解决并行多机提前/拖后调度问题的混合遗传算法方法

研究了带有公共交货期的并行多机提前/拖后调度问题.提出了一种混合遗传算法方法,以便于确定公共交货期和每台机器上加工的任务代号及其加工顺序,即找到一个最优公共交货期和最优调度,使加工完所有任务后交货期安排的成本、提前交货成本和拖后交货成本的总和最小.数值计算结果表明了该混合遗传算法优于启发式算法,并能适用于较大规模并行多机提前/拖后调度问题.算法计算量小,鲁棒性强.     

一类赋权诱导问题的多项式时间算法

本文介绍了赋权诱导推理的基本概念及其求解算法复杂性研究的现状。诱导推理在人工智能领域有广泛的应用前景，但现有的求解算法都未能从根本上排除ＮＰ－难解性的困扰，本文考虑了其中一类子问题；二阶独立赋权诱导问题，并给出求出其最优解的多项式时间算法。     

基于二维可变邻域编码方式的混合遗传算法

针对现有混合遗传算法无法兼顾有效性及高效性的问题,提出一种基于二维可变邻域编码方式的新型混合遗传算法(VNHGA)。首先提出了一种将个体“基因型”与“邻域型”分开编码、同步遗传的新型编码方式,以替换传统二进制编码方式;然后设计了一种稳定变异算子,以替换传统变异算子来提高效率。通过多维函数最小值问题对VNHGA进行测试:首先验证采用所提二维可变邻域编码方式后,使用“鲍德温(Baldwin)效应”作为将局部搜索嵌入传统遗传算法策略时,相对于基于“拉马克(Lamarckian)进化”的嵌入策略,仍然具有采用传统二进制编码方式时的特性,即具有良好有效性但高效性不足;其次验证引入稳定变异算子后,算法在保持其有效性的同时提升了效率,运行时间缩短到之前的50%左右;最后,与两种改进混合遗传算法进行比较,验证所提算法优势。结果表明VNHGA兼具有效性与高效性特点,可用于解决最优化问题。     

A population-based variable neighborhood search for the single machine total weighted tardiness problem

This paper investigates the single machine total weighted tardiness problem, in which a set of independent jobs with distinct processing times, weights, and due dates are to be scheduled on a single machine to minimize the sum of weighted tardiness of all jobs. This problem is known to be strongly NP-hard, and thus provides a challenging area for metaheuristics. A population-based variable neighborhood search (PVNS) algorithm is developed to solve it. This algorithm differs from the basic variable neighborhood search (VNS). First, the PVNS consists of a number of iterations of the basic VNS, and in each iteration a population of solutions is used to simultaneously generate multiple trial solutions in a neighborhood so as to improve the search diversification. Second, the PVNS adopts a combination of path-relinking, variable depth search and tabu search to act as the local search procedure so as to improve the search intensification. Computational experiments show that the proposed PVNS algorithm can obtain the optimal or best known solutions within a reasonable computation time for all standard benchmark problem instances from the literature.     

An improved scatter search algorithm for the single machine total weighted tardiness scheduling problem with sequence-dependent setup times

In this paper, a scatter search algorithm with improved component modules is proposed to solve the single machine total weighted tardiness problem with sequence-dependent setup times. For diversification generation module, both random strategy based heuristics and construction heuristic are adopted to generate the diversified population. For improvement module, variable neighborhood search based local searches are embedded into the algorithm to improve the trial solutions and the combined solutions. For reference set update module, the number of edges by which the two solutions differ from each other is counted to measure the diversification value between two solutions. We also propose a new strategy in which the length of the reference set could be adjusted adaptively to balance the computing time and solving ability. In addition, a discrete differential evolution operator is proposed with another two operators constitute the combination module to generate the new trial solutions with the solutions in the subsets. The proposed algorithm is tested on the 120 benchmark instances from the literature. Computational results indicate that the average relative percentage deviations of the improved algorithm from the ACO_AP, DPSO, DDE and GVNS are −5.16%, −3.33%, −1.81% and −0.08%, respectively. Comparing with the state-of-the-art and exact algorithms, the proposed algorithm can obtain 78 optimal solutions out of 120 instances within a reasonable computational time.     

HYBRID ALGORITHMS FOR THE CONSTRAINT SATISFACTION PROBLEM

It might be said that there are five basic tree search algorithms for the constraint satisfaction problem (csp), namely, naive backtracking (BT), backjumping (BJ), conflict-directed backjumping (CBJ), backmarking (BM), and forward checking (FC). In broad terms, BT, BJ, and CBJ describe different styles of backward move (backtracking), whereas BT, BM, and FC describe different styles of forward move (labeling of variables). This paper presents an approach that allows base algorithms to be combined, giving us new hybrids. The base algorithms are described explicitly, in terms of a forward move and a backward move. It is then shown that the forward move of one algorithm may be combined with the backward move of another, giving a new hybrid. In total, four hybrids are presented: backmarking with backjumping (BMJ), backmarking with conflict-directed backjumping (BM-CBJ), forward checking with backjumping (FC-BJ), and forward checking with conflict-directed backjumping (FC-CBJ). The performances of the nine algorithms (BT, BJ, CBJ, BM, BMJ, BM-CBJ, FC, FC-BJ, FC-CBJ) are compared empirically, using 450 instances of the ZEBRA problem, and it is shown that FC-CBJ is by far the best of the algorithms examined.     

