Heuristic for scheduling in a two-machine bicriteria dynamic flowshop with setup and processing times separated

This paper addresses the two-machine bicriteria dynamic flowshop problem where setup time of a job is separated from its processing time and is sequenced independently. The performance considered is the simultaneous minimization of total flowtime and makespan, which is more effective in reducing the total scheduling cost compared to the single objective. A frozen-event procedure is first proposed to transform a dynamic scheduling problem into a static one. To solve the transformed static scheduling problem, an integer programming model with N ² + 5N variables and 7N constraints is formulated. Because the problem is known to be NP-complete, a heuristic algorithm with the complexity of O (N ³) is provided. A decision index is developed as the basis for the heuristic. Experimental results show that the proposed heuristic algorithm is effective and efficient. The average solution quality of the heuristic algorithm is above 99%. A 15-job case requires only 0.0235 s, on average, to obtain a near or even optimal solution.     

Dynamic scheduling of steel casting and milling using multi-agents

This paper presents a case study on the use of multi-agents for integrated dynamic scheduling of steel milling and casting. Steel production is an extremely complex problem requiring the consideration of several different constraints and objectives of a range of processes in a dynamic environment. Most research in steel production scheduling considers static scheduling of processes in isolation. In contrast to earlier approaches, the multi-agent architecture proposed consists of a set of heterogeneous agents which integrate and optimize a range of scheduling objectives related to different processes of steel production, and can adapt to changes in the environment while still achieving overall system goals. Each agent embodies its own scheduling model and realizes its local predictive-reactive schedule taking into account local objectives, real-time information and information received from other agents. Agents cooperate in order to find a globally good schedule, which is able to effectively react to real-time disruptions, and to optimize the original production goals whilst minimising disruption carried by unexpected events occurring in real-time. The inter-agent cooperation is based on the Contract Net Protocol with commitment.     

A pairwise interchange algorithm for parallel machine scheduling

This paper considers the problem of non-preemptive scheduling n tasks on m identical parallel processors to minimize makespan for simultaneous arrivals. Based on a pairwise interchange method, an efficient algorithm ispresented which is able to give a near-optimal schedule in a short time through suitable pairwise interchange between tasks, after an initial solution is constructed. The behaviour of the algorithm is discussed. Testing results prove its high performance in comparison with available simple heuristic procedures. Finally, the algorithm is generalized for the problems of non-identical processors and non-simultaneous arrivals.     

物流敏捷调运决策支持系统的研究

由于物流敏捷调运优化方案的生成过程,本质上是一个决策过程。针对各决策环节,提出了借鉴IDEF0方法描述的敏捷调运优化适度递阶控制模型,在此基础上构建了决策支持系统总体框架。针对该决策支持系统运行模块,提出了基于敏捷性准则的运行控制思想。进而,对该决策支持系统中的数据库系统、模型库与方法库进行了研究,利用递阶设计思想设计了数据库的三大体系,提出了以"剩余装载总量"极小化为目标的物流敏捷调运优化(0-1)_h模型和对分搜索算法。通过对搜索次数的估计来衡量(0-1)_h模型求解算法的计算复杂性,结果表明该算法效率较高。     

A simulated annealing algorithm for job shop scheduling

In this paper, the job shop scheduling problem is considered with the objective of minimization of makespan time. We first reviewed the literature on job shop scheduling using meta-heuristics. Then a simulated annealing algorithm is presented for scheduling in a job shop. To create neighbourhoods, three perturbation schemes, viz. pairwise exchange, insertion, and random insertion are used, and the effect of them on the final schedule is also compared. The proposed simulated annealing algorithm is compared with existing genetic algorithms and the comparative results are presented. For comparative evaluation, a wide variety of data sets are used. The proposed algorithm is found to perform well for scheduling in the job shop.     

A simulated annealing algorithm for designing cellular manufacturing systems with productivity consideration

In this paper, we propose a productivity model for solving the machine-part grouping problem in cellular manufacturing (CM) systems. First, a non-linear 0-1 integer programming model is developed to identify machine groups and part families simultaneously. This model aims to maximize the system productivity defined as the ratio of total output to the total material handling cost. Second, an efficient simulated annealing (SA) algorithm is developed to solve large-scale problems. This algorithm provides several advantages over the existing algorithms. It forms part families and machine cells simultaneously. It also considers production volume, sales price, and maximum number of machines in each cell and total material handling cost. The proposed SA also has the ability to determine the optimum number of manufacturing cells. The performance of the developed models is tested on eight problems of different size and complexity selected from the literature. The results show the superiority of the SA algorithm over the mathematical programming model in both productivity and computational time.     

