Optimal order quantity and product modularity for a two-echelon returnable supply chain

This study considers a supply chain consisting of a manufacturer, a retailer, customers and a returnable and modular product with uncertain demand, in which customers return the unsatisfactory products to the retailer, and the retailer returns the unsold products to the manufacturer. We investigate the effects of the manufacturer’s refund for the retailer’s unsold products and the product modularity under the decentralised and the centralised strategies. We confirm that the order quantity and customer’s return probability both affect the optimal modularity level of the product, and that the optimal modularity level is related to the refund policy. Also, a strategic alliance between the supply chain members is more advantageous to product innovation based on modularity for a returnable chain. Finally, managerial insights and remarks are offered.     

Interactive AR-assisted product disassembly sequence planning (ARDIS)

This paper presents a proof-of-concept novel near real-time interactive AR-assisted product disassembly sequence planning system (ARDIS) based on product information, such as interference matrix and 3D models. The system is developed using Unity and consists of three modules, including an intelligent disassembly sequence planning module, an automatic content authoring module and an intuitive augmented reality (AR) user interface (UI) with various features, such as a virtual panel for customisation and an option panel for sequence regeneration. Given the retrieval targets specified by a user, optimised disassembly sequences are computed using an evolutionary computing algorithm. For the sequences computed, the respective AR disassembly instruction sequences, such as 2D text instructions and animated 3D models, are generated dynamically based on a taxonomy that links each disassembly step in a sequence with the corresponding Unity templates that have been created beforehand. Hence, the need for manual authoring to provide AR disassembly guidance is reduced. If necessary, the user can request for alternative disassembly sequences which can be re-computed in near real-time. Several case studies have been carried out to demonstrate and evaluate the performance of the system within the laboratory environment.     

A weight function methodology for the assessment of embedded and surface irregular plane cracks

A low cost numerical tool for the calculation of mode I stress intensity factors K in embedded and surface irregular cracks is presented in this paper. The proposed tool is an extension of the O-integral algorithm due to Oore and Burns for the assessment of embedded plane cracks using the weight function methodology. The performance of the O-integral is assessed first by comparing its K results to exact solutions for embedded elliptical and rectangular cracks. From the analysis of this data it is found that the error in the K results systematically depends on the crack aspect ratio and the local crack front curvature. Based on this evidence a corrective function is derived in order to remediate the limitations of the O-integral. Solutions due to Newman and Raju are used to account for the effects of free surfaces and finite thickness. The accuracy of the proposed procedure is assessed by solving a number of examples and by comparing the obtained results to those available in the literature.     

Flexible disassembly planning considering product conditions

To increase the economic benefit of product recovery at the end of life of a consumer product, the profit margins should be augmented. This can be realised by utilising the existing flexibility of today’s mostly manually-conducted disassembly processes. Moreover, with increased economic benefits, the recovery becomes even more attractive, which is also beneficial to the environment. A key component of product recovery is disassembly. Allowing different disassembly states (or levels) per core (i.e. recovered product) increases flexibility in planning. This should result in higher profits, as long as the flexibility is still manageable for the companies. This study focuses on flexible disassembly planning, i.e. the integration of sequencing aspects into disassembly (process) planning. In addition, we further incorporate the condition of items in the core, item damaging, purity requirements, special treatment of hazardous items and several limitations like core availability, item, module and material distribution, disposal and labour time limit. We base our developed mixed integer linear programme on graphs, such as the disassembly state graph for sequencing, and a hypergraph to model the core condition. Lastly, our considerations are illustrated by a numerical example.     

Disassembly process planning algorithms for end-of-life product recovery and environmentally conscious disposal

Disassembly process planning is an act of preparing detailed operation instructions for disassembling used or an end-of-life (EOL) product to recover or dispose of its constituent parts or subassemblies. The main decisions are: (a) disassembly level; (b) disassembly sequence; and (c) EOL options such as reuse, remanufacturing, recycling, incineration, landfill, etc. This study deals with the three decision variables simultaneously in the parallel disassembly environment for the objective of maximising the profit. Unlike previous studies, we consider practical constraints, i.e., reuse probability and environmental impacts of parts or subassemblies, sequence-dependent setup costs, regulation on recovery rate, and incineration capacity. To represent and solve the problem, we develop an extended AND/OR graph, and then suggest a two-phase algorithm. To show the effectiveness of the proposed algorithm, two case studies have been carried out on an automatic pencil and a telephone. Also, test results on other general product structures are reported.     

