基于有限缓存区的多任务并行分拣调度研究

通过对某物流车间的实际调研,将自动化立体仓库出货台空间限制作为优化问题的约束条件,建立订单并行分拣模式下堆垛机调度问题的模型,并采用蚁群算法进行求解。在求解过程中,根据问题假设设定了算法相关的状态转移概率公式,并采用动态更新信息素浓度的改进型方式避免传统蚁群算法早熟的情况。最后根据工厂的实际订单信息给出了算例,并通过两种不同算法和不同参数设置的比较,说明通过蚁群算法求解该优化问题的有效性。数值试验显示该蚁群算法相比传统优化算法效率提升了10.5%。     

Minimising makespan on parallel machines with precedence constraints and machine eligibility restrictions

The block erection problem is defined as a parallel machine scheduling problem with precedence constraints and machine eligibility restrictions. A heuristic algorithm combined of the largest total amount of processing first rule (LTAP) and the enhanced smallest machine load first rule (ESML) is proposed to minimise makespan for the block erection in a shipyard. Finally, four lower bounds and the percentage of the reduced makespan compared with the current solutions are defined to evaluate the performance of the proposed algorithm. The experiments are performed on data selected from a shipbuilding company, and the results demonstrate that the presented algorithm can effectively find a good solution to minimise the makespan of the block erection problem.     

A re-entrant hybrid flow shop scheduling problem with machine eligibility constraints

A production scheduling problem originating from a real rotor workshop is addressed in the paper. Given its specific characteristics, the problem is formulated as a re-entrant hybrid flow shop scheduling problem with machine eligibility constraints. A mixed integer linear programming model of the problem is provided and solved by the Cplex solver. In order to solve larger sized problems, a discrete differential evolution (DDE) algorithm with a modified crossover operator is proposed. More importantly, a new decoder addressing the machine eligibility constraints is developed and embedded to the algorithm. To validate the performance of the proposed DDE algorithm, various test problems are examined. The efficiency of the proposed algorithm is compared with two other algorithms modified from the existing ones in the literatures. A one-way ANOVA analysis and a sensitivity analysis are applied to intensify the superiority of the new decoder. Tightness of due dates and different levels of scarcity of machines subject to machine eligibility restrictions are discussed in the sensitivity analysis. The results indicate the pre-eminence of the new decoder and the proposed DDE algorithm.     

Minimising makespan on a single batch processing machine with dynamic job arrivals and non-identical job sizes

Batch scheduling is a prevalent policy in many industries such as burn-in operations in semiconductor manufacturing and heat treatment operations in metalworking. In this paper, we consider the problem of minimising makespan on a single batch processing machine in the presence of dynamic job arrivals and non-identical job sizes. The problem under study is NP-hard. Consequently, we develop a number of efficient construction heuristics. The performance of the proposed heuristics is evaluated by comparing their results to two lower bounds, and other solution approaches published in the literature, namely the first-fit longest processing time-earliest release time (FFLPT-ERT) heuristic, hybrid genetic algorithm (HGA), joint genetic algorithm and dynamic programming (GA+DP) approach and ant colony optimisation (ACO) algorithm. The computational experiments demonstrate the superiority of the proposed heuristics with respect to solution quality, especially for the problems with small size jobs. Moreover, the computational costs of the proposed heuristics are very low.     

考虑作业效率差异的同类岸桥作业调度优化

针对集装箱港口岸桥调度过程中,岸桥具有作业效率差异的特点,将其视为同类平行机调度问题,同时结合岸桥作业不可相互穿越与安全距离等特有约束,建立了更加符合实际的岸桥作业调度混合整数规划模型,其优化目标是最小化装卸作业的最大完工时间。针对问题的NP-hard特性,设计了求解模型的遗传算法,对算法搜索空间进行了讨论,并推导了问题的下界。最后,通过实验算例验证了模型与算法的有效性。     

A particle swarm optimization algorithm for hybrid flow-shop scheduling with multiprocessor tasks

The multistage hybrid flow-shop scheduling problem with multiprocessor tasks has been found in many practical situations. Due to the essential complexity of the problem, many researchers started to apply metaheuristics to solve the problem. In this paper, we address the problem by using particle swarm optimization (PSO), a novel metaheuristic inspired by the flocking behaviour of birds. The proposed PSO algorithm has several features, such as a new encoding scheme, an implementation of the best velocity equation and neighbourhood topology among several different variants, and an effective incorporation of local search. To verify the PSO algorithm, computational experiments are conducted to make a comparison with two existing genetic algorithms (GAs) and an ant colony system (ACS) algorithm based on the same benchmark problems. The results show that the proposed PSO algorithm outperforms all the existing algorithms for the considered problem.     

