Applying a hybrid simulated annealing and tabu search approach to non-permutation flowshop scheduling problems

Researchers have indicated that a permutation schedule can be improved by a non-permutation schedule in a flowshop with completion time-based criteria, such as makespan and total completion time. This study proposes a hybrid approach which draws on the advantages of simulated annealing and tabu search for the non-permutation flowshop scheduling problem, in which the objective function is the makespan of the schedule. To verify the effectiveness of the proposed hybrid approach, computational experiments are performed on a set of well-known non-permutation flowshop scheduling benchmark problems. The result shows that the performance of the hybrid approach is better than that of other approaches, including ant colony optimisation, simulated annealing, and tabu search. Further, the proposed approach found new upper bound values for all benchmark problems within a reasonable computational time.     

Heuristics for the generalised assignment problem: simulated annealing and tabu search approaches

The generalised assignment problem (GAP) is the problem of finding a minimum cost assignment of a set of jobs to a set of agents. Each job is assigned to exactly one agent. The total demands of all jobs assigned to any agent can not exceed the total resources available to that agent. A review of exact and heuristic methods is presented. A-generation mechanism is introduced. Different search strategies and parameter settings are investigated for the-generation descent, hybrid simulated annealing/tabu search and tabu search heuristic methods. The developed methods incorporate a number of features that have proven useful for obtaining optimal and near optimal solutions. The effectiveness of our approaches is established by comparing their performance in terms of solution quality and computional requirement to other specialized branch-and-bound tree search, simulated annealing and set partitioning heuristics on a set of standard problems from the literature.     

Lagrangean relaxation and hybrid simulated annealing tabu search procedure for a two-echelon capacitated facility location problem with plant size selection

In this paper, we study a two-echelon capacitated facility location problem with plant size selection (TECFLP-PSS). Given a set of potential sites for plants, each of which is associated with several possible sizes and corresponding unit production costs, a set of potential sites for capacitated depots and a set of customers with demands, the TECFLP-PSS aims to optimise the plant locations and sizes, the depot locations and the product flows from the opened plants to the opened depots and then to the end customers under single-source constraints. The objective is to satisfy all customers’ demands with a minimum total cost of facility opening, production and transportation. We develop a mixed integer programming model and propose a Lagrangean relaxation approach combined with new valid inequalities and core problem to achieve tight lower and upper bounds for this problem. We then improve the upper bound with a hybrid simulated annealing tabu search procedure. Computational experiments on benchmarks and randomly generated instances are conducted to validate the effectiveness and efficiency of the proposed method.     

Applying multiple objective tabu search to continuous optimization problems with a simple neighbourhood strategy

One of the first multiple objective versions of the tabu search (TS) algorithm is proposed by the author. The idea of applying TS to multiple objective optimization is inspired from its solution structure. TS works with more than one solution (neighbourhood solutions) at a time and this situation gives the opportunity to evaluate multiple objectives simultaneously in one run. The selection and updating stages are modified to enable the original TS algorithm to work with more than one objective. In this paper, the multiple objective tabu search (MOTS) algorithm is applied to multiple objective non‐linear optimization problems with continuous variables using a simple neighbourhood strategy. The algorithm is applied to four mechanical components design problems. The results are compared with several other solution techniques including multiple objective genetic algorithms. It is observed that MOTS is able to find better and much wider spread of solutions than the reported ones. Copyright © 2005 John Wiley & Sons, Ltd.     

A combined global and local search method to deal with constrained optimization for continuous tabu search

Heuristic methods, such as tabu search, are efficient for global optimizations. Most studies, however, have focused on constraint‐free optimizations. Penalty functions are commonly used to deal with constraints for global optimization algorithms in dealing with constraints. This is sometimes inefficient, especially for equality constraints, as it is difficult to keep the global search within the feasible region by purely adding a penalty to the objective function. A combined global and local search method is proposed in this paper to deal with constrained optimizations. It is demonstrated by combining continuous tabu search (CTS) and sequential quadratic programming (SQP) methods. First, a nested inner‐ and outer‐loop method is presented to lead the search within the feasible region. SQP, a typical local search method, is used to quickly solve a non‐linear programming purely for constraints in the inner loop and provides feasible neighbors for the outer loop. CTS, in the outer loop, is used to seek for the global optimal. Finally, another local search using SQP is conducted with the results of CTS as initials to refine the global search results. Efficiency is demonstrated by a number of benchmark problems. Copyright © 2008 John Wiley & Sons, Ltd.     

