A simulated annealing heuristic for the dynamic layout problem with budget constraint

Facility layout problem has been extensively studied in the literature because the total material handling cost can be a significant portion in the operational costs for a company and in the manufacturing cost of a product. Today’s severe global competition, rapid changes in technology and shortening life cycle of products force companies to evaluate and modify their facility layout in a periodic fashion. This type of layout problems is categorized as the dynamic facility layout problem (DFLP). As a realistic dimension of the problem, one has to consider also the limited budget to cover the cost of changing the layout. In this study, we propose a simulated annealing heuristic for the DFLP with budget constraint, and show the effectiveness of this heuristic on a set of numerical experiments.     

Solving dynamic double row layout problem via combining simulated annealing and mathematical programming

Double row layout problem (DRLP) is to allocate facilities on two rows separated by a straight aisle. Aiming at the dynamic environment of product processing in practice, we propose a dynamic double-row layout problem (DDRLP) where material flows change over time in different processing periods. A mixed-integer programming model is established for this problem. A methodology combining an improved simulated annealing (ISA) with mathematical programming (MP) is proposed to resolve it. Firstly, a mixed coding scheme is designed to represent both of sequence of facilities and their exact locations. Secondly, an improved simulated annealing algorithm is suggested to produce a solution to DDRLP. Finally, MP is used to improve this solution by determining the optimal exact location for each facility. Experiments show that this methodology is able to obtain the optimal solutions for small size problems and outperforms an exact approach (CPLEX) for problems of realistic size.     

Simulated annealing heuristics for the dynamic facility layout problem

In today's economy, manufacturing plants must be able to operate efficiently and respond quickly to changes in product mix and demand. Therefore, this paper considers the problem of arranging and rearranging (when there are changes between the flows of materials between departments) manufacturing facilities such that the sum of the material handling and rearrangement costs is minimized. This problem is known as the dynamic facility layout problem (DFLP). In this paper, two simulated annealing (SA) heuristics are developed for the DFLP. The first SA heuristic (SA I) is a direct adaptation of SA to the DFLP. The second SA heuristic (SA II) is the same as SA I with a look-ahead/look-back strategy added. To test the performance of the heuristics, a data set taken from the literature is used in the analysis. The results obtained show that the proposed heuristics are very effective for the dynamic facility layout problem.     

A simulated annealing algorithm for multi-manned assembly line balancing problem

Assembly line balancing problems with multi-manned workstations usually occur in plants producing high volume products (e.g. automotive industry) in which the size of the product is reasonably large to utilize the multi-manned assembly line configuration. In these kinds of assembly lines, usually there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. However, owing to the high computational complexity, it is quite difficult to achieve an optimal solution to the balancing problem of multi-manned assembly lines with traditional optimization approaches. In this study, a simulated annealing heuristic is proposed for solving assembly line balancing problems with multi-manned workstations. The line efficiency, line length and the smoothness index are considered as the performance criteria. The proposed algorithm is illustrated with a numerical example problem, and its performance is tested on a set of test problems taken from literature. The performance of the proposed algorithm is compared to the existing approaches. Results show that the proposed algorithm performs well.     

A hybrid simulated annealing metaheuristic algorithm for the two-dimensional knapsack packing problem

The rectangle knapsack packing problem is to pack a number of rectangles into a larger stock sheet such that the total value of packed rectangles is maximized. The paper first presents a fitness strategy, which is used to determine which rectangle is to be first packed into a given position. Based on this fitness strategy, a constructive heuristic algorithm is developed to generate a solution, i.e. a given sequence of rectangles for packing. Then, a greedy strategy is used to search a better solution. At last, a simulated annealing algorithm is introduced to jump out of the local optimal trap of the greedy strategy, to find a further improved solution. Computational results on 221 rectangular packing instances show that the presented algorithm outperforms some previous algorithms on average.     

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings,lot splitting and flexible reconfiguration by simulated annealing

This paper presents a novel mixed-integer non-linear programming model for the layout design of a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands are varying during a multi-period planning horizon. As a result, the best cell configuration for one period may not be efficient for successive periods, and thus it necessitates reconfigurations. Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL) and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions in a dynamic environment. The proposed model integrating the CF and GL decisions can be used by researchers and practitioners to design GL in practical and dynamic cell formation problems. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. Such a DCMS model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, intra-cell layout, inter-cell layout, multi-rows layout of equal area facilities and flexible reconfiguration. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead and machine processing. Linearization procedures are used to transform the presented non-linear programming model into a linearized formulation. Two numerical examples taken from the literature are solved by the Lingo software using a branch-and-bound method to illustrate the performance of this model. An efficient simulated annealing (SA) algorithm with elaborately designed solution representation and neighborhood generation is extended to solve the proposed model because of its NP-hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison with the Lingo software. The obtained results show that the proposed SA is able to find the near-optimal solutions in computational time, approximately 100 times less than Lingo. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems.     

