A Branch-and-Bound Approach for a Two-machine Flowshop Scheduling Problem

In this paper, we present a branch-and-bound approach for solving a two-machine flow shop scheduling problem, in which the objective is to minimize a weighted combination of job flowtime and schedule makespan. Experimental results show that the algorithm works very well for certain special cases and moderately well for others. In fact, it is able to produce optimal schedules for 500-job problems in which the second machine dominates the first machine. It is also shown that the algorithm developed to provide an upper bound for the branch-and-bound is optimal when processing times for jobs are the same on both machines. The primary reason for developing the branch-and-bound approach is that its results can be used to guide other heuristic techniques, such as simulated annealing, tabu search and genetic algorithms, in their search for optimal solutions for larger problems.     

Serial and parallel simulated annealing and tabu search algorithms for the traveling salesman problem

This paper describes serial and parallel implementations of two different search techniques applied to the traveling salesman problem. A novel approach has been taken to parallelize simulated annealing and the results are compared with the traditional annealing algorithm. This approach uses abbreviated cooling schedule and achieves a superlinear speedup. Also a new search technique, called tabu search, has been adapted to execute in a parallel computing environment. Comparison between simulated annealing and tabu search indicate that tabu search consistently outperforms simulated annealing with respect to computation time while giving comparable solutions. Examples include 25, 33, 42, 50, 57, 75 and 100 city problems.     

Good solutions to discrete noxious location problems via metaheuristics

An implementation of simulated annealing and tabu search is described for discrete versions of two noxious facility location problems —p-dispersion andp-defense-sum. A series of computational experiments leading to good choices of the parameters that drive simulated annealing and tabu search are presented for a 33-node data set. Using these parameter settings, the performance of simulated annealing and tabu search are compared to a semi-greedy heuristic for thirty randomly generated 25-node data sets.     

Best Practice Simulated Annealing for the Airline Crew Scheduling Problem

We report about a study of a simulated annealing algorithm for the airline crew pairing problem based on a run-cutting formulation. Computational results are reported for some real-world short- to medium-haul test problems with up to 4600 flights per month. Furthermore we find that run time can be saved and solution quality can be improved by using a problem specific initial solution, by relaxing constraints as far as possible, by combining simulated annealing with a problem specific local improvement heuristic and by multiple independent runs.     

Local search heuristics for single-machine scheduling with batching to minimize the number of late jobs

Local search heuristics are developed for a problem of scheduling jobs on a single machine. Jobs are partitioned into families, and a set-up time is necessary when there is a switch in processing jobs from one family to jobs of another family. The objective is to minimize the number of late jobs. Four alternative local search methods are proposed: multi-start descent, simulated annealing, tabu search and a genetic algorithm. The performance of these heuristics is evaluated on a large set of test problems. The best results are obtained with the genetic algorithm; multi-start descent also performs quite well.     

A comparison of local search methods for flow shop scheduling

Local search techniques are widely used to obtain approximate solutions to a variety of combinatorial optimization problems. Two important categories of local search methods are neighbourhood search and genetic algorithms. Commonly used neighbourhood search methods include descent, threshold accepting, simulated annealing and tabu search. In this paper, we present a computational study that compares these four neighbourhood search methods, a genetic algorithm, and a hybrid method in which descent is incorporated into the genetic algorithm. The performance of these six local search methods is evaluated on the problem of scheduling jobs in a permutation flow shop to minimize the total weighted completion time. Based on the results of extensive computational tests, simulated annealing is found to generate better quality solutions than the other neighborhood search methods. However, the results also indicate that the hybrid genetic descent algorithm is superior to simulated annealing.     

Local search metaheuristics for discrete–continuous scheduling problems

Problems of scheduling nonpreemptable jobs which require simultaneously a machine from a set of parallel, identical machines and a continuous, renewable resource are considered. For each job there are known: its processing speed as a continuous, concave function of a continuous resource allotted at a time and its processing demand. The optimization criterion is the schedule length. The problem can be decomposed into two interrelated subproblems: (i) to sequence jobs on machines, and (ii) to find an optimal (continuous) resource allocation among jobs already sequenced. Problem (ii) can be formulated as a convex programming problem with linear constraints and solved using proper solvers. Thus, the problem remains to generate a set of all feasible sequences of jobs on machines (this guarantees finding an optimal schedule in the general case). However, the cardinality of this set grows exponentially with the number of jobs. Thus, we propose to use heuristic search methods defined on the space of feasible sequences. Three metaheuristics: tabu search (TS), simulated annealing (SA) and genetic algorithm (GA) have been implemented and compared computationally with a random sampling technique. The computational experiment has been carried out on an SGI PowerChallenge XL computer with 12 RISC R8000 processors. Some directions for further research have been pointed out.     

