Modeling and solving mixed-model assembly line balancing problem with setups. Part I: A mixed integer linear programming model

This paper is the first one of the two papers entitled “modeling and solving mixed-model assembly line balancing problem with setups”, which has the aim of developing the mathematical programming formulation of the problem and solving it with a hybrid meta-heuristic approach. In this current part, a mixed-integer linear mathematical programming (MILP) model for mixed-model assembly line balancing problem with setups is developed. The proposed MILP model considers some particular features of the real world problems such as parallel workstations, zoning constraints, and sequence dependent setup times between tasks, which is an actual framework in assembly line balancing problems. The main endeavor of Part-I is to formulate the sequence dependent setup times between tasks in type-I mixed-model assembly line balancing problem. The proposed model considers the setups between the tasks of the same model and the setups because of the model switches in any workstation. The capability of our MILP is tested through a set of computational experiments. Part-II tackles the problem with a multiple colony hybrid bees algorithm. A set of computational experiments is also carried out for the proposed approach in Part-II.     

A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly

Some manufacturers outsource their disassembly tasks to professional factories, each factory of them has specialized in its disassembly ability. Different disassembly facilities are usually combined to execute disassembly tasks. This study proposes the cloud-based disassembly that abstracts ability of the disassembly factory as the disassembly resource, the disassembly resource is then able to be allocated to execute disassembly tasks. Based on this concept, the cloud-based disassembly system is proposed, which provides the disassembly service according to the user requirement. The disassembly service is the execution plan for disassembly tasks, which is the result of scheduling disassembly tasks and allocating disassembly resources. To formally describe the disassembly service, this paper builds a mathematical model that considers the uncertainty nature of the disassembly process and precedence relationships of disassembly tasks. Two objectives including minimizing the expected total makespan and minimizing the expected total cost of the disassembly service are also discussed. The mathematical model is NP-complete, a multi-objective genetic algorithm based on non-dominated sorting genetic algorithm II is designed to address the problem. Computation results show that the proposed algorithm performs well, the algorithm generates a set of Pareto optimal solutions. The user can choose a preferred disassembly service among Pareto optimal solutions.     

A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB

Facing current environment full of a variety of small quantity customized requests, enterprises must provide diversified products for speedy and effective responses to customers’ requests. Among multiple plans of product, both assembly sequence planning (ASP) and assembly line balance (ALB) must be taken into consideration for the selection of optimal product plan because assembly sequence and assembly line balance have significant impact on production efficiency. Considering different setup times among different assembly tasks, this issue is an NP-hard problem which cannot be easily solved by general method. In this study the multi-objective optimization mathematical model for the selection of product plan integrating ASP and ALB has been established. Introduced cases will be solved by the established model connecting to database statistics. The results show that the proposed Guided-modified weighted Pareto-based multi-objective genetic algorithm (G-WPMOGA) can effectively solve this difficult problem. The results of comparison among three different kinds of hybrid algorithms show that in terms of the issues of ASP and ALB for multiple plans, G-WPMOGA shows better problem-solving capability for four-objective optimization.     

Physics-based virtual reality for task learning and intelligent disassembly planning

Physics-based simulation is increasingly important in virtual manufacturing for product assembly and disassembly operations. This work explores potential benefits of physics-based modeling for automatic learning of assembly tasks and for intelligent disassembly planning in desktop virtual reality. The paper shows how realistic physical animation of manipulation tasks can be exploited for learning sequential constraints from user demonstrations. In particular, a method is proposed where information about physical interaction is used to discover task precedences and to reason about task similarities. A second contribution of the paper is the application of physics-based modeling to the problem of disassembly sequence planning. A novel approach is described to find all physically admissible subassemblies in which a set of rigid objects can be disassembled. Moreover, efficient strategies are presented aimed at reducing the computational time required for automatic disassembly planning. The proposed strategies take into account precedence relations arising from user assembly demonstrations as well as geometrical clustering. A motion planning technique has also been developed to generate non-destructive disassembly paths in a query-based approach. Experiments have been performed in an interactive virtual environment including a dataglove and motion tracker that allows realistic object manipulation and grasping.     