A simple simulated annealing algorithm for the maximum clique problem

Simulated annealing technique has mostly been used to solve various optimization and learning problems, and it is well known that the maximum clique problem is one of the most studied NP-hard optimization problems owing to its numerous applications. In this note, a simple simulated annealing algorithm for the maximum clique problem is proposed and tested on all 80 DIMACS maximum clique instances. Although it is simple, the proposed simulated annealing algorithm is efficient on most of the DIMACS maximum clique instances. The simulation results show that the proposed simulated annealing algorithm outperforms a recent efficient simulated annealing algorithm proposed by Xu and Ma, and the solutions obtained by the proposed simulated annealing algorithm have the equal quality with those obtained by a recent trust region heuristic algorithm of Stanislav Busygin.     

A hybrid multi-population genetic algorithm for the dynamic facility layout problem

Due to inherent complexity of the dynamic facility layout problem, it has always been a challenging issue to develop a solution algorithm for this problem. For more than one decade, many researchers have proposed different algorithms for this problem. After reviewing the shortcomings of these algorithms, we realize that the performance can be further improved by a more intelligent search. This paper develops an effective novel hybrid multi-population genetic algorithm. Using a proposed heuristic procedure, we separate solution space into different parts and each subpopulation represents a separate part. This assures the diversity of the algorithm. Moreover, to intensify the search more and more, a powerful local search mechanism based on simulated annealing is developed. Unlike the available genetic operators previously proposed for this problem, we design the operators so as to search only the feasible space; thus, we save computational time by avoiding infeasible space. To evaluate the algorithm, we comprehensively discuss the parameter tuning of the algorithms by Taguchi method. The perfectly tuned algorithm is then compared with 11 available algorithms in the literature using well-known set of benchmark instances. Different analyses conducted on the results, show that the proposed algorithm enjoys the superiority and outperformance over the other algorithms.     

基于Wang-Landau算法的动态设施布局方法

动态设施布局问题是设施在车间内多个阶段的布局规划问题。目前,针对动态设施布局问题,国内外学者对离散模型研究较多,而对连续模型的研究却较少。根据连续动态设施布局的特性与需求,构建了不等面积设施的动态设施布局连续模型。求解该模型的难点在于缺乏一种高效的布局优化方法。Wang-Landau算法是一种改进的蒙特卡罗算法。通过将Wang-Landau算法与空位点放置策略、外推移动策略、内压移动策略三种启发式策略相结合,提出一种基于Wang-Landau抽样的启发式算法,并以此求解该模型。使用文献中已有的测试算例对提出的算法进行测试,计算结果表明,所提出的算法在求解连续动态设施布局问题上是有效的。     

Ant algorithms and simulated annealing for multicriteria dynamic programming

The paper presents a comparison of ant algorithms and simulated annealing as well as their applications in multicriteria discrete dynamic programming. The considered dynamic process consists of finite states and decision variables. In order to describe the effectiveness of multicriteria algorithms, four measures of the quality of the nondominated set approximations are used.     

A hybrid immune simulated annealing algorithm for the job shop scheduling problem

A hybrid simulated annealing algorithm based on a novel immune mechanism is proposed for the job shop scheduling problem with the objective of minimizing total weighted tardiness. The proposed immune procedure is built on the following fundamental idea: the bottleneck jobs existing in each scheduling instance generally constitute the key factors in the attempt to improve the quality of final schedules, and thus, the sequencing of these jobs needs more intensive optimization. To quantitatively describe the bottleneck job distribution, we design a fuzzy inference system for evaluating the bottleneck level (i.e. the criticality) of each job. By combining the immune procedure with a simulated annealing algorithm, we design a hybrid optimization algorithm which is subsequently tested on a number of job shop instances. Computational results for different-sized instances show that the proposed hybrid algorithm performs effectively and converges fast to satisfactory solutions.     

An effective two-stage simulated annealing algorithm for the minimum linear arrangement problem

In this paper, an improved two-stage simulated annealing algorithm is presented for the minimum linear arrangement problem for graphs. This algorithm integrates several distinguished features including an efficient heuristic to generate good quality initial solutions, a highly discriminating evaluation function, a special neighborhood function and an effective cooling schedule. The algorithm is evaluated on a set of 30 well-known benchmark instances of the literature and compared with several state-of-the-art algorithms, showing improvements of 17 previous best results.     

Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing – tabu search algorithm to solve the symmetrical traveling salesman problem

This paper applies a hybrid simulated annealing – tabu search algorithm to solve the Traveling Salesman Problem (TSP). Fully considering the characteristics of the hybrid algorithm, we develop a dynamic neighborhood structure for the hybrid algorithm to improve search efficiency by reducing the randomness of the conventional 2-opt neighborhood. A circle-directed mutation is developed to achieve this dynamic neighborhood structure. Furthermore, we propose adaptive parameters that can be automatically adjusted by the algorithm based on context specific examples. This negates the need to frequently readjust algorithm parameters. We employ benchmarks obtained from TSPLIB (a library of sample instances for the TSP) to test our algorithm, and find that the proposed algorithm can obtain satisfactory solutions within a reasonable amount of time. The experimental results demonstrate that the proposed hybrid algorithm can overcome the disadvantages of traditional simulated annealing and tabu search methods. The results also show that the dynamic neighborhood structure is more efficient and accurate than the classical 2-opt. Also, adaptive parameters are appropriate for almost all of the numerical examples tested in this paper. Finally, the experimental results are compared with those of other algorithms, to demonstrate the improved accuracy and efficiency of the proposed algorithm.     

