带准备时间的作业车间分批调度算法

研究了带有准备时间的作业车间分批排序问题,通过模拟计算将3种算法进行了对比分析.结果表明,遗传退火两阶段算法不受等分批限制,产生的各子批大小可以不同,这使得解空间更大,因而得到满意解的可能性大大增加.     

可靠性优化的一种新的算法

建立了可靠性冗余优化模型,提出了一种基于粒子群优化算法的可靠性优化的新方法,该方法结合了遗传算法的思想。实例结果表明,粒子群算法比模拟退火算法和遗传算法效果好。     

多生产线且多节点环境下的订单选择模型

由于有限资源的约束,企业经常拒绝接受一些不能保证能按时交付的订单。因为延迟交付带来的惩罚往往使这些订单无利可图,其罚金一般与延期时间长度相关。Slotnick等人提出的延迟惩罚订单选择模型是优化决策此类问题的常用模型。本文将原模型由单节点扩展到较复杂的多节点且多生产线应用环境下,给出了相应的订单选择模型。并设计了多维交替优化的模拟退火算法来同时优化选择和排序两个维度,给出了详细的算法步骤。然后对模拟数据进行了仿真算例求解,验证算法的求解效果和计算效率。同时对比了两种不同订单排序策略,算例结果也表明：跨生产线的混合订单排序方式能比传统的订单不跨线同步方式获得更好的目标收益。     

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.     

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.     

工期不确定的模具车间柔性Flow-shop调度问题

由于模具制造属于非重复性单件订货生产,模具加工的任务工期具有较强的不确定性,导致生产调度混乱.为制定合理可行的生产调度方案,建立了任务工期离散概率模型,以最大完工时间的期望值最小为目标,建立不确定工期柔性Flow-shop调度模型;在遗传算法交叉、变异等操作中融入模拟退火操作,将遗传算法的全局搜索能力与模拟退火算法的良好局部搜索能力相结合,设计了不确定工期的柔性Flow-shop调度问题混合遗传模拟退火算法.利用混合遗传模拟退火算法对调度模型进行求解,通过仿真实验表明,该研究对于解决工期不确定的模具车间柔性Flow-shop调度问题是行之有效的.     

针对项目式生产调度的模拟退火算法

分析了某航空航天企业生产现状,考虑到工时不确定性,建立了数学模型。提出了模拟退火启发式算法,以此制定主动调度计划,并且结合企业生产的实际情况,进行了验证。应用实践表明,相较于单纯求解工期最短的调度计划,该算法的解能很大程度上提高计划的鲁棒性,不需要过多延长工期,对于工时可变范围大的生产项目尤其适用。     

Reliability-based maintenance and job scheduling for identical parallel machines

Quality has an important role in manufacturing, and on the other hand, machine condition has a significant effect on quality. Based on this fact, all manufacturers integrate the production scheduling with maintenance activities to keep the machines in perfect conditions. In this paper, we propose a mixed integer nonlinear model to optimise the quality cost, maintenance cost, earliness–tardiness cost and interruption cost simultaneously. We assume that if machines work in undesirable conditions, their quality is reduced, resulting in quality cost. On the other hand, if the machines are repaired to decrease the quality cost, maintenance cost and other cost such as earliness–tardiness cost and interruption cost are imposed to the manufacturer. Several numerical instances are implemented by the proposed model to show the model effectiveness to obtain the best maintenance and production scheduling with minimum quality cost.     

共享策略下甩挂车辆调度问题的分析及求解

目的为了克服现阶段下甩挂货运过程中存在的空载率高、牵引车利用率低等问题,从服务点间的货运关系出发,对货运单循环系统中共享策略下甩挂车辆调度问题进行设计研究。方法以牵引车行驶总里程为目标函数,构建共享策略下甩挂车辆调度模型,并基于模拟退火算法对其进行优化求解。结果以山东省的2家小型运输企业为案例进行了仿真分析,与普通车辆调度模式分析比较可知,共享策略下调度模式的空载率、单位货运成本、单位货运油耗、牵引车利用率分别优化了79.6%,50%,22.6%,171.4%。结论文中构建的共享策略下甩挂车辆调度模型及其求解算法可行有效,在减少空载行程、提高燃油利用率等方面具有良好的效果。     

退火-遗传算法寻优及其实现

分析了遗传算法及退火算法的优缺点,提出用退火算法改进遗传算法局部的最优值搜索效率低问题。退火算法与遗传算法融合后,使算法在寻优结果上更加迅速精确。通过水泥的配比工程实例,与单纯的遗传算法的结果进行对比,说明该方法是有效的。     

基于Q学习的任务调度问题的改进研究

论文针对协同工作中的任务调度问题,建立了相应的马尔可夫决策过程模型,在此基础上提出了一种改进的基于模拟退火的Q学习算法。该算法通过引入模拟退火,并结合贪婪策略,以及在状态空间上的筛选判断,显著地提高了收敛速度,缩短了执行时间。最后与其它文献中相关算法的对比分析,验证了本改进算法的高效性。     

The impact of the cost of quality on serial supply‐chain network design

This paper presents a model for supply‐chain design that considers the Cost of Quality as well as the traditional manufacturing and distribution costs (SC‐COQ model). It includes three main contributions: (1) the SC‐COQ model internally computes quality costs for the whole supply chain considering the interdependencies among business entities, whereas previous works have assumed exogenously given Cost of Quality functions; (2) the SC‐COQ model can be used at a strategic planning level to design a logistic route that achieves a maximum profit while considering the overall quality level within a supply chain; and (3) we provide two solution methods based on simulated annealing and a genetic algorithm and perform computational experiments on test instances.     

A multi-point simulated annealing heuristic for solving multiple objective unrelated parallel machine scheduling problems

This study considers the problem of job scheduling on unrelated parallel machines. A multi-objective multi-point simulated annealing (MOMSA) algorithm was proposed for solving this problem by simultaneously minimising makespan, total weighted completion time and total weighted tardiness. To assess the performance of the proposed heuristic and compare it with that of several benchmark heuristics, the obtained sets of non-dominated solutions were assessed using four multi-objective performance indicators. The computational results demonstrated that the proposed heuristic markedly outperformed the benchmark heuristics in terms of the four performance indicators. The proposed MOMSA algorithm can provide a new benchmark for future research related to the unrelated parallel machine scheduling problem addressed in this study.