International Transactions in Operational Research最新文献

In this work, new mixed integer nonlinear optimization models are proposed for two clustering problems: the unitary weighted Weber problem and the minimum sum of squares clustering. The proposed formulations are convex quadratic models with linear and second-order cone constraints that can be efficiently solved by interior point algorithms. Their continuous relaxation is convex and differentiable. The numerical experiments show the proposed models are more efficient than some classical models for these problems known in the literature.

QUBO (quadratic unconstrained binary optimization) has become the modeling language for quantum annealing and quantum-inspired annealing solvers. We present different approaches in QUBO for the magic square problem and the quadratic assignment problem (QAP), which can be modeled by linear equations and a permutation constraint over integer variables. Different ways of encoding integers by Booleans in QUBO amount to models, the implementation of which could have very different performance. Experiments performed on the Fixstars Amplify Annealer Engine, a quantum-inspired annealing solver, show that, compared to the classical one-hot encoding, using unary encoding for integers performs slightly better for the QAP and much better for magic square.

The $�p$-median problem has been widely studied in the literature. However, there are several variants that include new constraints to the classical problem that make it more realistic. In this work, we study the variant that considers interconnected facilities, that is, the distances between each pair of facilities are less than or equal to a certain threshold $�r$. This optimization problem, usually known as the median location problem with interconnected facilities, consists of locating a set of interconnected facilities to minimize the distance between those interconnected facilities and the demand points. The large variety of real-world applications that fit into this model makes it attractive to design an algorithm able to solve the problem efficiently. To this end, a procedure based on the variable neighborhood search methodology is designed and implemented by using problem-dependent neighborhoods. Experimental results show that our proposal is able to reach most of the optimal solutions when they are known. Additionally, it outperforms previous state-of-the-art methods in those instances where the optima are unknown. These results are further confirmed by conducting nonparametrical statistical tests.

For several reasons, recent attention has focused on urban agriculture in the context of sustainable and smart agriculture. Most of the global population has relocated from rural to urban areas. The ecological impact of agriculture is a rising concern. Furthermore, food insecurity, particularly food availability, remains a significant issue. Satisfying rising food demand with minimal environmental impact is a significant barrier to more sustainable food production. In this article, we study a sustainable location for urban farming that optimally balances economic and environmental objectives. We formulate the problem as a multi-objective linear program that considers maximizing the ecological benefit and crop production yield and minimizing transportation cost, sensor cost, and CO₂ emissions. The proposed mathematical model is then solved using a two-phase method. The first phase uses several multi-objective optimization (MOO) methods (weighted sum, epsilon-constraint, augmented epsilon) to generate a pool of compromise solutions. The second phase uses multi-criteria decision-making (MCDM) methods (MARCOS, VIKOR, and Possibility Degree) to rank compromise solutions. We performed a sensitivity analysis first by studying the effect of different criteria weights (balanced, environmentally, and economically oriented) and then by investigating the rank reversal. Furthermore, we developed a thorough validity test that examines the impact of the dynamic elements of rank reversal by changing the importance of the model's input parameters. To provide a fusion ranking of the MCDM rankings, we devised an aggregated ranking approach using the half-quadratic (HQ) method.