求解置换调度问题的改进混合遗传算法

构造了求解极小化总完工时间的置换调度问题的改进混合遗传算法：先采用构造型启发式算法和随机方法共同产生初始种群，然后在选择、交叉和变异等遗传操作之前借助禁忌搜索算法寻找每个个体的局部最优解组成当前种群，再应用种群整体替换策略保存种群中的优秀个体构成新一代种群。改进混合遗传算法有机地结合了禁忌搜索算法的局部搜索性能和遗传算法的全局搜索性能。仿真实验表明，改进混合遗传算法具有比构造型启发式算法和禁忌搜索算法更好的鲁棒性和寻优性能。     

Flexible job shop scheduling with tabu search algorithms

This paper presents a tabu search algorithm that solves the flexible job shop scheduling problem to minimize the makespan time. As a context for solving sequencing and scheduling problems, the flexible job shop model is highly complicated. Alternative operation sequences and sequence-dependent setups are two important factors that frequently appear in various manufacturing environments and in project scheduling. In this paper, we present a model for a flexible job shop scheduling problem while considering those factors simultaneously. The purpose of this paper is to minimize the makespan time and to find the best sequence of operations and the best choice of machine alternatives, simultaneously. The proposed tabu search algorithm is composed of two parts: a procedure that searches for the best sequence of job operations, and a procedure that finds the best choice of machine alternatives. Randomly generated test problems are used to evaluate the performance of the proposed algorithm. Results of the algorithm are compared with the optimal solution using a mathematical model solved by the traditional optimization technique (the branch and bound method). After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented. Computational results indicate that the proposed algorithm can produce optimal solutions in a short computational time for small and medium sized problems. Moreover, it can be applied easily in real factory conditions and for large size problems. The proposed algorithm should thus be useful to both practitioners and researchers.     

自动化制造单元最小完工时间调度问题的混合启发式算法

自动化制造单元最小完工时间调度问题属于NP-hard难题,目前尚缺乏有效的调度方法。为此,提出基于遗传和禁忌搜索的混合启发式算法,用以搜索一组最满意的机器人搬运作业排序。以遗传算法为基本结构,在初始种群产生和交叉、变异操作中引入禁忌搜索技术,以提高优化质量。基于搬运作业规则的初始种群构造算法和两阶段交叉、变异算子克服了传统算子对可行搬运作业排序的破坏,而邻域移动算子则保证了禁忌搜索的多样性和集中性。最后,随机实验结果验证了算法的有效性。     

The tricriteria flowshop scheduling problem

Multicriteria flowshop scheduling problems have been one of the most attractive subjects in recent years. In the multicriteria flowshop scheduling literature, a very limited number of studies have been performed on problems which include a tardiness criterion. In this paper a multicriteria (tricriteria) two-machine flowshop scheduling problem with a tardiness criterion is tackled. The objective is to minimise a weighted sum of total completion time, total tardiness and makespan. An integer programming model is proposed for the problem which belongs to NP-hard class. The modified NEH (Nawaz, Enscore and Ham) algorithm, a tabu search-based heuristic method, random search and the EDD rule (the earliest due date rule) are used to solve problems with up to 2,500 jobs. A computational analysis is conducted to evaluate the performance of the heuristics. The analysis shows that the heuristics are quite efficient, and the performance of the tabu search based heuristic is the best of all in terms of solution quality.     

机器人制造单元的建模与任务调度策略

机器人资源的合理分配和调度是提高制造单元柔性的关键。本文针对一类机器人制造单元的最小周期调度问题，应用极大代数方法建立了单元系统的调度模型，提出了基于禁忌搜索的启发式调度策略，并给出了初始可行解和搜索邻域的构造方法；最后，通过具体的运算实例，验证了所提出方法具有较高的效率，能够解决较大规模的最小周期调度问题，具有广泛的适用性。     

Sequencing of an M machine flow shop with setup, processing and removal times separated

In this research, a flow shop scheduling problem in which setup, processing and removal times are separated, with the objective of minimizing makespan, is considered. A tabu search based heuristic is presented for solving the addressed problem. The proposed heuristic begins with the construction of artificial processing times for each operation; then a modified NEH algorithm is used to generate an initial solution, followed by a designed tabu search procedure applied for further improvement of the solution. The proposed heuristic, as well as the existing one, is evaluated in a large number of randomly generated problems. The results of the experimental investigations of the proposed heuristic algorithm and the existing heuristic in meeting the objective of makespan are also reported. It is found that the solution quality of the proposed tabu search heuristic is better than that of the existing heuristic, but for large size problems more computational efforts are needed; however, the CPU time is still acceptable.     

