This paper considers the problem of computing a pool of near-optimal solutions to a given optimization problem while maximizing their total pairwise distance, and focuses on Mixed-Integer Linear Programming problems where a subset of variables are binary and consider the Hamming distance over these variables.
ABSTRACT This paper addresses the Winner Determination Problem (WDP) within a multiobjective framework, extending the traditional approach that focuses solely on revenue maximization. We develop a mathematical model that simultaneously integrates four strategic objectives: maximizing the number of accepted bids, total...
Ali Zaidi, M. Azi, Lamine Idir et al.· Pesquisa Operacional· 0 citations
The results show that the heuristic closes the optimality gap for a significant portion of the considered instances and often practically outperforms both heuristic and exact benchmark approaches.
It is shown that a class of binary optimization problems with complex non-quadratic objectives or constraints can be reformulated as multi-objective quadratic unconstrained binary optimization problems and evaluated classically on Pareto-optimal candidates rather than encoded as penalties.
Andres D. Ruiz, Soumyadip Ghosh, S. Woerner· 1 citation
Multi-objective optimization (MOO) problems are common in logistics, where routing decisions must balance conflicting objectives such as travel distance, delivery time, and operational risk. A recently proposed Quantum Approximate Optimization Algorithm (QAOA) parameter-transfer strategy solves multi-objective MAX-CUT...
E. Lussi, Alisson dos Passos Fumaco, Marcos Vinicius Reballo et al.· 0 citations
The algorithm is parameterized so as to address various stochastic formulations spanning from Expectation-focused to Value-at-Risk (VaR) as well as Conditional-Value-at-Risk (CVaR) as well as Conditional-Value-at-Risk (CVaR)-focused formulations.
M. Alamir· 0 citations
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