Recovery planning of industrial robots based on semantic information of failures and time-dependent utility

Industrial robots are required to recover from temporary errors and continue operations under a changing environment. In this paper, we propose a recovery planning system that considers the semantic information behind errors during robotic actions. In order to establish general repair strategies for feasible recovery plans under uncertainties, the proposed system uses a conceptual graph based on case grammar and a Bayesian network that is dynamically constructed according to the semantic information. In addition, we tackle the problem that the wealth of the recovery plan depends on the uncertainty of execution costs against the deadline at the production site. The proposed system controls the decision model by using a time-dependent utility. We demonstrate the effectiveness of the proposed system through simulations of assembly tasks by multiple robots.     

Optimal assembly plan generation: a simplifying approach

The main difficulty in the overall process of optimal assembly plan generation is the great number of different ways to assemble a product (typically thousands of solutions). This problem confines the application of most existing automated planning methods to products composed of only a limited number of components. The presented method of assembly plan generation belongs to the approach called “disassembly” and is founded on a new representation of the assembly process, with introduction of a new concept, the equivalence of binary trees. This representation allows to generate the minimal list of all non-redundant (really different) assembly plans. Plan generation is directed by assembly operation constraints and plan-level performance criteria. The method was tested for various assembly applications and compared to other generation approaches. Results show a great reduction in the combinatorial explosion of the number of plans. Therefore, this simplifying approach of assembly sequence modeling allows to handle more complex products with a large number of parts.     

Assembly Coplanner: co-operative assembly planner based on subassembly extraction

The distribution of assembly workstations enables assembly operations to be done in parallel, while the multiple routing of parts in flexible assembly systems allows the opportunistic scheduling of assembly operations. This paper presents an assembly planning system, called the Assembly Coplanner, which automatically constructs an assembly partial order and generates a set of assembly instructions from a liaison graph representation of an assembly based on the extraction of preferred subassemblies. Assembly planning in Coplanner is carried out by the co-operation of multiple planning agents, such as the geometric reasoner, the physical reasoner, the resource manager and the plan coordinator, under the constraints of finding a cost-effective assembly plan in a flexible assembly system. The Coplanner identifies spatial parallelism in assembly as a means of constructing temporal parallelism among assembly operations. This is achieved in the following way: (1) the selection of a set of tentative subassemblies by decomposing a liaison graph into a set of subgraphs based on feasibility and difficulty of disassembly; (2) the evaluation of each of the tentative subassemblies in terms of assembly cost represented by subassembly selection indices; and (3) the construction of a hierarchical partial order graph (HPOG) as an assembly plan. A case study applying the Coplanner to a mechanical assembly is illustrated in this paper.     

智能装配规划中的拆卸方向计算

提出了基于装配约束的离散化单位球面的拆卸方向计算方法．首先利用离散化的单位球面计算局部拆卸方向集,通过离散点坐标的加权平均求取拆卸方向集的质心;然后通过可拆卸性判断获得可行的全局拆卸方向．应用表明,该方法可有效地降低复杂产品拆卸方向求解的计算复杂度．     

A Minimum-Jerk Speed-Planning Algorithm for Coordinated Planning and Control of Automated Assembly Manufacturing

We previously proposed a general algorithm for coordinating the motions among multiple machines in a shared assembly environment based on a constant-speed motion model. In this paper, we extend this work to a minimum-jerk polynomial motion model and describe a new speed-planning algorithm to plan automated assembly machines' motions. Machines are planned sequentially, based on their priorities, by mapping the motions of higher-priority machines into forbidden regions in two-dimensional space-time graphs. Collision-free minimum-jerk motions are then planned between the forbidden regions in the graphs. The new speed-planning algorithm is evaluated on a dual-robot surface-mount technology assembly machine in which both robots share a common workspace. Note to Practitioners—Automated assembly processes, especially surface-mount technology manufacturing, require a high degree of precision when placing certain components. This motivated us to find a way of maintaining good positional accuracy by planning smooth motions for the machines that perform these tasks. Since many of these machines have two or more robots, their motions must also be coordinated. We developed an algorithm that combines coordinated motion concepts with a minimum-jerk motion model that can solve these problems. The algorithm plans segmented paths for the robots and then sequentially plans their speeds to prevent collisions between them. The planned speeds ensure position, velocity, and acceleration continuity between path segments. The smooth motions resulting from this method enable high-accuracy component placement. The tradeoff for this improvement is increased cycle time compared to other speed-planning methods.     

