Job shop scheduling with delay-renewable resources: A comparison of decomposition strategies

Manufacturing systems are often characterized by the use of resources which need to be 'renewed' after a period of use, such as tools and dies. The resources are not available during their maintenance and if it is too expensive to purchase a suitable number of copies, this may introduce delays and lower the quality of the schedule. We refer to such resources as delay-renewable . In the paper we consider a two-fold generalization of the classical job shop scheduling problem; the operations require the concurrent use of different resources (not only one machine) and the resources are delay-renewable. The resulting scheduling problem is obviously rather difficult and calls for suitable heuristics. A natural way to devise a heuristic approach is to decompose the overall problem into sub-problems and to exploit high-quality meta-heuristics, such as tabu search, for their solution. The purpose of the paper is to discuss and compare two decomposition approaches, differing in the way sub-problems interact, i.e. through constraints and/or information.     

基于直线往复单智能RGV系统单工序调度研究

针对直线布局轨道式智能加工系统单自动引导车(rail guide vehicle, RGV)单工序作业调度进行研究。考虑到智能RGV与数控机床(CNC machine tools)的特点,建立了给定时间内RGV与CNC机床配合物料加工下料用时之和最小化为目标的非线性整数规划模型。当加工物料数目比较多时,求解该问题非常耗时。根据RGV与CNC机床加工物料调度特征,提出计算机模拟仿真算法对问题进行求解。利用3组系统参数对模型及算法的有效性和实用性进行了检验分析,最后给出了最优调度计划的实际运行结果及结论。     

A genetic algorithm based procedure for more realistic job shop scheduling problems

This work presents a robust procedure to solve job shop scheduling problems with large number of more realistic constraints such as jobs with several subassembly levels, alternative processing plans for parts and alternative resources for operations, requirement of multiple resources to process an operation (e.g. machine, tools, fixtures, staff), resource calendars, batch overlap and sequence dependent setups. Also, the approach considers multi-objective evaluation functions. The system uses modified schedule generation algorithms to obtain a set of initial solutions. Each initial solution is enhanced by a local improvement procedure. Then a hybrid genetic algorithm, which incorporates a local hill climbing procedure, is applied to the set of local optimum schedules.     

Tuner: A tool for designing and optimizing ion optical systems

Designing and optimizing ion optical systems is often a complex and difficult task, which requires the use of computational tools to iterate and converge towards the desired characteristics and performances of the system. Very often these tools are not well adapted for exploring the numerous degrees of freedom, rendering the process long and tedious, as well as somewhat random due to the very large number of local minima typically found when looking for a particular optical solution. This paper presents a novel approach to finding the desired solution of an optical system, by providing the user with an instant feedback of the effects of changing parameters. The process of finding an approximate solution by manually adjusting parameters is greatly facilitated, at which point the final tune can be calculated by minimization according to a number of constraints.     

Agent-based project scheduling: computational study of large problems

We present in this paper the results of a computational study for project scheduling based on new ideas for project representation taken from digital circuit technology (Knotts et al. , 1998a) and a solution approach based on the artificial intelligence notion of agent technology. We experimented with projects with up to 10 000 stochastic duration activities which can be executed in a number of modes requiring renewable, nonrenewable, and periodically renewable resources. This study is about agent implementation in a project scheduling domain. It compares agent types and priority rules with respect to their impact on project schedule duration and computational performance. This work demonstrates: (i) that artificial intelligence concepts of agent technology can be successfully implemented for project scheduling; and (ii) in conducting project scheduling studies we can experiment successfully with large project networks. Both points made in this research are new.     