CAPACITATED LOT SIZING AND SCHEDULING PROBLEMS USING HYBRID GA／TS APPROACHES

The capacitated lot sizing and scheduling probbm that involves in determining the production amounts and release dates for several items over a given planning horizon are given to meet dynamic order demand without incurring backloggings. Thi: problem considering overtime capacity is studied. The mathematical model is presented, and a genelic algorithm (GA) approach is developed to solve the problem. The initial solutions are generated after using heuristic method. Capacity balancing procedure is employed to stipulate the feasibility of the solutions. In addition, a technique based on Tabu search (TS) is inserted into the genetic algorithm to d;al with the scheduled overtime and help the convergence of algorithm. Computational simulation is conducted to test the efficiency of the proposed hybrid approach, which turns out to improve both the solution quality and execution speed.     

A Tabu Search Algorithm for Single Machine Scheduling with Release Times,Due Dates,and Sequence-Dependent Set-up Times

In this paper, we present a tabu search algorithm that schedules N jobs to a single machine in order to minimise the maximum lateness of the jobs. The release times, due dates, and sequence-dependent set-up times of the jobs are assumed to exist. We modified the original tabu search method to be suitable for the scheduling problem. The proposed tabu search algorithm is composed of two parts: a MATCS (modified apparent tardiness cost with set-ups) rule for finding an efficient initial solution, and the tabu search method to seek a near optimal solution from the initial solution. The experimental results show that the tabu search algorithm obtains much better solutions more quickly than the RHP (rolling horizon procedure) heuristic suggested by Ovacik and Uzsoy.     

A genetic-algorithm-based optimization model for scheduling flexible assembly lines

In this paper, a scheduling problem in the flexible assembly line (FAL) is investigated. The mathematical model for this problem is presented with the objectives of minimizing the weighted sum of tardiness and earliness penalties and balancing the production flow of the FAL, which considers flexible operation assignments. A bi-level genetic algorithm is developed to solve the scheduling problem. In this algorithm, a new chromosome representation is presented to tackle the operation assignment by assigning one operation to multiple machines as well as assigning multiple operations to one machine. Furthermore, a heuristic initialization process and modified genetic operators are proposed. The proposed optimization algorithm is validated using two sets of real production data. Experimental results demonstrate that the proposed optimization model can solve the scheduling problem effectively.     

Solving a new fuzzy multi-objective model for a multi-skilled manpower scheduling problem by particle swarm optimization and elite tabu search

Manpower scheduling is a complicated problem to solve that strives to satisfy employers’ objectives and employees’ preferences as much as possible by generating fairly desirable schedules. But sometimes, objectives and preferences may not be determined precisely. This problem causes manpower scheduling takes the fuzzy nature. This paper presents a new fuzzy multi-objective mathematical model for a multi-skilled manpower scheduling problem considering imprecise target values of employers’ objectives and employees’ preferences. Hence, a fuzzy goal programming model is developed for the presented mathematical model and two fuzzy solution approaches are used to convert the fuzzy goal programming model to two single-objective models. Since the complexity of a manpower scheduling problem is NP-hard, the single-objective models are solved by two meta-heuristics, namely particle swarm optimization and elite tabu search. Eventually, the performance of the proposed algorithms is verified and the results are compared with each other to select the best schedules.     

面向模具车间机器人制造单元的任务调度策略

分析了模具生产加工的特点,针对模具车间机器人制造单元作业任务难以合理分配的问题,以工件组的总体完工时间最小为优化目标,提出了任务调度的数学模型,并建立了一种遗传禁忌混合优化算法.最后,通过实例分析说明了所提方法的可靠性和有效性.     

Scheduling in a two-machine flow-shop for earliness/tardiness under learning effect

In the literature, earliness/tardiness (E/T) problem was known as weighted absolute deviation problem, and both tardiness and earliness is very important performance criteria for scheduling problem. While total tardiness criteria provides adaptation for due date (ignoring results of earliness done jobs), it deals with only cost of tardiness. However this phenomenon has been started to change with just-in-time (JIT) production concept. On JIT production, earliness is as important as tardiness. The phenomenon of the learning effect has been extensively studied in many different areas of operational research. However, there have been a few studies in the general context of production scheduling such as flow-shop scheduling. This paper addresses the minimization of the total earliness/tardiness penalties under learning effects in a two-machine flow-shop scheduling problem. Jobs have a common due date. We present mathematical model to obtain an optimal schedule for a given job sequence. We also present heuristics that use genetic algorithm and tabu search, based on proposed properties. Furthermore, random search was used for showing the significance of the study by comparison purpose. A new set of benchmark problems is presented with the purpose of evaluating the heuristics. The experimental results show that the performance of proposed approach is quite well, especially for the instances of large size.     

