Kitting optimisation in Just-in-Time mixed-model assembly lines: assigning parts to pickers in a hybrid robot–operator kitting system

With increasing components’ variety in mixed-model assembly lines, industrials show interest in kitting operations using robots. This paper deals with a hybrid kitting system that consists of a robot and an operator working in series to deliver parts to a Just-In-Time mixed-model assembly line. We develop a mathematical model that optimally assigns stock keeping units to either the robot or the operator so that the cycle time of the overall system is optimised. To test the model, a case study from the automotive sector is presented and a sensitivity analysis is carried out on relevant system parameters.     

Optimal inventory control with sequential online auction in agriculture supply chain: an agent-based simulation optimisation approach

With the development of e-commerce, in agriculture supply chain, online auction is adopted as an inventory clearing tool. Comparing to mathematical models studying inventory control over online sequential auctions, our agent-based simulation model could systematically describe the complexities of bidders’ information interactions and behaviour preferences caused from financial and production perspectives, and by other supply chain members. In addition, we take into account the complex and dynamic market environment, which will impact the operation effect of auction policies. With identical auction items, the profit-maximising firm must decide auction lot-size, which is the number of units in each auction, minimum initial bid, and the time interval between auctions. To obtain the optimal solution, nested partitions framework and optimal expected opportunity cost algorithm are integrated to improve computation accuracy and efficiency. A case study based on real data is conducted to implement and validate the proposed approach. Furthermore, based on the model, the paper studies the sensitivities of the decision variables under different supply and demand scenarios.     

Optimal age replacement scheduling for a random work system with random lead time

System maintenance and spare parts are two closely related logistics activities since maintenance generates the demand for spare parts. Most studies on integrated models of preventive replacement and inventory of spare parts have focused on age replacement scheduling, while random replacement policy, which is sensible and necessary in practice, is rarely discussed and applied. The purpose of this paper is to present a generalised age replacement policy for a system which works at random time and considers random lead time for replacement delivery. To model an imperfect maintenance action, we consider that the system undergoes minimal repairs at minor failures and corrective replacements at catastrophic failures. Before catastrophic failures, the system is replaced preventively at age T or at the completion of a working time, whichever occurs first. The main objective is to determine an optimal schedule of age replacement that minimises the mean cost rate function of the system in a finite time horizon. The existence and uniqueness of optimal replacement policy are derived analytically and computed numerically. It can be seen that the proposed model is a generalisation of the previous works in maintenance theory.     

Assessment of optical emission analysis for in-process monitoring of powder bed fusion additive manufacturing

ABSTRACT

Developing methods which allow real-time monitoring of powder bed fusion (PBF) additive manufacturing (AM) processes is key to enabling in situ assessments of build quality (e.g. lack of fusion and porosity). Here, we investigate the use of optical emission spectroscopy and high-speed (100?kHz) measurement of select wavelength emissions, based on a line-to-continuum approach, to determine if a correlation between PBF AM process inputs, sensor outputs, and build quality exists. Using an open protocol system interfaced with a 3D Systems ProX 200 machine, sensor data were synchronised with the scanner position and the laser state during the buildup of Inconel-718 components under varying powers, scan speeds, and hatch spacing parameters. Sensor measurements were then compared against post-build computed tomography scans. We show evidence that sensor data, when combined with appropriate analyses, are related to both processing conditions and build quality.     

An integrated design methodology for components produced by laser powder bed fusion (L-PBF) process

Laser powder bed fusion (L-PBF) is an additive manufacturing (AM) process that allows to build full dense metal complex part. However, despite the obvious benefits of L-PBF process, it is affected by specific technological drawbacks and it suffers from issues regarding design support tools. In order to fully exploit the advantages of L-PBF, it is necessary to know the technological constraints, such as material availability and the need to minimise the support structures. In this paper, an integrated design procedure that involves topology optimisation, design for laser powder bed fusion rules and finishing requirements is presented in order to define practical guidelines for successful AM of metal parts. The procedure is tested using a case study to prove the effectiveness of the proposed approach.     

SPECT断层显象心肌中心和内外膜半径的确定

提出了一种自动计算单光子断层扫描仪（ＳＰＥＣＴ）心肌短轴图象心肌中心和内外膜半径的方法。该方法基于对心肌边缘位置信息的统计分析和非线性最优化拟合算法，具有可靠性好、精度高、速度快等优点。结果同有经验的医师手工确定的中心位置基本一致，有较高的临床应用价值     

递进多目标粒子群算法的设计及应用

在现有递进进化技术研究的基础上,提出了一种递进多目标PSO算法,该方法每进化一定代数后以一定策略对群体进行重构,以提高算法对解空间的遍历性,从而较大程度上避免算法的早熟。该算法采用非劣解等级优先的选择方式复制后代,通过递进层次间对部分非劣解个体执行局部搜索,加快全局非劣解集的进化。采用递进PSO算法与非递进PSO算法对一些典型优化问题进行对比分析,验证了算法求解多目标函数优化问题的有效性。研究表明,通过研究惯性因子确定的随机数方法,比目前的固定数确定方法具有一定的先进性。     

An AHP-based decision-making tool for the solution of multiproduct batch plant design problem under imprecise demand

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessarily involves the way that equipment may be utilized, which means that plant scheduling and production must form an integral part of the design problem. This work relies on a previous study, which proposed an alternative treatment of the imprecision (demands) by introducing fuzzy concepts, embedded in a multi-objective Genetic Algorithm (GA) that takes into account simultaneously maximization of the net present value (NPV) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The results showed that an additional interpretation step might be necessary to help the managers choosing among the non-dominated solutions provided by the GA. The analytic hierarchy process (AHP) is a strategy commonly used in Operations Research for the solution of this kind of multicriteria decision problems, allowing the apprehension of manager subjective judgments. The major aim of this study is thus to propose a software integrating the AHP theory for the analysis of the GA Pareto-optimal solutions, as an alternative decision-support tool for the batch plant design problem solution.     

Supply chain modeling in uncertain environment with bi-objective approach

Supply chain is viewed as a large-scale system that consists of production and inventory units, organized in a serial structure. Uncertainty is the main attribute in managing the supply chains. Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships among various entities exist in the SC. Uncertainty may result from customer’s demand variability or unreliability in external suppliers. In this paper we develop an inventory model for an assembly supply chain network (each unit has at most one immediate successor, but any number of immediate predecessors) which fuzzy demand for single product in one hand and fuzzy reliability of external suppliers in other hand affect on determination of inventory policy in SCM. External supplier’s reliability has determined using a fuzzy expert system. Also the performance of supply chain is assessed by two criteria including total cost and fill rate. To solve this bi-criteria model, hybridization of multi-objective particle swarm optimization and simulation optimization is considered. Results indicate the efficiency of proposed approach in performance measurement.     

An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And, TS is a meta-heuristic which is designed for finding a near optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.