A simulated annealing solution to the cell formation problem

This research develops a simulated annealing (SA) based heuristic for cell formation problems. We apply the SA based heuristic to many popular examples for cell formation problems. The computational results show that the SA based heuristic performs very well in all these examples, and the SA based heuristic has several advantages that most of the existing algorithms do not have.     

Genetic algorithms and simulated annealing for scheduling in agile manufacturing

In this paper, genetic algorithms and simulated annealing are applied to scheduling in agile manufacturing. The system addressed consists of a single flexible machine followed by multiple identical assembly stations, and the scheduling objective is to minimize the makespan. Both genetic algorithms and simulated annealing are investigated based on random starting solutions and based on starting solutions obtained from existing heuristics in the literature. Overall, four new algorithms are developed and their performance is compared to the existing heuristics. A 2³ factorial experiment, replicated twice, is used to compare the performance of the various approaches, and identify the significant factors that affect the frequency of resulting in the best solution and the average percentage deviation from a lower bound. The results show that both genetic algorithms and simulated annealing outperform the existing heuristics in many instances. In addition, simulated annealing outperforms genetic algorithms with a more robust performance. In some instances, existing heuristics provide comparable results to those of genetic algorithms and simulated annealing with the added advantage of being simpler. Significant factors and interactions affecting the performance of the various approaches are also investigated.     

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.     

Solving structural optimization problems with genetic algorithms and simulated annealing

In this paper we study the performance of two stochastic search methods: Genetic Algorithms and Simulated Annealing, applied to the optimization of pin‐jointed steel bar structures. We show that it is possible to embed these two schemes into a single parametric family of algorithms, and that optimal performance (in a parallel machine) is obtained by a hybrid scheme. Examples of applications to the optimization of several real steel bar structures are presented. Copyright © 1999 John Wiley & Sons, Ltd.     

Comparative study of simulated annealing,genetic algorithms and tabu search for solving binary and comprehensive machine-grouping problems

Machine/part grouping problems have proven to be NP-complete and cannot be solved in polynomial time. Solving such problems of reasonable size often relies on heuristic approaches. Recently, several metaheuristic approaches have emerged as efficient tools for solving such problems. However, the development and implementation of such meta-heuristics have not been a trivial issue. The merits of each method and the problems involved in implementation may not be easily apprehended by practitioners, thereby posing difficulties in the selection of an efficient heuristic for industrial applications. For this reason, a comparative study of three important metaheuristic approaches--simulated annealing, genetic algorithms and tabu search for both binary (considering only machines and part types) and comprehensive (involving machine/part types, processing times, lot sizes, and machine capacities) machine grouping problems--was carried out. To test the performance of the three metaheuristics, two binary performance indices and two generalized performance indices were respectively used for binary and comprehensive machine/part grouping problems. The comparisons were made in terms of solution quality, search convergence behaviour and presearch effort. The results indicate that simulated annealing outperforms both genetic algorithm and tabu search particularly for large problems. The genetic algorithm seems slightly better than the tabu search method for the comprehensive grouping problems.     

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.     

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.     

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.     

A two-stage simulated annealing procedure for block layout problems

Rules for setting simulated annealing control parameters are proposed for block layout problems where different material-handling devices are dynamically assigned to individual material movements as layout solutions are perturbed. Recognizing the high cost of computing materials-handling cost in this type of problem, the rules are based on adapting an existing two-stage simulated annealing procedure to accelerate convergence. Experimental results suggest that the application of these rules yields solution quality comparable with other single and two-stage simulated annealing algorithms but with significantly fewer re-evaluations of the objective function.     

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.     