Exploiting semantics of temporal multi‐scale methods to optimize multi‐level mesh partitioning

Multi‐scale problems are often solved by decomposing the problem domain into multiple subdomains, solving them independently using different levels of spatial and temporal refinement, and coupling the subdomain solutions back to obtain the global solution. Most commonly, finite elements are used for spatial discretization, and finite difference time stepping is used for time integration. Given a finite element mesh for the global problem domain, the number of possible decompositions into subdomains and the possible choices for associated time steps is exponentially large, and the computational costs associated with different decompositions can vary by orders of magnitude. The problem of finding an optimal decomposition and the associated time discretization that minimizes computational costs while maintaining accuracy is nontrivial. Existing mesh partitioning tools, such as METIS, overlook the constraints posed by multi‐scale methods and lead to suboptimal partitions with a high performance penalty. We present a multi‐level mesh partitioning approach that exploits domain‐specific knowledge of multi‐scale methods to produce nearly optimal mesh partitions and associated time steps automatically. Results show that for multi‐scale problems, our approach produces decompositions that outperform those produced by state‐of‐the‐art partitioners like METIS and even those that are manually constructed by domain experts. Copyright © 2017 John Wiley & Sons, Ltd.     

Simultaneous resource scheduling with batching to minimize weighted flow times

This paper examines scheduling problems in which individual tasks require several resources concurrently. We analyze the situation where, in addition to the need to schedule resources concurrently, it is possible to choose the batch sizes of the tasks. Although such situations arise in both manufacturing and service firms, the likely application of these results will be to repetitive batch manufacturing situations. Several formulations for this problem are presented. Both a lagrangian relaxation and a surrogate relaxation are developed. Heuristics based on these relaxations as well as extensive computational experiments are discussed. The simultaneous resource scheduling problem without batching is quite difficult because jobs need to be synchronized so that individual jobs can capture their required simultaneous resources. Nevertheless, if batch size is introduced as an additional variable, we show that the resulting problem is in some sense easier because of the added flexibility provided by the ability to adjust production batch sizes, which allows us to eliminate 'gaps' in the schedule.     

An optimisation approach for pre-runtime scheduling of tasks and communication in an integrated modular avionic system

In modern integrated modular avionic systems, applications share hardware resources on a common avionic platform. Such an architecture necessitates strict requirements on the spatial and temporal partitioning of the system to prevent fault propagation between different aircraft functions. One way to establish a temporal partitioning is through pre-runtime scheduling of the system, which involves creating a schedule for both tasks and a communication network. While avionic systems are growing more and more complex, so is the challenge of scheduling them. The scheduling of the system has an important role in the development of new avionic systems, since functionality is typically added to the system over a period of several years and a scheduling tool is used both to detect if the platform can host the new functionality and, if this is possible, to create a new schedule. For this reason an exact solution strategy for avionics scheduling is preferred over a heuristic one. In this paper we present a mathematical model for an industrially relevant avionic system and present a constraint generation procedure for the scheduling of such systems. We apply our optimisation approach to instances provided by our industrial partner. These instances are of relevance for the development of future avionic systems and contain up to 20,000 tasks to be scheduled. The computational results show that our optimisation approach can be used to create schedules for such instances within a reasonable time.     

A New Task Scheduling Scheme Based on Genetic Algorithm for Edge Computing

With the continuous evolution of smart grid and global energy interconnection technology, amount of intelligent terminals have been connected to power grid, which can be used for providing resource services as edge nodes. Traditional cloud computing can be used to provide storage services and task computing services in the power grid, but it faces challenges such as resource bottlenecks, time delays, and limited network bandwidth resources. Edge computing is an effective supplement for cloud computing, because it can provide users with local computing services with lower latency. However, because the resources in a single edge node are limited, resource-intensive tasks need to be divided into many subtasks and then assigned to different edge nodes by resource cooperation. Making task scheduling more efficient is an important issue. In this paper, a two-layer resource management scheme is proposed based on the concept of edge computing. In addition, a new task scheduling algorithm named GA-EC(Genetic Algorithm for Edge Computing) is put forth, based on a genetic algorithm, that can dynamically schedule tasks according to different scheduling goals. The simulation shows that the proposed algorithm has a beneficial effect on energy consumption and load balancing, and reduces time delay.     

