• Journal of the military operations research society of Korea
• /
• v.11 no.2
• /
• pp.53-63
• /
• 1985

This paper presents a multi-costraint optimization model for redundant system reliability. The optimization model is usually formulated as a nonlinear integer programming (NIP) problem. This paper reformulates the NIP problem into a linear integer programming (LIP) problem. Then an efficient 'Branch and Straddle' algorithm is proposed to solve the LIP problem. The efficiency of this algorithm stems from the simultaneous handling of multiple variables, unlike in ordinary branch and bound algorithms. A numerical example is given to illustrate this algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.11a
• /
• pp.403-406
• /
• 2006

This paper provides an M/G/1 queueing model for the operations management problem at the toll plaza. This queueing process is incorporated with two non-linear integer programming models - the user cost minimization model during the peak times and the operating cost minimization model during the off-peak hours.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.11 no.18
• /
• pp.7-15
• /
• 1988

This study is concerned with selecting mutually dependent quality improvement alternatives with resource constraints. These qualify improvement alternatives art different fro the tradition at alternatives which are independent from each other. In other words, selection of any improvement alternative requires other related specific improvement. Also the overall product quality in a multi stage manufacturing process is characterized by a complex multiplication method rather than a simple addition method which dose not allow to solve a linear knapsack problem despite its popularity in the traditional study. This study suggests a non-linear integer programming model for selecting mutually dependent quality improvement alternatives in multi-stage manufacturing process. In order to apply the model to selecting alternatives. This study also suggests a heuristic mode1 based on a dynamic programming model which is more practical than the non-linear integer programming model. The logic of the heuristic model enables 1) to estimate improvement effectiveness values on all improvement alternatives specifically defined for this study. 2) to arrange the effectiveness values in a descending order, and 3) to select the best one among the alternatives based on their forward and backward linkage relationships. This process repeats to selects other best alternatives within the resource constraints. This process is presented in a Computer programming in Appendix A. Alsc a numerical example of model application is presented in Chapter 4.

• Journal of the Society of Naval Architects of Korea
• /
• v.42 no.6 s.144
• /
• pp.654-665
• /
• 2005

In this paper, we present a non-linear integer programming by genetic algorithm (GA) for available sizes of stiffener or thickness of plate in a job site. GA can rapidly search for the approximate global optimum under complicated design environment such as ship. Meanwhile it can handle the optimization problem involving discrete design variable. However, there are many parameters have to be set for GA, which greatly affect the accuracy and calculation time of optimum solution. The setting process is hard for users, and there are no rules to decide these parameters. In order to overcome these demerits, the optimization for these parameters has been also conducted using GA itself. Also it is proved that the parameters are optimal values by the trial function. Finally, we applied this method to compass deck of ship where the vibration problem is frequently occurred to verify the validity and usefulness of nonlinear integer programming.

• Journal of the military operations research society of Korea
• /
• v.23 no.1
• /
• pp.14-24
• /
• 1997

This study is to develop an optimal surveillance units assignment model in order to obtain the maximized surveillance efficiency with the limited surveillance units. There are many mathematical models which deal with problems to assign weapons such as aircrafts, missiles and guns to targets. These models minimize the lost required to attack, the threat forecast from the enemy, or both of them. However, a problem of the efficient assignment of surveillance units is not studied yet, nevertbless it is important in the battlefield surveillance system. This paper is concerned with the development of the optimal surveillance units assignment model using integer programming. An optimal integer solution of the model can be obtained by using linear programming and branch and bound method.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.14 no.4
• /
• pp.49-59
• /
• 2004

Boolean function synthesize problem is to find a boolean expression with in/outputs of original function. This problem can be modeled into a 0-1 integer programming. In this paper, we find a boolean expressions of S-boxes of DES for an example, whose algebraic structure has been unknown for many years. The results of this paper can be used for efficient hardware implementation of a function and cryptanalysis using algebraic structure of a block cipher.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2002.05a
• /
• pp.179-184
• /
• 2002

We consider the heterogeneous fleet vehicle routing problem (HVRP), a variant of the classical vehicle routing problem (VRP). The HVRP differs from the classical VRP in that it deals with a heterogeneous fleet of vehicles having various capacities, fixed costs, and variables costs. Therefore the HVRP is to find the fleet composition and a set of routes with minimum total cost. We give an integer programming formulation of the problem and propose an algorithm to solve it. Although the formulation has exponentially many variables, we can efficiently solve the linear programming relaxation of it by using the column generation technique. To generate profitable columns we solve a shortest path problem with capacity constraints using dynamic programming. After solving the linear programming relaxation, we apply a branch-and-bound procedure. We test the proposed algorithm on a set of benchmark instances. Test results show that the algorithm gives best-known solutions to almost all instances.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.31 no.1
• /
• pp.91-103
• /
• 2006

Given a bill of materials (BOM) tree T labeled by the breadth first search (BFS) order from node 0 to node n and a general network ${\Im}=(V,A)$, where V={1,2,...,m} is the set of production facilities and A is the set of arcs representing transportation links between any of two facilities, we assume that each node of T stands for not only a component. but also a production stage which is a possible stocking point and operates under a periodic review base-stock policy, We also assume that the random demand which can be achieved by a suitable service level only occurs at the root node 0 of T and has a normal distribution $N({\mu},{\sigma}^2)$. Then our integrated model of facility location problems and safety stock optimization problem (FLP&SSOP) is to identify both the facility locations at which partitioned subtrees of T are produced and the optimal assignment of safety stocks so that the sum of production cost, inventory holding cost, and transportation cost is minimized while meeting the pre-specified service level for the final product. In this paper, we first formulate (FLP&SSOP) as a nonlinear integer programming model and show that it can be reformulated as a 0-1 linear integer programming model with an exponential number of decision variables. We then show that the linear programming relaxation of the reformulated model has an integrality property which guarantees that it can be optimally solved by a column generation method.

• Proceedings of the Korea Society of Information Technology Applications Conference
• /
• 2005.11a
• /
• pp.311-314
• /
• 2005

Supply chain optimization is one of the most important components in the optimization of a company's value chain. This paper considers the problem of designing the supply chain for a product that is represented as an assembly bill of material (BOM). In this problem we are required to identify the locations at which different components of the product arc are produced/assembled. The objective is to minimize the overall cost, which comprises production, inventory holding and transportation costs. We assume that production locations are known and that the inventory policy is a base stock policy. We first formulate the problem as a 0-1 nonlinear integer programming model and show that it can be reformulated as a 0-1 linear integer programming model with an exponential number of decision variables.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.4
• /
• pp.85-92
• /
• 2018

Reductions of the electricity charge are achieved by demand management of the load. The demand management method of the load using ESS involves peak shifting, which shifts from a high demand time to low demand time. By shifting the load, the peak load can be lowered and the energy charge can be saved. Electricity charges consist of the energy charge and the basic charge per contracted capacity. The energy charge and peak load are minimized by Linear Programming (LP) and Quadratic Programming (QP), respectively. On the other hand, each optimization method has its advantages and disadvantages. First, the LP cannot separate the efficiency of the ESS. To solve these problems, the charge and discharge efficiency of the ESS was separated by Mixed Integer Linear Programming (MILP). Nevertheless, both methods have the disadvantages that they must assume the reduction ratio of peak load. Therefore, QP was used to solve this problem. The next step was to optimize the formula combination of QP and LP to minimize the electricity charge. On the other hand, these two methods have disadvantages in that the charge and discharge efficiency of the ESS cannot be separated. This paper proposes an optimization method according to the situation by analyzing quantitatively the advantages and disadvantages of each optimization method.