abstract the muskingum method is frequently used to route floods in hydrology. however, application of the model is still difficult because of the parameter estimation’s. recently, some of heuristic methods have been used in order to estimate the nonlinear muskingum model. this paper presents a efficient heuristic algorithm, simulated annealing, which has been used to estimate the three paramet...

in this paper, a mixed-integer linearized programming (minlp) model is presented to design a group layout (gl) of a cellular manufacturing system (cms) in a dynamic environment with considering production planning (pp) decisions. this model incorporates with an extensive coverage of important manufacturing features used in the design of cmss. there are also some features that make the presented...

this study is devoted to the formulation of the network design problem of one-way streets and the application of simulated annealing (sa) algorithm to solve this problem for a large real network. it discusses some points of views on one-way street networks, the objective function used for design, the way in which design constraints may be considered, and the traffic problems concerning one-way ...

This paper addresses the job shop scheduling problem with minimizing the number of tardy jobs as the objective. This problem is usually treated as a job sequencing problem, and the permutation-based representation of solutions was commonly used in the existing search-based approaches. In this paper, the flaw of the permutation-based representation is discussed, and a rule-centric concept is pro...

The choice of a good annealing schedule is necessary for good performance of simulated annealing for combinatorial optimization problems. In this paper, we pose the simulated annealing task decision-theoretically for the first time, allowing the user to explicitly define utilities of time and solution quality. We then demonstrate the application of reinforcement learning techniques towards appr...

We develop a quantum algorithm to solve combinatorial optimization problems through quantum simulation of a classical annealing process. Our algorithm combines techniques from quantum walks, quantum phase estimation, and quantum Zeno effect. It can be viewed as a quantum analogue of the discrete-time Markov chain Monte Carlo implementation of classical simulated annealing. Our implementation re...

There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very ...

The question of satissability for a given proposi-tional formula arises in many areas of AI. Especially nding a model for a satissable formula is very important though known to be NP-complete. There exist complete algorithms for satissability testing like the Davis-Putnam-Algorithm, but they often do not construct a satisfying assignment for the formula , are not practically applicable for more...

We propose a variant of the Simulated Annealing method for optimization in the multivariate analysis of diierentiable functions. The method uses the Hybrid Monte Carlo algorithm for the proposal of new conngurations. We show how this choice can improve the performance of simulated annealing methods by allowing much faster annealing schedules.