在Pro/E中自动生成零件拆卸优先约束矩阵

拆卸优先约束矩阵为建立产品的拆卸层次信息图模型提供了基础信息,保证了拆卸序列规划结果的正确性和有效性.在Pro/E中用C语言完成了零件拆卸优先约束矩阵自动生成的二次开发,包括模型调入和预处理、简单拆卸路径生成和包容盒逐级分解的干涉检验等3个模块.解决了自动判别节点可拆卸性的关键问题,为机电产品基于拆卸层次信息图建模的自动化提供了有力的支持.     

Dynamic disassembly planning for remanufacturing of multiple types of products

With the increased need for remanufacturing of end-of-life products, achieving economic efficiency in remanufacturing is urgently needed. The purpose of this study was to devise a cost-minimisation plan for disassembly and remanufacturing of end-of-life products returned by consumers. A returned end-of-life product is disassembled into remanufacturable parts, which are supposed to be used for new products after being remanufactured. Each end-of-life product is disassembled into parts at variable levels and through variable sequences as needed, taking into account not only disassembly but also manufacturing, remanufacturing, and holding inventory of remanufacturable parts. This study proposes a mixed integer linear programming (MILP) model for derivation of the optimal disassembly plan for each returned product, under deterministically known demand and return flows. For the purposes of an illustrative example, the proposed model was applied to the formulation of an optimal disassembly and remanufacturing plan of ‘fuser assembly’ of laser printers. The solution reveals that variable-level disassembly of products saves a significant remanufacturing cost compared with full disassembly.     

Optimising resource portfolio planning for capital-intensive industries under process-technology progress

This paper addresses the problem of resource portfolio planning of firms in high-tech, capital-intensive manufacturing industries. In light of the strategic importance of resource portfolio planning in these industries, we offer an alternative approach to modelling capacity planning and allocation problems that improves the deficiencies of prior models in dealing with three salient features of these industries, i.e. fast technological obsolescence, volatile market demand, and high capital expenditure. This paper first discusses the characteristics of resource portfolio planning problems including capacity adjustment and allocation. Next, we propose a new mathematical programming formulation that simultaneously optimises capacity planning and task assignment. For solution efficiency, a constraint-satisfied genetic algorithm (CSGA) is developed to solve the proposed mathematical programming problem on a real-time basis. The proposed modelling scheme is employed in the context of a semiconductor testing facility. Experimental results show that our approach can solve the resource portfolio planning problem more efficiently than a conventional optimisation solver. The overall contribution is an analytical tool that can be employed by decision makers responding to the dynamic technological progress and new product introduction at the strategic resource planning level.     

Automatic derivation of transition matrix for end-of-life decision making

Recently strengthened environmental regulations have obligated manufacturing companies to treat end-of-life (EOL) products both environmentally consciously and economically. EOL treatment begins with disassembling a product into recyclable or disposable sub-assemblies. Therefore, the economic value of an EOL product is largely a function of the plan for its disassembly: the means by which it is to be disassembled into smaller sub-assemblies, and the choice of sub-assemblies to be disassembled first. In order to make these decisions, a disassembly structure describing every possible sub-assembly division and its disassembly path from the original product has to be presented in a typical form. A widely used form of such a structure is a transition matrix. A transition matrix shows all feasible sub-assemblies and their disassembly hierarchy. Whereas it can be easily transformed into mathematical disassembly planning problem, the tedious work required for its generation limits its practical use. In this paper, we propose an algorithm for automatic derivation of a transition matrix. The proposed algorithm provides an efficient way to derive a transition matrix based on a product's architectural information, which includes the product's physical connections and the relative geometric locations between individual parts. The algorithm was validated in deriving a transition matrix of a car door-trim. Our algorithm can significantly expand the applicability of transition-matrix-based disassembly planning research.     

