• Management Science and Financial Engineering
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• 제7권1호
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• pp.41-56
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• 2001

This linear fractional - quadratic bilevel programming problem, in which the leader's objective function is a linear fractional function and the follower's objective function is a quadratic function, is studied in this paper. The leader's and the follower's variables are related by linear constraints. The derivations of the optimality conditions are based on Kuhn-Tucker conditions and the duality theory. It is also shown that the original linear fractional - quadratic bilevel programming problem can be solved by solving a standard linear fractional program and the optimal solution of the original problem can be achieved at one of the extreme point of a convex polyhedral formed by the new feasible region. The algorithm is illustrated with the help of an example.

• Management Science and Financial Engineering
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• 제16권3호
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• pp.71-84
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• 2010

In this study, we present the exact methods of transforming the continuous solutions into the discrete ones for two types of bit-loading problem, marginal adaptive (MA) and rate adaptive (RA) problem, in multicarrier communication systems. While the computational complexity of existing solution methods for discrete optimal solutions depends on the number of bits to be assigned (R), the proposed method determined by the number of subcarriers (N), making ours be more efficient in most cases where R is much larger than N. Furthermore our methods have some strength of their simpler form to make a practical use.

• Management Science and Financial Engineering
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• 제2권1호
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• pp.147-176
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• 1996

The main objective of this study is to uncover the differences in the programming behavior between methodology creators and methodology users. We conducted an experiment with methodology creators who have invented one of the major object-oriented methodologies and with professional programmers who have used the same methodology for their software-development projects. In order to explain the difference between the two groups, we propose a theoretical framework that views programming as search in four problem spaces: representation, rule, instance and paradigm spaces. The main problem spaces in programming are the representation and rule spaces, while the paradigm and instance spaces are the supporting spaces. The results of the experiment showed that the methodology creators mostly adopted the paradigm space as their supporting space, while the methodology users chose the instance space as their supporting space. This difference in terms of the supporting space leads to different search behaviors in the main problem spaces, which in turn resulted in different final programs and performance.

• Management Science and Financial Engineering
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• 제3권1호
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• pp.1-14
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• 1997

We address the problem of determining the sequence of a vehicle with fixed capacity to visit n nodes at which a predetermined amount is picked up and/or delivered. The objective is to minimize the total travel distance of the vehicle, while satisfying the pick-up/delivery requirements and feasibility at all nodes. Existing methods for the problem allows the vehicle to visit a node twice, which is impractical in many real situations. We propose a heuristic algorithm, in which every node is visited exactly once. Computational results using random problems indicate that the proposed heuristic outperforms existing methods for practical range of the number of nodes in reasonable computation time.

• 교육녹색환경연구
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• 제10권2호
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• pp.46-60
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• 2011

Nowadays, the change of the social structure such as the elderly society and the broken traditional family conception is progressing rapidly. The social change produced a lot of problems, and the welfare from among these is the urgent problem. The welfare for the aged is very important problem in an aging society. The changes of industrial and economic structures also caused the increase of double-income families. The numbers of schoolchildren decreased rapidly because of the low birth and elderly society. So, many idle spaces of existing school facilities have emerged, and these left spaces is being blamed for the fiscal waste. If these idle spaces were used for the welfare-facilities, it will be of assistant to solve the problem about welfare and use the school facilities effectively. And then, the big profit from these enterprises can be used for the better welfare for education.

• Management Science and Financial Engineering
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• 제17권2호
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• pp.1-21
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• 2011

This paper considers an integrated inventory-distribution system with a fleet of heterogeneous vehicles employed where a single warehouse distributes a single type of products to many spatially distributed retailers to satisfy their dynamic demands. The problem is to determine order planning at the warehouse, and also vehicle schedules and delivery quantities for the retailers with the objective of minimizing the sum of ordering cost at the warehouse, inventory holding cost at both the warehouse and retailers, and transportation cost. For the problem, we give a Mixed Integer Programming formulation and develop a Lagrangean heuristic procedure for computing lower and upper bounds on the optimal solution value. The Lagrangean dual problem of finding the best Lagrangrean lower bound is solved by subgradient optimization. Computational experiments on randomly generated test problems showed that the suggested algorithm gives relatively good solutions in a reasonable amount of computation time.

• Management Science and Financial Engineering
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• 제9권2호
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• pp.47-68
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• 2003

We consider the customer order scheduling problem with job capacity restriction where the number of jobs in the shop at the same time is fixed. In the customer order scheduling problem, each job is part of some batch (customer order) and the composition of the jobs (product) in the batch is pre-specified. The objective function is associated with the completion time of the batches instead of the completion time of the jobs. We first summarize the known results for the general customer order scheduling problems. Then, we establish some new properties for the problems with job capacity restriction. For the case of unit processing time with the objective of minimizing makespan, we develop a polynomial-time optimal procedure for the two machine case. For the same problem with a variation of no batch alternation, we also develop a polynomial-time optimal procedure. Then, we show that the problems with the objectives of minimizing makespan and minimizing average batch completion time become NP-hard when there exist arbitrary number of machines. Finally, We propose optimal solution procedures for some special cases.

• Management Science and Financial Engineering
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• 제17권1호
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• pp.95-116
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• 2011

This paper considers a flowshop scheduling problem where a customer orders multiple products (jobs) from a production facility. The objectives are to minimize makespan and to minimize the sum of order (batch) completion times. The order cannot be shipped unless all the products in the order are manufactured. This problem was motivated by numerous real world problems encountered by a variety of manufacturers. For the makespan objective, we develop an optimal solution procedure which runs in polynomial time. For the sum of order completion time objective, we establish the complexity of the problem including several special cases. Then, we introduce a simple heuristic and find an asymptotically tight worst case bound on relative error. Finally, we conclude the paper with some implications.

• Management Science and Financial Engineering
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• 제18권2호
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• pp.19-22
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• 2012

In the bin-packing problem, we deal with how to pack the items by using a minimum number of bins. In the inverse bin-packing number problem, IBPN for short, we are given a list of items and a fixed number of bins. The objective is to perturb at the minimum cost the item-size vector so that all items can be packed into the prescribed number of bins. We show that IBPN is NP-hard and provide an approximation algorithm. We also consider a variant of IBPN where the prescribed solution value should be returned by a pre-selected specific approximation algorithm.

• 산업경영시스템학회지
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• 제32권4호
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• pp.63-71
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• 2009

The traditional portfolio optimization problem is to find an investment plan for securities with reasonable trade-off between the rate of return and the risk. The seminal work in this field is the mean-variance model by Markowitz, which is a quadratic programming problem. Since it is now computationally practical to solve the model, a number of alternative models to overcome this complexity have been proposed. In this paper, among the alternatives, we focus on the Mean Absolute Deviation (MAD) model. More specifically, we developed an algorithm to obtain an optimal portfolio from the MAD model. We showed mathematically that the algorithm can solve the problem to optimality. We tested it using the real data from the Korean Stock Market. The results coincide with our expectation that the method can solve a variety of problems in a reasonable computational time.