A heuristic algorithm for master production scheduling problem with controllable processing times and scenario-based demands

Master production scheduling (MPS) is widely used by manufacturing industries in order to handle the production scheduling decisions in the production planning hierarchy. The classical approach to MPS assumes infinite capacity, fixed (i.e. non-controllable) processing times and a single pre-determined scenario for the demand forecasts. However, the deterministic optimisation approaches are sometimes not suitable for addressing the real-world problems with high uncertainty and flexibility. Accordingly, in this paper, we propose a new practical model for designing an optimal MPS for the environments in which processing times may be controllable by allocating resources such as facilities, energy or manpower. Due to the NP-hardness of our model, an efficient heuristic algorithm using local search technique and theory of constraints is developed and analysed. The computational results especially for large-sized test problems show that the average optimality gap of proposed algorithm is four times lower than that of exact solution using GAMS while it consumes also significantly smaller run times. Also, the analysis of computational results confirms that considering the controllable processing times may improve the solution space and help to more efficiently utilise the available resources. According to the model structure and performance of the algorithm, it may be proposed for solving large and complex real-world problems particularly the machining and steel industries.     

A parallel run‐time iterative load balancing algorithm for solution‐adaptive finite element meshes on hypercubes

Abstract

To efficiently execute a finite element program on a hypercube, we need to map nodes of the corresponding finite element graph to processors of a hypercube such that each processor has approximately the same amount of computational load and the communication among processors is minimized. If the number of nodes of a finite element graph will not be increased during the execution of a program, the mapping only needs to be performed once. However, if a finite element graph is solution‐adaptive, that is, the number of nodes will be increased discretely due to the refinement of some finite elements during the execution of a program, a run‐time load balancing algorithm has to be performed many times in order to balance the computational load of processors while keeping the communication cost as low as possible. In this paper, we propose a parallel iterative load balancing algorithm (ILB) to deal with the load imbalancing problem of a solution‐adaptive finite element program. The proposed algorithm has three properties. First, the algorithm is simple and easy to be implemented. Second, the execution of the algorithm is fast. Third, it guarantees that the computational load will be balanced after the execution of the algorithm. We have implemented the proposed algorithm along with two parallel mapping algorithms, parallel orthogonal recursive bisection (ORB) [19] and parallel recursive mincut bipartitioning (MC) [8], on a 16‐node NCUBE‐2. Three criteria, the execution time of load balancing algorithms, the computation time of an application program under different load balancing algorithms, and the total execution time of an application program (under several refinement phases) are used for performance evaluation. Experimental results show that (1) the execution time of ILB is very short compared to those of MC and ORB; (2) the mappings produced by ILB are better than those of ORB and MC; and (3) the speedups produced by ILB are better than those of ORB and MC.     

A project scheduling approach to production planning with feeding precedence relations

In Manufacturing-to-Order or Engineering-to-Order systems producing complex and highly customised items, each item has its own characteristics that are often tailored for a specific customer. Project scheduling approaches are suitable for production planning in such environments. However, when we consider the production of complex items, the distinct production operations are often aggregated into activities representing whole production phases. In such cases, the planning and scheduling problem works on the aggregate activities, considering that, in most cases, such activities also have to be manually executed. Moreover, simple finish-to-start precedence relations no longer correctly represent the real production process, but overlapping among activities should be allowed. In this paper, a project scheduling approach is proposed for production planning in Manufacturing-to-Order systems. The Variable Intensity formulation is used to allow the effort committed to the execution of activities to vary over time. Feeding precedences are developed to model generalised precedence relations when the execution mode of activities is not known a priori. Two mathematical formulations of these precedence relations are proposed. The formulations are applied both to randomly generated instances and to an industrial system producing machining centres and are compared in terms of computational efficiency.     