Multi-threaded simulated annealing for a bi-objective maintenance scheduling problem

A parallel Simulated Annealing algorithm with multi-threaded architecture is proposed to solve a real bi-objective maintenance scheduling problem with conflicting objectives: the minimisation of the total equipment downtime caused by maintenance jobs and the minimisation of the multi-skilled workforce requirements over the given horizon. The maintenance jobs have different priorities with some precedence relations between different skills. The total weighted flow time is used as a scheduling criterion to measure the equipment availability. The multi-threaded architecture is used to speed up a multi-objective Simulated Annealing algorithm to solve the considered problem. Multi-threading is a form of parallelism based on shared memory architecture where multiple logical processing units, so-called threads, run concurrently and communicate via shared memory. The performance of the parallel method compared to the exact method is verified using a number of test problems. The obtained results imply the high efficiency and robustness of the proposed heuristic for both solution quality and computational effort.     

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.     

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.     

Variable neighbourhood simulated annealing algorithm for capacitated vehicle routing problems

This article presents the variable neighbourhood simulated annealing (VNSA) algorithm, a variant of the variable neighbourhood search (VNS) combined with simulated annealing (SA), for efficiently solving capacitated vehicle routing problems (CVRPs). In the new algorithm, the deterministic ‘Move or not’ criterion of the original VNS algorithm regarding the incumbent replacement is replaced by an SA probability, and the neighbourhood shifting of the original VNS (from near to far by k← k+1) is replaced by a neighbourhood shaking procedure following a specified rule. The geographical neighbourhood structure is introduced in constructing the neighbourhood structures for the CVRP of the string model. The proposed algorithm is tested against 39 well-known benchmark CVRP instances of different scales (small/middle, large, very large). The results show that the VNSA algorithm outperforms most existing algorithms in terms of computational effectiveness and efficiency, showing good performance in solving large and very large CVRPs.     

A simulated annealing approach for optimization of multi-pass turning operations

In this paper, an optimization algorithm based on the simulated annealing (SA) algorithm and the Hooke-Jeeves pattern search (PS) is developed for optimization of multi-pass turning operations. The cutting process is divided into multi-pass rough machining and finish machining. Machining parameters are determined to optimize the cutting conditions in the sense of the minimum unit production cost under a set of practical machining constraints. Experimental results indicate that the proposed nonlinear constrained optimization algorithm, named SA/PS, is effective for solving complex machining optimization problems. The SA/PS algorithm can be integrated into a CAPP system for generating optimal machining parameters.     

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.     

A new simulated annealing algorithm for the facility layout problem

In this paper we present an application of simulated annealing to facility layout problems with single and multiple floors. The facility layout problem is highly combinatorial in nature and generally exhibits many local minima. These properties make it a suitable candidate for simulated annealing. Using a new candidate layout generation routine and spacefilling curves, we develop an improvement-type layout algorithm based on simulated annealing that considers an expanded set of department exchanges. The resulting algorithm achieves low-cost solutions that are much less dependent on the initial layout than other approaches. We compare the performance of the simulated-annealing based algorithm with both steepest-descent and randomized approaches from the literature. Unlike other simulated annealing papers which typically present a statistical experiment to evaluate the effect of numerous control settings, all the experiments presented in this paper were conducted with control settings that are constant or easily specified. This approach facilitates the application of the proposed algorithm to real-life facility layout problems in both single and multiple floor facilities. Although the algorithm presented here can be applied to many types of facilities, our primary focus is on production facilities.     

Large production line optimization using simulated annealing

We present a robust generalized queuing network algorithm as an evaluative procedure for optimizing production line configurations using simulated annealing. We compare the results obtained with our algorithm to those of other studies and find some interesting similarities but also striking differences between them in the allocation of buffers, numbers of servers, and their service rates. While context dependent, these patterns of allocation are one of the most important insights which emerge in solving very long production lines. The patterns, however, are often counter-intuitive, which underscores the difficulty of the problem we address. The most interesting feature of our optimization procedure is its bounded execution time, which makes it viable for optimizing very long production line configurations. Based on the bounded execution time property, we have optimized configurations of up to 60 stations with 120 buffers and servers in less than five hours of CPU time.