Cooperation Requirements Planning (CRP) for multiprocessors: Optimal assignment and execution planning

Cooperation is considered an essential attribute of intelligent multi-machine systems. It enhances their flexibility and reliability. Cooperation Requirement Planning (CRP) is the process of generating a consistent and coordinated global execution plan for a set of tasks to be completed by a multi-machine system based on the task cooperation requirements and interactions. CRP is divided into two steps: CRP-I which matches the task requirements to machine and system capabilities to generate cooperation requirements. It also generates task precedence, machine operation, and system resource constraints. CRP-II uses the cooperation requirements and various constraints to generate a task assignment and coordinated and consistent global execution plan. The global execution plan specifies an ordered sequence of actions and the machine sets that execute them such that the assigned tasks are successfully completed, all the constraints are resolved, and the desired performance measure optimized.In this paper, we describe the CRP-II methodology based on the concepts of planning for multiple goals with interactions. Each task is considered to be a goal, and the CRP-I process is viewed as generating alternate plans and associated costs to accomplish each goal. Five different interactions are specified between the various plans: action combination, precedence relation, resource sharing, cooperative action, and independent action. The CRP-II process is viewed as selecting a plan to satisfy each goal and resolving the interactions between them. A planning strategy is proposed which performs plan selection and interaction resolution simultaneously using a best-first search process to generate the optimal global plan.     

Multi-agent collaboration in time-constrained domains

Timeliness is usually an indispensable attribute of planning and problem solving for resource allocation in command, control and communication systems. The success of such a system is judged on its ability to respond to scheduled and unscheduled tasks within a permissible time period. The response is based on a plan that covers the following activities: resource allocation, plan execution and monitoring and dynamic plan mending, if necessary. Decision making for resource selection can become very time consuming when there are many resources and the number of constraints is large. In a changing environment of multiple agents, restrictive organizational structures and strict communication protocols may cause intolerable further delays.Traditional approaches to planning in deterministic environments require a predictable amount of time to produce and execute plans. However, given more time, such systems usually cannot improve on the plans. In this paper we describe a multi-agent resource scheduler which uses a prioritized rule base to model decision making under the constraints of time. We also discuss dynamic scoping as a negotiation technique for inter-agent cooperation and constrained lattice-like communications as an optimized message routing strategy. Finally, we present some empirical results from a sequence of experiments.     

Coordinating Self-interested Planning Agents

We consider planning problems where a number of non-cooperative agents have to work on a joint problem. Such problems consist in completing a set of interdependent, hierarchically ordered tasks. Each agent is assigned a subset of tasks to perform for which it has to construct a plan. Since the agents are non-cooperative, they insist on planning independently and do not want to revise their individual plans when the joint plan has to be assembled from the individual plans. We present a general formal framework to study some computational aspects of this non-cooperative coordination problem and we establish some complexity results to identify some of the factors that contribute to the complexity of this problem. Finally, we illustrate our approach with an application to coordination in multi-modal logistic planning.     

Mechanical assembly planning using ant colony optimization

In mechanical assembly planning research, many intelligent methods have already been reported over the past two decades. However, those methods mainly focus on the optimal assembly solution search while another important problem, the generation of solution space, has received little attention. This paper proposes a new methodology for the assembly planning problem. On the basis of a disassembly information model which has been developed to represent all theoretical assembly/disassembly sequences, two decoupled problems, generating the solution space and searching for the best result, are integrated into one computation framework. In this framework, using an ant colony optimization algorithm, the solution space of disassembly plans can be generated synchronously during the search process for best solutions. Finally, the new method’s validity is verified by a case study.     

Dynamic scheduling in flexible assembly system based on timed Petri nets model

This paper investigates a scheduling model for optimal production sequencing in a flexible assembly system. The system features a set of machines working together in the same workspace, with each machine performing a subset of operations. Three constraints are considered: (1) the precedence relation among the operations specified by the assembly tree; (2) working space that limits concurrent operations; and (3) the variation of process time. The objective is to find both a feasible assignment of operations to machines and schedule tasks in order to minimize the completion time for a single product or a batch of products. The assembly process is modeled using timed Petri nets and task scheduling is solved with a dynamic programming algorithm. The method calculates the time required precisely. A detailed case study is discussed to show the effectiveness of the model and algorithm.     

