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Intensifying the fumigation policy to minimize the risk of mosquito-borne diseases implementing a stochastic optimization approach

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CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2024年 197卷

作者： Alfaro-Bernardino, Angel Diaz-Trujillo, Luis A. Cerda-Flores, Sandra C. Ponce-Ortega, Jose M. Napoles-Rivera, Fabricio Univ Michoacana Chem Engn Dept Ave Francisco J MugSNBldg V1 Morelia 58060 Michoacan Mexico Univ Intercultural Indigena Michoacan Acad Program Masters Engn Sustainable Energy Finca Tzipekua S-N Carretera Patzcuaro Huecorio Km Patzcuaro 61614 Michoacan Mexico

This paper presents a stochastic mathematical model for the planning, dosage, and strategic scheduling of fumigation policies to reduce mosquito-borne diseases in a geographic location. multiple scenarios were generated to account for uncertainty in the existing mosquito population based on a random normal distribution. In addition, the model includes occupational health, considering the permissible limits of each insecticide applied and their residual effect. This stochastic mathematical model applies a Pareto solution method, using a Conditional Value at Risk approach to select solutions that trade off the different objective functions. Results show that the optimal solutions found by the model provide a compromise between the expected infected people and the total cost of applying the insecticides. This methodology improves the current practice by an intensified approach that considers optimal scheduling alternatives that minimize cost while considering the permissible concentration limits to guarantee the health and comfort of the population to minimize the possible incidences of infections.

关键词： Intensifying fumigation policy multiple objective programming Stochastic mathematical model Mosquito population Fumigation strategy

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Biobjective robust optimization over the efficient set for Pareto set reduction

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第2期252卷 573-586页

作者： Jornada, Daniel Leon, V. Jorge Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA Texas A&M Univ Dept Engn Technol & Ind Distribut College Stn TX 77843 USA

This paper presents a biobjective robust optimization formulation for identifying robust solutions from a given Pareto set. The objectives consider both solution and model robustness when the exact values of the selected solution are affected by uncertainty. The problem is formulated equivalently as a model with uncertainty on the constraint parameters and objective function coefficients. Structural properties and a solution algorithm are developed for the case of multiobjective linear programs. The algorithm is based on facial decomposition;each subproblem is a biobjective linear program and is related to an efficient face of the multiobjective program. The resulting Pareto set reduction methodology allows the handling of continuous and discrete Pareto sets, and can be generalized to consider criteria other than robustness. The use of secondary criteria to further break ties among the many efficient solutions provides opportunities for additional trade-off analysis in the space of the secondary criteria. Examples illustrate the algorithm and characteristics of solutions obtained. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multiple criteria analysis multiple objective programming Robustness and sensitivity analysis Pareto set reduction

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Bi-objective robust optimisation

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第2期252卷 418-431页

作者： Kuhn, K. Raith, A. Schmidt, M. Schoebel, A. RAND Corp Santa Monica CA 90406 USA Univ Auckland Dept Engn Sci Auckland New Zealand Erasmus Univ Dept Technol & Operat Management Rotterdam Netherlands Univ Gottingen Inst Numer & Appl Math D-37073 Gottingen Germany

It is important, in practice, to find robust solutions to optimisation problems. This issue has been the subject of extensive research focusing on single-objective problems. Recently, researchers also acknowledged the need to find robust solutions to multi-objective problems and presented some first results on this topic. In this paper, we deal with bi-objective optimisation problems in which only one objective function is uncertain. The contribution of our paper is three-fold. Firstly, we introduce and analyse four different robustness concepts for bi-objective optimisation problems with one uncertain objective function, and we propose an approach for defining a meaningful robust Pareto front for these types of problems. Secondly, we develop an algorithm for computing robust solutions with respect to these four concepts for the case of discrete optimisation problems. This algorithm works for finite and for polyhedral uncertainty sets using a transformation to a multi-objective (deterministic) optimisation problem and the recently published concept of Pareto robust optimal solutions (lancu & Trichakis, 2014). Finally, we apply our algorithm to two real-world examples, namely aircraft route guidance and the shipping of hazardous materials, illustrating the four robustness concepts and their solutions in practical applications. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Robust optimisation OR in transportation

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Local search and suffix tree for car-sequencing problem with colors

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2008年第3期191卷 972-980页

作者： Gavranovic, Haris Univ Sarajevo Fac Nat Sci Dept Math Sarajevo 71000 Bosnia & Herceg

This paper describes an approximation solution method for the car sequencing problem with colors. Firstly, we study the optimality of problems with a single ratio constraint. This study also introduces a data structure for efficient calculation of the penalties related to ratio constraints. We describe the constructive greedy algorithm and variable neighborhood search adjusted for the problem with colors. Tabu metaheuristic is used to improve the results obtained by VNS. We then represent the cars with their constraints as letters over an alphabet and apply the algorithm to spell the motifs in order to improve the number of batch colors without decreasing the costs associated to the set of ratio constraints. The algorithm achieves 19 out of the 64 best results for instance sets A and B. These instances are the reference instances for Challenge ROADEF. (C) 2007 Elsevier B.V. All rights reserved.

