A novel MILP model for job shop scheduling problem with mobile robots

The mobile robot is the essential equipment for automated logistics in the intelligent workshop, but the literature on shop scheduling rarely considers transport resources. This paper studies the integrated scheduling of machines and mobile robots, which can facilitate the efficiency of production systems. For the job shop scheduling problem with mobile robots (JSPMR), the existing mathematical models are too complex to obtain the optimal solution in an efficient time. Therefore, a novel mixed integer linear programming (MILP) model is proposed to minimize the makespan. Firstly, in view of the property of the problem, a disjunctive graph model is modified to describe the relationship between transport and processing tasks. Secondly, a more accurate and simplified MILP is proposed based on the modified disjunctive graph model. Two related proofs are given to prove the proposed model satisfies all special situations. Thirdly, the proposed MILP is tested on the well-known benchmark, including 82 instances. The proposed model is the first MILP model to obtain optimal solutions for all instances. Finally, 40 larger-scale instances are presented based on a real-world engineering case and used to validate the performance of models further. The comparison results verify the effectiveness and superior computational performance of the proposed model.     

智能仓储中的多机器人调度方法

近年来,传统仓储系统已满足不了日益增长的订单需求并已渐渐向智能仓储转变。针对智能仓储中移动机器人的调度问题,以移动机器人执行任务时的转向次数、路程代价、最大任务等待时间为优化目标,提出一种兼顾任务分配和路径规划的调度算法。算法采用遗传算法进行任务分配,同时以多个移动机器人为目标进行任务分配,保证每个机器人分配到的任务没有重复。然后采用Q-learning算法对机器人分配到的任务进行路径规划,根据转向次数和路程代价约束路径,对于路径转向和每一步可行的动作均设有惩罚值,最终形成一条转向次数少、行程较短的路径。通过将该算法与其他算法进行对比,证实了该算法的有效性。     

路网约束下异构机器人系统路径规划方法

由无人机(Unmanned aerial vehicles, UAV)和地面移动机器人组成的异构机器人系统在协作执行任务时,可以充分发挥两类机器人各自的优势.无人机运动灵活,但通常续航能力有限;地面机器人载荷多,适合作为无人机的着陆平台和移动补给站,但运动受路网约束.本文研究这类异构机器人系统协作路径规划问题.为了降低完成任务的时间代价,提出一种由蚁群算法(Ant colony optimization, ACO)和遗传算法(Genetic algorithm, GA)相结合的两步法对地面机器人和无人机的路线进行解耦,同时规划地面机器人和无人机的路线.第1步使用蚁群算法为地面机器人搜索可行路线.第2步对无人机的最优路径建模,采用遗传算法求解并将无人机路径长度返回至第1步中,用于更新路网的信息素参数,从而实现异构协作系统路径的整体优化.另外,为了进一步降低无人机的飞行时间代价,研究了无人机在其续航能力内连续完成多任务的协作路径规划问题.最后,通过大量仿真实验验证了所提方法的有效性.     

Pre-emptive versus non-pre-emptive real time scheduling in intelligent mobile robotics

Autonomous and semi-autonomous mobile robots have to perform a multiplicity of concurrent activities in order to carry out useful tasks in unstructured human-populated environments. Even if it is commonly accepted that a successful accomplishment of assigned tasks requires some sort of real time capability to quickly react and adapt to environmental changes, it is not clear which operating system support is best suited for the scheduling and synchronizing of concurrent activities with different timing requirements. This paper discusses this problem, comparing two different real time scheduling policies for autonomous robot applications: pre-emptive rate monotonic and non pre-emptive Earliest Deadline First (EDF). Experimental results are presented and evaluated.     

MIP formulation and heuristics for multi-stage capacitated lot-sizing and scheduling problem with availability constraints

In this paper, the problem of lot-sizing and scheduling of multiple product types in a capacitated flow shop with availability constraints for multi-period planning horizon is considered. In many real production systems, machines may be unavailable due to breakdowns or preventive maintenance activities, thus integrating lot-sizing and scheduling with maintenance planning is necessary to model real manufacturing conditions. Two variants are considered to deal with the maintenance activities. In the first, the starting times of maintenance tasks are fixed, whereas in the second one, maintenance must be carried out in a given time window. A new mixed-integer programming (MIP) model is proposed to formulate the problem with sequence-dependent setups and availability constraints. The objective is to find a production and preventive maintenance schedule that minimizes production, holding and setup costs. Three MIP-based heuristics with rolling horizon framework are developed to generate the integrated plan. Computational experiments are performed on randomly generated instances to show the efficiency of the heuristics. To evaluate the validity of the solution methods, problems with different scales have been studied and the results are compared with the lower bound. Computational experiments demonstrate that the performed methods have good-quality results for the test problems.     