维修拆装工艺规划系统的研究

结合装备维修活动中的拆卸与装配问题,明确了研发面向维修的拆装工艺规划系统的必要性.提出了一个拆装工艺规划系统的构架,论述了系统的主要功能模块、工作流程以及所采用的关键技术.实现了装配建模、拆装序列规划、拆装序列评估与优化,以及拆装作业仿真等关键功能.实践表明该原型系统的设计切实可行,具有良好的应用前景.     

A guided search genetic algorithm using mined rules for optimal affective product design

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.     

Automatic generation of a section building planning for constructing complex ships in European shipyards

Efficient planning of the section building process is important for European shipyards since delays in this process can disrupt the on-time delivery of a ship. Automatically generating production schedules of the section building process can result in higher quality schedules compared to those created manually. Recently, the production processes of European shipyards have shifted to focus heavily on outsourcing and outfitting, yet existing automatic planning methods for section building fail to sufficiently consider these factors. This paper develops a mathematical model of the section building process which includes the effects of outfitting and outsourcing. The objective of this model is to simultaneously minimise the fluctuations in workload and the number of outsourced man-hours. The mathematical model was solved by implementing the non-dominated sorting generic algorithm-II (NSGA-II) using a custom heuristic as the fitness function. Due to the multi-objective nature of the problem definition and solution approach, a Pareto front of optimal solutions is created instead of a single, best solution. A test case showed that gains in both objectives are achievable compared to the planning developed manually. Implementing the Section Building Planning methodology developed in this paper could potentially improve the efficiency and controllability of the overall shipbuilding process.     

Hybrid evolutionary multi-objective algorithms for integrating assembly sequence planning and assembly line balancing

Assembly sequence planning (ASP) and assembly line balancing (ALB) play critical roles in designing product assembly systems. In view of the trend of concurrent engineering, pondering simultaneously over these two problems in the development of assembly systems is significant for establishing a manufacturing system. This paper contemplates the assembly tool change and the assembly direction as measurements in ASP; and further, Equal Piles assembly line strategy is adopted and the imbalanced status of the system employed as criteria for the evaluation concerning ALB. Focus of the paper is principally on proposing hybrid evolutionary multiple-objective algorithms (HEMOAs) for solutions with regard to integrate the evolutionary multi-objective optimization and grouping genetic algorithms. The results provide a set of objectives and amend Pareto-optimal solutions to benefit decision makers in the assembly plan. In addition, an implemented decision analytic model supports the preference selection from the Pareto-optimal ones. Finally, the exemplifications demonstrate the effectiveness and performance of the proposed algorithm. The consequences definitely illustrate that HEMOAs search out Pareto-optimal solutions effectively and contribute to references for the flexible change of assembly system design.     

GA-based adaptive setup planning toward process planning and scheduling integration

Setup planning of a part for more than one available machine is a typical combinatorial optimisation problem under certain constraints. It has significant impact not only on the whole process planning but also on scheduling, as well as on the integration of process planning and scheduling. Targeting the potential adaptability of process plans associated with setups, a cross-machine setup planning approach using genetic algorithms (GA) for machines with different configurations is presented in this paper. First, based on tool accessibility analysis of different machine configurations, partially sequenced machining features can be grouped into certain setups; then by responding to the requirements from a scheduling system, optimal or near-optimal setup plans are selected for certain criteria, such as cost, makespan and/or machine utilisation. GA is adopted for the combinatorial optimisation, which includes gene pool generation based on tool accessibility examination, setup plan encoding and fitness evaluation, and optimal setup plan selection through GA operations. The proposed approach is implemented in a GA toolbox, and tested using a sample part. The results demonstrate that the proposed approach is applicable to machines with varying configurations, and adaptive to different setup requirements from a scheduling system due to machine availability changes. It is expected that this approach can contribute to process planning and scheduling integration when a process plan is combined with setups for alternative machines during adaptive setup planning.     