关键词： multiple objective programming car-sequencing problem heuristic tabu search suffix tree

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A genetic algorithm approach for the time-cost trade-off in PERT networks

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APPLIED MATHEMATICS AND COMPUTATION 2005年第2期168卷 1317-1339页

作者： Azaron, A Perkgoz, C Sakawa, M Hiroshima Univ Dept Artificial Complex Syst Engn Grad Sch Engn Higashihiroshima Hiroshima 7398527 Japan

We develop a multi-objective model for the time-cost trade-off problem in PERT networks with generalized Erlang distributions of activity durations, using a genetic algorithm. The mean duration of each activity is assumed to be a non-increasing function and the direct cost of each activity is assumed to be 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 project direct cost (to be minimized), the mean of the project completion time (min), the variance of the project completion time (min), and the probability that the project completion time does not exceed a certain threshold (max). It is impossible to solve this problem optimally. Therefore, we apply a "Genetic Algorithm for Numerical Optimizations of Constrained Problems" (GENOCOP) to solve this multi-objective problem using a goal attainment technique. Several factorial experiments are performed to identify appropriate genetic algorithm parameters that produce the best results within a given execution time in the three typical cases with different configurations. Finally, we compare the genetic algorithm results against the results of a discrete-time approximation method for solving the original optimal control problem. (c) 2004 Elsevier Inc. All rights reserved.

关键词： project management and scheduling genetic algorithm multiple objective programming optimal control design of experiments

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Partitioning multiple objective optimal solutions with applications in radiotherapy design

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OPTIMIZATION AND ENGINEERING 2006年第4期7卷 501-526页

作者： Holder, Allen Trinity Univ San Antonio TX 78212 USA

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 multiple objectives 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.

关键词： optimal partition multiple objective programming interior point algorithms radiation oncology

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Multitime multiobjective variational problems and vector variational-like inequalities

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第3期254卷 739-745页

作者： Jayswal, Anurag Singh, Shipra Kurdi, Alia Indian Sch Mines Dept Appl Math Dhanbad 826004 Jharkhand India Polytech Univ Fac Sci Appl Splaiul Independentei 313 Bucharest 060042 Romania

In this paper, we introduce vector variational-like inequality with its weak formulation for multitime multiobjective variational problem. Moreover, we establish the relationships between the solutions of introduced inequalities and (properly) efficient solutions of multitime multiobjective variational problem, involving the invexities of multitime functionals. Some examples are provided to illustrate our results. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Multitime multiobjective variational problem Vector variational-like inequalities Invex functionals Properly efficient solutions

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objectives and methods in multi-objective routing problems: a survey and classification scheme

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期290卷 1-25页

作者： Zajac, Sandra Huber, Sandra Ruhr Univ Bochum Univ Str 150 D-44801 Bochum Germany Helmut Schmidt Univ Holstenhofweg 85 D-22043 Hamburg Germany

Transportation of goods and passengers plays an important role for companies and individuals in today's globalized world. There exists a variety of road-based routing problems at all three strategic, tactical and operational planning levels. Examples include location routing problems, fleet management, vehicle routing and shortest path problems. To evaluate solution quality, multiple, usually conflicting objectives are considered. Aiming to provide practice-oriented decision support, multi-objective routing problems attract more and more attention in academic literature. This contribution offers a wide overview of which application-oriented multi-objective routing problems are treated and what kind of trade-off is investigated. Furthermore, the algorithmic approach is analyzed with regard to its fitness assignment strategy, i.e. how the multiple objectives are handled, and its search strategy to solve the problem. In order to structure the literature, we propose a classification in which every identified objective is sorted into a category and related to problem elements. Both problem-specific and general research gaps in multi-objective routing problems are identified and offer a starting point for future research. Lastly, fitness assignment strategies are extensively discussed and insights regarding their usage are given. (C) 2020 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Literature review Logistics Transportation Routing

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A hybrid immune multiobjective optimization algorithm

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第2期204卷 294-302页

作者： Chen, Jianyong Lin, Qiuzhen Ji, Zhen Shenzhen Univ Dept Comp Sci & Technol Shenzhen 518060 Peoples R China

In this paper, we develop a hybrid immune multiobjective optimization algorithm (HIMO) based on clonal selection principle. In HIMO, a hybrid mutation operator is proposed with the combination of Gaussian and polynomial mutations (GP-HM operator). The GP-HM operator adopts an adaptive switching parameter to control the mutation process, which uses relative large steps in high probability for boundary individuals and less-crowded individuals. With the generation running, the probability to perform relative large steps is reduced gradually. By this means, the exploratory capabilities are enhanced by keeping a desirable balance between global search and local search, so as to accelerate the convergence speed to the true Pareto-optimal front in the global space with many local Pareto-optimal fronts. When comparing HIMO with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that HIMO performs better evidently. (C) 2009 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Artificial immune systems Clonal selection principle Hybrid mutation Artificial intelligence

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Multiobjective programming methods in the reserve selection problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2003年第3期150卷 640-652页

作者： Memtsas, DP Univ Aegean Dept Environm Studies Biodivers Conservat Lab GR-81100 Mitilini Lesbos Greece

Many ecological criteria have been proposed to assign conservation values to nature reserves in the reserve selection problem. Multiobjective programming is used to identify the best compromise solution among a set of alternative solutions that have been obtained from single objective linear programming methods based upon different criteria. Endemic plant species from the island of Crete in Greece are used as a model and a number of cells, as they have been implemented by ARC/INFO, are selected based on four criteria: (1) species richness, (2) species rarity, (3) cell richness, (4) cell rarity. Best compromise solution is identified by (i) a simple multiattribute rating technique, (ii) geometrical methods based on four distance metrics. The two methods are compared and the degree to which they fulfil the four criteria is examined. (C) 2002 Elsevier B.V. All rights reserved.

关键词： multiple objective programming multiple criteria analysis reserve selection

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