain cost (transportation cost, time window violation penalty cost and inventory cost).
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A Heuristic Method for the vehicle routing problem with backhauls and inventory
Journal of Intelligent Manufacturing, 2008Co-Authors: S. C. Liu, Chich-hung ChungAbstract:The purpose of this paper is to determine the route of the vehicle routing problem with backhauls (VRPB), delivering new items and picking up the reused items or wastes, and resolve the inventory control decision problem simultaneously since the regular VRPB does not. Both the vehicle routing decision for delivery and pickup, and the inventory control decision affect each other and must be considered together. Hence, a mathematical model of vehicle routing problem with backhauls and inventory (VRPBI) is proposed. Since finding the optimal solution(s) for VRPBI is a NP-hard problem, this paper proposes a Heuristic Method, variable neighborhood tabu search (VNTS), adopting six neighborhood searching approaches to obtain the optimal solution. Moreover, this paper compares the proposed Heuristic Method with two other existing Heuristic Methods. The experimental results indicate that the proposed Method is better than the two other Methods in terms of average logistic cost (transportation cost and inventory cost).
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A Heuristic Method for the combined location routing and inventory problem
The International Journal of Advanced Manufacturing Technology, 2004Co-Authors: S. C. Liu, C.c. LinAbstract:The combined location routing and inventory problem (CLRIP) is used to allocate depots from several potential locations, to schedule vehicles’ routes to meet customers’ demands, and to determine the inventory policy based on the information of customers’ demands, in order to minimize the total system cost. Since finding the optimal solution(s) for this problem is a nonpolynomial (NP) problem, several Heuristics for searching local optima have been proposed. However, the solutions for these Heuristics are trapped in local optima. Global search Heuristic Methods, such as tabu search, simulated annealing Method, etc., have been known for overcoming the combinatorial problems such as CLRIP, etc. In this paper, the CLRIP is decomposed into two subproblems: depot location-allocation problem, and routing and inventory problem. A Heuristic Method is proposed to find solutions for CLRIP. First of all, an initial solution for CLRIP is determined. Then a hybrid Heuristic combining tabu search with simulated annealing sharing the same tabu list is used to improve the initial solution for each subproblem separately and alternatively. The proposed Heuristic Method is tested and evaluated via simulation. The results show the proposed Heuristic Method is better than the existing Methods and global search Heuristic Methods in terms of average system cost.
John M Hickey - One of the best experts on this subject based on the ideXlab platform.
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a Heuristic Method for fast and accurate phasing and imputation of single nucleotide polymorphism data in bi parental plant populations
Theoretical and Applied Genetics, 2018Co-Authors: Serap Gonen, Valentin Wimmer, Chris R Gaynor, Ed Byrne, Gregor Gorjanc, John M HickeyAbstract:Key message New fast and accurate Method for phasing and imputation of SNP chip genotypes within diploid bi-parental plant populations. This paper presents a new Heuristic Method for phasing and imputation of genomic data in diploid plant species. Our Method, called AlphaPlantImpute, explicitly leverages features of plant breeding programmes to maximise the accuracy of imputation. The features are a small number of parents, which can be inbred and usually have high-density genomic data, and few recombinations separating parents and focal individuals genotyped at low density (i.e. descendants that are the imputation targets). AlphaPlantImpute works roughly in three steps. First, it identifies informative low-density genotype markers in parents. Second, it tracks the inheritance of parental alleles and haplotypes to focal individuals at informative markers. Finally, it uses this low-density information as anchor points to impute focal individuals to high density. We tested the imputation accuracy of AlphaPlantImpute in simulated bi-parental populations across different scenarios. We also compared its accuracy to existing software called PlantImpute. In general, AlphaPlantImpute had better or equal imputation accuracy as PlantImpute. The computational time and memory requirements of AlphaPlantImpute were tiny compared to PlantImpute. For example, accuracy of imputation was 0.96 for a scenario where both parents were inbred and genotyped at 25,000 markers per chromosome and a focal F2 individual was genotyped with 50 markers per chromosome. The maximum memory requirement for this scenario was 0.08 GB and took 37 s to complete.
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a Heuristic Method for fast and accurate phasing and imputation of single nucleotide polymorphism data in bi parental plant populations
bioRxiv, 2018Co-Authors: Serap Gonen, Valentin Wimmer, Chris R Gaynor, Ed Byrne, Gregor Gorjanc, John M HickeyAbstract:This paper presents a new Heuristic Method for phasing and imputation of genomic data in diploid plant species. Our Method, called AlphaPlantImpute, explicitly leverages features of plant breeding programs to maximise the accuracy of imputation. The features are a small number of parents, which can be inbred and usually have high-density genomic data, and few recombinations separating parents and focal individuals genotyped at low-density (i.e. descendants that are the imputation targets). AlphaPlantImpute works roughly in three steps. First, it identifies informative low-density genotype markers in parents. Second, it tracks the inheritance of parental alleles and haplotypes to focal individuals at informative markers. Finally, it uses this low-density information as anchor points to impute focal individuals to high-density. We tested the imputation accuracy of AlphaPlantImpute in simulated bi-parental populations across different scenarios. We also compared its accuracy to existing software called PlantImpute. In general, AlphaPlantImpute had better or equal imputation accuracy as PlantImpute. The computational time and memory requirements of AlphaPlantImpute were tiny compared to PlantImpute. For example, accuracy of imputation was 0.96 for a scenario where both parents were inbred and genotyped at 25,000 markers per chromosome and a focal F2 individual was genotyped with 50 markers per chromosome. The maximum memory requirement for this scenario was 0.08 GB and took 37 seconds to complete.
