Abstract: Bicycle-sharing systems are commonly established at geographically dispersed locations to create their rental service networks. To provide customers with flexibility and convenience, bicycle-sharing systems commonly allow them to pick up bicycles from one station and return them to a different one. However, allowing customers to return their rented bicycles to different stations can possibly lead to an imbalance in the bicycle rental network. One of the approaches to overcome the bicycle imbalance problem is to apply dynamic pricing to motivate consumers to return the rented bicycles to stations without a sufficient number of bicycles. This study developed a constrained dynamic…pricing model to address the bicycle imbalance problem. Moreover, this study aimed to maximize the total revenues over a planning horizon through dynamic pricing strategies. We identify some necessary conditions for optimal sale prices. Using these conditions, we develop a heuristic algorithm based on linear programming and an evolutionary algorithm to efficiently produce comprise solutions. The proposed model and solution procedure were applied to analyze the bicycle system in Taiwan. Sensitivity analyses were also conducted to investigate the effects of various system parameters.
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Abstract: Leveraging their networks, bike rental companies usually provide customers with services for renting and returning bikes at different bike stations. Over time, however, rental networks may encounter problems with unbalanced bike stocks. The potential imbalance between supply and demand at bike stations may result in lost sales for stations with relatively high demand and underutilization for stations with relatively low demand. This paper proposed a constrained mixed-integer programming model that uses operator-based redistribution and user-based price approach to rebalance bikes across bike stations. This paper aims to maximize total profit over a planning horizon by determining operator-based bike transfers and…dynamic pricing. The proposed model is a non-deterministic polynomial-time problem, and thus, a heuristic was developed based on linear programming and evolutionary computation to perform model solving. Numerical experiments reveal that the proposed method performed better than Lingo, a well-known commercial software. Sensitivity analyses were also performed to investigate the impact of changes in system parameters on computational results.
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Abstract: In this study, we investigate a new multi-maintenance with sequential operation (MMSO) problem, in which a variety of tasks must be processed on multiple machines. In the MMSO problem, each task has multiple sequential operations that must be processed for each machine. In addition to maintenance, the MMSO problem has many other practical applications, such as physical examination scheduling. The proposed MMSO, which is an NP-hard problem, generalizes typical job shop scheduling problems. Thus, a novel encoding scheme, which is embedded into an immune-based algorithm (IBA), is proposed in this study to convert any sequence of random numbers into a…feasible solution of the MMSO problem to solve the MMSO problem. Numerical results of applications in aircraft maintenance and physical examination scheduling are reported and compared with those of particle swarm optimization and genetic algorithm. Experimental results show that IBA outperforms the two other algorithms.
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Abstract: In this paper, we study the forest harvesting problem (FHP). A forest is assumed to be divided into several identical square units, and each unit has its harvesting value based on its type. Harvesting a unit will affect the growth and values of its neighboring units. In this FHP, the best harvesting plan of a unit must be identified to maximize three various objectives simultaneously. The FHP is a multiobjective mathematical and an NP-hard problem. We apply three artificial intelligence algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, for maximizing the weighted objective to solve the FHP. We…also solve the following two sets of test problems: (i) a set of randomly generated FHP problems and (ii) a practical problem in Taiwan. Numerical results show the performance of the three algorithms for the test problems. Finally, we compare and discuss the effects of various weights for the three objectives.
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Abstract: According to the reports of the World Health Organization, the abnormal total cholesterol is one of the major risk factors for cardiovascular diseases. Supporting this, a number of prospective studies have examined that the risk of cardiac morbidity and mortality is directly associated with the concentration of plasma total cholesterol. Traditional Chinese medicine has been used to treat cardiovascular diseases since it creates milder healing effects and incurs fewer side effects than Western approaches. However, in real world, the traditional Chinese medicine diagnosis using the meridian system is highly complicated in nature so that it is difficult to create a…general model. In our research, a hybrid evolutionary rule mining approach is proposed to assess the total cholesterol data patterns detected from the traditional Chinese medicine meridian energy. Based on the proposed approach, a rule-based decision-making model can be developed with maximum classification accuracy. Through a numerical experiment, the results of this study were compared with the commercial data mining software and the proposed approach is shown to be a promising method for improving prediction accuracy with fewer type II errors. The research outcomes may have benefits to help diagnose and treat cholesterol abnormalities in traditional Chinese medicine clinical therapy.
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Keywords: Rule mining, traditional Chinese medicine, total cholesterol, decision-making