A multi-objectiveoptimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggreg...
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A multi-objectiveoptimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objectiveoptimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.
We present a model for multi-objective decision analysis with respect to the location of public facilities as schools in areas near to coasts, taking risks of inundation by tsunamis into account. A mathematical progra...
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We present a model for multi-objective decision analysis with respect to the location of public facilities as schools in areas near to coasts, taking risks of inundation by tsunamis into account. A mathematical programming formulation with three objective functions is given. The first objective function is a weighted mean of a minisum and a maximum coverage criterion. The second objective function expresses risk by possible tsunami events;for quantifying this risk, a statistical model for tsunami occurrences by Kaistrenko and Pinegina is applied. The third criterion represents costs. For the solution of the multi-objectiveoptimization problem, we propose a heuristic approach based on the NSGA-II algorithm and compare it with a decomposition technique where the region under consideration is partitioned into smaller sub-regions, and the problem is solved for each separate subregion either exactly or heuristically. Both approaches are tested on two real-life instances from southern Sri Lanka.
This paper deals with the Bi-objective Set Covering Problem, which is a generalization of the well-known Set Covering Problem. The proposed approach is a two-phase heuristic method which has the particularity to be a ...
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This paper deals with the Bi-objective Set Covering Problem, which is a generalization of the well-known Set Covering Problem. The proposed approach is a two-phase heuristic method which has the particularity to be a constructive method using the primal-dual Lagrangian relaxation to solve single objective Set Covering problems. The results show that this algorithm finds several potentially supported and unsupported solutions. A comparison with an exact method (up to a medium size), shows that many Pareto-optimal solutions are retrieved and that the other solutions are well spread and close to the optimal ones. Moreover, the method developed compares favorably with the Pareto Memetic Algorithm proposed by Jaszkiewicz.
We study the problem of the optimal design of routes and frequencies in urban public transit systems, the Transit Network Design Problem (TNDP), which is modeled as a multi-objective combinatorial optimization problem...
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We study the problem of the optimal design of routes and frequencies in urban public transit systems, the Transit Network Design Problem (TNDP), which is modeled as a multi-objective combinatorial optimization problem. A new heuristic based on the GRASP metaheuristic is proposed to solve the TNDP. As a multi-objective metaheuristic, it produces in a single run a set of non-dominated solutions representing different trade-off levels between the conflicting objectives of users and operators. Previous approaches have dealt with the multi-objective nature of the problem by weighting the different objectives into a single objective function. The case proposed by Mandl is used to show that the multi-objective metaheuristic is capable of producing a diverse set of solutions, which are compared with solutions obtained by other authors. We show that the proposed algorithm produces more non-dominated solutions than the Weighted Sum Method with the same computational effort, using the case of Mandl and another real test case.
Crew rostering system is a daily grind in the management of both corporation and enterprise. A fair and reasonable rostering method plays a very important role in the arousing worker’s enthusiasm and improving the wo...
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Crew rostering system is a daily grind in the management of both corporation and enterprise. A fair and reasonable rostering method plays a very important role in the arousing worker’s enthusiasm and improving the work efficiency. This paper presents a method of building models for automatic crew rostering mode with computer and advancing the multi-objective optimum scheme. The method to build models for crew rostering system is also discussed. The question to crew rostering system model is solved by genetic algorithms and simulated annealing algorithms. Simulation results show the correctness of algorithms. The actual data of the airways have justified its reasonability and efficiency.
This paper introduces the element of beneficiaries' choice into a location-routing problem for disaster relief logistics suited for decision support systems. Decision makers in humanitarian logistics face the chal...
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This paper introduces the element of beneficiaries' choice into a location-routing problem for disaster relief logistics suited for decision support systems. Decision makers in humanitarian logistics face the challenge where to establish distribution centers (DCs) for relief goods. For this purpose, two objectives are considered: the impact of the relief operations on the beneficiaries and the efficient use of monetary resources. The proposed multi-objective location-routing model minimizes unserved demand as well as cost for opening DCs and for routing relief goods. It anticipates the choice of beneficiaries to which DC to go (if at all), based on a model adopted from the literature on competitive location analysis. A mathematical programming formulation is presented. For small instances, the Pareto front can be determined exactly using an epsilon constraint method. For solving also realistic instances, an evolutionary algorithm has been implemented and evaluated. The algorithms are tested on real-world instances from Mozambique. The results show that when designing a distribution network, improvements can be achieved by taking the predicted behavior of beneficiaries into account.
