• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.2
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• pp.177-198
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• 1994

We consider an algorithm for the Crew Scheduling Problem (CSP) based on the Transportation Problem approach. The main flows of the algorithm are arranged in three steps. First we propose a heuristic algorithm of the greedy principle to obtain an initial feasible solution. Secondary we present a method of formulating CSP into a Modified Transportation Problem format. Lastly the procedures of network search to get the optimal solution are presented. This algorithm can be applied to the general GSP and also to most combinatorial problems like the Vehicle Routing Problems. The computational results show that the large size CSP's could be tackled.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.159-170
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• 1992

The size of time constaints is the critical bottleneck of the Crew Scheduling Problem (CSP). This paper deals with a method to extract the minimum required time constraints by k-shortest path algorithm. These time constraints are added as the "insurance constraints" to avoid the unnecessary tree search, which are very time-consuming procedures, for the integer solutions. The computational results show that the problem size in LP formulation could be reduced by our method.

• Korean Journal of Construction Engineering and Management
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• v.20 no.5
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• pp.42-51
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• 2019

The train operation institution establishes a transportation plan based on the forecast of transport demand and the ability of train vehicles to transport, and establishes a train operation plan accordingly. The train operation plan adjusts the intervals between trains, creates a timetable (train diagram) for trains, and establishes a plan for the operation of train vehicles used for train operation. The train operation institution shall establish a crew schedule to determine and place the crew members of the trains arranged in the diagram in order to enhance the efficiency of the operation management of the trains. In this study, the authors apply the neighborhood search method that satisfies the constraints at the phase of generating the crew diagram. This suggests a methodology for efficient management of crew schedule plan. The crew diagram generated in this study compared with the existing crew diagram in accordance with the actual operating train timetable, and verified the effectiveness of the suggested method.

• International Journal of Quality Innovation
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• v.10 no.2
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• pp.19-27
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• 2009

This paper considers the short term fleet scheduling problem as described by Keskinocak and Tayur (1998). Fleet scheduling may directly affect the service quality of fractional jet aircraft business. The contributions of this paper are two: (i) we show how their model is easily implemented in a standard modeling language, LINGO, and (ii) an alternate formulation is given which is expected to perform better on large, difficult problems.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.69-75
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• 2015

This paper suggests heuristic polynomial time algorithm for crew scheduling problem that is a kind of optimization problems. This problem has been solved by linear programming, set cover problem, set partition problem, column generation, etc. But the optimal solution has not been obtained by these methods. This paper sorts transit costs $c_{ij}$ to ascending order, and the task i and j crew paths are merged in case of the sum of operation time ${\Sigma}o$ is less than day working time T. As a result, we can be obtain the minimum number of crews $_{min}K$ and minimum transit cost $z=_{min}c_{ij}$. For the transit cost of specific number of crews $K(K>_{min}K)$, we delete the maximum $c_{ij}$ as much as the number of $K-_{min}K$, and to partition a crew path. For the 5 benchmark data, this algorithm can be gets less transit cost than state-of-the-art algorithms, and gets the minimum number of crews.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.2
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• pp.195-205
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• 2002

Crew scheduling is the problem of pairing crews with each of the vehicles in operation during a certain period of time. A typical procedure of crew schedule optimization consists of enumerating all possible pairings and then selecting the subset which can cover all the operating vehicles, with the goal of minimizing the number of pairings in the subset. The linear programming approach popularly adopted for optimal selection of pairings, however, is not applicable when the objective function cannot be expressed in a linear form. This paper proposes a method of integrating integer programming and heuristic search to solve difficult crew scheduling problems in which the objective function cannot be expressed in linear form and at the same time the number of crews available is limited. The role of heuristic search is to improve the incomplete solution generated by integer programming through iterative repair. Experimental results show that our method outperforms human experts in terms of both solution quality and execution time when applied to real world crew scheduling Problems which can hardly be solved by traditional methods.

• Journal of Navigation and Port Research
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• v.30 no.7
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• pp.561-566
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• 2006

In this paper, we propose a new ship scheduling set packing model considering limited risk or variance. The set packing model is used in many applications, such as vehicle routing, crew scheduling, ship scheduling, cutting stock and so on. As long as the ship scheduling is concerned, there exits many unknown external factors such as machine breakdown, climate change and transportation cost fluctuation. However, existing ship scheduling models have not considered those factors apparently. We use a quadratic set packing model to limit the variance of expected cost of ship scheduling problems under stochastic spot rates. Set problems are NP-complete, and additional quadratic constraint makes the problems much harder. We implement Kelley's cutting plane method to replace the hard quadratic constraint by many linear constrains and use branch-and-bound algorithm to get the optimal integral solution. Some meaningful computational results and comments are provided.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.133-141
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• 2019

Crew rostering in railway operations is usually done by arranging a crew diagram in accordance with working standards every month. This study was done to identify the problems related to the creation of crew rosters in railway operations and to suggest an optimum crew rostering method that can be applied in railway operations planning. To do this, the work standards of a railway company were identified, and a genetic algorithm was used to develop an optimal roster with equal working time while considering actual working patterns. The optimization process is composed of analysis of the input data, creation of work patterns, creation of a solution, and optimization steps. To verify the method, the roster derived from the proposed process was compared with a manually created roster. The results of the study could be used to reduce the deviation of business hours when generating a roster because the standard deviation of working time is the objective function.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.139-149
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• 1994

In many project-oriented production systems, e. g., shipyards or large-scale steel products manufacturing, resource loading by an activity is flexible, and the activity duration is a function of resource allocation. For example, if one doubles the size of the crew assigned to perform an activity, it may be feasible to complete the activity in half the duration. Such flexibility has been modeled by Weglarz [13] and by Leachman, Dincerler, and Kim [7[ in extended formulations of the resource-constrained poject scheduling problem. This paper presents a new algorithmic approach to the problem that combines the ideas proposed by the aforementioned authors. The method we propose involves a two-step approach : (1) solve the resource-constrained scheduling problem using a heuristic, and (2) using this schedule as an initial feasible solution, find improved resource allocations by solving a linear programming model. We provide computational results indicating the superiority of this approach to previous methodology for the resource-constrained scheduling problem. Extensions to the model to admit overlap relationship of the activities also are presented.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.410-420
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• 1994

In many project-oriented production systems, e.g., shipyards or large-scale steel products manufacturing, resource loading by an activity is flexible, and the activity duration is a function of resource allocation. For example, if one doubles the size of the crew assigned to perform an activity, it may be feasible to complete the activity in half the duration. Such flexibility has been modeled by Weglarz [131 and by Leachman, Dincerler, and Kim [7] in extended formulations of the resource-constrained project scheduling problem. This paper presents a new algorithmic approach to the problem that combines the ideas proposed by the aforementioned authors. The method we propose involves a two-step approach: (1) solve the resource-constrained scheduling problem using a heuristic, and (2) using this schedule as an initial feasible solution, find improved resource allocations by solving a linear programming model. We provide computational results indicating the superiority of this approach to previous methodology for the resource-constrained scheduling problem. Extensions to the model to admit overlap relationships of the activities also are presented.