面向订单的瓶颈资源识别与单机成组作业调度

具有分类设置与提交时间的单机成组作业调度问题明显是NP-Hard问题。一些问题的多项式求解方法不能保证求取最优解。一些启发式算法无法保证瓶颈资源多目标最优。基于改进禁忌搜索算法,建立了此类单机成组作业调度模型,可搜索到该问题的最优解。仿真结果表明,该算法性能优于WSPT启发式算法,能够解决面向订单的多品种小批量生产企业中的瓶颈识别与多目标排产问题。     

A drastic hybrid heuristic algorithm to approach to JIT policy considering controllable processing times

Job scheduling has always been a challenging task in modern manufacturing and the most real life scheduling problems which involves multi-criteria, multi-machine environments. In this research, the single-machine scheduling problem is studied in which job processing times are controllable, namely, they may vary within a specified interval. The goal of this research is to minimize total tardiness and earliness on a single machine, simultaneously. In this context, we first propose a mathematical model for the considered problem and then a net benefit compression–net benefit expansion heuristic is presented for obtaining the set of amounts of compression and expansion of jobs processing times in a given sequence. Two meta-heuristic approaches are then employed to solve medium-to-large-sized problems as local search methods. Thereafter, we apply a hybrid method based on our heuristic as well as these two meta-heuristics in order to obtain solutions with higher quality within lesser computational time. The addressed problem is NP-hard since the single machine total tardiness problem is already NP-hard. The computational results show that our proposed heuristics can effectively solve such Just-In-Time problem with a high-quality solution.     

A heuristic method for the combined location routing and inventory problem

The combined location routing and inventory problem (CLRIP) is used to allocate depots from several potential locations, to schedule vehicles’ routes to meet customers’ demands, and to determine the inventory policy based on the information of customers’ demands, in order to minimize the total system cost. Since finding the optimal solution(s) for this problem is a nonpolynomial (NP) problem, several heuristics for searching local optima have been proposed. However, the solutions for these heuristics are trapped in local optima. Global search heuristic methods, such as tabu search, simulated annealing method, etc., have been known for overcoming the combinatorial problems such as CLRIP, etc. In this paper, the CLRIP is decomposed into two subproblems: depot location-allocation problem, and routing and inventory problem. A heuristic method is proposed to find solutions for CLRIP. First of all, an initial solution for CLRIP is determined. Then a hybrid heuristic combining tabu search with simulated annealing sharing the same tabu list is used to improve the initial solution for each subproblem separately and alternatively. The proposed heuristic method is tested and evaluated via simulation. The results show the proposed heuristic method is better than the existing methods and global search heuristic methods in terms of average system cost.     

Parallel machine scheduling with earliness-tardiness penalties and space limits

In order to maximize an availability of machine and utilization of space, the parallel machines scheduling problem with space limit is frequently discussed in the industrial field. In this paper, we consider the parallel machine scheduling problem in which n jobs having different release times, due dates, and space limits are to be scheduled on m parallel machines. The objective function is to minimize the weighted sum of earliness and tardiness. To solve this problem, a heuristic is developed which is divided into three modules hierarchically: job selection, machine selection and job sequencing, and solution improvement. To illustrate its effectiveness, a proposed heuristic is compared with genetic algorithm (GA), hybrid genetic algorithm (HGA), and tabu search (TS), which are well-known meta-heuristics in a large number of randomly generated test problems based on the field situation. Also, we determine the job selection rule that is suitable to the problem situation considered in this paper and show the effectiveness of our heuristic method.     

考虑工序相关性的动态Job shop调度问题启发式算法

提出一类考虑工序相关性的、工件批量到达的动态Job shop 调度问题,在对工序相关性进行了定义和数学描述的基础上,进一步建立了动态Job shop 调度问题的优化模型。设计了一种组合式调度规则RAN(FCFS,ODD),并提出了基于规则的启发式算法以及该类动态Job shop 调度问题的算例生成方法。为验证算法和比较评估调度规则的性能,对算例采用文献提出的7种调度规则和RAN(FCFS,ODD)进行了仿真调度,对调度结果的分析表明了算法的有效性和RAN(FCFS,ODD)调度规则求解所提出的动态Job Shop 调度问题的优越性能。     

CMS scheduling problem considering material handling and routing flexibility

Cell manufacturing as an application of group technology increases the flexibility and efficiency of the production. Cell scheduling problem, one of the subjects in cell manufacturing, has not been widely studied by researchers compared with other problems in cell manufacturing. In spite of great importance of material handling in cell scheduling, it has not been paid enough attention by researches. In this paper, a new mathematical model for cell scheduling problem considering material handling time and routing flexibility is proposed. The proposed model belongs to the mixed-integer nonlinear programs (MINLP). A linearization procedure is proposed to convert the MINLP to an integer program (IP) in order to develop more powerful optimization tools. Furthermore, a simulated annealing-based heuristic is developed to solve the large-size problems.     

Minimization of maximum tardiness on unrelated parallel machines with process restrictions and setups

We consider the problem of scheduling N jobs on M unrelated parallel machines to minimize maximum tardiness. Each job has a due date and requires a single stage of processing. A setup for dies is incurred if the type of the job scheduled is different from the previous one on that machine. For each die type, the number of dies is restricted. Because of the mechanical structure of the machines and the fitness of dies to each machine, the processing time depends on both the job and the machine. In this paper, an efficient heuristic based on guided search, record-to-record travel, and tabu lists is presented to minimize maximum tardiness. Computational characteristics of the proposed heuristic are evaluated through extensive experiments, which show that the proposed heuristic outperforms a simulated annealing method tested and is able to prescribe the optimal solutions for problems in small scales.