Capabilities of enhanced simulated-annealing-based algorithms in solving process planning problems in reconfigurable manufacturing are investigated. The algorithms are enhanced by combining variants of the simulated annealing technique with other algorithm concepts such as (i) knowledge exploitation and (ii) parallelism. Four configurations of simulated annealing algorithms are devised and enga...

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...

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...

scheduling for job shop is very important in both fields of production management and combinatorial op-timization. however, it is quite difficult to achieve an optimal solution to this problem with traditional opti-mization approaches owing to the high computational complexity. the combination of several optimization criteria induces additional complexity and new problems. in this paper, we pro...

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 ...

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...