卫星的智能规划与调度

以对地观测卫星为例，分析卫星的结构功能和飞行任务的特点，并在此基础上建立卫星智能规划与调度系统。规划与调度系统由卫星模型和推理机组成，其中模型描述卫星结构功能和各种约束条件，推理机分析并解决这些约束条件，形成一个没有冲突的飞行计划。规划与调度系统还具有修正计划的能力，能满足任务删除、更改和新任务插入等需求。     

多项目多任务选择计划模型及其免疫遗传算法

针对多资源受限下项目群选择计划及项目流程调度问题,依据项目的权重和承继约束,将项目群按权值从大到小进行排序,各项目内部的任务分别采用网络计划图进行拓扑排序,并以资源约束和合同期等因素为项目选择标准,建立多项目多任务选择计划的资源配置数学模型。进而,在遗传算法中引入免疫系统的记忆性和多样性功能,设计免疫遗传算法求解所获模型的最佳决策方案。比较性的数值实验结果说明了模型的合理性和算法的有效性。     

以低碳为目标的集装箱拖车运输问题及其时间窗离散化算法

研究以低碳为目标的集装箱拖车运输问题. 该问题需同时调度隐含的运输资源和具有双重时间窗限制的运输任务. 基于扩展的确定的活动在顶点上(DAOV) 的图建立该问题的具有双时间窗约束的混合整数非线性规划模型,设计一个基于时间窗离散化的求解算法, 并将该模型转化为纯整数线性规划模型. 实验结果表明, 所提出的方法有很好的求解速度和精度, 与给定车辆行驶速度情形的对比进一步验证了所提出模型的有效性.

     

Reliable decentralized stabilization via extended linear matrix inequalities and constrained dissipativity

This paper considers the problem of reliable decentralized stabilization with multicontroller configurations when some of the controllers are faulty in the sense that they fail to act optimally or do not function in the way that they were originally intended to function. Specifically, we introduce a solution concept that requires controllers to respond optimally (i.e. in the sense of best‐response correspondences) to the nonfaulty controllers regardless of the identity or actions of the faulty controllers. At any time, we assume that the nonfaulty controllers know only that there can be at most one faulty controller in the system, but they know neither the identity of the faulty controller nor how this faulty controller behaves. We present a design framework using an extended linear matrix inequality technique for deriving reliable stabilizing state‐feedback gains; whereas a set of filters whose estimation‐error dynamics satisfy certain quadratic integral constraints is used as decentralized observers within the subsystems for extending the result to the output‐feedback case. Moreover, a sufficient condition for solvability of the problem is provided in terms of the minimum‐phase condition of the subsystems. We also present an application of the results to a power system problem. Copyright © 2013 John Wiley & Sons, Ltd.     

Improving assembly precedence constraint generation by utilizing motion planning and part interaction clusters

In this paper, we present a technique that combines motion planning and part interaction clusters to improve generation of assembly precedence constraints. In particular, this technique automatically finds, and clusters, parts that can mutually affect each other’s accessibility, and hence may impose assembly constraints. This enables the generation of accurate precedence constraints without needing to examine all possible assembly sequences. Given an assembly model, our technique generates potential disassembly layers: spatial clustering is used to generate part sets. Next, motion planning based on rapidly-exploring random trees (RRT) with multiple trees is used to evaluate the interaction between these part sets. Specifically, motion planning is used to determine which part sets can be removed from the assembly. These sets are added to the first disassembly layer and removed from the assembly. Part sets that can be removed from the simplified assembly are then added to the second layer. If the process gets stuck, parts in the parent set are regrouped, and the process continues until all disassembly layers are found. The resulting structure reveals precedence relationships among part sets, which can be used to generate feasible assembly sequences for each part set and the whole assembly. We present theoretical results related to the algorithms developed in the paper. Computational results from tests on a variety of assemblies are presented to illustrate our approach.