Parallel machine scheduling problem with ready times,due times and sequence-dependent setup times using meta-heuristic algorithms

This article considers a parallel machine scheduling problem with ready times, due times and sequence-dependent setup times. The objective of this problem is to determine the allocation policy of jobs and the scheduling policy of machines to minimize the weighted sum of setup times, delay times and tardy times. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, two meta-heuristics, genetic algorithm (GA) and a new population-based evolutionary meta-heuristic called self-evolution algorithm (SEA), are proposed. The performances of the meta-heuristic algorithms are evaluated through comparison with optimal solutions using several randomly generated examples.     

A discrete artificial bee colony algorithm for single machine scheduling problems

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.     

Coupling ant colony optimization and the extended great deluge algorithm for the discrete facility layout problem

This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experimental results indicate also that the ACO/EGD heuristic methodology offers advantages over other algorithms based on meta-heuristics in terms of solution quality.     

Parallel machine scheduling with step-deteriorating jobs and setup times by a hybrid discrete cuckoo search algorithm

This article considers the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times. The objective is to minimize the total tardiness by determining the allocation and sequence of jobs on identical parallel machines. In this problem, the processing time of each job is a step function dependent upon its starting time. An individual extended time is penalized when the starting time of a job is later than a specific deterioration date. The possibility of deterioration of a job makes the parallel machine scheduling problem more challenging than ordinary ones. A mixed integer programming model for the optimal solution is derived. Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem. In order to generate a good initial swarm, a modified Biskup–Hermann–Gupta (BHG) heuristic called MBHG is incorporated into the population initialization. Several discrete operators are proposed in the random walk of Lévy flights and the crossover search. Moreover, a local search procedure based on variable neighbourhood descent is integrated into the algorithm as a hybrid strategy in order to improve the quality of elite solutions. Computational experiments are executed on two sets of randomly generated test instances. The results show that the proposed hybrid algorithm can yield better solutions in comparison with the commercial solver CPLEX^® with a one hour time limit, the discrete cuckoo search algorithm and the existing variable neighbourhood search algorithm.     

Ant colony optimization system for a multi-quantitative and qualitative objective job-shop parallel-machine-scheduling problem

This paper addresses a multi-stage job-shop parallel-machine-scheduling problem with an ant colony optimization system developed. The problem is practically important and yet more complex, especially when customer order splitting in multiple lots for the reduction of operation times in each workstation is allowed. It also includes the decisions of the numbers of parallel machines in workstations dynamically scheduled. In addition, this paper also addresses the multiple-objectives scheduling. For the practical concern, in addition to the production (or quantitative) objectives, the marketing (strategic or qualitative) criteria are also considered. A soft constraint thus may be realized from a thus-called qualitatively evaluated order sequence. The soft constraint with the ant colony optimization solution constructs a penalty function for the multiple qualitative objectives and the results of scheduling obtained by ant colony optimization. For this problem, the ant colony optimization components (including the network representation, tabu lists, transition probabilities, and pheromone trail updating) are also developed and adapted for the multiple objectives. The experiment results of parameter design and different problem sizes are provided. The results of a genetic algorithm also developed for the present problem under the developed system concept are also provided, since in the literature the genetic algorithm has also not been explored for the present problem with multiple objectives and order splitting. The results of both solution techniques show the potential usefulness of the system and are comparable, but the ant colony optimization provides a more computationally efficient better result.     

求解一类无关并行机调度的遗传迭代贪心算法

以最小化最大完工时间为优化目标,建立带工单加工约束和序相关设置时间无关并行机调度问题的混合整数规划模型;考虑现实生产对求解算法在质量、收敛速度和鲁棒性等方面的较高要求,构建一种混合遗传-迭代贪心算法。在遗传变异操作中嵌入一种迭代贪心策略的破坏和构建机制,用于提高算法的种群多样性;引入基于破坏与构建操作设计而成的快速局部搜索算法来增强算法的局部开发能力;基于实际生产数据的相关特征随机生成了一系列计算案例,并通过实验说明所提新型混合算法相较于传统混合算法的优越性。     

资源约束项目排序问题的一种修正蚁群算法

根据蚁群算法的性质与资源约束项目排序问题(CPSP:Resource-Constrained Project Schedul- ing Problem)的特征,本文给出了蚁群算法中信息素的表示及更新方案、启发信息的计算方法等,由此提出了一种求解RCPSP的修正蚁群算法。最后,通过对项目排序问题库中的标准问题集进行计算,结果表明本文提出的修正蚁群算法是可行优良的。     