Exact and approximate methods for parallel multiple-area spatial scheduling with release times

Spatial scheduling problems involve scheduling jobs that each require certain amounts of two-dimensional space within a processing area of limited width and length. Thus, this requires not only assigning time slots to each job but also locations and orientations within the limited physical processing space as well. Such problems, often encountered in shipbuilding and aircraft manufacturing, are generally difficult to solve, and there is a relatively small amount of literature addressing these problems compared to other types of scheduling. In this paper, we consider a particularly complex class of spatial scheduling problems that involve scheduling each job into one of several possible processing areas in parallel to minimize the total amount of tardy time. In addition, each job has a release time before which it may not be processed. We introduce two methods for solving this type of problem: an integer programming (IP) model and a heuristic algorithm. We perform computational tests and comparisons of each method over a large number of generated benchmark problems with varying characteristics, and also compare these to a more naïve heuristic. Solving the IP model was effective for small problems but required excessive amounts of time for larger ones. The heuristic was effective and produced solutions of comparable quality to the IP model for many problems while requiring very little computational time.     

Heuristic algorithms for scheduling heat-treatment furnaces of steel casting industries

This paper addresses a research problem of scheduling parallel, non-identical batch processors in the presence of dynamic job arrivals, incompatible job-families and non-identical job sizes. We were led to this problem through a real-world application involving the scheduling of heat-treatment operations of steel casting. The scheduling of furnaces for heat-treatment of castings is of considerable interest as a large proportion of the total production time is the processing times of these operations. In view of the computational intractability of this type of problem, a few heuristic algorithms have been designed for maximizing the utilization of heat-treatment furnaces of steel casting manufacturing. Extensive computational experiments were carried out to compare the performance of the heuristics with the estimated optimal value (using the Weibull technique) and for relative effectiveness among the heuristics. Further, the computational experiments show that the heuristic algorithms proposed in this paper are capable of obtaining near (statistically estimated) optimal utilization of heat-treatment furnaces and are also capable of solving any large size real-life problems with a relatively low computational effort.     

生物计算网格中的在线调度技术研究

研究了在异构网格环境下的生物应用集成,定义了服务的提供者、部署者和使用者三种用户角色,设计了网格环境下的服务和资源整合机制,重点实现了用户管理以及作业调度控制等功能.根据计算资源的异构特点,设计了多种启发式调度算法.考虑到生物计算应用的不同类型,提出了自适应调度算法,该算法根据应用的特点动态选择启发式调度算法.实验表明,非阻塞调度优于阻塞调度方式;自适应调度算法比静态的在线调度算法有更好的性能,而在异构的网络中,带宽优先调度算法的性能比其他静态调度算法性能更好.     

Sequential resource allocation utilizing agents

Many manufacturing systems allocate resources, such as machines, sequentially. Sequential allocation of resources can be viewed as a digraph where each vertex represents resources forming nodes in a distributed system and the arcs represent the allocation. The allocation of such resources can be considered to be a distributed problem. Agents are a distributed artificial intelligence paradigm applicable to distributed problems and, therefore, have a potential to be applicable to sequential resource allocation. This paper presents a method of sequential resource allocation utilizing agents, and an AGV system is presented as an example application area. This system was utilized in experiments to test the agent application. Results and an analysis are also presented.     

Mixed Self-adapting GA Optimal Scheduling Algorithm for a Multiple Resource Job-shop

With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.     

A multi-agent system for chemical supply chain simulation and management support

Modern chemical production is customer-driven and the desired delivery time for the products is often shorter than their campaign length. In addition, the raw materials supplying time is often long. These features make it desirable to provide tools to support collaborative supply chain decision making, preferably over the Internet, and where there are conflicts, compromise decisions can be quickly reached and the effects of the decisions can be quantitatively simulated. This paper des cribes such a multi-agent system (MAS) that can be used to simulate the dynamic behaviour and support the management of chemical supply chains over the Internet. Geographically distributed retailers, logistics, warehouses, plants and raw material suppliers are modelled as an open and re-configurable network of co-operative agents, each performing one or more supply chain functions. Communication between agents is made through the common agent communication language KQML (knowledge query message language). A t the simulation layer, the MAS allows distributed simulation of the chain behaviour dynamically, so that compromise decisions can be rapidly and quantitatively evaluated. Because in a chemical supply chain the scheduling of the plant often dominates the chain performance, an optimum scheduling system for batch plants is integrated into the MAS. The functions of the system are illustrated by reference to a case study for the supply and manufacture using a multi-purpose batch plant of paints and coatings.