In this study, biobjective mixed-integer linear programming problems are considered and a search-andremove (SR) algorithm is presented. At each stage of the algorithm, Pareto outcomes are searched with the dichotomic search algorithm and these outcomes are excluded from the objective space with the help of Tabu constraints. The algorithm is also enhanced with lower and upper bounds, which are u...

We propose a life cycle optimization framework for the design of sustainable product systems and supply chains considering the concept of “functional unit” under economic and environmental criteria. This general modeling framework integrates the life cycle analysis methodology with multiobjective optimization and measures both the economic and environmental performances based on a standard quan...

We consider a general framework, where each agent has an outside option of privately known value, that encompasses object allocation, matching with contracts, provision of excludable public goods, and more. First, we show that if a strategyproof mechanism is required to (weakly) Pareto-improve an individually rational and participation-maximal benchmark mechanism, there is at most one choice. A...

With the popularity of efficient multi-objective evolutionary optimization (EMO) techniques and the need for such problem-solving activities in practice, EMO methodologies and EMO research and application have received a great deal of attention in the recent past. The first decade of research in EMO area has been spent on developing efficient algorithms for finding a well-converged and well-dis...

Abstract The key contribution of this paper is a theoretical framework to analyse humans’ decision-making strategies under uncertainty, and more specifically how human subjects manage the trade-off between information gathering (exploration) reward seeking (exploitation) in particular active learning black-box optimization task. Humans’ decisions making according these two objectives can be mod...

This paper proposes a novel Self-Adaptive algorithm for Multi-Objective Constrained Optimization by using Radial Basis Function Approximations, SAMO-COBRA. The automatically determines the best Function-fit as surrogates objectives well constraints, to find new feasible Pareto-optimal solutions. also uses hyper-parameter tuning on fly improve its local search strategy. In every iteration one so...

General economic equilibrium with incomplete markets and default represents an important tool in competitive economic theory, and has led to fundamental insights into the behavior of real and financial markets. The numerical computation of these models is important for understanding the behavior of the real world economy, and may lead to insights on the effects of regulation and welfare. Howeve...

We study Pareto optimal matchings in the context of house allocation problems. We present an O( √ nm) algorithm, based on Gale’s Top Trading Cycles Method, for finding a maximum cardinality Pareto optimal matching, where n is the number of agents and m is the total length of the preference lists. By contrast, we show that the problem of finding a minimum cardinality Pareto optimal matching is N...

Abstract In this paper, we propose an approach to explore reinsurance optimization for a non-life multi-line insurer through simulation model that combines alternative treaties. Based on the Solvency II framework, maximises both solvency ratio and portfolio performance under user-defined constraints. Data visualisation helps understanding numerical results and, together with concept of Pareto f...