Thin-film designs by simulated annealing

With the increasing power of computers, new methods in synthesis of optical multilayer systems have appeared. Among these, the simulated-annealing algorithm has proved its efficiency in several fields of physics. We propose to show its performances in the field of optical multilayer systems through different filter designs.     

Bandwidth reduction by simulated annealing

The method of Simulated Annealing (SA) is investigated in the concrete problem of bandwidth reductio. We develop a very efficient way to compute the transitions, and this allows long annealing sessions (Monte Carlo runs) in reasonable time, enabling meaningful experimenting. It is demonstrated (empirically) that the neighbourhood relation in the space of states of the system greatly influences the convergence of the annealing. A pure SA algorithm is developed and used to obtain new lower bounds for the bandwidth in the 30 Everstine problems used as a benchmark for testing bandwidth reduction algorithms. An hybrid algorithm is also developed to be used in practice. It consists of an annealing session (without previous melting) run in cascade after the Cuthill-McKee algorithm. The running times are of the same order as those of the deterministic algorithms now in use, and improvements of (typically) 20 per cent are obtained on the 30 Everstine problems.     

Resource-constrained project scheduling by simulated annealing

This paper presents a new adaptation of the simulated annealing algorithm for solving non-preemptive resource-constrained project scheduling problems in which resources are limited but renewable from period to period. This algorithm is able to handle single-mode and multi-mode problems and to optimize different objective functions. Statistical experiments show the efficiency of the proposed algorithm even in comparison to some Tabu search heuristics.     

Modelling and analysis of wafer fabrication scheduling via generalized stochastic Petri net and simulated annealing

Some of the important characteristics of the semiconductor wafer fabrication factories are re-entrant process flows, a dynamic and uncertain environment, stringent production control requirements, etc. that pose a major challenge to the scheduling decisions in integrated circuit wafer fabrication process. Keeping in view the high capital investment and quick response to the market changes, it is essential for the integrated circuit fabrication process to exercise effective control on its production operations so that production resources can be employed in a flexible and efficient manner. The present research has focussed on the development of a generalized stochastic Petri net model that faithfully captures dynamic behaviours such as re-entrant processing, machine failures, loading and unloading, etc., pertaining to wafer fabrication. A simulated annealing-based scheduling strategy using mean cycle time and tardiness as performance measures was also developed to obtain an efficient and robust schedule for a known hard problem. Analysis of variance was applied to examine the interaction effects of various scheduling rules and to identify the main as well as the interaction effects of dispatching rules, dispatching rules and set-up rules, and set-up rules and batching rules. Paired t-tests were applied to assess the performance of rule combinations for lot and batch scheduling. The proposed simulated annealing-based solution methodology was tested on a well-known data set adopted from the literature and its performance reveals that simulated annealing-based scheduling rules work better than existing rules in terms of the two performance measures mean cycle time and tardiness.     

Solving a stochastic berth allocation problem using a hybrid sequence pair-based simulated annealing algorithm

In this article, a continuous berth allocation problem is studied with stochastic ship arrival and handling times. The objective is to minimize a weighted sum of the expected waiting costs, berthing deviation costs and expected overtime costs. The sequence pair representation is utilized to project the solution space of the problem into two permutations. Then, a scenario-based method is used to capture the uncertainty. To effectively solve the problem over the sequence pair solution space, a simulated annealing is combined with two algorithms. One of the algorithms is used to determine the berthing positions and the other one is used to determine the berthing times. Computational experiments are conducted to evaluate the performance of the solution method and to verify the advantages of the proposed stochastic approach. The results indicate that the proposed methodology is both efficient and effective.     

用模拟退火算法解旅行商问题

对解旅行商问题的模拟退火算法作了改进,增加了产生新解的函数,修改了原算法计算旅行回路总长度的代价函数,并用混沌随机序列替代不适宜的随机函数.从而用TurboC实现了改进算法.实验表明,改进算法对于解旅行商问题是实用的.     

Controlling asynchronous team design environments by simulated annealing

An organizational strategy for design environments,asynchronous teams, is reviewed. Simulated annealing is used to implement the necessarycontracting search behavior of asynchronous teams. An example of an asynchronous team design environment controlled by simulated annealing is given from the building design domain. The simulated annealing algorithm used, which has been modified for distributed use and multi-criteria, non-preference objectives, is described.     