A heuristic algorithm for minimizing maintenance workforce level

We develop a heuristic algorithm for minimizing the workforce level required to accommodate all the maintenance jobs requested within a specific time interval. Each maintenance job has its own release and due dates as well as the required man-days, and must be scheduled in a noninterrupted time interval, i.e. without preemption. However, the duration of each job is not fixed, but to be determined within a specific range. We show that this problem can be seen as a variant of the two-dimensional bin-packing problem with some additional constraints. We develop a non-linear mixed integer programming model for the proposed problem, and employ some well-known bin-packing algorithms to develop an efficient heuristic algorithm. In order to evaluate the performance of the proposed heuristic, we present a computationally efficient scheme for getting a good lower bound for the actual minimum. The computational experiment shows that the proposed heuristic algorithm performs quite satisfactorily in practice.     

Dynamic selection of dispatching rules for job shop scheduling

Although the academic contribution to job shop scheduling is abundant, its impact on practice has been minimal. The most preferred approach to job shop scheduling in the industry is dispatching rules. A major criticism against dispatching rules is that there is no single universal rule. The effective choice of dispatching rules depends on the scheduling criterion and existing job shop conditions. In this paper, the authors have proposed a scheduling method based on the analytic hierarchy process, that dynamically selects the most appropriate dispatching rule from several candidate rules. The selection is based on the existing job shop conditions. This method is applied to two formal job shop problems, and the results for single dispatching rules are inferior to the method proposed in this paper.     

Intelligent scheduling model and algorithm for manufacturing

This paper puts forward an intelligent scheduling model based on Hopfield neural network and a unified algorithm for manufacturing. The energy computation function and its dynamic state equation are derived and discussed in detail about their coefficients (parameters) and steps (Delta t) in iteration towards convergence. The unified model is focused on the structure of the above function and equation, in which the goal and penalty items must be involved and meet different schedule models. The applications to different schedule mode including jobshop static scheduling, scheduling with due-date constraint or priority constraint, dynamic scheduling, and JIT (just in time) scheduling are discussed, and a series of examples with Gantt charts are illustrated.     

A memetic differential evolution algorithm for energy-efficient parallel machine scheduling

This paper considers an energy-efficient bi-objective unrelated parallel machine scheduling problem to minimize both makespan and total energy consumption. The parallel machines are speed-scaling. To solve the problem, we propose a memetic differential evolution (MDE) algorithm. Since the problem involves assigning jobs to machines and selecting an appropriate processing speed level for each job, we characterize each individual by two vectors: a job-machine assignment vector and a speed vector. To accelerate the convergence of the algorithm, only the speed vector of each individual evolves and a list scheduling heuristic is applied to derive its job-machine assignment vector based on its speed vector. To further enhance the algorithm, we propose efficient speed adjusting and job-machine swap heuristics and integrate them into the algorithm as a local search approach by an adaptive meta-Lamarckian learning strategy. Computational results reveal that the incorporation of list scheduling heuristic and local search greatly strengthens the algorithm. Computational experiments also show that the proposed MDE algorithm outperforms SPEA-II and NSGA-II significantly.     

综合面试中的均衡分组方法

本文针对综合面试中的分组问题,首先分析了分组对综合面试结果的影响,提出了均衡分组的思想;在此基础上,提出了综合面试中的均衡分组方法,该方法构建了双目标优化模型,并开发了一个求解问题的遗传算法,该算法还可以解决一类分组问题;最后,给出算例说明方法的应用及可行性。     

A Heuristic Algorithm for the Capacitated Multiple Supplier Inventory Grouping Problem

This paper presents and solves a model for the multiple supplier inventory grouping problem, which involves the minimization of logistics costs for a firm that has multiple suppliers with capacity limitations. The costs included in the model are purchasing, transportation, ordering, and inventory holding, while the firm's objective is to determine the optimal flows and groups of commodities from each supplier. We present an algorithm, which combines subgradient optimization and a primal heuristic, to quickly solve the multiple supplier inventory grouping problem. Our algorithm is tested extensively on problems of various sizes and structures, and its performance is compared to that of OSL, a state-of-the-art integer programming code. The computational results indicate that our approach is extremely efficient for solving the multiple supplier inventory grouping problem.     

Implications of selected production planning functions: Impact analysis through simulation

When capacity differences are minimized through an efficient algorithm, and integration of capacity planning with any production planning system is performed, it affects some elements of production planning functions. In the reverse way, some elements of production planning and management techniques also affect the effectiveness of capacity planning. These happen because capacity planning processes, production planning processes and production management techniques are not standalone sub-systems, rather these are totally dependent on each other. This paper aims at determining and formulating the effects of some of the selected elements of capacity and production planning functions on each other. This study is conducted using simulation in object-oriented SIMPLE+ + system.     

