‎this paper proposes a multi-supplier multi-product inventory model in which the suppliers have unlimited production capacity, allow delayed payment, and offer either an all-unit or incremental discount. the retailer can delay payment until after they have sold all the units of the purchased product. the retailer’s warehouse is limited, but the surplus can be stored in a rented warehouse at a...

Population annealing Monte Carlo is an efficient sequential algorithm for simulating k-local Boolean Hamiltonians. Because of its structure, the algorithm is inherently parallel and therefore well-suited for large-scale simulations of computationally hard problems. Here we present various ways of optimizing population annealing Monte Carlo using 2-local spin-glass Hamiltonians as a case study. ...

Assigning cells to switches in cellular mobile networks is an NP-hard problem which, for realsize mobile networks, could not be solved by using exact methods. In this context, heuristic approaches like genetic algorithms, tabu search and simulated annealing can be used. This paper proposes a comparative study of three heuristics—tabu search, simulated annealing and Parallel Genetic Algorithms w...

Simulated Annealing is a family of randomized algorithms used to solve many combinatorial optimization problems. In practice they have been applied to solve some presumably hard (e.g., NP-complete) problems. The level of performance obtained has been promising [5, 2, 6, 14]. The success of this heuristic technique has motivated analysis of this algorithm from a theoretical point of view. In par...

We propose an e cient MFA formulation for mapping un structured domains to hypercube connected distributed memory archi tectures In the general MFA formulation N P spin variables are main tained and an individual MFA iteration requires davgP P time for the mapping of a sparse domain graph withN vertices and average vertex degree of davg to a parallel architecture with P processors The proposed ...

We study errors of quantum annealing and simulated annealing to highlight better performance of quantum annealing over simulated annealing. Quantum annealing and simulated annealing perform optimization through a quantum adiabatic evolution and a quasi-static evolution respectively. In both methods, dynamics across a phase transition plays a crucial role. The Kibble-Zurek mechanism is known as ...

the markowitz issue of optimization can’t be solved by precise mathematical methods such as second order schematization, when real world condition and limitations are considered. on the other hand, most managers prefer to manage a small portfolio of available assets in place of a huge portfolio. it can be analogized to cardinal constrains, that is, constrains related to minimum and maximum curr...

As multiobjective optimization problems have many solutions, evolutionary algorithms have been widely used for complex multiobjective problems instead of simulated annealing. However, simulated annealing also has favorable characteristics in the multimodal search. We developed several simulated annealing schemes for the multiobjective optimization based on this fact. Simulated annealing and evo...