We develop a multi-objective model for resource allocation problem in PERT networks with exponentially or Erlang distributed activity durations, where the mean duration of each activity is a non-increasing function an...
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We develop a multi-objective model for resource allocation problem in PERT networks with exponentially or Erlang distributed activity durations, where the mean duration of each activity is a non-increasing function and the direct cost of each activity is a non-decreasing function of the amount of resource allocated to it. The decision variables of the model are the allocated resource quantities. The problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. The objective functions are the total direct costs of the project (to be minimized), the mean of project completion time (min), the variance of project completion time (min), and the probability that the project completion time does not exceed a certain threshold (max). The surrogate worth trade-off method is used to solve a discrete-time approximation of the original problem. (c) 2005 Elsevier B.V. All rights reserved.
In this paper we consider a simulated annealing algorithm for multiobjective optimization problems. With a suitable choice of the acceptance probabilities, the algorithm is shown to converge asymptotically, that is, t...
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In this paper we consider a simulated annealing algorithm for multiobjective optimization problems. With a suitable choice of the acceptance probabilities, the algorithm is shown to converge asymptotically, that is, the Markov chain that describes the algorithm converges with probability one to the Pareto optimal set.
The optimal partition for linear programming is induced by any strictly complementary solution, and this partition is important because it characterizes the optimal set. However, constructing a strictly complementary ...
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The optimal partition for linear programming is induced by any strictly complementary solution, and this partition is important because it characterizes the optimal set. However, constructing a strictly complementary solution in the presence of degeneracy was not practical until interior point algorithms became viable alternatives to the simplex algorithm. We develop analogs of the optimal partition for linear programming in the case of multipleobjectives and show that these new partitions provide insight into the optimal set (both pareto optimality and lexicographic ordering are considered). Techniques to produce these optimal partitions are provided, and examples from the design of radiotherapy plans show that these new partitions are useful.
In multiobjective optimization methods, the multiple conflicting objectives are typically converted into a single objective optimization problem with the help of scalarizing functions and such functions may be constru...
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In multiobjective optimization methods, the multiple conflicting objectives are typically converted into a single objective optimization problem with the help of scalarizing functions and such functions may be constructed in many ways. We compare both theoretically and numerically the performance of three classification-based scalarizing functions and pay attention to how well they obey the classification information. In particular, we devote special interest to the differences the scalarizing functions have in the computational cost of guaranteeing Pareto optimality. It turns out that scalarizing functions with or without so-called augmentation terms have significant differences in this respect. We also collect a set of mostly nonlinear benchmark test problems that we use in the numerical comparisons. (c) 2005 Elsevier B.V. All rights reserved.
In this paper we develop an open queueing network for optimal design of multi-stage assemblies, in which each service station represents a manufacturing or assembly operation. The arrival processes of the individual p...
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In this paper we develop an open queueing network for optimal design of multi-stage assemblies, in which each service station represents a manufacturing or assembly operation. The arrival processes of the individual parts of the product are independent Poisson processes with equal rates. In each service station, there is a server with exponential distribution of processing time, in which the service rate is controllable. The transport times between the service stations are independent random variables with exponential distributions. By applying the longest path analysis in queueing networks, we obtain the distribution function of time spend by a product in the system or the manufacturing lead time. Then, we develop a multi-objective optimal control problem, in which the average lead time, the variance of the lead time and the total operating costs of the system per period are minimized. Finally, we use the goal attainment method to obtain the optimal service rates or the control vector of the problem. (c) 2005 Elsevier B.V. All rights reserved.
multipleobjective linear fractional programming (MOLFP) is an important field of research. Using some branch and bound techniques, we have developed a new interactive method for MOLFP that drastically reduces the com...
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multipleobjective linear fractional programming (MOLFP) is an important field of research. Using some branch and bound techniques, we have developed a new interactive method for MOLFP that drastically reduces the computational effort needed, while providing guidance for the decision maker in the choice of his/her preferred solutions. The basic idea of the computation phase of the algorithm is to optimize one of the fractional objective functions while constraining the others. Several linear programming problems, organized in a tree structure, are generated as the search evolves. The whole idea is simple and it results in a fast and very intuitive approach to exploring the non-dominated set of solutions in MOLFP, and eventually to finding the preferred solution.
In this paper we apply a multiobjective optimization model of Smart Growth to land development. The term Smart Growth is meant to describe development strategies-that do not promote urban sprawl. However, the term is ...
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This paper studies the multipleobjectives shortest path problem with stochastic weights on arcs. We propose a hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm for the expected valu...
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This paper studies the multipleobjectives shortest path problem with stochastic weights on arcs. We propose a hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm for the expected values model to derive a satisfying path for the decision maker.
作者：
Sun, MHUniv Texas
Coll Business Div Management & Mkt San Antonio TX 78249 USA
A multiple objective programming approach is proposed as a new analytical toot to fit a model which is used to project faculty salaries. The projected salaries are used as a basis to allocate the available budget for ...
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A multiple objective programming approach is proposed as a new analytical toot to fit a model which is used to project faculty salaries. The projected salaries are used as a basis to allocate the available budget for faculty salary equity adjustments, The model is also used to classify all faculty members into two categories with one category consisting of those faculty members being underpaid and the other consisting of those being overpaid. The multiple objective programming approach is much more powerful than regression analysis for this purpose because budgetary and policy restrictions can be included in the model as constraints. It is also more flexible than the goal programming approach because it has desirable solution properties. Salary data from a public university are used as an example to demonstrate the use of the approach. In addition, determinants of faculty salaries and variables to be included in the model are briefly discussed. (C) 2002 Elsevier Science B.V. All rights reserved.
Efficiency evaluation in Data Envelopment Analysis (DEA) depends on different factors. The most important factors arc the values of input and output. In this paper, we present an alternative proof that, if one compone...
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Efficiency evaluation in Data Envelopment Analysis (DEA) depends on different factors. The most important factors arc the values of input and output. In this paper, we present an alternative proof that, if one component of output or input vectors of a DMU dominates the corresponding component of other DMUs whatever the value of other components of this DMU may be, then that DMU is efficient in some of the DEA models. An important outcome of such an analysis is a set of virtual multipliers or weights accorded to each factor taken into account. These sets of weights are, typically, different for each of the participating DMUs. In this paper, by means of solving only one problem, we can determine common set of weights (CSW) for all DMUs and their efficiencies. Finally, a method for ranking DMUs, is presented. In this method by solving only two problems, efficient DMUs are ranked. (c) 2004 Elsevier Inc. All rights reserved.
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