Optimizing a linear function over the efficient set of a multiobjective integer linear programming (MOILP) problem is a topic of unquestionable practical as well as mathematical interest within the field of multiple c...
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Optimizing a linear function over the efficient set of a multiobjective integer linear programming (MOILP) problem is a topic of unquestionable practical as well as mathematical interest within the field of multiple criteria decision making. As known, those problems are particularly difficult to deal with due to the discrete nature of the efficient set, which is not explicitly known, nor a suitable implicit description is available. In this work an exact algorithm is presented to optimize a linear function over the efficient set of a MOILP. The approach here proposed defines a sequence of progressively more constrained single-objective integer problems that successively eliminates undesirable points from further consideration. The algorithm has been coded in C Sharp, using CPLEX solver, and computational experiments have been undertaken in order to analyze performance properties of the algorithm over different problem instances randomly generated. (c) 2008 Elsevier B.V. All rights reserved.
This paper addresses the problem of scheduling medical residents that arises in different clinical settings of a hospital. The residents are grouped according to different seniority levels that are specified by the nu...
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This paper addresses the problem of scheduling medical residents that arises in different clinical settings of a hospital. The residents are grouped according to different seniority levels that are specified by the number of years spent in residency training. It is required from the residents to participate in the delivery of patient care services directly by working weekday and weekend day shifts in addition to their regular daytime work. A monthly shift schedule is prepared to determine the shift duties of each resident considering shift coverage requirements, seniority-based workload rules, and resident work preferences. Due to the large number of constraints often conflicting, a multi-objectiveprogramming model has been proposed to automate the schedule generation process. The model is implemented on a real case in the pulmonary unit of a local hospital for a 6-month period using sequential and weighted methods. The results indicate that high quality solutions can be obtained within a few seconds compared to the manually prepared schedules expending considerable effort and time. It is also shown that the employed weighting procedure based on seniority levels performs much better compared to the preemptive method in terms of computational burden. (C) 2008 Elsevier B.V. All rights reserved.
A genetic algorithm approach is used to solve a multi-objective discrete reliability optimization problem in a k dissimilar-unit non-repairable cold-standby redundant system. Each unit is composed of a number of indep...
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A genetic algorithm approach is used to solve a multi-objective discrete reliability optimization problem in a k dissimilar-unit non-repairable cold-standby redundant system. Each unit is composed of a number of independent components with generalized Erlang distributions arranged in a series-parallel configuration. There are multiple component choices with different distribution parameters available for being replaced with each component of the system. The objective of the reliability optimization problem is to select the best components, from the set of available components, to be placed in the standby system in order to minimize the initial purchase cost of the system, maximize the system MTTF (mean time to failure), minimize the system VTTF (variance of time to failure) and also maximize the system reliability at the mission time. Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed GA approach. (c) 2008 Elsevier Ltd. All rights reserved.
Transportation systems can be represented by graphs with travel weights accorded to each of the edges that represent the roads to be travelled. This paper gives brief introduction of the Euler's path and the descr...
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ISBN:
(纸本)9781424441358
Transportation systems can be represented by graphs with travel weights accorded to each of the edges that represent the roads to be travelled. This paper gives brief introduction of the Euler's path and the description of Chinese postman problem. The said problem is then extended to multi-objective problem by considering multiple weights for each edge. Finally, this paper presents an algorithm to solve this multi-objective problem and implements the same on a biobjective Chinese postman problem.
Goal programming (GP) can be regarded as one of the most widely used multicriteria decision-making techniques. In this paper, two surveys are carried out. First, the evolution of GP since its birth to the present time...
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Goal programming (GP) can be regarded as one of the most widely used multicriteria decision-making techniques. In this paper, two surveys are carried out. First, the evolution of GP since its birth to the present time, in terms of number of publications, references, journals, etc., has been studied. Second, a more in-depth survey has been carried out, which covers the publications from year 2000 to the present time. All the references are listed, and some conclusions and future research lines have been extracted about the late trends of GP. Copyright (C) 2010 John Wiley & Sons, Ltd.
Increasing regulatory legislations for carbon and waste management and the focus on corporate social responsibility are driving a major focus on supply chain sustainability. In this research, a goal programming model ...