Order picker routing with product returns and interaction delays

E-commerce companies often use manual order-picking systems in their warehouses since these systems can provide the required flexibility and scalability. Manual systems have been widely studied, but the operating policies may require significant changes for e-commerce settings. First, to maintain consumers’ loyalty, it is important to maintain delivery reliability even on the busiest days. When the number of order pickers in an area increases, however, more delays due to interactions may occur. For example, travel speed may need to be lowered when order pickers pass each other in narrow aisles. Second, many products sold through e-commerce are returned by consumers. Before these returned products can be sold again, they must be reintegrated in the stock. This paper presents hybrid genetic algorithms to determine routes for simultaneous pickup of products in response to consumers’ orders and delivery of returned products to storage locations. Furthermore, interactions between the order pickers are considered in the routing decisions. The developed algorithms use specific warehouse problem characteristics. We identify the mix of pickups and deliveries to realise the highest savings in practice. It is shown that order-picker interactions can be a significant cause for delay and should be accounted for in the routing.     

An adaptive genetic algorithm for large-size open stack problems

The problem of minimising the maximum number of open stacks arises in many contexts (production planning, cutting environments, very-large-scale-integration circuit design, etc.) and consists of finding a sequence of tasks (products, cutting patterns, circuit gates, etc.) that determines an efficient utilisation of resources (stacks). We propose a genetic approach that combines classical genetic operators (selection, order crossover and pairwise interchange mutation) with an adaptive search strategy, where intensification and diversification phases are obtained by neighbourhood search and by a composite and dynamic fitness function that suitably modifies the search landscape. Computational tests on random and real-world benchmarks show that the proposed approach is competitive with the state of the art for large-size problems, providing better results for some classes of instances.     

基于遗传算法的码垛机器人路径规划应用

目的为了改进传统遗传算法在码垛机器人路径规划中可能出现的局部陷阱和过早收敛问题,以及机器人的能耗和路线平滑性问题,提出一种改进的遗传算法机器人路径规划方法。方法针对传统遗传算法存在的问题,分别对种群初始化、适应度函数、选择算子、交叉算子、变异算子的算法和方式进行调整和改进,对优秀算法进行融合。针对基本遗传算法主要着重于路径最短,从而忽视了机器人的能耗及路径平滑性等问题,设计一种综合考虑距离和转弯次数控制的适应度函数,最后将改进的算法应用于码垛机器人的路径规划中。结果仿真结果表明,相较于基本遗传算法,提出的算法搜索到的路径质量更高,不仅距离更短,同时转弯次数远远小于其他算法,路径更为平滑,验证了该算法的有效性。结论基于该算法的码垛机器人路径在兼顾距离最优的同时,路线更加平滑。由于减少了转向次数,机器人的能耗更低,同时仿真结果表明,该算法的实时性也较好。     

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Integrated process planning and scheduling (IPPS) is a manufacturing strategy that considers process planning and scheduling as an integrated function rather than two separated functions performed sequentially. In this paper, we propose a new heuristic to IPPS problem for reconfigurable manufacturing systems (RMS). An RMS consists mainly of reconfigurable machine tools (RMTs), each with multiple configurations, and can perform different operations with different capacities. The proposed heuristic takes into account the multi-configuration nature of machines to integrate both process planning and scheduling. To illustrate the applicability and the efficiency of the proposed heuristic, a numerical example is presented where the heuristic is compared to a classical sequential process planning and scheduling strategy using a discrete-event simulation framework. The results show an advantage of the proposed heuristic over the sequential process planning and scheduling strategy.     

A weight function method for the assessment of partially closed three-dimensional plane cracks

The interference and physical contact between mating fracture surfaces can lead, even under the action of traction loads, to the closure of a crack. A low-cost numerical tool for the assessment of three-dimensional partially closed mode-I cracks is presented in this paper. The devised tool is based on the weight function methodology and it allows computing the geometry of the open part of the crack and the stress intensity factor along the complete crack front. The accuracy and versatility of the proposed procedure is assessed by solving a number of examples and comparing the obtained results with those available in the literature.     

An efficient methodology for fatigue damage assessment of bridge details using modal superposition of stress intensity factors

The objective of this paper is to propose an accurate and computationally efficient method for the fatigue assessment of bridge details, using Fracture Mechanics and crack propagation laws. The proposed workflow benefits from the combination of finite element submodeling techniques and modal superposition method with the new concept of modal stress intensity factors.The new methodology was applied to the fatigue analysis of a complex bridge under real traffic conditions. The simulation of fatigue crack propagation in a critical detail of the structure was achieved with minimal computer resources and within a short time frame.