Level Set方法求解机器人路径规划的探讨

移动机器人路径规划是机器人学的一个最基本也是最复杂的问题,路径规划的主要方法有势能方法、单元分解方法、神经网络(NN)等。水平集(level set)方法已经广泛应用于图像处理和计算机图形学领域,因为其具有能够处理拓扑改变、数值稳定性好和独立于参数化的优势。为了探讨Level set方法在求解机器人路径规划中的应用,在介绍水平集法的基本思想和相关技术,以及路径规划的求解方法等的基础上,引入路径规划问题的隐式主动轮廊模型,即水平集模型,并采用快速推进方法(FMM)求解此模型方程,进而给出了路径规划模型的计算结果及其可视化界面,并且与经典势能法的计算结果进行了比较。理论和计算结果证明,Level set方法求解机器人路径规划是可行和有效的,从而为机器人路径规划研究提供了新的思路和方法。     

Bacterial memetic algorithm for simultaneous optimization of path planning and flow shop scheduling problems

The paper deals with simultaneous optimization of path planning of mobile robots and flow shop scheduling problem. The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. The objective is to minimize the path length without colliding with an obstacle. On the other hand, shop scheduling problems deal with processing a given set of jobs on a given number of machines. Each operation has an associated machine on which it has to be processed for a given length of time. The problem is to minimize the overall time demand of the whole process. In this paper, we deal with two robots carrying items between the machines. Bacterial memetic algorithm is proposed for solving this combined problem. The algorithm is verified by experimental simulations and compared to classical techniques.     

Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation

This paper addresses the interest of using punctual versus continuous coordination for mobile multi-robot systems where robots use auction sales to allocate tasks between them and to compute their policies in a distributed way. In continuous coordination, one task at a time is assigned and performed per robot. In punctual coordination, all the tasks are distributed in Rendezvous phases during the mission execution. However, tasks allocation problem grows exponentially with the number of tasks. The proposed approach consists in two aspects: (1) a control architecture based on topological representation of the environment which reduces the planning complexity and (2) a protocol based on sequential simultaneous auctions (SSA) to coordinate Robots’ policies. The policies are individually computed using Markov Decision Processes oriented by several goal-task positions to reach. Experimental results on both real robots and simulation describe an evaluation of the proposed robot architecture coupled wih the SSA protocol. The efficiency of missions’ execution is empirically evaluated regarding continuous planning.     

An energy-efficient task scheduling for mobile devices based on cloud assistant

Mobile cloud computing is an emerging service model to extend the capability and the battery life of mobile devices. Mostly one network application can be decomposed into fine-grained tasks which consist of sequential tasks and parallel tasks. With the assistance of mobile cloud computing, some tasks could be offloaded to the cloud for speeding up executions and saving energy. However, the task offloading results in some additional cost during the communication between cloud and mobile devices. Therefore, this paper proposes an energy-efficient scheduling of tasks, in which the mobile device offloads appropriate tasks to the cloud via a Wi-Fi access point. The scheduling aims to minimize the energy consumption of mobile device for one application under the constraint of total completion time. This task scheduling problem is reconstructed into a constrained shortest path problem and the LARAC method is applied to get the approximate optimal solution. The proposed energy-efficient strategy decreases 81.93% of energy consumption and 25.70% of time at most, compared with the local strategy. Moreover, the applicability and performance of the proposed strategy are verified in different patterns of applications, where the time constraint, the workload ratio between communication and computation are various.     

Reactive scheduling using a multi-agent model: the SCEP framework

Multi-agent systems have been successfully applied to the scheduling problem for some time. However, their use often leads to poorly unsatisfactory disappointing results. A new multi-agent model, called supervisor, customers, environment, producers (SCEP), is suggested in this paper. This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation. Its two main interests are the following: first it provides a more efficient control of the consequences generated by the local decisions than usual systems to each agent, then the adopted architecture and behaviour permit an easy co-operation between the different SCEP models, which can represent different production functions such as manufacturing, supply management, maintenance or different workshops. As a consequence, the SCEP model can be adapted to a great variety of scheduling/planning problems. This model is applied to the basic scheduling problem of flexible manufacturing systems, and it permits a natural co-habitation between infinite capacity scheduling processes, performed by the manufacturing orders, and finite capacity scheduling processes, performed by the machines. It also provides a framework in order to react to the disturbances occurring at different levels of the workshop.     