求解柔性作业车间调度的混合粒子群算法

将粒子群算法运用于求解柔性作业车间调度问题,采用基于轮盘赌的编码方法以及基于邻域互换的局部搜索方法。通过两个不同规模算例的试验计算,与基于粒子位置取整的编码方法进行对比分析,说明了轮盘赌编码方法求解柔性作业车间调度问题的有效性。且采用该编码方法的混合粒子群算法在求解柔性作业车间调度问题时具有更好的求解性能。     

批量流水线调度问题的混合差分进化算法

针对E/T指标的批量流水线调度问题,提出了差分进化调度算法。该算法采用基于实数的编码方式,利用最优目标个体的扰动产生变异个体,通过变异个体与目标个体的交叉产生试验个体,提高了最优目标个体信息共享,并结合模拟退火算法给出了两种混合求解策略。仿真试验表明了所得算法的可行性和高效性。     

混和进化算法求解具有分段恶化效应的并行机调度问题

本文提出了一种新的混合进化算法求解具有线性恶化的并行机调度问题,目标是使总完工时间最小.该算法采用对立策略以及最小比率优先规则生成初始种群,并且引入种群多样度指标加快算法的收敛;同时加入含有3-opt扰动算子的变邻域搜索算法对遗传算法得到的结果进行局部搜索.通过对不同规模算例的实验进行仿真,其结果与传统GA和VNS算法相比,效果均有所提升.     

A discrete differential evolution algorithm for the single machine total weighted tardiness problem with sequence dependent setup times

This paper is concerned with solving the single machine total weighted tardiness problem with sequence dependent setup times by a discrete differential evolution algorithm developed by the authors recently. Its performance is enhanced by employing different population initialization schemes based on some constructive heuristics such as the well-known NEH and the greedy randomized adaptive search procedure (GRASP) as well as some priority rules such as the earliest weighted due date (EWDD) and the apparent tardiness cost with setups (ATCS). Additional performance enhancement is further achieved by the inclusion of a referenced local search (RLS) in the algorithm together with the use of destruction and construction (DC) procedure when obtaining the mutant population. Furthermore, to facilitate the greedy job insertion into a partial solution which will be employed in the NEH, GRASP, DC heuristics as well as in the RLS local search, some newly designed speed-up methods are presented for the insertion move for the first time in the literature. They are novel contributions of this paper to the single machine tardiness related scheduling problems with sequence dependent setup times. To evaluate its performance, the discrete differential evolution algorithm is tested on a set of benchmark instances from the literature. Through the analyses of experimental results, its highly effective performance with substantial margins both in solution quality and CPU time is shown against the best performing algorithms from the literature, in particular, against the very recent newly designed particle swarm and ant colony optimization algorithms of Anghinolfi and Paolucci [A new discrete particle swarm optimization approach for the single machine total weighted tardiness scheduling problem with sequence dependent setup times. European Journal of Operational Research 2007; doi:10.1016/j.ejor.2007.10.044] and Anghinolfi and Paolucci [A new ant colony optimization approach for the single machine total weighted tardiness scheduling problem. http://www.discovery.dist.unige.it/papers/Anghinolfi_Paolucci_ACO.pdf, respectively. Ultimately, 51 out of 120 overall aggregated best known solutions so far in the literature are further improved while other 50 instances are solved equally.     

一种具有混合编码的二进制差分演化算法

差分演化(DE)是Storn和Price于1997年提出的一种基于个体差异重组思想的演化算法,非常适用于求解连续域上的最优化问题.首先引入“差异算子”等概念,给出DE的一种简洁算法描述,并分析了它所具有的特性.然后,为了使DE能够求解离散域上的最优化问题,基于数学变换思想引入“辅助搜索空间”和“个体混合编码”等概念,通过定义一个特殊的满射变换,在辅助搜索空间的作用下将连续域上的高效差分演化搜索变换为离散域上的同步演化搜索,由此提出了第1个二进制差分演化算法：具有混合编码的二进制差分演化算法(HBDE).接着,给出了HBDE的依概率收敛和完全收敛的定义,并利用离散Markov随机理论证明了HBDE是完全收敛的. HBDE不仅完全具有DE的各种特性和所有优点,而且非常适用于求解离散域上的最优化问题,对随机生成的大规模3-SAT问题实例和典型0/1背包问题实例的数值计算表明：该算法具有很好的全局收敛性和稳定性,其性能远远超过二进制粒子群优化算法和遗传算法.     

A multiobjective GRASP–VND algorithm to solve the waste collection problem

In this paper, the waste collection problem (WCP) of a city in the south of Spain is addressed as a multiobjective routing problem that considers three objectives. From the company's perspective, the minimization of the travel cost is desired as well as that of the total number of vehicles. Additionally, from the employee's point of view, a set of balanced routes is also sought. Four variants of a multiobjective hybrid algorithm are proposed. Specifically, a GRASP (greedy randomized adaptive search procedure) with a VND (variable neighborhood descent) is combined. The best GRASP–VND algorithm found is applied in order to solve the real‐world WCP of a city in the south of Spain.     

A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops

Obtaining an optimal solution for a permutation flowshop scheduling problem with the total flowtime criterion in a reasonable computational timeframe using traditional approaches and optimization tools has been a challenge. This paper presents a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime. Iterated greedy algorithms are comprised of local search procedures based on insertion and swap neighborhood structures. In the same context, we also consider a discrete differential evolution algorithm from our previous work. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the discrete artificial bee colony and hybrid differential evolution algorithms is compared against the best performing algorithms from the existing literature in terms of both solution quality and CPU times. Ultimately, 44 out of the 90 best known solutions provided very recently by the best performing estimation of distribution and genetic local search algorithms are further improved by the proposed algorithms with short-term searches. The solutions known to be the best to date are reported for the benchmark suite of Taillard with long-term searches, as well.