A goal geometric programming problem (G 2 P 2) with logarithmic deviational variables and its applications on two industrial problems

A very useful multi-objective technique is goal programming. There are many methodologies of goal programming such as weighted goal programming, min-max goal programming, and lexicographic goal programming. In this paper, weighted goal programming is reformulated as goal programming with logarithmic deviation variables. Here, a comparison of the proposed method and goal programming with weighted sum method is presented. A numerical example and applications on two industrial problems have also enriched this paper

Swarm lexicographic goal programming for fuzzy open shop scheduling

Debido a restricciones de copyright, la versión del artículo que se envía es la versión de autoarchivo de los autores, y no su versión impresa.In this work we consider a multiobjective open shop scheduling problem with uncertain processing times and flexible due dates, both modelled using fuzzy sets. We adopt a goal programming model based on lexicographic multiobjective optimisation of both makespan and due-date satisfaction and propose a particle swarm algorithm to solve the resulting problem.We present experimental results which show that this multiobjective approach achieves as good results as single-objective algorithms for the objective with the highest priority, while greatly improving in the second objective.Gobierno de España(FEDER TIN2010-20976-C02-02 y MTM2010-16051) Principado de Asturias (Beca Severo Ochoa BP13106

Boolean lexicographic optimization: algorithms & applications

Multi-Objective Combinatorial Optimization (MOCO) problems find a wide range of practical application problems, some of which involving Boolean variables and constraints. This paper develops and evaluates algorithms for solving MOCO problems, defined on Boolean domains, and where the optimality criterion is lexicographic. The proposed algorithms build on existing algorithms for either Maximum Satisfiability (MaxSAT), Pseudo-Boolean Optimization (PBO), or Integer Linear Programming (ILP). Experimental results, obtained on problem instances from haplotyping with pedigrees and software package dependencies, show that the proposed algorithms can provide significant performance gains over state of the art MaxSAT, PBO and ILP algorithms. Finally, the paper also shows that lexicographic optimization conditions are observed in the majority of the problem instances from the MaxSAT evaluations, motivating the development of dedicated algorithms that can exploit lexicographic optimization conditions in general MaxSAT problem instances.This work was partially funded by SFI PI Grant 09/IN.1/I2618, EU grants FP7-ICT-217069 and FP7-ICT-214898, FCT grant ATTEST (CMU-PT/ELE/0009/2009), FCT PhD grant SFRH/BD/ 28599/2006, CICYT Projects TIN2009-14704-C03-01 and TIN2010-20967-C04-03, and by INESC-ID multiannual funding from the PIDDAC program funds

Swarm lexicographic goal programming for fuzzy open shop scheduling

In this work we consider a multiobjective open shop scheduling problem with uncertain processing times and flexible due dates, both modelled using fuzzy sets. We adopt a goal programming model based on lexicographic multiobjective optimisation of both makespan and due-date satisfaction and propose a particle swarm algorithm to solve the resulting problem. We present experimental results which show that this multiobjective approach achieves as good results as single-objective algorithms for the objective with the highest priority, while greatly improving on the second objectiv

Preference programming and inconsistent interval matrices

The problem of derivation of the weights of altematives from pairwise comparison matrices is long standing. In this paper,Lexicographic Goal Programming (LGP) has been used to find out weights from pairwise inconsistent interval judgment matrices. A number of properties and advantages of LGP as a weight determination technique have been explored. An algorithm for identification and modification of inconsistent bounds is also provided. The proposed technique has been illustrated by means of numerical examples.Analytic hierarchy process; Interval judgment; Preferente programming

Continuous multi-criteria methods for crop and soil conservation planning on La Colacha (Río Cuarto, Province of Cordoba, Argentina)

Agro-areas of Arroyos Menores (La Colacha) west and south of Rand south of R?o Cuarto (Prov. of Cordoba, Argentina) basins are very fertile but have high soil loses. Extreme rain events, inundations and other severe erosions forming gullies demand urgently actions in this area to avoid soil degradation and erosion supporting good levels of agro production. The authors first improved hydrologic data on La Colacha, evaluated the systems of soil uses and actions that could be recommended considering the relevant aspects of the study area and applied decision support systems (DSS) with mathematic tools for planning of defences and uses of soils in these areas. These were conducted here using multi-criteria models, in multi-criteria decision making (MCDM); first of discrete MCDM to chose among global types of use of soils, and then of continuous MCDM to evaluate and optimize combined actions, including repartition of soil use and the necessary levels of works for soil conservation and for hydraulic management to conserve against erosion these basins. Relatively global solutions for La Colacha area have been defined and were optimised by Linear Programming in Goal Programming forms that are presented as Weighted or Lexicographic Goal Programming and as Compromise Programming. The decision methods used are described, indicating algorithms used, and examples for some representative scenarios on La Colacha area are given

Assessing partnership alternatives in an IT network employing analytical methods

One of the main critical success factors for the companies is their ability to build and maintain an effective collaborative network. This is more critical in the IT industry where the development of sustainable competitive advantage requires an integration of various resources, platforms, and capabilities provided by various actors. Employing such a collaborative network will dramatically change the operations management and promote flexibility and agility. Despite its importance, there is a lack of an analytical tool on collaborative network building process. In this paper, we propose an optimization model employing AHP and multiobjective programming for collaborative network building process based on two interorganizational relationships’ theories, namely, (i) transaction cost theory and (ii) resource-based view, which are representative of short-term and long-term considerations. The five different methods were employed to solve the formulation and their performances were compared. The model is implemented in an IT company who was in process of developing a large-scale enterprise resource planning (ERP) system. The results show that the collaborative network formed through this selection process was more efficient in terms of cost, time, and development speed. The framework offers novel theoretical underpinning and analytical solutions and can be used as an effective tool in selecting network alternatives