• Korean Management Science Review
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• v.29 no.2
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• pp.91-103
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• 2012

Revenue management problems originated in the 1970's in the context of the airline industry have been successfully introduced in airline industries. It has started on the capacity control by booking classes for available seats, and has been recognized as a powerful tool to maximize the total revenue. Changing customer behavior and airline market environments, however, has required a new mechanism for improving the revenue. Dynamic pricing is one of innovative tools which is to adjust prices according to the market status. In this paper, we consider a dynamic pricing and seat control problem for discrete time horizon. The problem can be modeled as a stochastic programming problem. Applying the linear approximation technique and given the price set for each time, we suggest a mixed Integer Programming model to solve our problem efficiently. From the simulation results, we can find our model makes good performance and can be expanded to other comprehensive problems.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.30-35
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• 1993

Simple recurrence relations for calculating completion times of various storage polices (unlimited, intermediate storages(FIS), finite intermediate storages(FIS), no intermediate storage(NIS), zero wait(ZW) for serial multi-product multi-unit processes are suggested. Not only processing times but also transfer times, set-up (clean-up) times of units and set-up times of storages are considered. Optimal scheduling strategies with zero transfer times and zero set-up times had been developed as a mixed integer linear programniing(MILP) formulation for several intermediate storage policies. In this paper those with non-zero transfer times, non-zero set-up times of units and set-up times of storages are newly proposed as a mixed integer nonlinear programming(MINLP) formulation for various storage polices (UIS, NIS, FIS, and ZW). Several examples are tested to evaluate the robustness of this strategy and reasonable computation times.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.73-80
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• 1986

This paper deals with the problem of the optimal location of warehouses in the two stage distribution system, i.e., the distribution system where the product is transported from plants to customer areas via warehouses. The Problem is formulated with a zero-one mixed integer programming and an efficient branch and bound algorithm is then used to solve the problem. In order to obtain the solution of this problem, this paper shows the procedure of conversion of two stage distribution system into one stage distribution system. An improved method of solving the linear programming at the nodes and branching decision rule is also showed by this study.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.2
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• pp.47-65
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• 1998

We consider a multiperiod capacity planning problem for determining a mix of flexible and dedicated capacities under budget restriction. These capacities are controlled by purchasing flexible machines and/or new dedicated machines and disposing old dedicated machines. Acquisition and replacement schedules are determined and operations are assigned to the flexible or dedicated machines for the objective of minimizing the sum of discounted costs of acquisition and operation of flexible machines, new dedicated machines, and old dedicated machines. In this research, the Problem is formulated as a mixed integer linear Program and solved by a Lagrangian relaxation approach. A subgradient optimization method is employed to obtain lower bounds and a multiplier adjustment method is devised to improve the bounds. We develop a linear programming based Lagrangian heuristic algorithm to find a good feasible solution of the original problem. Results of tests on randomly generated test problems show that the algorithm gives relatively good solutions in a reasonable amount of computation time.

• Korean Chemical Engineering Research
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• v.57 no.1
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• pp.51-57
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• 2019

A plant layout problem has a large impact on the overall construction cost of a plant. When determining a plant layout, various constraints associating with safety, environment, sufficient maintenance area, passages for workers, etc have to be considered together. In general plant layout problems, the main goal is to minimize the length of piping connecting equipments as satisfying various constraints. Since the process may suffer from the heat and friction loss, the piping length between equipments should be shorter. This problem can be represented by the mathematical formulation and the optimal solutions can be investigated by an optimization solver. General researches have overlooked many constraints such as maintenance spaces and safety distances between equipments. And, previous researches have tested benchmark processes. What the lack of general researches is that there is no realistic comparison. In this study, the plant layout of a real industrial C3MR (Propane precooling Mixed Refrigerant) process is studied. A MILP (Mixed Integer Linear Programming) including various constraints is developed. To avoid the violation of constraints, penalty functions are introduced. However, conventional optimization solvers handling the derivatives of an objective functions can not solve this problem due to the complexities of equations. Therefore, the PSO (Particle Swarm Optimization), which investigate an optimal solutions without differential equations, is selected to solve this problem. The results show that a proposed method contributes to saving the capital expenditures.

