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Local Non-overlapping Evolutionary Algorithm II to Solve Fixed Charge Transportation Problems
Byungki Kim,Shinichiro Ataka 대한산업공학회 2011 대한산업공학회 추계학술대회논문집 Vol.2011 No.11
The fixed-charge transportation planning model (fcTP) is one of the basic subsystems in the Supply Chain Management (SCM) network system. The fcTP problem is to find determinations of a minimum-cost transportation plan for a homogeneous commodity from a number of plants to a number of warehouses. This problem is a well-known NP-hard problem. Many practical transportation and distribution problems in SCM such as the minimum cost network flow problem with fixed charge in logistics can be formulated as fcTP problem. In this paper, we propose the Local Non-Overlapping Evolutionary Algorithm II (LNEA-II) for solving fcTP problem. The experimental results of propose method and two different Hybrid Genetic Algorithms (hGA) are compared to validate the effectiveness of proposed method.
A Possibility Approach to Solve Fuzzy Fixed Charge Transportation Problem
Tlig, Houssine,Benhamed, Adel,Rebai, Abdelwaheb Korean Institute of Industrial Engineers 2017 Industrial Engineeering & Management Systems Vol.16 No.4
This paper studies the fixed charge transportation problem under uncertain environment, in which the capacities of sources, the direct costs, the fixed charges and the demands of destinations are not known with a precise manner. As a result, the transportation problem takes the form of fuzzy mixed-integer programming problem. In this paper, we propose a solution method based on the possibility approach. With this approach, the obtained transportation problem takes the form of a crisp mixed-integer linear programming problem and provides crisp values to different variables at different possibility levels. A numerical example with trapezoidal fuzzy parameters is given to demonstrate the effectiveness of the proposed method.
ByungKi Kim,JongRyul Kim,Mitsuo Gen,JungBok Jo 한국경영과학회 2009 한국경영과학회 학술대회논문집 Vol.2009 No.5
The fixed-charge transportation planning model (fcTP) is one of the basic subsystems in the Supply Chain Management (SCM) network system. The fcTP problem is to find determinations of a minimum-cost transportation plan for a homogeneous commodity from a number of plants to a number of warehouses. This problem is a well-known NP-hard problem. Many practical transportation and distribution problems in SCM such as the minimum cost network flow problem with fixed charge in logistics can be formulated as fcTP problem. In this paper, we propose the new Stochastic Evolutionary Algorithm for solving fcTP problem. The experimental results of propose method and three different Hybrid Genetic Algorithms (hGA) are compared to validate the effectiveness of proposed method. The compared hGAs are hybridized with (1) Alteration of Population Size Method, (2)Fuzzy logic control of GA parameter auto-tuning, (3)Local search techniques.
ByungKi Kim,JongRyul Kim,Mitsuo Gen,JungBok Jo 대한산업공학회 2009 대한산업공학회 춘계학술대회논문집 Vol.2009 No.5
The fixed-charge transportation planning model (fcTP) is one of the basic subsystems in the Supply Chain Management (SCM) network system. The fcTP problem is to find determinations of a minimum-cost transportation plan for a homogeneous commodity from a number of plants to a number of warehouses. This problem is a well-known NP-hard problem. Many practical transportation and distribution problems in SCM such as the minimum cost network flow problem with fixed charge in logistics can be formulated as fcTP problem. In this paper, we propose the new Stochastic Evolutionary Algorithm for solving fcTP problem. The experimental results of propose method and three different Hybrid Genetic Algorithms (hGA) are compared to validate the effectiveness of proposed method. The compared hGAs are hybridized with (1) Alteration of Population Size Method, (2)Fuzzy logic control of GA parameter auto-tuning, (3)Local search techniques.
A Possibility Approach to Solve Fuzzy Fixed Charge Transportation Problem
Houssine Tlig,Adel Benhamed,Abdelwaheb Rebai 대한산업공학회 2017 Industrial Engineeering & Management Systems Vol.16 No.4
This paper studies the fixed charge transportation problem under uncertain environment, in which the capacities of sources, the direct costs, the fixed charges and the demands of destinations are not known with a precise manner. As a result, the transportation problem takes the form of fuzzy mixed-integer programming problem. In this paper, we propose a solution method based on the possibility approach. With this approach, the obtained transportation problem takes the form of a crisp mixed-integer linear programming problem and provides crisp values to different variables at different possibility levels. A numerical example with trapezoidal fuzzy parameters is given to demonstrate the effectiveness of the proposed method.
조정복(Jo, Jung bok),김종율(Kim, Jong ryul),김동훈(Kim, Dong hun) 실천경영학회 2009 실천경영연구 Vol.3 No.1
This paper concerned one of the most popular issues, transportation problem (TP), among the production/logistics system optimization problems. Especially, this paper focused on Fixed Charged Transportation problem (fcTP) considering simultaneous a variable cost proportioned to the transportation amount and fixed cost occurred in all route additionally. Usually, these problems have known as the NP-hard problems which are difficult to solve it by conventional methods. Therefore, to solve these problems, we adopt genetic algorithm method, most widely known as Meta-heuristic method. In this paper, wed propose the genetic algorithm method with priority based genetic representation and try to display the performance of the proposed method comparing other representation methods by numerical experiments.
고정비용과 비선형 단위운송비용을 가지는 수송문제를 위한 이단유전알고리즘에 관한 연구
성기석(Kiseok Sung) 한국경영과학회 2016 韓國經營科學會誌 Vol.41 No.4
This paper proposes a Bi-level Genetic Algorithm for the Fixed Charge Transportation Problem with Non-linear Unit Cost. The problem has the property of mixed integer program with non-linear objective function and linear constraints. The bi-level procedure consists of the upper-GA and the lower-GA. While the upper-GA optimize the connectivity between each supply and demand pair, the lower-GA optimize the amount of transportation between the pairs set to be connected by the upper-GA. In the upper-GA, the feasibility of the connectivity are verified, and if a connectivity is not feasible, it is modified so as to be feasible. In the lower-GA, a simple method is used to obtain a pivot feasible solution under the restriction of the connectivity determined by the upper-GA. The obtained pivot feasible solution is utilized to generate the initial generation of chromosomes. The computational experiment is performed on the selected problems with several non-linear objective functions. The performance of the proposed procedure is analyzed with the result of experiment