Distributed multi-robot patrol: A scalable and fault-tolerant framework

This paper addresses the Multi-Robot Patrolling Problem, where agents must coordinate their actions while continuously deciding which place to move next after clearing their locations. This problem is commonly addressed using centralized planners with global knowledge and/or calculating a priori routes for all robots before the beginning of the mission. In this work, two distributed techniques to solve the problem are proposed. These are motivated by the need to adapt to the changes in the system at any time and the possibility to add or remove patrolling agents (e.g., due to faults).The first technique presented is greedy and aims to maximize robot’s local gain. The second one is an extension of the former, which takes into account the distribution of agents in the space to reduce interference and foster scalability.The validation of the proposed solution is preliminarily conducted through realistic simulations as well as experiments with robot platforms in a small lab scenario. Subsequently, the work is verified in a large indoor real-world environment with a team of autonomous mobile robots with scalability and fault-tolerance assessment.     

Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments

Reinforcement learning (RL) is a popular method for solving the path planning problem of autonomous mobile robots in unknown environments. However, the primary difficulty faced by learning robots using the RL method is that they learn too slowly in obstacle-dense environments. To more efficiently solve the path planning problem of autonomous mobile robots in such environments, this paper presents a novel approach in which the robot’s learning process is divided into two phases. The first one is to accelerate the learning process for obtaining an optimal policy by developing the well-known Dyna-Q algorithm that trains the robot in learning actions for avoiding obstacles when following the vector direction. In this phase, the robot’s position is represented as a uniform grid. At each time step, the robot performs an action to move to one of its eight adjacent cells, so the path obtained from the optimal policy may be longer than the true shortest path. The second one is to train the robot in learning a collision-free smooth path for decreasing the number of the heading changes of the robot. The simulation results show that the proposed approach is efficient for the path planning problem of autonomous mobile robots in unknown environments with dense obstacles.     

PWB装配线综合生产能力计划模型及其近似求解算法

提出了多产品柔性制造环境中市场需求确定动态且完全由生产满足的条件下PWB 装配线的再设计模型.由于该模型为大规模混合整数规划问题,提出了一种首先求解若干递 归线性规划以减小搜索空间,然后应用启发式搜索的近似求解方法.实际问题的计算结果表 明了所提出算法的有效性.     

A PATH PLANNING METHOD BASED ON CELLULAR AUTOMATA FOR COOPERATIVE ROBOTS

A Cellular Automaton-based technique suitable for solving the path planning problem in a distributed robot team is outlined. Real-time path planning is a challenging task that has many applications in the fields of artificial intelligence, moving robots, virtual reality, and agent behavior simulation. The problem refers to finding a collision-free path for autonomous robots between two specified positions in a configuration area. The complexity of the problem increases in systems of multiple robots. More specifically, some distance should be covered by each robot in an unknown environment, avoiding obstacles found on its route to the destination. On the other hand, all robots must adjust their actions in order to keep their initial team formation immutable. Two different formations were tested in order to study the efficiency and the flexibility of the proposed method. Using different formations, the proposed technique could find applications to image processing tasks, swarm intelligence, etc. Furthermore, the presented Cellular Automaton (CA) method was implemented and tested in a real system using three autonomous mobile minirobots called E-pucks. Experimental results indicate that accurate collision-free paths could be created with low computational cost. Additionally, cooperation tasks could be achieved using minimal hardware resources, even in systems with low-cost robots.     

A programming environment for real-time control of distributed multiple robotic systems

In recent years there has been great interest in robot software control architectures. However, although many interesting solutions have been presented, most of the research problems tackled related to a single robot perception, navigation and action in everyday environments. Instead, most of the practical applications of mobile robotics for service tasks in civilian environments consist of systems composed of multiple robots communicating with each other, with external sensing and actuating devices, and with external supervising workstations. RoboCup offers a great opportunity to deal with this problem. In fact the software architecture of a robot soccer player must allow successful intra-robot integration of the different activities (visual perception, path planning, strategy planning, motion control, etc.) spanning many different types of representation (raw sensor data, images, symbolic plans, etc.) and it must also guarantee successful inter-robot integration by supporting communication and cooperation. This paper focuses on this problem, presenting ETHNOS-IV - a programming environment for the design of a real-time control system composed of different robots, devices and external supervising or control stations - which has been successfully used within the Italian ART robot team in the RoboCup-99 competition. ETHNOS provides support from three main point of views which will be addressed in detail: inter-robot and intra-robot communication, realtime task scheduling, and software engineering and code reuse. Experimental results illustrating the advantages of this approach will also be presented.     