• Korean Chemical Engineering Research
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• v.52 no.4
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• pp.475-480
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• 2014

In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines for connecting equipment. However, what is the lacking of considerations in previous researches is to handle the multi floor processes considering the safety distances for domino impacts on a complex plant. The mathematical programming formulation can be transformed into MILP (Mixed Integer Linear Programming) problems as considering safety distances, maintenance spaces, and economic benefits for solving the multi-floor plant layout problem. The objective function of this problem is to minimize piping costs connecting facilities in the process. However, it is really hard to solve this problem due to complex unequality or equality constraints such as sufficient spaces for the maintenance and passages, meaning that there are many conditional statements in the objective function. Thus, it is impossible to solve this problem with conventional optimization solvers using the derivatives of objective function. In this study, the PSO (Particle Swarm Optimization) technique, which is one of the representative sampling approaches, is employed to find the optimal solution considering various constraints. The EO (Ethylene Oxide) plant is illustrated to verify the efficacy of the proposed method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.530-533
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• 2000

Recently, Manufacturers have recognized the importance of recycling because of the limitation of natural resources and new and proposed laws and legislations that assign responsibility to manufacturer for the ultimate disposal of their products and the environmental problems. In this paper, products assembled recyclable parts and non-recyclable parts are collected after consumer usage and go into a plant. And the collected recyclable parts are disassembled through the disassembly process and have three attributes - re-usable attribute, re-manufacturing attribute and dumping attribute. In this situation, we deal with a production planning for recycle-oriented manufacturing system. The proposed model maximizing the profit in a system is formulated as a mixed-integer linear programming. And then a numerical example is used to evaluate the effectiveness of the proposed model

• Industrial Engineering and Management Systems
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• v.14 no.2
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• pp.159-174
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• 2015

Remanufacturing used, end-of-life products is a complex problem involving multiple types of products that may share common parts. Recovery targets assigned by market demand and environmental legislation add more difficulty to the problem. Manufacturers now need to achieve specified take-back and recovery rates while fulfilling demands for remanufactured products. To assists in the demand- and legislation-driven remanufacturing of a family of products (i.e., multiple products that share common parts), this paper introduces a bi-objective mixed integer linear programming (MILP) model for optimizing remanufacturing. The model identifies optimal remanufacturing plans for a product family, whereby, the remanufacturer can achieve demand and recovery targets more profitably and in an environmentally-friendly manner. The model can also be used to quantify and justify the economic and environmental benefits of a product family from a remanufacturing perspective. A case study is presented for remanufacturing an alternatorfamily of products.

• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.35-44
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• 2003

Recovery of end-of-life (EOL) products is an environmentally and economically sound way to achieve many of the goals of sustainable development. Many product recovery systems are dependent upon destructive disassembly such as shredding, which undesirably causes a large volume of shredder dust and makes parts reuse impossible. Although non-destructive disassembly has been considered as an alternative for solving the problems, the classification of disassembled items has not been sufficiently investigated. In this paper, we propose a model that mathematically optimizes the disassembly and classification of EOL products. Based on the AND/OR graph that illustrates all possible disassembly sequences of a given product, we identify the physical properties that are considered as constraints in the model. As a result of the solution procedure, the recovery problem can be transformed into a mixed integer linear programming (MILP) model. We show an example that illustrates the concept of our model.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.162-163
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• 2016

This paper presents a mathematical model for optimizing earthwork allocation plan that minimizes earthwork cost. The model takes into account operational constraints in the real-world earthwork such as material-type (i.e., quality level of material) and quantities excavated from cut-sections, required quality of material and quantities for each embankment layer, top-down cutting and bottom-up filling constraints, and allocation orders. These constraints are successfully handled by assuming the rock-earth material as the three dimensional (3D) blocks. The study is of value to project scheduler because the model identifies the optimal earth allocation plan (i.e., haul direction (cut and fill pairs), quantities of soil, type of material, and order of allocations) expeditiously and is developed as an automated system for usability. It is also relevant to estimator in that it computes more realistic earthworks costs estimation. The economic impact and validity of the mathematical model was confirmed by performing test cases.