Thinning and weighting of large planar arrays by simulated annealing

Two-dimensional arrays offer the potential for producing three-dimensional acoustic imaging. The major problem is the complexity arising from the large number of elements in such arrays. In this paper, a synthesis method is proposed that is aimed at designing an aperiodic sparse two-dimensional array to be used with a conventional beam-former. The stochastic algorithm of simulated annealing has been utilized to minimize the number of elements necessary to produce a spatial response that meets given requirements. The proposed method is highly innovative, as it can design very large arrays, optimize both positions and weight coefficients, synthesize asymmetric arrays, and generate array configurations that are valid for every steering direction. Several results are presented, showing notable improvements in the array characteristics and performances over those reported in the literature.     

Unit commitment computation by fuzzy adaptive particle swarm optimisation

A fuzzy adaptive particle swarm optimisation (FAPSO) for unit commitment (UC) problem has been proposed. FAPSO reliably and accurately tracks a continuously changing solution. By analyzing the social model of standard PSO for the UC problem of variable resource size and changing load demand, the fuzzy adaptive criterion is applied for the PSO inertia weight based on the diversity of fitness. In this method, the inertia weight is dynamically adjusted using fuzzy IF/THEN rules to increase the balance between global and local searching abilities. Velocity is digitised (0/1) by a logistic function for the binary UC schedule. To improve knowledge, the global best location is also moved instead of a fixed one in each generation. To avoid the system to be frozen, stagnated/idle particles are reset from time to time. Finally, benchmark data and methods are used to show effectiveness of the proposed method     

Solving the dynamic berth allocation problem by simulated annealing

Berth allocation, the first operation when vessels arrive at a port, is one of the major container port optimization problems. From both the port operator's and the ocean carriers’ perspective, minimization of the time a ship spends at berth is a key goal of port operations. This article focuses on two versions of the dynamic berth allocation problem (DBAP): discrete and continuous cases. The first case assigns ships to a given set of berth positions; the second one permits them to be moored anywhere along the berth. Simulated annealing (SA) approaches are proposed to solve the DBAP. The proposed SAs are tested with numerical instances for different sizes from the literature. Experimental results show that the proposed SA can obtain the optimal solutions in all instances of discrete cases and update 27 best known solutions in the continuous case.     

Linear sequencing for machine layouts by a modified simulated annealing

Machine sequencing is an essential step towards the physical layout of machines as it determines the relative positions of machines in a layout. Linear machine sequencing is most popular due to its efficient flow structure and its ability to arrange machines in various flow layouts. For example, in a conveyor or an AGV system, the layout can be a straight line, a U-shape line, a serpentine line, or a loop. This paper addresses the problem of determining a common linear machine sequence (also known as a linear flowline) for multi-products with different operation sequences. Each machine type has a limited number of duplicates available for use. The objective is to minimize the total flow distance travelled by the products on this linear flowline. The flows of products are allowed in the forward direction, either in-sequence or by-pass (i.e. no backtrack movements are allowed). To solve this problem, we first construct a feasible flow network that satisfies all operation sequences and then transform it into a linear machine sequence. To improve the solution, a modified simulated annealing is utilized. The new algorithm was tested on several examples in the literature.     

Task assignment problems in distributed computing systems by simulated annealing

Abstract

The stochastic, heuristic search algorithm called simulated annealing is considered for the problems of static task assignment in distributed computing systems. The purposes of task assignment problems are to assign modules of programs over a set of interconnected processors in order to both maximize the utilization of processors and minimize interprocessor communication costs. This problem has been proven to be NP‐hard. Although simulated annealing has been applied to a broad class of combinatorial optimization problems, but it requires a long computation time in order to converge to the globally optimal solution. In this paper, we design a very efficient annealing schedule with good move generation strategies and use the concept of specific heat and the frozen condition to obtain near‐optimal solutions for task assignment problems with a significantly large reduction in the number of iterations.     

Computer-generated multiple-object discriminant correlation filters: design by simulated annealing

The computer generation of multiple-object discriminant correlation filters is studied. The quantization of filter functions influences the correlation response. This may cause misdetection or incorrect classification of patterns and is especially serious in the case of multiple-object discriminant filters. We propose synthesizing the matched-filter functions by the simulated-annealing algorithm. The recording of Lohmann-type computer-generated holograms is considered. By this method the filter functions can be encoded with a reduction in the quantization levels of amplitude and phase. Acomputer simulation is performed, and the expected correlation responses are obtained.     