Constraint-based model for the cyclic multi-hoists scheduling problem

This article presents a method for the resolution of a material handling scheduling problem. The case studied is a real industrial problem. It consists of finding a cyclic schedule for hoist movements in a treatment surface shop. In this kind of facility, several hoists are used for all the handling operations and they have to share common zones. Then it is necessary to control that there is no collision. The mathematical formulation of the problem is based on a combination of disjunctive constraints. The constraints describe either movement schedule or collision avoidance. The resolution procedure presented identifies all the collision configurations and then uses a branch and bound-like algorithm to find the optimal solution of a given problem. The language chosen for our implementation is the constraint logic programming language: Prolog IV, which is able to solve constraints with rational variables. It actively uses the constraint propagation mechanism that can be found in several languages.     

Two new robust genetic algorithms for the flowshop scheduling problem

The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms. The optimization criterion considered is the minimization of the total completion time or makespan (_Cmax$C_{\max }$). We show a robust genetic algorithm and a fast hybrid implementation. These algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed algorithms are very effective and at the same time are easy to implement.     

一种求解双目标flow shop排序问题的进化算法

提出一种求解双目标flow shop排序的递进多目标进化算法.算法采用改进的精英复制策略,在实现精英保留的前提下降低了计算复杂性;通过递进进化模式增加群体多样性,改善了算法收敛性;通过群体进化过程中对非劣解集进行竞争型可变邻域启发式搜索,增强了算法局部搜索性能.采用新算法和参照算法NSGA-II对31个标准双目标flow shop算例进行优化.研究结果表明,新算法在所有算例的求解中均获得了优于NSGA-II的非劣解集,验证了算法的有效性.     

A heuristic for scheduling in a flowshop with the bicriteria of makespan and maximum tardiness minimization

This article deals with the development of a heuristic for scheduling in a flowshop with the objective of minimizing the makespan and maximum tardiness of a job. The heuristic makes use of the simulated annealing technique. The proposed heuristic is relatively evaluated against the existing heuristic for scheduling to minimize the weighted sum of the makespan and maximum tardiness of a job. The results of the computational evaluation reveal that the proposed heuristic performs better than the existing one.     

An improved distributed data aggregation scheduling in wireless sensor networks

This paper focuses on the distributed data aggregation collision-free scheduling problem, which is one of very important issues in wireless sensor networks. Bo et al. (Proc. IEEE INFOCOM, 2009) proposed an approximate distributed algorithm for the problem and Xu et al. (Proc. ACM FOWANC, 2009) proposed a centralized algorithm and its distributed implementation to generate a collision-free scheduling for the problem, which are the only two existing distributed algorithms. Unfortunately, there are a few mistakes in their performance analysis in Bo et al. (Proc. IEEE INFOCOM, 2009), and the distributed algorithm can not get the same latency as the centralized algorithm because the distributed implementation was not an accurate implementation of the centralized algorithm (Xu et al. in Proc. ACM FOWANC, 2009). According to those, we propose an improved distributed algorithm to generate a collision-free schedule for data aggregation in wireless sensor networks. Not an arbitrary tree in Bo et al. (Proc. IEEE INFOCOM, 2009) but a breadth first search tree (BFS) rooted at the sink node is adopted, the bounded latency 61R+5Δ?67 of the schedule is obtained, where R is the radius of the network with respect to the sink node and Δ is the maximum node degree. We also correct the latency bound of the schedule in Bo et al. (Proc. IEEE INFOCOM, 2009) as 61D+5Δ?67, where D is a diameter of the network and prove that our algorithm is more efficient than the algorithm (Bo et al. in Proc. IEEE INFOCOM, 2009). We also give a latency bound for the distributed implementation in Xu et al. (Proc. ACM FOWANC, 2009).     

A new three-machine shop scheduling: complexity and approximation algorithm

The paper investigates a new three-machine shop scheduling problem that arises from many production systems, such as the garment assembly line, etc. In such scenarios, each job consists of three operations, each of which has to be non-preemptively processed by one specific machine. In contrast with the classical three-machine shop scheduling, the processing order of the operations of each job is partially restricted. In particular, the first two operations are ordered and all the same for all jobs, while the third operation is not restricted. The objective is to minimize the makespan. We show the problem is NP-hard in the ordinary sense and present a polynomial time approximation algorithm with a worst case performance ratio of $\frac{3}{2}$ .     

Development and implementation of a knowledge-based system for the dynamic scheduling of a two-stage production process

Abstract

This paper discusses the implementation of a knowledge-based system for the dynamic scheduling of a two-stage production process. It is an interactive, real-time, menu-driven system written in Prolog. The system architecture is delineated and the rules and problem-solving logic to be used under various dynamic situations are described. Results of a sample system test session are included to illustrate its use.