The objective of this paper is to search the efficient arrangements of huge multi-objective assignment problem (MOAP). Here, we have modified the Jaya algorithm with the help of product operator in exponential members...
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The objective of this paper is to search the efficient arrangements of huge multi-objective assignment problem (MOAP). Here, we have modified the Jaya algorithm with the help of product operator in exponential membership functions for solving MOAP. We have also provided one numerical illustration from the real world for verifying the results of proposed approach. At last, the results of the proposed approach are contrasted with reputed approaches and from that we can conclude that the proposed approach is better for solving the MOAP.
In the era of globalization, institutions face increasing challenges in effectively managing their resources to achieve diverse and differentiated objectives. The unbalanced multi-objective assignment problem reflects...
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In the era of globalization, institutions face increasing challenges in effectively managing their resources to achieve diverse and differentiated objectives. The unbalanced multi-objective assignment problem reflects these challenges, as the distribution of limited resources requires achieving a balance between objectives such as minimizing time, cost, etc. In this study, an application case in the industrial field of rolling process was investigated, where the objective of the assignmentproblem is to minimize total assignment. In order to tackle this problem, the multi-objectiveproblem was addressed by converting it into a single-objectiveproblem through the weighted sum method by assigning a weight to each objective. A new heuristic method was then applied to solve the unbalanced single-objectiveassignmentproblem where the machines were loaded with more than one job. The Hungarian method was also used to solve the problem in order to assess the proposed method, and the outcomes were then compared. The proposed method proved efficient as it reduced idle time by 28%, increased machine efficiency by 5% and reduced costs by an average of 15%.
This paper presents a genetic algorithm based hybrid approach for solving a fuzzy multi-objective assignment problem (FMOAP) by using an exponential membership function in which the coefficient of the objective functi...
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This paper presents a genetic algorithm based hybrid approach for solving a fuzzy multi-objective assignment problem (FMOAP) by using an exponential membership function in which the coefficient of the objective function is described by a triangular possibility distribution. Moreover, in this study, fuzzy judgment was classified using alpha-level sets for the decision maker (DM) to simultaneously optimize the optimistic, most likely, and pessimistic scenarios of fuzzy objective functions. To demonstrate the effectiveness of the proposed approach, a numerical example is provided with a data set from a realistic situation. This paper concludes that the developed hybrid approach can manage FMOAP efficiently and effectively with an effective output to enable the DM to take a decision.
In this paper, we consider the problem of determining a best compromise solution for the multi-objective assignment problem. Such a solution minimizes a scalarizing function, such as the weighted Tchebychev norm or re...
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In this paper, we consider the problem of determining a best compromise solution for the multi-objective assignment problem. Such a solution minimizes a scalarizing function, such as the weighted Tchebychev norm or reference point achievement functions. To solve this problem, we resort to a ranking (or k-best) algorithm which enumerates feasible solutions according to an appropriate weighted sum until a condition, ensuring that an optimal solution has been found, is met The ranking algorithm is based on a branch and bound scheme. We study how to implement efficiently this procedure by considering different algorithmic variants within the procedure: choice of the weighted sum, branching and bounding schemes. We present an experimental analysis that enables us to point out the best variants, and we provide experimental results showing the remarkable efficiency of the procedure, even for large size instances. (C) 2014 Elsevier Ltd. All rights reserved.
In this paper, a crew-scheduling problem has been formulated considering the daily assignment of a set of crew staff to their round-trips for all the public transports (e.g. train, bus or air bus) that will minimize t...
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In this paper, a crew-scheduling problem has been formulated considering the daily assignment of a set of crew staff to their round-trips for all the public transports (e.g. train, bus or air bus) that will minimize the waiting times and total service times (including waiting times) of all crews separately. In this approach, the waiting times as well as the service times of crews have been taken as intervals. Here the problem has been formulated as a multi-objective assignment problem using interval arithmetic and the order relations (for the case of pessimistic decision making) that represent the decision maker's preference between interval times. For this purpose, we have used the existing complete definition of order relations recently developed. Finally, the reformulated problem has been converted to a single objective optimization problem using Global Criterion Method and then solved the same by tournament genetic algorithm. The experimental results of the proposed approach to a crew-scheduling problem have been reported.
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