Two-dimensional equilibrium constraint layout using simulated annealing

This paper studies the layout optimization problem with equilibrium constraint. It is a two-dimensional packing problem with the industrial background of simplified satellite module layout design, and is known as NP-hard problem. By incorporating the heuristic neighborhood search mechanism and the adaptive gradient method into the simulated annealing procedure, a heuristic simulated annealing algorithm is put forward for this problem. The special neighborhood search mechanism can avoid the disadvantage of blind search in the simulated annealing algorithm, and the adaptive gradient method is used to execute local search and speed up finding the global optimal solution. Numerical examples are illustrated to verify the effectiveness of the proposed algorithm.     

Damage detection by an adaptive real-parameter simulated annealing genetic algorithm

An effective algorithm, which combined an adaptive real-parameter genetic algorithm with simulated annealing, is proposed to detect damage occurrence in beam-type structures. The proposed algorithm uses the displacements of static response and natural frequencies of modal analysis, which are obtained by finite element software ANSYS. There are three different kinds of beam structures to verify the performance of the proposed algorithm. These three cases have different boundary conditions and different damage scenarios. From the results, it is demonstrated that the proposed algorithm is efficient in flexural stiffness damage identification for beam-type structures under free of noise condition. Even under the case of noise, the results show that the searched solutions are still in reasonable precision.     

A simulated annealing heuristic for the open location-routing problem

This paper introduces the open location-routing problem (OLRP) that is a variant of the capacitated location-routing problem (CLRP). OLRP is motivated from the rise in contracting with third-party logistic (TPL) companies and is different from CLRP in that vehicles do not return to the distribution center after servicing all customers. The goal of OLRP is to minimize the total cost, consisting of facility operation costs, vehicle fixed costs, and traveling costs. We propose a simulated annealing (SA)-based heuristic for solving OLRP, which is tested on OLRP instances that have been adopted from three sets of well-known CLRP benchmark instances with up to 318 customers and 4 potential depots. The computational results indicate that the proposed heuristic efficiently solves OLRP.     

A simulated annealing algorithm for the job shop cell scheduling problem with intercellular moves and reentrant parts

This paper addresses the problem of scheduling parts in job shop cellular manufacturing systems by considering exceptional parts that need to visit machines in different cells and reentrant parts which need to visit some machines more than once in non-consecutive manner. Initially, an integer linear programming (ILP) model is presented for the problem to minimize the makespan, which considers intercellular moves and non-consecutive multiple processing of parts on a machine. Due to the complexity of the model, a simulated annealing (SA) based solution approach is developed to solve the problem. To increase the efficiency of the search algorithm, a neighborhood structure based on the concept of blocks is applied. Subsequently, the efficiency of the ILP model and the performance of the proposed SA are assessed over a set of problem instances taken from the literature. The proposed ILP model was coded in Lingo 8.0 and the solution obtained by the proposed SA was compared to the optimal values. The computational results demonstrate that the proposed ILP model and SA algorithm are effective and efficient for this problem.     

A distributed implementation of simulated annealing for the travelling salesman problem

This paper describes and analyses a novel distributed implementation of the simulated annealing algorithm to find a good solution to the travelling salesman problem. The implementation runs on a linear chain of processors driven by a host processor, which plays only a supervisory role, so that the bulk of processing takes place on the chain and the efficiency of the algorithm remains high as the number of processors is increased.     

A theoretical framework for simulated annealing

Simulated Annealing has been a very successful general algorithm for the solution of large, complex combinatorial optimization problems. Since its introduction, several applications in different fields of engineering, such as integrated circuit placement, optimal encoding, resource allocation, logic synthesis, have been developed. In parallel, theoretical studies have been focusing on the reasons for the excellent behavior of the algorithm. This paper reviews most of the important results on the theory of Simulated Annealing, placing them in a unified framework. New results are reported as well.This research was sponsored by SRC and DARPA monitored by SNWSC under contract numbers N00039-87-C-012 and N00039-88-C-0292.     

A simulated annealing approach to the traveling tournament problem

Automating the scheduling of sport leagues has received considerable attention in recent years, as these applications involve significant revenues and generate challenging combinatorial optimization problems. This paper considers the traveling tournament problem (TTP) which abstracts the salient features of major league baseball (MLB) in the United States. It proposes a simulated annealing algorithm (TTSA) for the TTP that explores both feasible and infeasible schedules, uses a large neighborhood with complex moves, and includes advanced techniques such as strategic oscillation and reheats to balance the exploration of the feasible and infeasible regions and to escape local minima at very low temperatures. TTSA matches the best-known solutions on the small instances of the TTP and produces significant improvements over previous approaches on the larger instances. Moreover, TTSA is shown to be robust, because its worst solution quality over 50 runs is always smaller or equal to the best-known solutions. A Preliminary version of this paper was presented at the CP'AI'OR'03 Workshop.