Optimal Layout Design for Agricultural Facility Using Simulated Annealing

A new concept of adjacency for concurrent consideration of economic and safety aspects in design of facility layout problems

This article proposes a general fixture layout design framework that directly integrates the system equation with the convex relaxation method. Note that the optimal fixture design problem is a large-scale combinatorial optimization problem; we relax it to a convex Semi-Definite Programming (SDP) problem by adopting sparse learning and SDP relaxation techniques. It can be solved efficiently by existing convex optimization algorithms and thus generates a near-optimal fixture layout. A real case study in the half-to-half fuselage assembly process indicates the superiority of our proposed algorithm compared to the current industry practice and state-of-art methods.

An MILP model for safe multi-floor process plant layout using the domino hazard index

Cutter head is one of the most important components for the full-face rock tunnel boring machine (TBM), in which the layout design of the disc cutters is a key issue. Considering the complex engineering technical requirements, this paper studied an ant colony optimization (ACO) algorithm to solve the disc cutters plane layout problem. The searching space of the design variables (disc cutters’ position) were dispersed by setting several different search steps, then an ACO algorithm was adopted to search the best searching step of each design variable dynamically during the whole optimization process. Finally, the disc cutters layout design instance was solved by the ACO method to demonstrate its feasibility and effectiveness. The computational results showed that that ACO method can provide various disc cutters layout schemes for engineers choosing from.KeywordsFull-face tunnel boring machineDisc cutterslayoutAnt Colony Optimization

An evolutionary optimal green layout design for a production facility by simulated annealing algorithm

Facility layout design problem considers the departments’ physcial layout design with area requirements in some restrictions such as material handling costs, remoteness and distance requests. Briefly, facility layout problem related to optimization of the layout costs and working conditions. This paper proposes a new multi objective simulated annealing algorithm for solving of the unequal area in layout design. Using of the different objective weights are generated with entropy approach and used in the alternative layout design. Multi objective function takes into the objective function and constraints. The suggested heuristic algorithm used the multi-objective parameters for initialization. Then prefered the entropy approach determines the weight of the objective functions. After the suggested improved simulated annealing approach applied to whole developed model. A multi-objective simulated annealing algorithm is implemented to increase the diversity and reduce the chance of getting layout conditions in local optima.

Parallel implementations of metaheuristics appear quite naturally as an effective alternative to speed up the search for approximate solutions of combinatorial optimization problems. They not only allow solving larger problems or finding improved solutions with respect to their sequential counterparts, but also lead to more robust algorithms. We review some trends in parallel computing and report recent results about linear speedups that can be obtained with parallel implementations using multiple independent processors. Parallel implementations of tabu search, GRASP, genetic algorithms, simulated annealing, and ant colonies are reviewed and discussed to illustrate the main strategies used in the parallelization of different metaheuristics and their hybrids.

This paper deals with the topic of optimal wind farm layout design on complex terrains. The paper proposes optimization model that reflects the actual requirements of realistic wind farms. Fuzzy based computational efficiency model is proposed. Computational efficiency model takes account of environmental impact of a wind farm, land costs, costs of auxiliary roads, installation and maintenance costs, costs of electrical infrastructure, etc. Fuzzy logic deals with uncertainties that can occur while quantifying computational efficiency. Using genetic algorithm, the proposed model has been tested on a realistic wind farm terrain in Serbia.

The optimal logical layout design for a type of machining transfer lines is addressed. Such transfer lines are made of many machine-tools (workstations) located in sequence. On each workstation there are several spindle-heads. A spindle-head does not execute a single operation but a block of machining operations; all operations of a block are executed simultaneously (in parallel). Spindle-heads of the same workstation are activated sequentially in a fixed order. The transfer line design problem considered in the paper consists of finding the best partition in blocks and workstations of the set of all operations to be executed on the line. The objective is to minimize the number of spindle-heads and workstations (i.e. transfer line investment cost). An optimal decision must satisfy a desired productivity rate (cycle time) as well as precedence and compatibility constraints for machining operations. A heuristic algorithm is proposed: it is based on the COMSOAL technique and a backtracking approach. Results from computer testing are reported.

We suggest solution method for optimization problems using correlated noises. The correlated noises are introduced to neural networks to discuss mechanism of synfire chain. Kawamura and Okada have introduced correlated noises to associative memory models and have analyzed those dynamics. In the associative memory models, memory patterns are memorized as attractors in the minimum of the system. They found the correlated noise can make the state transit between the attractors. However, the mechanism of the state transition has not been known enough yet. One the other hand, for combinational optimization problems, the energy function of a problem can be defined. Therefore, finding a optimum solution is finding a minima of the energy function. The steepest descent method searches one of the solutions by going down along the gradient direction. By this method, however, the state is usually trapped in a local minimum of the system. In order to escape from the local minimum, the simulated annealing, i.e. Metropolis method, or chaotic disturbance is introduced. These methods can be represented by adding thermal noises or chaotic inputs to the dynamic equation. In this paper, we show that correlated noises introduced to neural networks can be applied to solve the optimization problems. We solve the TSP that is a typical combinational optimization problem of NP-hard, and evaluated solutions obtained by using the steepest descent method, the simulated annealing and the proposed method with the correlated noises. As results, in the case of ten cities, the proposed method with correlated noises can obtain more optimum solutions than the steepest descent method and the simulated annealing. In the cases of large numbers of cites, where it is hard to find one of the optimum solutions, our method can obtain solutions at least as same level as the simulated annealing.