图的度量维数问题的0-1蚁群条件着色分辨算法研究

图的度量维数问题（MDP）是一类在机器导航、声呐系统布置、化学、数据分类等领域有重要应用的组合优化问题．针对该问题,本文通过引入图的分辨表存储结构,建立了非线性求解模型;同时,通过改进现有蚁群算法的参数设计,利用全局搜索和局部搜索相结合的策略,建立了求解模型的改进型蚁群算法．数值对比分析验证了算法的有效性：全局搜索和局部搜索的结合较大程度的改进了算法求解质量;在规则图上提高算法求解质量具有一定挑战;与遗传算法计算结果相比较,本文提出的算法不仅在求解质量方面有所提升,而且在最坏的情况下能为图提供极小分辨集. 最后,本文探索了部分算法参数对算法求解质量的影响,并给出了进一步研究课题．     

混合模拟植物生长算法在包装件配送中的应用

目的针对改进模拟植物生长算法(IPGSA)容易陷入局部最优解及其算法运行时间较长,提出混合模拟植物生长算法(HPGSA)来求解带时间窗车辆调度问题(VSPTW)。方法在IPGSA基础上,提出求解包装件物流配送中VSPTW的混合模拟植物生长算法(HPGSA)。改进IPGSA初始调度方案的构造方式,设计求解VSPTW的C-W算法用于构造HPGSA的初始调度方案;改进IPGSA的邻域搜索算子,选择插入搜索算子和互换搜索算子对HPGSA进行邻域搜索;对18个不同规模的Solomon算例进行仿真测试。结果相对于其他智能算法,HPGSA具有更好的求解性能,能够保证VSPTW对求解算法的要求。结论 HPGSA的全局优化能力、稳定性和运行速度均优于IPGSA、遗传算法、蚁群算法和禁忌搜索算法。     

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.     

Multi-objective unrelated parallel machine scheduling: a Tabu-enhanced iterated Pareto greedy algorithm

This work proposes a high-performance algorithm for solving the multi-objective unrelated parallel machine scheduling problem. The proposed approach is based on the iterated Pareto greedy (IPG) algorithm but exploits the accessible Tabu list (TL) to enhance its performance. To demonstrate the superior performance of the proposed Tabu-enhanced iterated Pareto greedy (TIPG) algorithm, its computational results are compared with IPG and existing algorithms on the same benchmark problem set. Experimental results reveal that incorporating the accessible TL can eliminate ineffective job moves, causing the TIPG algorithm to outperform state-of-the-art approaches in the light of five multi-objective performance metrics. This work contributes a useful theoretical and practical optimisation method for solving this problem.     

Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times

In this paper, a genetic algorithm (GA) with local search is proposed for the unrelated parallel machine scheduling problem with the objective of minimising the maximum completion time (makespan). We propose a simple chromosome structure consisting of random key numbers in a hybrid genetic-local search algorithm. Random key numbers are frequently used in GAs but create additional difficulties when hybrid factors are implemented in a local search. The best chromosome of each generation is improved using a local search during the algorithm, but the better job sequence (which might appear during the local search operation) must be adapted to the chromosome that will be used in each successive generation. Determining the genes (and the data in the genes) that would be exchanged is the challenge of using random numbers. We have developed an algorithm that satisfies the adaptation of local search results into the GAs with a minimum relocation operation of the genes’ random key numbers – this is the main contribution of the paper. A new hybrid approach is tested on a set of problems taken from the literature, and the computational results validate the effectiveness of the proposed algorithm.     

Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem

In this article, the genetic algorithm (GA) and fully informed particle swarm (FIPS) are hybridized for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. In the proposed hybrid genetic algorithm–fully informed particle swarm algorithm (HGFA), FIPS is a popular variant of the particle swarm optimization algorithm. A random key and the related mode list representation schemes are used as encoding schemes, and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. Furthermore, the existing mode improvement procedure in the literature is modified. The results show that the proposed mode improvement procedure remarkably improves the project makespan. Comparing the results of the proposed HGFA with other approaches using the well-known PSPLIB benchmark sets validates the effectiveness of the proposed algorithm to solve the MRCPSP.     

A hybrid particle swarm optimisation for scheduling just-in-time single machine with preemption,machine idle time and unequal release times

This paper addresses preemption in just-in-time (JIT) single–machine-scheduling problem with unequal release times and allowable unforced machine idle time as realistic assumptions occur in the manufacturing environments aiming to minimise the total weighted earliness and tardiness costs. Delay in production systems is a vital item to be focussed to counteract lost sale and back order. Thus, JIT concept is targeted including the elements required such as machine preemption, machine idle time and unequal release times. We proposed a new mathematical model and as the problem is proven to be NP-hard, three meta-heuristic approaches namely hybrid particle swarm optimisation (HPSO), genetic algorithm and imperialist competitive algorithm are employed to solve the problem in larger sizes. In HPSO, cloud theory-based simulated annealing is employed with a certain probability to avoid being trapped in a local optimum. Taguchi method is applied to calibrate the parameters of the proposed algorithms. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The performance of the proposed algorithms is evaluated in terms of relative percent deviation and computational time where the computational results clarify better performance of HPSO than other algorithms in quality of solutions and computational time.