The use of intelligent techniques in the manufacturing field has been growing the last decades due to the fact that most manufacturing optimization problems are combinatorial and NP hard. This paper examines recent developments in the field of evolutionary computation for manufacturing optimization. Significant papers in various areas are highlighted, and comparisons of results are given wherever data are available. A wide range of problems is covered, from job shop and flow shop scheduling, to process planning and assembly line balancing.

Genetic programming has rarely been applied to manufacturing optimisation problems. In this paper the potential use of genetic programming for the solution of the one-machine total tardiness problem is investigated. Genetic programming is utilised for the evolution of scheduling policies in the form of dispatching rules. These rules are trained to cope with different levels of tardiness and tightness of due dates. q

This paper provides a review on current developments in genetic algorithms. The discussion includes theoretical aspects of genetic algorithms and genetic algorithm applications. Theoretical topics under review include genetic algorithm techniques, genetic operator technique, niching techniques, genetic drift, method of benchmarking genetic algorithm performances, measurement of difficulty level of a test-bed function, population genetics and developmental mechanism in genetic algorithms. Examples of genetic algorithm application in this review are pattern recognition, robotics, artificial life, expert system, electronic circuit design, cellular automata, and biological applications. While the paper covers many works on the theory aiid application of genetic algorithms, not much details are reported on genetic programming, parallel genetic algorithms, in addition to more advanced techniques e.g. micro-genetic algorithms and multiobjective optimisation.

This article presents a genetic algorithm approach to multi‐criteria motion planning of mobile manipulator systems. For mobile robot path planning, traveling distance and path safety are considered. The workspace of a mobile robot is represented as a grid by cell decomposition, and a wave front expansion algorithm is used to build the numerical potential fields for both the goal and the obstacles. For multi‐criteria position and configuration optimization of a mobile manipulator, least torque norm, manipulability, torque distribution and obstacle avoidance are considered. The emphasis of the study is placed on using genetic algorithms to search for global optimal solutions and solve the minimax problem for manipulator torque distribution. Various simulation results from two examples show that the proposed genetic algorithm approach performs better than the conventional methods.

This study examines the adoption of the Internet of Things (IoT) based innovations by urban poor communities. In recent years, IoT applications in social networking, smart cities and m-health have been explored, but the social acceptance of such applications remain largely unknown. A consumer segment on which IoT can have a major positive impact is the urban poor, where IoT can provide access to services such as healthcare, education and food security. However, to facilitate adoption among the poor, IoT-based innovations must incorporate the unique characteristics of this segment viz. low levels of technology awareness, social acceptance and consumer need. The Disruption of Things refers to the process of distribution the innovations that are based on IoT and replacing existing market leaders and prevalent systems. The study was conducted in four stages -a literature review, a survey with the target users, interviews with experts (both technological and sociological) and a usability test with a prototype technology system. The results from the surveys, interviews and usability tests were used to develop a model for adoption of IoT-based innovations by the urban poor. The model identifies five sources of innovation -nutrition, healthcare, employment, education and finances. Based on these sources, a participative design process is undertaken. Once developed the innovation must provide excellent service based on three parameters -benefits (value of using the system to the users), support provided to the users, and training/instructions given to them regarding system use. Satisfied system's users would then be leveraged through three channelsadvertising, social media and word-of-mouth, in order to spur greater adoption of the innovation among the urban poor. Accomplishing these systematic processes would enable the Disruption of Things -replacement of existing market leaders by an IoT-based innovation. Scope and BenefitsThis study aims to develop a model that facilitates the adoption of IoT-based innovations by the urban poor. IoT is a global information infrastructure that enables advanced services by interconnecting devices based on existing and evolving interoperable information and communication technologies [1]. IoT facilitates communication forms beyond the traditional human-human to human-thing and thing-thing (also referred to as M2M). Radio Frequency Identification (RFID) is believed to be a foundation for IoT [2].While the original intent of IoT was to increase supply chain efficiencies for organizations [3], more recently potential IoT applications in social networking [4], smart cities [5] and m-health [6] have been explored. However, all such potential applications face a road block in the form of societal acceptance. Sanchez [7] noted that social acceptance was a major challenge in the implementation of smart cities. Coughlan [8] considered that social acceptance of IoT applications depended on a number of human factors such as sociology, psychology and human-computer interaction.

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