A multi-objective combinatorial optimization model is formulated for hot rolling lot planning problem in the production scheduling of iron and steel enterprises and a new modified multi-objective genetic local search ...
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ISBN:
(纸本)1424403316
A multi-objective combinatorial optimization model is formulated for hot rolling lot planning problem in the production scheduling of iron and steel enterprises and a new modified multi-objective genetic local search algorithm is designed to solve the model. The model can solve the problem more precisely than previous methods. The algorithm can provide the schedulers with more than one solution in order to help schedulers make further decisions. Simulation experiment using production data shows that the model and algorithm are effective.
This paper considers the p-median problem that consists in finding p-locals from a set of m candidate locals to install facilities minimizing simultaneously two functions: the sum of the distances from each customer t...
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ISBN:
(纸本)9783642169519
This paper considers the p-median problem that consists in finding p-locals from a set of m candidate locals to install facilities minimizing simultaneously two functions: the sum of the distances from each customer to its nearest facility and the sum of costs for opening facilities. Since this is a NP-Hard problem, heuristic algorithms are the most suitable for solving such a problem. To determine nondominated solutions, we propose a multi-objective genetic algorithm (MOGA) based on a nondominated sorting approach. The algorithm uses an efficient elitism strategy and an intensification operator based on the Path Relinking technique. To test the performance of the proposed MOGA, we develop a Mathematical Programming Algorithm, called epsilon-Constraint, that finds Pareto-optimal solutions by solving iteratively the mathematical model of the problem with additional constraints. The results show that the proposed approach is able to generate good approximations to the nondominated frontier of the bi-objective problem efficiently.
Various multicriteria sorting methods have been proposed in the literature to assign the feasible alternatives into predefined categories. We consider here problems involving a set of totally ordered categories repres...
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Various multicriteria sorting methods have been proposed in the literature to assign the feasible alternatives into predefined categories. We consider here problems involving a set of totally ordered categories representing different achievement levels in the satisfaction of criteria. As in many existing methods, the assignment rule of an alternative to a category is based on the comparison of its performance vector to reference profiles defining lower bounds of the categories. Within this standard setting we address a new problem that consists in finding how to modify a given solution, within a combinatorial set of alternatives, to upgrade it in the upper category (or higher) at minimum cost. We also consider the problem of identifying the sequence of solutions that minimize the total cost while satisfying some budget constraint at every step, and the problem of determining how to modify the current solution to save money while staying in the same category. We first propose a general approach based on mixed integer (linear or quadratic) programming to solve these problems. Then, we implement this approach on various multiobjectivecombinatorial problems, such as multi-agent assignment problems and multiobjective knapsack problems. Numerical tests are provided to establish the feasibility of the approach on instances of different sizes.
The well-known NP-hard traveling salesman problem (TSP) primarily considers distance as its single objective. However, applications modeled from real world systems repeatedly involve more than one objective giving ris...
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ISBN:
(纸本)9781538678756
The well-known NP-hard traveling salesman problem (TSP) primarily considers distance as its single objective. However, applications modeled from real world systems repeatedly involve more than one objective giving rise to multi-objectiveoptimization. Fusing ideas of dimension reduction, decomposition approaches, and genetic algorithms, this paper presents a multi-objective minimum matrix search algorithm (MOMMS) for the heuristic resolution of the bi-objective TSP (bTSP). The MOMMS uses dimension reduction to obtain a reduce matrix network that is used to obtain or to approximate the set of efficient solutions. The reduce matrix network aids in the decomposition of a multi-objective combinatorial optimization (MOCO) problem into a single objectivecombinatorialoptimization problem. Moreover, using the reduce matrix network MOMMS introduces a population generator that creates an initial population composed of an approximation to the extreme supported efficient solutions. The MOMMS does not use any numerical parameter. Also, MOMMS uses family competitive metamorphosis and short-term memory selection to maintain population diversity in MOCO problems. The proposed algorithm showed respectable results in testing on well-known benchmark problems of the bTSP. Comparisons are performed with the results of state-of-the-art algorithms from the literature. Moreover, the MOMMS is tested on large-scale instances of the bTSP. The computational study shows that the proposed algorithm is able to solve large-scale instances in reasonable time. Therefore, the MOMMS is a competitive tool for solving the bTSP.
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