多工作日历下流水作业调度遗传优化方法

针对多工作日历下的流水作业调度问题，提出了一种遗传优化方法。首先，提出了基于多工作日历的时间推算方法，解决了多工作日历下流水作业调度的关键问题：以Excel为平台设计了“工作制”工作表和“设备”工作表，在“设备”工作表中为每台设备指定工作制并设定工作时段；在此基础上，以Excel VBA为平台设计了5个基于工作日历的时间推算函数。其次，以Excel VBA为平台设计了遗传算法用于求解问题：个体采用整数编码方式，交叉操作采用“交换交叉”方式，变异操作采用“交换变异”方式，解码过程采用基于多工作日历的时间推算方法准确计算各工序开工和完工时刻。最后，通过案例分析验证了所提方法的有效性。     

Cognitive and affective processes for learning science in immersive virtual reality

As immersive virtual reality (IVR) systems proliferate in classrooms, it is important to understand how they affect learning outcomes and the underlying affective and cognitive processes that may cause these outcomes. Proponents argue that IVR could improve learning by increasing positive affective and cognitive processing, thereby supporting improved performance on tests of learning outcome, whereas opponents of IVR contend that it could hurt learning by increasing distraction, thereby disrupting cognitive learning processes and leading to poorer learning outcomes. In a media comparison study, students viewed a biology lesson either as an interactive animated journey in IVR or as a slideshow on a desktop monitor. Those who viewed the IVR lesson performed significantly worse on transfer tests, reported higher emotional arousal, reported more extraneous cognitive load and showed less engagement based on EEG measures than those who viewed the slideshow lesson, with or without practice questions added to the lessons. Mediational analyses showed that the lower retention scores for the IVR lesson were related to an increase in self-reported extraneous cognitive load and emotional arousal. These results support the notion that immersive environments create high affective and cognitive distraction, which leads to poorer learning outcomes than desktop environments.     

Soft Matter Technology at KIT: Chemical Perspective from Nanoarchitectures to Microstructures

Bioinspiration has emerged as an important design principle in the rapidly growing field of materials science and especially its subarea, soft matter science. For example, biological cells form hierarchically organized tissues that not only are optimized and designed for durability, but also have to adapt to their external environment, undergo self‐repair, and perform many highly complex functions. Being able to create artificial soft materials that mimic those highly complex functions will enable future materials applications. Herein, soft matter technologies that are used to realize bioinspired material structures are described, and potential pathways to integrate these into a comprehensive soft matter research environment are addressed. Solutions become available because soft matter technologies are benefitting from the synergies between organic synthesis, polymer chemistry, and materials science.     

A note on “minimizing makespan in three machine flow shop with deteriorating jobs”

This paper is a note on “minimizing makespan in three machine flow shop with deteriorating jobs” [J.-B. Wang, M.-Z. Wang, minimizing makespan in three machine flow shop with deteriorating jobs, Computers & Operation Research 40 (2013) 547–557]. Wang and Wang presented a branch-and-bound algorithm with several dominance properties and a lower bound; however, we think that the dominance properties may not be true as they are neither necessary nor sufficient. We first show by means of a counter-example that the published dominance properties are incorrect, and then present a necessary and sufficient condition for them to be true. Moreover, a simplifying remark is provided for the above dominance properties.     

Whither Physical Organic Chemistry? Wither? Or Wider?

This article presents a personal view of the field of physical organic chemistry (broadly construed), its history, its current status, and what it may become in the future.     

Sustainability Science and Planning: A Crucial Collaboration?

In this age of public skepticism about science, sustainability science offers a way to increase the application of science in planning and policy. This paper explores the relationship between science and society and the emergence of sustainability science. A comparison of the theory and practice of sustainability science and planning reveals the potential for a natural partnership. Based on the analysis, I suggest ways that planning may bridge the gap and build on the opportunity to strengthen its purpose and impact.     

OSPM: A design methodology for open strategic planning

This study employs a design science perspective to propose a methodology for open strategic planning (OSP). Habermas’ discourse theory and Bryson’s strategy change cycle are used as informing kernel theories. A methodology is proposed to satisfy the requirements retrieved from the kernel theories. The proposed methodology contains modules for a planning system and a planning process. Design principles are explained through a blueprint of the system and process. The proposed methodology is applied and evaluated in two cases. Contributions to the literature involve extending the literature on OSP to an applicable methodology with guidelines on how to implement open strategy.     

An effective heuristic for adaptive control of job sequences subject to variation in processing times

Variation in sequential task processing times is common in manufacturing systems. This type of disturbance challenges most scheduling methods since they cannot fundamentally change job sequences to adaptively control production performance as jobs enter the system because actual processing times, are not known in advance. Some research literature indicates that simple rules are more suitable than algorithmic scheduling methods for adaptive control. In this work, a ‘state space – average processing time’ (SS-APT) heuristic is proposed and compared to four most commonly used scheduling rules and two well-established heuristics based on Taillard’s benchmarks. It is shown that the adaptive control is made possible under variation in processing times given the flexibility and strong performance of the SS-APT heuristic, especially for work-in-process inventory control.     

Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling

Factory management plays an important role in improving the productivity and quality of service in the production process. In particular, the distributed permutation flow shop scheduling problem with multiple factories is considered a priority factor in the factory automation. This study proposes a novel model of the developed distributed scheduling by supplementing the reentrant characteristic into the model of distributed reentrant permutation flow shop (DRPFS) scheduling. This problem is described as a given set of jobs with a number of reentrant layers is processed in the factories, which compromises a set of machines, with the same properties. The aim of the study is to determine the number of factory needs to be used, jobs assignment to certain factory and sequence of job assigned to the factory in order to simultaneously satisfy three objectives of minimizing makespan, total cost and average tardiness. To do this, a novel multi-objective adaptive large neighborhood search (MOALNS) algorithm is developed for finding the near optimal solutions based on the Pareto front. Various destroy and repair operators are presented to balance between intensification and diversification of searching process. The numerical examples of computational experiments are carried out to validate the proposed model. The analytical results on the performance of proposed algorithm are checked and compared with the existing methods to validate the effectiveness and robustness of the proposed potential algorithm in handling the DRPFS problem.     

A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem

In most real manufacturing environments, schedules are usually inevitable with the presence of various unexpected disruptions. In this paper, a rescheduling method based on the hybrid genetic algorithm and tabu search is introduced to address the dynamic job shop scheduling problem with random job arrivals and machine breakdowns. Because the real-time events are difficult to express and take into account in the mathematical model, a simulator is proposed to tackle the complexity of the problem. A hybrid policy is selected to deal with the dynamic feature of the problem. Two objectives, which are the schedule efficiency and the schedule stability, are considered simultaneously to improve the robustness and the performance of the schedule system. Numerical experiments have been designed to test and evaluate the performance of the proposed method. This proposed method has been compared with some common dispatching rules and meta-heuristic algorithms that have been widely used in the literature. The experimental results illustrate that the proposed method is very effective in various shop-floor conditions.