Optimal design method with biomechanical analysis for a work environment reducing physical workload: illustration by application to work table height design

A work environment should be designed to minimise physical workload. We propose an optimal work environment design method to accomplish this, in which joint moment ratios were calculated by biomechanical analyses through digital human modelling (DHM) and were used to indicate physical workload. The work environment design problem was formulated as a multi-objective optimisation problem, minimising the average and maximum joint moment ratio values to determine the optimal work environment. Sequential approximate optimisation, which improves the accuracy of the response surface by sequentially adding new sampling points from a simulation, was applied to efficiently obtain a precise optimal solution. The proposed method was applied to designing a work table height for light assembly tasks. This method determined the optimal work table height considering the anthropometric diversity of workers from a relatively small number of subjects. Through this case study, the validity of the proposed method is discussed.     

An optimal and a heuristic algorithm for the single-item retrieval problem in puzzle-based storage systems with multiple escorts

Puzzle-based storage systems consist of densely stored unit loads on a square grid. The problem addressed in this paper is to retrieve a stored unit load from a puzzle-based storage using the minimum number of item moves. While previous research contributed optimal algorithms for only up to two empty locations (escorts), our approach solves configurations where multiple empty locations are arbitrarily positioned in the grid. The problem is formulated as a state space problem and solved to optimality using an exact search algorithm. To reduce the search space, we derive bounds on the number of eligible empty locations and develop several search-guiding estimate functions. Furthermore, we present a heuristic variant of the search algorithm to solve larger problem instances. We evaluate both solution algorithms on a large set of problem instances. Our computational results show that the algorithms clearly outperform existing approaches where they are applicate and solve more general configurations, which could not be solved to optimality before. The heuristic variant efficiently yields high-quality solutions for significantly larger instances of practically relevant size.     

Cloud manufacturing service composition and optimal selection with sustainability considerations: a multi-objective integer bi-level multi-follower programming approach

The process of service composition and optimal selection (SCOS) is an important issue in cloud manufacturing (CMfg). However, the current studies on CMfg and SCOS have generally focused on optimising the allocation of resources against quality of service (QoS), in terms of e.g. cost, quality, and time. They have seldom taken the perspective of sustainability into discussion, although sustainability is indispensable in the CMfg environment. Addressing this gap, we aim to (1) propose a comprehensive method to assess the sustainability of cloud manufacturing (SoM) in terms of the economic, environmental, and social aspects; (2) establish a multi-objective integer bi-level multi-follower programming (MOIBMFP) model to simultaneously maximise SoM and QoS from the perspectives of both platform operator and multiple service demanders; and (3) design a hybrid particle swarm optimisation algorithm to solve the proposed MOIBMFP model. The experimental results show that the proposed algorithm is more feasible and effective than the typical multi-objective particle swarm optimisation algorithm when solving the proposed model. In other words, the proposed model and algorithm suggest better alternatives to meet the needs of the platform operator and service demanders in the CMfg environment.     

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.     

Optimisation approach to target costing under uncertainty with application to ICT-service

Target costing is a modern approach applied during product development that defines cost targets for products and its components. These cost targets are driven by customer requirements and achievable revenues. The intention of this paper is the integration of target costing with modern concepts of modelling uncertainty and management of risk based on optimisation. Contrary to the traditional focus of target costing on cost targets, this paper prefers a strategy for achieving a target profit. Moreover, in this paper target costing is understood as a continuous process with incremental changes of cost drivers, product and component design as well as product prices. Therefore, the change in costs and profit with respect to aforementioned control parameters is modelled by linear approximations. Hence, improved decisions concerning design and prices are derived by linear programming models. In practice, information concerning product and component costs, demand or customer preferences are not given with certainty. Therefore, we apply a stochastic programming approach to manage the risk inherent in the target costing process. After a general presentation, we apply our approach to the provision of an information and communication technology service where the level of uncertainty is considerable.     

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.     

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.     

一种新颖的重构发射的混沌信号的跟踪器

Coauthors of the paper propose a novel tracker for reconstructing the transmitted chaotic signal whtch uses a modified radial-basis-ruction neural network incorporating a learning algorithm for tracking the noisy chaotic signal with varying parameters. The results show that application of the tracker to noncoherent detection in chaos-shiftkeying digital communication system is very effective under practical communication environment.     

基于位平面的渐近式数字水印技术

将JPEG 2000中的EBCOT编码与现代水印技术相结合提出了基于图像内容中位平面特征的渐近式水印技术，利用位平面特征来同步水印的顺序，图像被分解为多个位平面上的数据，对各个位平面的重要数据按照顺序先后在压缩的同时嵌入水印信息。实验结果证明该方法改善了水印的不可见性和同步性的矛盾。     

飞行转台系统混沌故障建模分析

在一定条件下，系统本身的非线性或某种故障给系统增加的非线性因素可能使系统变成一个混沌动力系统，该文针对一个实际实验中转台出现的异常不规则运动，证明了该系统中转台的运动具有混沌特性。对转台系统数学模型进行分析，通过仿真加入了一些非线性环节，证明了在一定的初试条件下，转台系统有可能进入混沌状态。