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Increasing regulatory legislations for carbon and waste management and the focus on corporate social responsibility are driving a major focus on supply chain sustainability. In this research, a goal programming model is proposed to address a supply chain design problem with environmental considerations. Carbon emissions (environmental dimension) and total logistics cost (economic dimension) are considered in order to evaluate the supply chain performance. A crucial contribution of our work is that, together with incorporating regulatory environmental constraints such as emissions “cap” and putting a price tag on carbon emissions, we comprehensively model compliance strategies for the supply chain including suppliers and sub-contractors selection, technology acquisition, and transportation modes configuration. The results obtained show that this approach is a viable decision tool and offer a good framework for designing and evaluating efficient and environmental supply chains.
The paper describes the Multi-Criteria Branch and Bound (MCBB) algorithm. a vector maximization algorithm capable of deriving all efficient extreme points, for small and medium-sized Mixed 0-1 multipleobjective Linea...
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The paper describes the Multi-Criteria Branch and Bound (MCBB) algorithm. a vector maximization algorithm capable of deriving all efficient extreme points, for small and medium-sized Mixed 0-1 multipleobjective Linear programming (Mixed 0-1 MOLP). Particular emphasis is given to computational aspects aiming principally at accelerating the solution procedure. For facilitating the decision maker's search toward the most preferred efficient solution, the notion of efficient combinations of the binary variables is further exploited. It is also shown that the MCBB algorithm can be used in single objective problems (Mixed Integer LP problems) in order to determine all alternative optima, as well as in Mixed Integer MOLP problems and Pure 0-1 MOLP problems that frequently arise in practice. A computational experiment is included in the paper in order to illustrate the performance of the algorithm. (c) 2005 Elsevier Inc. All rights reserved.
In this paper we consider solution generation method for multipleobjective linear programming problems. The set of efficient or Pareto optimal solutions for the problems can be regarded as global information in multi...
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In this paper we consider solution generation method for multipleobjective linear programming problems. The set of efficient or Pareto optimal solutions for the problems can be regarded as global information in multipleobjective decision making situation. In the past three decades as solution generation techniques various conventional algorithms based on simplex-like approach with heavy computational burden were developed. Therefore, the development of novel and useful directions in efficient solution generation method have been desired. The purpose of this paper is to develop theoretical results and computational techniques of the efficient solution generation method based on extreme ray generation method that sequentially generates efficient points and rays by adding inequality constraints of the polyhedral feasible region. (C) 2003 Elsevier B.V. All rights reserved.
In this paper we consider solution generation method for multipleobjective linear programming problems. The set of efficient or Pareto optimal solutions for the problems can be regarded as global information in multi...
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In this paper we consider solution generation method for multipleobjective linear programming problems. The set of efficient or Pareto optimal solutions for the problems can be regarded as global information in multipleobjective decision making situation. In the past three decades as solution generation techniques various conventional algorithms based on simplex-like approach with heavy computational burden were developed. Therefore, the development of novel and useful directions in efficient solution generation method have been desired. The purpose of this paper is to develop theoretical results and computational techniques of the efficient solution generation method based on extreme ray generation method that sequentially generates efficient points and rays by adding inequality constraints of the polyhedral feasible region. (C) 2003 Elsevier B.V. All rights reserved.
We have already proposed a similarity-based mating scheme to recombine extreme and similar parents for evolutionary multiobjective optimization. In this paper, we examine the effect of the similarity-based mating sche...
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We have already proposed a similarity-based mating scheme to recombine extreme and similar parents for evolutionary multiobjective optimization. In this paper, we examine the effect of the similarity-based mating scheme on the performance of evolutionary multiobjective optimization (EMO) algorithms. First we examine which is better between recombining similar or dissimilar parents. Next we examine the effect of biasing selection probabilities toward extreme solutions that are dissimilar from other solutions in each population. Then we examine the effect of dynamically changing the strength of this bias during the execution of EMO algorithms. Computational experiments are performed on a wide variety of test problems for multiobjective combinatorial optimization. Experimental results show that the performance of EMO algorithms can be improved by the similarity-based mating scheme for many test problems. (C) 2007 Elsevier B.V. All rights reserved.
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