Fuquan Zhao - One of the best experts on this subject based on the ideXlab platform.
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Heuristic Method for automakers' technological strategy making towards fuel economy regulations based on genetic algorithm: A China's case under corporate average fuel consumption regulation
Applied Energy, 2017Co-Authors: Sinan Wang, Fuquan ZhaoAbstract:The vehicle fuel economy standards have been implemented worldwide. However, it is quite difficult for the automakers to secure an optimal portfolio of fuel-efficient technologies which complies with these strengthened standards and minimizes the overall cost at the same time. In this paper, a genetic-algorithm-based Heuristic Method is proposed for technological strategy planning. In particular, a case study of the Corporate Average Fuel Economy standards in China is presented. Moreover, the mathematical model is constructed with the considerations of the technology cost, effect of reducing fuel consumption and technology physical weight. Problem complexity is analyzed and proven NP-hard. Moreover, a comparison analysis of performance is carried out between the elaborated genetic algorithm and the greedy algorithm that is currently used by most automakers to determine the technological strategies in China. The results imply that genetic algorithm outperforms the common Method because it provides more economical and reasonable strategies. In addition, the incremental cost under the greedy algorithm is 16.4% higher than that under genetic algorithm. Due to the counteractive effect under the weight-based standards in China, the mass reduction technologies should be given lower priorities compared with current strategies. To satisfy the standards by 2020, automakers should implement more conventional engine and transmission technologies instead of the hybrid electric vehicle technologies. It is recommended that automakers should develop Heuristic algorithms to make strategic decisions more reasonably.
Christopher C. Yang - One of the best experts on this subject based on the ideXlab platform.
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A Heuristic Method based on a statistical approach for Chinese text segmentation
Journal of the American Society for Information Science and Technology, 2005Co-Authors: Christopher C. YangAbstract:The authors propose a Heuristic Method for Chinese automatic text segmentation based on a statistical approach. This Method is developed based on statistical information about the association among adjacent characters in Chinese text. Mutual information of bi-grams and significant estimation of tri-grams are utilized. A Heuristic Method with six rules is then proposed to determine the segmentation points in a Chinese sentence. No dictionary is required in this Method. Chinese text segmentation is important in Chinese text indexing and thus greatly affects the performance of Chinese information retrieval. Due to the lack of delimiters of words in Chinese text, Chinese text segmentation is more difficult than English text segmentation. Besides, segmentation ambiguities and occurrences of out-of-vocabulary words (i.e., unknown words) are the major challenges in Chinese segmentation. Many research studies dealing with the problem of word segmentation have focused on the resolution of segmentation ambiguities. The problem of unknown word identification has not drawn much attention. The experimental result shows that the proposed Heuristic Method is promising to segment the unknown words as well as the known words. The authors further investigated the distribution of the errors of commission and the errors of omission caused by the proposed Heuristic Method and benchmarked the proposed Heuristic Method with a previous proposed technique, boundary detection. It is found that the Heuristic Method outperformed the boundary detection Method.
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ICADL - Segmenting Chinese Unknown Words by Heuristic Method
Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access, 2003Co-Authors: Christopher C. YangAbstract:Chinese text segmentation is important in Chinese text indexing. Due to the lack of word delimiters in Chinese text, Chinese text segmentation is more difficult than English text segmentation. Besides, the segmentation ambiguities and the occurrences of out-of-vocabulary words (i.e. unknown words) are the major challenges in Chinese segmentation. Many research works dealing with the problem of word segmentation have focused on the resolution of segmentation ambiguities. The problem of unknown word identification has not drawn much attention. In this paper, we propose a Heuristic Method for Chinese test segmentation based on the statistical approach. The experimental result shows that our proposed Heuristic Method is promising to segment the unknown words as well as the known words. We have further investigated the distribution of the errors of commission and the errors of omission caused by the proposed Heuristic Method and benchmarked the proposed Heuristic Method with our previous proposed technique, boundary detection.
I J Wassell - One of the best experts on this subject based on the ideXlab platform.
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lagrangian Heuristic Method for the wireless sensor network design problem in railway structural health monitoring
Mechanical Systems and Signal Processing, 2012Co-Authors: Akio Hada, Kenichi Soga, Ruoshui Liu, I J WassellAbstract:Abstract In this paper, we study a design Method for minimizing the total cost of a wireless sensor network (WSN) used for health monitoring of railway structures. First, we present the problem, that is to simultaneously determine the number of relays and their deployment locations, the transmission power level for each sensor and relay, and the routes for transferring sensor data to a gateway using multi-hop wireless communication. Second, we formulate this task as a mathematical programming problem, and to solve this problem, we propose a near optimal algorithm based on the Lagrangian Heuristic Method. Finally, we verify the effectiveness of our algorithm through computational experiments carried out using data acquired from a real WSN used for railway structural health monitoring.