Despite more than a decade of research, the magnitude of wastewater leakage from defective sewer systems into groundwater supplies is still largely unknown, partly because reliable measurement methods are lacking. Although recently suggested in‐sewer tracer studies present a promising solution, it is unclear how to optimally design such studies in network settings. In this study we present a formal experimental design procedure, which uses Bayesian data analysis to improve the diagnosis of sewer leakage by combining tracer test data with prior knowledge on network topology and condition. From a simulation study, we show that (1) if a single sewer section is expected to have high leakage, that section should be distinguished in measurement layouts through isolated tests or appropriate overlapping of multiple tests; (2) if multiple sections are expected to have high leakage, layouts with tests that cover more than one high‐leakage section should be avoided; and (3) if a robust experimental design is desired, a balanced layout of tests that overlap multiple sections of high leakage, yet minimizes stretch length, should be chosen. This design will have the additional benefit of inducing covariance in the posterior distribution of exfiltration estimates, which can be used to advantage in subsequent studies. We apply these guidelines to a case study of a catchment in Zurich, Switzerland, and find that optimal layout design can improve the anticipated gain of information substantially relative to designs based on practical considerations alone. Remaining concerns regarding the procedure include (1) the generally poor understanding of the mechanisms governing sewer leakage, which can hamper reliable prior information on exfiltration; (2) the currently low measurement precision of sewer tracer studies, which might only allow for the detection of large leaks; and (3) the need for numerical implementation of the Bayesian inference procedure, which requires careful tuning and long computation times. In general, we were able to demonstrate that the incorporation of prior information through a Bayesian procedure adds significant value to experimental design, especially in situations with few “hard” data but good site‐specific knowledge, which is common in water resources research.

The usefulness of a special kind of approximability-preserving transformations (called continuous reductions) among combinatorial optimization problems is demonstrated. One common measure for the approximability of an optimization problem is its best performance ratio. This parameter attains the same value for two problems (up to a bounded factor) whenever they are mutually related by continuous reductions. Therefore, lower and upper bounds or gap-theorems valid for a particular problem are transferred along reduction chains. In this paper, continuous reductions are used for the analysis of several basic combinatorial problems including graph coloring, consistent deterministic finite automaton, covering by cliques, covering by complete bipartite subgraphs, independent set, set packing, and others. The results obtained and the methods involved are a contribution towards a systematic classification of NP-complete problems with regard to their approximability.

Dynamic positron emission tomography (PET) is a powerful tool of measuring biological activities in vivo. Due to the inherent high noise level, there are always concerns about how to increase the signal-to-noise ratio. One possible approach is to optimize the experimental design. In this paper, we propose a discretized representation of the experimental design and transform it to a combinatorial problem. This combinatorial optimization problem then can be solved using simulated annealing with component-wise Metropolis Monte Carlo simulation. We showed that using this novel approach one can design an optimal input function as well as an optimal sampling schedule efficiently. Our results show that the current dynamic scanning of approximately 20 frames does not give us much more information than an optimized four-frame schedule, and needlessly increases storage requirements. This is consistent with the conclusion given by Li et al. (1996). We also reproduced the optimal sampling schedule for the fluorodeoxy-glucose (FDG) study proposed. Moreover, we show that the single bolus injection is almost optimal in the sense of D-optimal design, as well as many other measures.

Combinatorial optimizations are widely adopted in scientific and engineering applications, such as VLSI design, automated machine learning (AutoML), and compiler design. Combinatorial optimization problems are notoriously challenging to exactly solve due to the NP-hardness. Scientists have long discovered that numerically simulating classical nonlinear Hamiltonian systems can effectively solve many well-known combinatorial optimization problems. However, such physical simulation typically requires a massive amount of computation, which even outstrips the logic capability of modern reconfigurable digital fabrics. In this work, we proposed an FPGA-based general combinatorial optimization problem solver which achieved ultra-high performance and scalability. Specifically, we first reformulated a broad range of combinatorial optimization problems with a general graph-based data structure called the Ising model. Second, instead of utilizing classical simulated annealing to find an approximate solution, we utilized a new heuristic algorithm, simulated bifurcation, to search for solutions. Third, we designed an efficient hardware architecture to fully exploit FPGAs' potentials to accelerate the algorithm, and proposed three hardware-software co-optimizations to further improve the performance. By experimenting on benchmarks, our proposal outperformed the state-of-the-art simulated annealing optimization solver by up to 10.91 times.

Finite-element approximation of 2D elliptic optimal design