Compared to the booming industry of AIMDSS, the usage of AIMDSS among healthcare professionals is relatively low in the hospital. Thus, a research on the acceptance and adoption intention of AIMDSS ...by health professionals is imperative. In this study, an integration of Unified theory of user acceptance of technology and trust theory is proposed for exploring the adoption of AIMDSS. Besides, two groups of additional factors, related to AIMDSS (task complexity, technology characteristics, and perceived substitution crisis) and health professionals’ characteristics (propensity to trust and personal innovativeness in IT) are considered in the integrated model. The data set of proposed research model is collected through paper survey and Internet survey in China. The empirical examination demonstrates a high predictive power of this proposed model in explaining AIMDSS adoption. Finally, the theoretical contribution and practical implications of this research are discussed.
Integrated optimisation of production scheduling and distribution decision is necessary for reducing the whole cost of the supply chain in the make-to-order business environment. This paper studies a ...new integrated production scheduling and vehicle routing problem (IPSVRP) with inventory holding, delivery, and tardiness costs. The considered IPSVRP is modelled as a triple-objective optimisation problem, where the first objective aims to obtain the minimal total holding cost in the inventory, the second one attempts to achieve the minimal total travelling cost, and the third one tries to acquire the minimal total tardiness cost. To obtain a set of diverse non-dominated solutions in the Pareto-optimal front of the problem, we first derive several key structural properties used to provide necessary conditions for any solution to be Pareto-optimal through theoretical investigation. Based on the derived structural properties, a level-based multi-objective particle swarm optimizer (LMPSO) is subsequently designed. The performance of LMPSO is analysed by conducting a set of experiments, and its superiority is verified through comparing with other optimisation algorithms. Moreover, the convergence behaviour of LMPSO is also investigated, and the experimental results prove that it has the ability to achieve a set of non-dominated solutions proximity to the true Pareto front.
Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited ...to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.
Although support vector machine (SVM) and its variants have been combined successfully with partitioning strategies for multiclass classification, a series of individual classifiers have to be ...considered separately, which significantly limits the sparseness of the solution. In this work, from an absolutely novel perspective, we proposed a regular simplex support vector machine (RSSVM) for the K-class classification. RSSVM maps the K classes to K vertices of a (K−1)-dimensional regular simplex so that the K-class classification becomes a (K−1)-output learning task. We measured the training loss by comparing the square distances between the output point of each sample and the vertices. As a result, we were able to implement RSSVM by constructing a single primal problem with linear inequality constraints, endowing it with excellent sparseness. This method, however, made the computational complexity of RSSVM higher than commonly used K-class classification methods. To overcome this drawback, we added an appropriate regularized term to the primal problem, making the dual problem a quadratic programming problem, and we developed an exclusive sequential minimization optimization–type solver to accelerate our ability to solve it. Numerical experiments indicated that our proposed RSSVM not only demonstrated excellent sparseness but also superior overall accuracy, efficiency, and scalability.
According to Dolgui, Alexandre, and Jean Marie Proth. 2010. Supply Chain Engineering: Useful Methods and Techniques. Vol. 539. Springer., advancing tactical levels in production systems has led to ...the disappearance of static scheduling in favour of dynamic scheduling. Additionally, the evolving challenges in the supply chain paradigm have significantly impacted the organisation of production systems. This shift has moved scheduling issues from the tactical to the strategic level, resulting in linear organisations encompassing scheduling decisions. Proth, Jean Marie. 2007. "Scheduling: New Trends in Industrial Environment." Annual Reviews in Control 31 (1): 157-166.
https://doi.org/10.1016/j.arcontrol.2007.03.005
. emphasised that real-time scheduling in production systems has become a pivotal area of research. He presented several open problems for researchers to address in this context, including (1) the development of real-time algorithms capable of handling multiple operations on the same product and unrelated resources, (2) adapting previous schedules with certain modifications, (3) addressing unforeseen actions that arise randomly in real-time planning, and (4) exploring cyclic scheduling problems with size limits as alternative solutions to heuristic approaches. This paper reviews the evolving trends in light of J.M. Proth's predictions and advice within the aforementioned domains.