A dynamic scheme for scheduling complex tasks in manufacturing systems based on collaboration of agents

The characteristics of scheduling tasks in the real world is a dynamic and challenging issue as the processes and the companies involved may change from time to time. For small flexible enterprises to respond to business opportunities, an effective scheme to facilitate dynamic coalition, share the core competencies and resources and support inter-enterprise collaboration must be developed. Although multi-agent systems (MAS) provide a paradigm for modeling these characteristics, scheduling tasks in MAS is a complex problem due to the computational complexity involved, distributed architecture for scheduling tasks by individual agents and dependency of different agents’ workflows. How to develop a problem solver that can be applied in MAS to achieve coherent and consistent workflow schedules that can meet a customer’s order is an important issue. In this paper, we propose a solution methodology for scheduling workflows in MAS. Our solution combines the multi-agent system architecture to dynamically discover services, workflow and activity models to specify the capabilities of agents, contract net protocol to facilitate negotiation and coordination of agents and optimization theories to optimize the cost for fulfilling an order. A problem solver for scheduling tasks in MAS has been implemented. An application scenario has also been provided to verify our solution methodology.     

Project-oriented task scheduling for mobile robot team

This paper presents a project-oriented framework for multi-robot task scheduling. An agent-based architecture is designed to schedule tasks where robots are considered as resources. The study focused on the problems when the number of available robots is less than the required number. In this case, the problem becomes a resource-constrained scheduling problem. Initially, tasks are scheduled by using Critical path method (CPM), resource leveling method is used to smooth the deviation between the resource requirements and available resource levels, and tasks are allocated to robots. As robots perform their tasks, a monitoring agent observes them and tasks are rescheduled if the difference between the planned and actual completion time of tasks exceeds a predefined threshold. Effectiveness of the proposed approach is shown by using a nine-task two-robot project simulation.     

Spillover Algorithm: A decentralised coordination approach for multi-robot production planning in open shared factories

Open and shared manufacturing factories typically dispose of a limited number of industrial robots and/or other production resources that should be properly allocated to tasks in time for an effective and efficient system performance. In particular, we deal with the dynamic capacitated production planning problem with sequence independent setup costs where quantities of products to manufacture need to be determined at consecutive periods within a given time horizon and products can be anticipated or back-ordered related to the demand period. We consider a decentralised multi-agent variant of this problem in an open factory setting with multiple owners of robots as well as different owners of the items to be produced, both considered self-interested and individually rational. Existing solution approaches to the classic constrained lot-sizing problem are centralised exact methods that require sharing of global knowledge of all the participants’ private and sensitive information and are not applicable in the described multi-agent context. Therefore, we propose a computationally efficient decentralised approach based on the spillover effect that solves this NP-hard problem by distributing decisions in an intrinsically decentralised multi-agent system environment while protecting private and sensitive information. To the best of our knowledge, this is the first decentralised algorithm for the solution of the studied problem in intrinsically decentralised environments where production resources and/or products are owned by multiple stakeholders with possibly conflicting objectives. To show its efficiency, the performance of the Spillover Algorithm is benchmarked against state-of-the-art commercial solver CPLEX 12.8.     

基于总体势减小的动态调度技术解决多机器人的路径规划

本文提出了一种解决多机器人路径规划与协调问题的新方法：基于总体势减小的优 先级动态调度策略．文中引入了总体势的概念,机器人从起始点向目标点运动过程中,始终 沿着总体势减小的方向进行,逐步引导机器人导航任务的完成．     

Planning,scheduling and dispatching tasks in production control

"What is the difference between planning and scheduling?" Production control encompasses many tasks performed by humans, three of which are planning, scheduling, and dispatching. In the past, the only criterion that could distinguish between the tasks was that planning is usually on a higher level than scheduling and scheduling is on a higher level than dispatching. Hence, the tasks are often ambiguous, unclear, and subject to speculation. There are few formal studies on the actual tasks of planning, scheduling, and dispatching, and there are no known studies that compare or discuss all three. In this paper it is argued that it is important to understand the differences between the tasks. An action science and ethnographic case study is presented as the empirical basis for the discussion, and the implications for decision support systems in production control tasks are presented.