Acoustic behavior design with simulated annealing

The present work extends the matrix method formalism, by using a supplementary computational method based on a simulated annealing algorithm, with the aim to design acoustical structures, especially acoustic filters. The algorithm introduces a cost function, which is minimized by the simulated annealing algorithm. Also, some numerical computations have been carried out to design some special acoustic filters and an experimental analysis of the designed acoustic filters is provided to test the validity of the method.     

Accounting-based heuristics for order processing and review in a stochastic environment

As manufacturers evolve from make-to-stock to make-to-order strategies, they are forced to rely less on inventory to meet demands and more on the effective usage of existing capacity as they determine which orders to accept and schedule. Several accounting-based heuristics are used to identify and accept those jobs that maximize plant throughput. Accounting heuristics have been suggested as surrogates for approximating the opportunity costs of manufacturing one order instead of another order. Research results suggest that the best heuristic is dependent on the balance of the manufacturing resources. Moreover, these tendencies are affected by product mix and load.     

Integrated design of the block layout and aisle structure by simulated annealing

In a manufacturing layout, it is widely agreed that the design of the layout and aisles for material handling needs to be considered simultaneously. This paper presents an integrated method to optimize the manufacturing layout and aisle structure by simulated annealing. In the method, the layout design problem is represented as a slicing floorplan and the slicing lines in the floorplan are used as the aisle structure for the material-handling system. A new representation for a sliced floorplan is provided so that it can be handled by simulated annealing to perform the optimization. The method minimizes the material-handling cost, which is calculated by the aisle distances between delivery stations, while designing the aisle structure at the same time.     

Solving the location-routing problem with simultaneous pickup and delivery by simulated annealing

The study concerns the location-routing problem with simultaneous pickup and delivery (LRPSPD) in which the pickup and delivery take place at the same time for each customer. The goal is to determine the facility locations and vehicle routes in order to minimise the total system cost as a sum of facility opening cost, vehicle fixed cost and vehicle travel cost. A simulated annealing (SA) heuristic is proposed for the problem and extensive computational experiments are conducted. The results show that the proposed SA effectively solves LRPSPD and outperforms existing exact approaches in terms of solution quality.     

Setup planning using Hopfield net and simulated annealing

This paper reports a new approach to setup planning of prismatic parts using Hopfield neural net coupled with simulated annealing. The approach deals with setup planning in two stages, i.e.: (1) sequence all the features of a workpiece according to geometric and technological constraints; and (2) identify setups from the sequenced features. In the first stage, the task of feature sequencing is converted to a constraint optimization problem (COP) which is similar to the travelling salesman problem (TSP). The setup time due to setup and tool changes is incorporated into the 'distance' between features, while the precedence and critical tolerance relationships between features are treated as constraints. The Hopfield neural net approach for TSP, i.e. energy function, is adopted to model the COP mathematically where the constraints are attached as additional penalty functions. Simulated annealing is then used to search for the minimum energy state of the net while avoiding the local minima. The feature sequence obtained aims at minimizing the number of setups and tool changes while ensuring little or no violation of feature precedence relationship, thus keeping critical tolerance violation to a minimum. In the second stage, setups are generated from the sequenced features using a vector intersection approach based on common tool approach directions. A case study is presented to demonstrate the effectiveness of this approach. A comparison study between this approach and an existing integer programming setup planning system is also given which indicates the superior efficiency of the proposed approach when dealing with problems with large number of features.     

Multicriteria airport gate assignment and Pareto simulated annealing

This paper addresses an airport gate assignment problem with multiple objectives. The objectives are to minimize the number of ungated flights and the total passenger walking distances or connection times as well as to maximize the total gate assignment preferences. The problem examined is an integer program with multiple objectives (one of them being quadratic) and quadratic constraints. Of course, such a problem is inherently difficult to solve. We tackle the problem by Pareto simulated annealing in order to get a representative approximation for the Pareto front. Results of computational experiments are presented. To the best of our knowledge, this is the first attempt to consider the airport gate assignment problem with multiple objectives.     

Area-adaptive simulated annealing for image reconstruction and restoration

Simulated annealing (SA) is a robust, stable, but computationally costly method for solving ill-posed image-restoration problems. We describe the use of a backprojection operator that identifies those regions of an object estimate that have the greatest likelihood of being in error at each step of the SA process. This reduces computational time by concentrating the computing effort of SA on those pixels most effective in reducing the reconstruction error. The performance of an area-adaptive SA algorithm is evaluated for the restoration of images blurred by a simple pillbox space-invariant and a biconical space-variant point-spread function typical of a depth-measuring optical system.