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1 – 10 of 25Ackmez Mudhoo, Gaurav Sharma, Khim Hoong Chu and Mika Sillanpää
Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However…
Abstract
Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.
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Mostafa Abd-El-Barr, Kalim Qureshi and Bambang Sarif
Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued…
Abstract
Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso-MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.
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Xiaohui Jia, Chunrui Tang, Xiangbo Zhang and Jinyue Liu
This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single…
Abstract
Purpose
This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations.
Design/methodology/approach
A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy.
Findings
The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems.
Originality/value
This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.
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Sayan Chakraborty, Charandeep Singh Bagga and S.P. Sarmah
Being the final end of the logistic distribution, attended home delivery (AHD) plays an important role in the distribution network. AHD typically refers to the service provided by…
Abstract
Purpose
Being the final end of the logistic distribution, attended home delivery (AHD) plays an important role in the distribution network. AHD typically refers to the service provided by the distribution service provider to the recipient's doorstep. Researchers have always identified AHD as a bottleneck for last-mile delivery. This paper addresses a real-life stochastic multi-objective AHD problem in the context of the Indian public distribution system (PDS).
Design/methodology/approach
Two multi-objective models are proposed. Initially, the problem is formulated in a deterministic environment, and later on, it is extended to a multi-objective AHD model with stochastic travel and response time. This stochastic AHD model is used to extensively analyze the impact of stochastic travel time and customer response time on the total expected cost and time-window violation. Due to the NP-hard nature of the problem, an ant colony optimization (ACO) algorithm, tuned via response surface methodology (RSM), is proposed to solve the problem.
Findings
Experimental results show that a change in travel time and response time does not significantly alter the service level of an AHD problem. However, it is strongly correlated with the planning horizon and an increase in the planning horizon reduces the time-window violation drastically. It is also observed that a relatively longer planning horizon has a lower expected cost per delivery associated.
Research limitations/implications
The paper does not consider the uncertainty of supply from the warehouse. Also, stochastic delivery failure probabilities and randomness in customer behavior have not been taken into consideration in this study.
Practical implications
In this paper, the role of uncertainty in an AHD problem is extensively studied through a case of the Indian PDS. The paper analyzes the role of uncertain travel time and response time over different planning horizons in an AHD system. Further, the impact of the delivery planning horizon, travel time and response time on the overall cost and service level of an AHD system is also investigated.
Social implications
This paper investigates a unique and practical AHD problem in the context of Indian PDS. In the present context of AHD, this study is highly relevant for real-world applications and can help build a more efficient delivery system. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.
Originality/value
The most challenging part of an AHD problem is the requirement of the presence of customers during the time of delivery, due to which the probability of failed delivery drastically increases if the delivery deviates from the customer's preferred time slot. The paper modelled an AHD system to incorporate uncertainties to attain higher overall performance and explore the role of uncertainty in travel and response time with respect to the planning horizon in an AHD, which has not been considered by any other literature.
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Ahmed Abdullah Danook and Omar-F Hasan Al.obaidy
The purpose of this study is to examine the dimensions of strategic intent (SI; see Appendix 1) according to the Hamel and Prahalad model as a building for the future, relying on…
Abstract
Purpose
The purpose of this study is to examine the dimensions of strategic intent (SI; see Appendix 1) according to the Hamel and Prahalad model as a building for the future, relying on today’s knowledge-based and proactive strategic directions of management as long-term and deep-perspective creative directions, objective vision and rational analysis, integrative in work, survival structure and comprehensiveness in perception.
Design/methodology/approach
The quantitative approach was used based on research, detection and proof, as data were collected from leaders amounting to 70 respondents and analyzed in the SPSS program and ant colony optimization (see Appendix 1) algorithm and interpretation of the results.
Findings
It was found that the SI dimensions are poorly available due to problems related to foresight, capabilities and the strategic structure that represents the head of strategic objectives.
Practical implications
The empirical evaluation view of the study is valuable for leaders to draw a future strategy of advantage for organizations in developing countries to enrich their core capabilities by activating the SI dimensions.
Originality/value
It stems from a vital issue related to strategic dimensions aimed at a better future for strategic executive work, especially after the crisis that led to a significant decline in the level of performance of organizations.
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This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.
Abstract
Purpose
This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.
Design/methodology/approach
In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.
Findings
The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.
Originality/value
In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.
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Zaheer Doomah, Asish Seeboo and Tulsi Pawan Fowdur
This chapter provides an overview of the potential use of Intelligent Transport Systems (ITS) and associated artificial intelligence (AI) techniques in the land transport sector…
Abstract
This chapter provides an overview of the potential use of Intelligent Transport Systems (ITS) and associated artificial intelligence (AI) techniques in the land transport sector in an attempt to achieve related United Nations Sustainable Development Goals (SDGs) targets. ITS applications that have now been extensively tested worldwide and have become part of the everyday transport toolkit available to practitioners have been discussed. AI techniques applied successfully in specific ITS applications such as automatic traffic control systems, real-time image processing, automatic incident detection, safety management, road condition assessment, asset management and traffic enforcement systems have been identified. These methods have helped to provide traffic engineers and transport planners with novel ways to improve safety, mobility, accessibility and efficiency in the sector and thus move closer to achieving the various SDG targets pertaining to transportation.
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Mohammad Mahdi Vali-Siar and Emad Roghanian
This study addresses resilient mixed supply chain network design (SCND) and aims to minimize the expected total cost of the supply chain (SC) considering disruptions. The capacity…
Abstract
Purpose
This study addresses resilient mixed supply chain network design (SCND) and aims to minimize the expected total cost of the supply chain (SC) considering disruptions. The capacity of facilities is considered uncertain. In order to get closer to real-world situations, competition between SCs is considered.
Design/methodology/approach
A two-stage stochastic programming model is developed for designing the SC network. The location of facilities and selection of suppliers are considered first-stage decisions, and the determination of materials and products flows are second-stage decisions. Some resilience strategies are applied to mitigate the negative impacts of disruptions.
Findings
The results indicate that considering resilience and applying the related strategies are vitally important, and resilience strategies can significantly improve the SC objective and maintain market share. Also, it is confirmed that unrealistic decisions will be made without considering the competition.
Originality/value
This study contributes to the literature by proposing a novel mathematical model for the resilient mixed SCND problem. The other contribution is considering the chain-to-chain competition in collecting returned products and selling recycled products to other SCs in a mixed SC under disruptions. Also, a novel hybrid metaheuristic is developed to cope with the complexity of the model.
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Ting Zhou, Yingjie Wei, Jian Niu and Yuxin Jie
Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a…
Abstract
Purpose
Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).
Design/methodology/approach
The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.
Findings
The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.
Originality/value
This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.
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