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Articles by Peerayuth Charnsethikul
Total Records ( 11 ) for Peerayuth Charnsethikul
  Peerayuth Charnsethikul
  This study proposes an algorithm capable of working in parallel for solving large and sparse linear equations under given right hand side (RHS) ranges. A comparative study to the direct linear programming method is reported theoretically, computationally and discussed. Moreover, the approach can be adapted for the system under domain decompositions structure leading to a better efficiency experimentally.
  Suwitchaporn Witchakul , Prapaisri Sudasna-na-Ayudthya , Peerayuth Charnsethikul and Kamlesh Mathur
  An efficient and effective special purpose method is proposed for solving a class of one-stage single constrained linear programming problems with a finite number of right hand side scenarios and bounded variables. We compare our proposed method with a general purpose method using CPLEX interactive optimizer. For various m and n, by using elapsed computational time as the criteria, our procedure outperformed the general purpose method as the problem size grew.
  Chanin Srisuwannapa and Peerayuth Charnsethikul
  We address a variant of the unbounded knapsack problem (UKP) into which the processing time of each item is also put and considered, referred as MMPTUKP. The MMPTUKP is a decision problem of allocating amount of n items, such that the maximum processing time of the selected items is minimized and the total profit is gained as at least as determined without exceeding capacity of knapsack. In this study, we proposed a new exact algorithm for this problem, called MMPTUKP algorithm. This pseudo polynomial time algorithm solves the bounded knapsack problem (BKP) sequentially with the updated bounds until reaching an optimal solution. We present computational experience with various data instances randomly generated to validate our ideas and demonstrate the efficiency of the proposed algorithm.
  Chansiri Singhtaun and Peerayuth Charnsethikul
  In this paper, a squared-Euclidean distance multifacility location problem with inseparable demands under balanced transportation constraints is analyzed. Using calculus to project the problem onto the space of allocation variables, the problem becomes minimizing concave quadratic integer programming problem. The algorithm based on extreme point ranking method combining with logical techniques is developed. The numerical experiments are randomly generated to test efficiency of the proposed algorithm compared with a linearization algorithm. The results show that the proposed algorithm provides a better solution on average with less processing time for all various sizes of problems.
  Chansiri Singhtaun and Peerayuth Charnsethikul
  Problem statement: The objective of this study is to develop efficient exact algorithms for a single source capacitated multi-facility location problem with rectilinear distance. This problem is concerned with locating m capacitated facilities in the two dimensional plane to satisfy the demand of n customers with minimum total transportation cost which is proportional to the rectilinear distance between the facilities and their customers. Approach: Two exact algorithms are proposed and compared. The first algorithm, decomposition algorithm, uses explicit branching on the allocation variables and then solve for location variable corresponding to each branch as the original Mixed Integer Programming (MIP) formulation with nonlinear objective function of the problem. For the other algorithm, the new formulation of the problem is first created by making use of a well-known condition for the optimal facility locations. The problem is considered as a p-median problem and the original formulation is transformed to a binary integer programming problem. The classical exact algorithm based on this formulation which is branch-and-bound algorithm (implicit branching) is then used. Results: Computational results show that decomposition algorithm can provide the optimum solution for larger size of the studied problem with much less processing time than the implicit branching on the discrete reformulated problem. Conclusion: The decomposition algorithm has a higher efficiency to deal with the studied NP-hard problems but is required to have efficient MIP software to support.
  Suwitchporn Witchakul , Prapaisri Sudas Na Ayudhaya and Peerayuth Charnsethikul
  Problem Statement: The problem of allocating a set of items in order to maximize the toal linear profit under uncertain capacity referred as a stochastic knapsack problem with continuous random capacity was studied theoretically and computationally. Approach: Two optimization based heuristic algorithms were proposed and developed for solving the problem and both efficiency and effectiveness are compared with the general purpose method using the Monte Carlo simulation. Results: For both the relaxed and the original problems, both algorithms with appropriate stepping size parameter were superior on average to the simulation approach in both computing time and solution quality. Conclusion: Therefore, both proposed approaches can be practical for large scale problem and can be used as a basic algorithm for a more complex nature in case of simultaneously random in both items weight and knapsack capacity.
  Peerayuth Charnsethikul and Saeree Svetasreni
  Two classes of the bottleneck transportation problem with an additional budget constraint are introduced. An exact approach was proposed to solve both problem classes with proofs of correctness and complexity. Moreover, the approach was extended to solve a class of multi-commodity transportation network with a special case of the multi-period constrained bottleneck assignment problem.
  Peerayuth Charnsethikul
  This study presents a computational procedure for analyzing statistics of steady state probabilities in a discrete time Markov chain with correlations among their transition probabilities. The proposed model simply uses the first order Taylor’s series expansion and statistical expected value properties to obtain the resulting linear matrix equations system. Computationally, the bottleneck is O(n4) but can be improved by distributed and parallel processing. A preliminary computational experience is reported.
  Sirirat Wongprakornkul and Peerayuth Charnsethikul
  In this work, the integration of the one-dimensional cutting stock problem with multiple cutting facilities and the transportation problem was formulated mathematically as a large-scale discrete optimization problem. Benders partitioning approach and the column-generation technique with the direct method and the proposed heuristic method for solving corresponding integer programming (IP) were developed into three approaches and were used to solve a set of various sizes test problems within a controllable computation time. The computation time and the relative-difference percentage between the lower and upper bounds are criterions. The results indicated that the approach based on the column-generation technique with the proposed heuristic method is the most efficient method for solving this studied large-scale problems. Hence, this approach could be used in practical manners; to manage both production and transportation plans simultaneously.
  Sirirat Wongprakornkul and Peerayuth Charnsethikul
  Problem statement: One-dimensional cutting stock problem with discrete demands and capacitated planning objective is an NP hard problem. Approach: The mathematical model with column-generation technique by a branch-and-bound procedure and the heuristic based on the first fit decreasing method are proposed. Then, both approaches were compared and some characteristics were investigated such as upper-bound value, percentage above lower-bound value, computation time, and number of patterns. Results: The 24 instances were examined. The proposed heuristic provides the upper-bound value above the lower-bound around 0-16.78%. All upper-bound values from column-generation and integer programming are better than the proposed heuristic but all computation times are higher. Conclusion: The proposed heuristic has consistently high performance in computation times.
  Sirirat Muenvanichakul and Peerayuth Charnsethikul
  Problem statement: The Dynamic Quadratic Assignment Problem (DQAP), an NP-hard problem, is outlined and reformulated in two alternative models: Linearized model and logic-based model. Approach: The solution methods for both models based on combinatorial methods (Benders’ Decomposition and Approximate Dynamic Programming) and constraint logic programming, respectively, are proposed. Results: Proofs of model equivalence and solution methodology are presented. Conclusion: Both proposed models are more simplified leading to possible hybrid adaptations of existing techniques for more practical approaches.
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