A multi-class multi-level capacitated lot sizing model

When demand loading is higher than available capacity, it takes a great deal of effort for a traditional MRP system to obtain a capacity-feasible production plan. Also, the separation of lot sizing decisions and capacity requirement planning makes the setup decisions more difficult. In a practical application, a production planning system should prioritize demands when allocating manufacturing resources. This study proposes a planning model that integrates all MRP computation modules. The model not only includes multi-level capacitated lot sizing problems but also considers multiple demand classes. Each demand class corresponds to a mixed integer programming (MIP) problem. By sequentially solving the MIP problems according to their demand class priorities, this proposed approach allocates finite manufacturing resources and generates feasible production plans. In this paper we experiment with three heuristic search algorithms: (1) tabu search; (2) simulated annealing, and (3) genetic algorithm, to solve the MIP problems. Experimental designs and statistical methods are used to evaluate and analyse the performance of these three algorithms. The results show that tabu search and simulated annealing perform best in the confirmed order demand class and forecast demand class, respectively.     

一种求解延迟工件数最小的混合流水车间调度问题的模拟退火算法

针对延迟工件数最小的混合流水车间调度问题,给出了一种改进的模拟退火求解算法. 该算法首先给出一个启发式算法来获得初始解,然后用模拟退火算法对初始解改进. 通过交换工件在第一阶段的排序来获得一个新的解,采用最先空闲设备分配规则和先到先被加工规则,对工件在剩余各级的工序进行调度. 实验仿真表明算法是可行有效的.     

Constructing covering codes by tabu search

The problem of finding good covering codes in Hamming spaces is considered. Many different local search methods have been used to find packing codes (the dual problem), whereas practically all published results on searches for covering codes are based on simulated annealing. In this article tabu search is evaluated and compared against the simulated annealing method. A novel neighborhood function is also presented. The combination of a new optimization method and a new neighborhood function turns out to speed up the search for covering codes remarkably compared to the traditional simulated annealing approach. Using the new approach, the best known upper bound for the football pool problem for 9 matches is improved to 1341. © 1997 John Wiley & Sons, Inc.     

基于禁忌算法的中枢辐射航线网络鲁棒优化方法

文章针对航空公司构建中枢辐射航线网络问题展开研究,采用鲁棒优化的方法建立数学模型,针对鲁棒解的特点以及问题的复杂性,将禁忌算法和最短路算法相结合并加以改进,提出了一种适应于求解大型问题的启发式算法,并通过实例进行了仿真实现.     

A comparative study of a new heuristic based on adaptive memory programming and simulated annealing: The case of job shop scheduling

In this study, a general framework is proposed that combines the distinctive features of three well-known approaches: the adaptive memory programming, the simulated annealing, and the tabu search methods. Four variants of a heuristic based on this framework are developed and presented. The performance of the proposed methods is evaluated and compared with a conventional simulated annealing approach using benchmark problems for job shop scheduling. The unique feature of the proposed framework is the use of two short-term memories. The first memory temporarily prevents further changes in the configuration of a provisional solution by maintaining the presence of good elements of such solutions. The purpose of the second memory is to keep track of good solutions found during an iteration, so that the best of these can be used as the starting point in a subsequent iteration. Our computational results for the job shop scheduling problem clearly indicate that the proposed methods significantly outperform the conventional simulated annealing.     

A morph-based simulated annealing heuristic for a modified bin-packing problem

This paper presents a local-search heuristic, based on the simulated annealing (SA) algorithm for a modified bin-packing problem (MBPP). The objective of the MBPP is to assign items of various sizes to a fixed number of bins, such that the sum-of-squared deviation (across all bins) from the target bin workload is minimized. This problem has a number of practical applications which include the assignment of computer jobs to processors, the assignment of projects to work teams, and infinite-loading machine scheduling problems. The SA-based heuristic we developed uses a morph-based search procedure when looking for better allocations. In a large computational study we evaluated 12 versions of this new heuristic, as well as two versions of a previously published SA-based heuristic that used a completely random search. The primary performance measure for this evaluation was the mean percent above the best known objective value (MPABKOV). Since the MPABKOV associated with the best version of the random-search SA heuristic was more than 290 times larger than that of the best version of the morph-based SA heuristic, we conclude that the morphing process is a significant enhancement to SA algorithms for these problems.     