•A novel bi-objective load balancing model is developed.•This problem has been inspired from implementing HLA simulation system.•Two meta-heuristic algorithms i.e. NSGA-II and MOPSO with improving ...strategies are proposed to solve the problem.•A novel approach of Taguchi method is used to tune the parameters of the algorithms.•The results indicate that MOPSO with ELS is more powerful in solving the problem.
High level architecture (HLA) is a software-architecture specification of a distributed simulation system which does not involve load balancing. As a result, problems of long simulation time and distortion caused by unequally distributed simulation tasks cannot be solved in the simulation process of HLA. In this paper, the simulation system based on the component level will be studied, and problems of load imbalances will be tackled. The goal is to find a set of Pareto optimal solutions to minimize the imbalance of the computation load and the total communication load with load limitation constraints in the model. To formulate this problem, a new integer programming model is presented. Both a non-dominated sorting genetic algorithm with elitist strategy (NSGA-II) and a multi-objective particle swarm optimization (MOPSO) are adopted to solve the problem. Different global best selection methods (crowding distance, adaptive grids and comprehensive ranking) and perturbation methods (rapidly decreasing and elitist learning strategy) for MOPSO are analyzed. Since the parameters of the algorithms have significant effects on their performance, the Taguchi method with a novel response value is utilized to tune the parameters of the proposed algorithms. Five performance metrics are used to evaluate the results of these algorithms. Based on the results, a networked control simulation platform will be used to test and verify the schemes. Numerical results show that the proposed MOPSO with ELS operator outperforms other proposed algorithms in solving the problem. In addition, the proposed strategies could solve problems of load imbalances in systems based on HLA.
This paper provides a comprehensive survey of research on operating room planning and scheduling problems. Aiming to give a comprehensive classification on the studied problems, we review the ...literature from the perspectives of decision level, scheduling strategy, patient characteristics, problem setting, uncertainty, mathematical models, and solutions and methods. The papers are reviewed in diversified ways so as to obtain a detailed overview in this area, and the fields that need to be focused on are summarized. It shows that mathematical programming and heuristics are frequently applied in the complex linear and combinatorial optimization problems. Furthermore, future research trends and directions on operating room planning and scheduling are also identified.
This paper seeks to optimise fleet management for companies that utilise both internal combustion engine vehicles (ICEVs) and electric vehicles by considering various costs, including fixed costs, ...variable costs, time window penalty costs, fuel costs, and electricity consumption costs. Additionally, the study examines the influence of four types of regulations, i.e. carbon cap, carbon tax, carbon trading, and carbon offsetting, on carbon emissions and fleet configuration. The optimisation model was solved using a Clarke-Wright savings heuristic algorithm followed by an improved adaptive genetic algorithm (IAGA), and a sensitivity analysis was conducted under different regulatory policies. The results show that all four types of regulations can effectively reduce fleet emissions. While carbon prices have a greater impact on carbon regulations than carbon quota, carbon trading was more effective under similar circumstances. Therefore, governments should implement appropriate regulatory strategies to reduce energy consumption and encourage enterprises to reduce their emissions.
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•A variable neighborhood search for the production routing problem (PRP).•Skewed general variable neighborhood search for delivery schedule.•Guided variable neighborhood search for ...VRP subproblem.•Outperform existing heuristics overall in reasonable computing times.•Excel on instance classes of standard and high transportation cost.
In a production routing problem (PRP), the aim is to integrate planning of production, inventory, delivery schedules and vehicle routes in supply chains. In this work, we present a variable neighborhood search metaheuristic for the PRP. In the metaheuristic, variables of delivery and routing decisions are handled by local search based on skewed general variable neighborhood search and guided variable neighborhood descent, respectively. Binary variables for production setups and continuous variables for production quantities and the depot inventory are determined in a production-inventory subproblem with a mixed integer programming solver. The computational results show that the proposed heuristic is competitive with the state-of-the-art algorithms on the benchmark instances. Furthermore, the proposed heuristic outperforms existing heuristics on the standard and high transportation cost benchmark instances in Archetti et al. 1 and large size benchmark instances in Boudia et al. 2 within comparable computing times.
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