Optimization of truss topology using tabu search

A design procedure for integrating topological considerations in the framework of structural optimization is presented. The proposed approach is capable of considering multiple load conditions, stress, displacement and local/global buckling constraints, and multiple objective functions in the problem formulation. Further, since the proposed method permits members to be added to or deleted from an existing topology and the topology is not defined by member areas, the difficulty of not being able to reach singular optima is also avoided. These objectives are accomplished using a discrete optimization procedure which uses 0–1 topological variables to optimize alternate designs. Since the topological variables are discrete in nature and the member cross-sections are assumed to be continuous, the topological optimization problem has mixed discrete-continuous variables. This non-linear programming problem is solved using a memory-based combinatorial optimization technique known as tabu search. Numerical results obtained using tabu search for single and multiobjective topological optimization of truss structures are presented. To model the multiple objective functions in the problem formulation, a cooperative game theoretic approach is used. The results indicate that the optimum topologies obtained using tabu search compare favourably, and in some instances, outperform the results obtained using the ground–structure approach. However, this improvement occurs at the expense of a significant increase in computational burden owing to the fact that the proposed approach necessitates that the geometry of each trial topology be optimized.     

Simulated annealing applied to the optimal plastic design of circular steel plates

This article describes an application of the simulated annealing (SA) algorithm to a structural problem, consisting in finding a minimum‐weight circular plate of the imposed limit load. For technological reasons, the considered plates are divided into rings of constant thickness. The boundaries of these rings may vary, so the thickness and size of each ring are the design variables to be considered. Although SA is mostly applied to handle combinatorial optimization problems, the present study shows that it can also be efficient in the field of mechanical engineering. Some particular aspects of the study are thoroughly described. For instance, technological constraints have to be considered: lower and upper bounds are imposed on the thickness of each ring, and a lower bound is imposed on the ring width. These constraints are not taken into account by means of a classical penalty method, but by a set of dedicated procedures. The particular constraint of the imposed limit load is treated by solving the inverse problem at each iteration: the limit load of the current solution is computed and the geometry of the plate is adapted to fit the assigned value. After discussion about points of attention, general conclusions are drawn from the performances of SA in its present implementation. The obtained results show that the algorithm exhibits satisfactory levels of reliability and efficiency. Copyright © 2007 John Wiley & Sons, Ltd.     

Hybrid simulated annealing with memory: an evolution-based diversification approach

This study presents an efficient metaheuristic approach for combinatorial optimisation and scheduling problems. The hybrid algorithm proposed in this paper integrates different features of several well-known heuristics. The core component of the proposed algorithm is a simulated annealing module. This component utilises three types of memories, one long-term memory and two short-term memories. The main characteristics of the proposed metaheuristic are the use of positive (reinforcement) and negative (inhibitory) memories as well as an evolution-based diversification approach. Job shop scheduling is selected to evaluate the performance of the proposed method. Given the benchmark problem, an extended version of the proposed method is also developed and presented. The extended version has two distinct features, specifically designed for the job shop scheduling problem, that enhance the performance of the search. The first feature is a local search that partially explores alternative solutions on a critical path of any current solution. The second feature is a mechanism to resolve possible deadlocks that may occur during the search as a result of shortage in acceptable solutions. For the case of job shop scheduling, the computational results and comparison with other techniques demonstrate the superior performance of the proposed methods in the majority of cases.     

A multi-objective simulated annealing approach to reactive power compensation

Reactive power compensation is an important problem in electrical distribution systems, involving the sizing and location of capacitors (sources of reactive power). The installation of capacitors also contributes to releasing system capacity and improving voltage level. A multi-objective simulated annealing approach to provide decision support in this problem is presented. This approach is able to compute a set of well-distributed and diversified solutions underlying distinct trade-offs, even for a challenging network. The characterization of the non-dominated front is relevant information for aiding planning engineers to select satisfactory compromise solutions (compensation schemes) to improve the network operation conditions.     

稀疏矢量阵设计的模拟退火算法

对于均匀间隔线列阵,由采样定理可知,当阵元间距超过信号波长的一半时,指向性图会产生与目标等高的栅瓣。为了获得高分辨率,同时避免栅瓣出现,需要大量的传感器。为了减少设备复杂度,可以采用稀疏布阵技术,以较少的阵元获得较高的分辨力。将模拟退火算法应用到稀疏矢量水听器阵的设计中。通过优化阵元位置控制指向性图,可以获得无栅瓣的指向性图。给出了优化后的稀疏矢量阵与均匀间隔矢量阵的指向性图比较。     

Single and multiobjective structural optimization in discrete-continuous variables using simulated annealing