Particle swarm optimization approach to portfolio optimization

The survey of the relevant literature showed that there have been many studies for portfolio optimization problem and that the number of studies which have investigated the optimum portfolio using heuristic techniques is quite high. But almost none of these studies deals with particle swarm optimization (PSO) approach. This study presents a heuristic approach to portfolio optimization problem using PSO technique. The test data set is the weekly prices from March 1992 to September 1997 from the following indices: Hang Seng in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei in Japan. This study uses the cardinality constrained mean-variance model. Thus, the portfolio optimization model is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. The results of this study are compared with those of the genetic algorithms, simulated annealing and tabu search approaches. The purpose of this paper is to apply PSO technique to the portfolio optimization problem. The results show that particle swarm optimization approach is successful in portfolio optimization.     

A neighbourhood scheme with a compressed solution space for the early/tardy scheduling problem

The single machine, distinct due date, early/tardy machine scheduling problem closely models the situation faced by Just-In-Time manufacturers. This paper develops a new method of finding good quality solutions to this scheduling problem by using the concept of a ‘compressed solution space’, based on a binary representation of the early/tardy scheduling problem, and tabu search. A heuristic which simultaneously sequences and schedules the jobs is developed to perform the conversion between the compressed and physical solution spaces. Results show that the compressed solution space performs well with small problems, and is superior to standard tabu search solution spaces for large-scale, realistically sized problems.     

混合启发式算法求解多配送人员车辆路径问题

为解决带时间窗和多配送人员的车辆路径问题,本文采用混合启发式算法对其进行求解。该算法主要由整数规划重组、局部搜索算法和模拟退火算法三部分组成。在算法中,整数规划重组有效提高了解的质量,局部搜索算法和模拟退火算法保证了算法搜索的深入性和广泛性。通过与CPLEX和禁忌搜索算法进行对比,证实了混合启发式算法实用价值更高,求解效果更好。     

Solving the flight perturbation problem with meta heuristics

When there is a perturbation in a carefully constructed aircraft schedule, e.g. an aircraft breakdown, it is important to minimize the negative consequences of this disturbance. Here, a tabu search and a simulated annealing approach to the flight perturbation problem are presented. The heuristics use a tree-search algorithm to find new schedules for the aircraft, and utilize a path relinking strategy to explore paths between structurally different solutions. The computational results indicate that the solution strategies, especially the tabu search, can be successfully used to solve the flight perturbation problem.     

An adaptive tabu-simulated annealing for concave cost transportation problems

The transportation problem (TP) is one of the most popular network problems because of its theoretical and practical importance. If the transportation cost linearly depends on the transported amount of the product, then TP is solvable in polynomial time with linear programming methods. However, in the real world, the transportation costs are generally nonlinear, frequently concave where the unit cost for transporting products decreases as the amount of products increases. Since concave cost transportation problems (ccTPs) are NP-hard, solving large-scale problems is time consuming. In this study, we propose a hybrid algorithm based on the concepts borrowed from tabu search (TS) and simulated annealing (SA) to solve the ccTP. This algorithm, called ATSA (adaptive tabu-simulated annealing), is an SA approach supplemented with a tabu list and adaptive cooling strategy. The effectiveness of ATSA has been investigated in two stages using a set of TPs with different sizes. The first stage includes performance analysis of ATSA using SA, ASA (adaptive simulated anealing) and TS, which are basic forms of ATSA. In the second stage, ATSA has been compared with the heuristic approaches given in the literature for ccTP. Statistical analysis shows that ATSA exhibits better performance than its basic forms and heuristic approaches.     

Comparative performance of tabu search and simulated annealing heuristics for the quadratic assignment problem

For almost two decades the question of whether tabu search (TS) or simulated annealing (SA) performs better for the quadratic assignment problem has been unresolved. To answer this question satisfactorily, we compare performance at various values of targeted solution quality, running each heuristic at its optimal number of iterations for each target. We find that for a number of varied problem instances, SA performs better for higher quality targets while TS performs better for lower quality targets.     

Performance of simulated annealing-based heuristic for the unconstrained binary quadratic programming problem

The unconstrained binary quadratic programming problem (BQP) is known to be NP-hard and has many practical applications. This paper presents a simulated annealing (SA)-based heuristic for the BQP. The new SA heuristic for the BQP is based on a simple (1-opt) local search heuristic and designed with a simple cooling schedule, but the multiple annealing processes are adopted. To show practical performances of the SA, we test on publicly available benchmark instances of large size ranging from 500 to 2500 variables and compare them with other heuristics such as multi-start local search, the previous SA, tabu search, and genetic algorithm incorporating the 1-opt local search. Computational results indicate that our SA leads to high-quality solutions with short times and is more effective than the competitors particularly for the largest benchmark set. Furthermore, the values of new best-known solutions found by the SA for several large instances are also reported.