A multivariable optimization technique based on the Monte-Carlo method used in statistical mechanics studies of condensed systems is adapted for solving single and multiobjective structural optimization problems. This procedure, known as simulated annealing, draws an analogy between energy minimization in physical systems and objective function minimization in structural systems. The search for a minimum is simulated by a relaxation of the statistical mechanical system where a probabilistic acceptance criterion is used to accept or reject candidate designs. To model the multiple objective functions in the problem formulation, a cooperative game theoretic approach is used. Numerical results obtained using three different annealing strategies for the single and multiobjective design of structures with discrete-continuous variables are presented. The influence of cooling schedule parameters on the optimum solutions obtained is discussed. Simulation results indicate that, in several instances, the optimum solutions obtained using simulated annealing outperform the optimum solutions obtained using some gradient-based and discrete optimization techniques. The results also indicate that simulated annealing has substantial potential for additional applications in optimization, especially for problems with mixed discrete-continuous variables.     

A tabu search approach for buffer allocation in production lines with unreliable machines

The optimal allocation of buffers is an important research issue in designing production lines. In this study, a tabu search (TS) algorithm is proposed to find near-optimal buffer allocation plans for a serial production line with unreliable machines. The main objective is to maximize the production rate, i.e. throughput, of the line. The efficiency of the proposed method is also tested to solve buffer allocation problems with the objective of total buffer size minimization. To estimate the throughput of the line with a given specific buffer allocation, an analytical decomposition approximation method is used. The performance of the tabu search algorithm is demonstrated on existing benchmark problems. The results obtained by the TS algorithm are clearly encouraging, as the TS algorithm is much better than the other algorithms for all considered benchmark problems.     

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.     

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.     

Distillation separation sequence optimization synthesis based on application of adaptive parallel tabu search

Abstract

Distillation separation sequences can be described as binary tree data structures, because of the analogous structures of distillation separation sequences and binary trees, and then by applying graph theory, the change mechanism of neighborhood separation points based on binary trees is built, correspondingly a kind of highly effective, evolutionary neighborhood structure is constructed. For the purpose of researching further tabu search algorithms, adaptive mechanisms and parallel techniques are introduced. That is, according to memory frequency information, the tabu length and the number of candidates are adaptively adjusted, and multitask parallel technology is realized through the arrangement of the search assignments. The example shows that adaptive parallel tabu searches can solve, successfully, large‐scale distillation separation sequence synthesis problems.     

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.     

A multilevel tabu search algorithm for balanced partitioning of unstructured grids

This paper presents a multilevel algorithm for balanced partitioning of unstructured grids. The grid is partitioned such that the number of interface elements is minimized and each partition contains an equal number of grid elements. The partition refinement of the proposed multilevel algorithm is based on iterative tabu search procedure. In iterative partition refinement algorithms, tie‐breaking in selection of maximum gain vertices affects the performance considerably. A new tie‐breaking strategy in the iterative tabu search algorithm is proposed that leads to improved partitioning quality. Numerical experiments are carried out on various unstructured grids in order to evaluate the performance of the proposed algorithm. The partition results are compared with those produced by the well‐known partitioning package Metis and k‐means clustering algorithm and shown to be superior in terms of edge cut, partition balance, and partition connectivity. Copyright © 2015 John Wiley & Sons, Ltd.     

JIT mixed-model sequencing with batching and setup considerations via search heuristics

This research addresses the problem of sequencing items for production when it is desired that the production sequences result in minimal usage rates–surrogate measures for flexibility in a JIT environment. While seeking sequences with minimal usage rates, the number of required setups for the sequences is also considered, along with feasible batch-sizing combinations. The general intent is to find minimum usage-rate sequences for each associated number of setups and total batches. This multiple objective problem is addressed via a three-dimensional efficient frontier. Because the combinatorial nature of sequencing problems typically provides an intractable search space for problems of ‘real world’ size, the search heuristics of simulated annealing and genetic algorithms are presented and used to find solutions for several problem sets from the literature. Experimentation shows that the simulated annealing approach outperforms the genetic algorithm approach in both objective function and CPU performance.     

Using queuing theory and simulated annealing to design the facility layout in an AGV-based modular manufacturing system

An automated guided vehicle-based flow production system is used for manufacturing prefabricated bathroom units. One unit can occupy a space of more than 10?m². Due to large time deviations in sequential processes, queues are formed and greater plant space is needed. Reducing work-in-progress helps to save plant space but renders manufacture less efficient. The research explores better workstation arrangements. An open queuing network (OQN) model was used to approximate the flow production system. Since the problem of workstation arrangement is a combinatorial optimisation problem, simulated annealing (SA) was applied to search for a good solution. The combination of an OQN model and SA provides a powerful tool to solve the facility layout problem for a stochastic flow production system. The experimental results show that the proposed approach has the potential to guide industrial layout design and practice.