A rural bus integrating passenger and freight transport is a new effective public transit mode to realise village interconnection and solve the first-last mile rural logistics service. Mixed services reduce logistics ...
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A rural bus integrating passenger and freight transport is a new effective public transit mode to realise village interconnection and solve the first-last mile rural logistics service. Mixed services reduce logistics costs and generate additional income for transit operators. However, the mutual interpenetration of logistics and passenger services in a single bus trip may lead to a decline in the service quality of both passengers and goods. With this in mind, we designed a government subsidy incentive contract and a logistics alliance payment incentive contract respectively from the perspective of participants based on the principal-agent theory. In addition, a bi-level pro-gramming model consisting of two principals (government and logistics alliance) and one agent (transit operator) was proposed to incentivise bus operators and improve passenger and freight service quality. It scientifically realises the coordination of interests between the principals and agents. A Chinese case study was conducted to examine the proposed models and methods, and a passenger-based incentive subsidy programme from the government was proposed to replace the mileage-based one. With both incentive policies (government and logistics alliance), passenger travel time can be reduced by 4.2 min, and logistics transportation time can be shortened by 30 min. In addition, the total annual government budget can be reduced by 17.2% ( yen 450,000, $64,000) and annual fleet mileage savings by approximately 9.4% (62,000 km) compared with the mileage-based incentive programme in the case study. A sensitivity analysis is conducted to explore the impact on the collaborative system. This innovative concept, combined with incentive schemes, is a good reference for public administration to avoid the dilemma of having passenger and freight transport in low-demand areas.
In recent years, researchers have been focused on solving optimization problems in order to determine the global optimum. Increasing the dimension of a problem increases its computational cost and complexity as well. ...
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In recent years, researchers have been focused on solving optimization problems in order to determine the global optimum. Increasing the dimension of a problem increases its computational cost and complexity as well. In order to solve these types of problems, metaheuristicalgorithms are used. The whale optimization algorithm (WOA) is one of the most well-known algorithms based on whale hunting behavior. In this paper, the WOA algorithm is combined with the Sine Cosine algorithm (SCA), which is based on the principle of trigonometric sine-cosine. The WOA algorithm has superior performance in the exploration phase in contrast with the exploitation phase, whereas the SCA algorithm has weaknesses in the exploitation phase. The levy flight dis-tribution has been used in the hybrid WOA and SCA algorithm to improve these deficiencies. This study intro-duced a novel hybridalgorithm named WOASCALF. In this algorithm, the search agents' position updates are based on a hybridization of the WOA, SCA, and levy flight. Each of these metaheuristicalgorithms has reasonable performance, however, the Levy distribution caused small and large distance leaps in each phase of the algo-rithm. Thus, it is possible for the appropriate search agent to move in different directions of the search space. The performance of the WOASCALF has been evaluated by the 23 well-known benchmark functions and three real -world engineering problems. The result analysis demonstrates that the exploration ability of WOASCALF has strong superiority over other compared algorithms.
The non-permutation flow-shop scheduling problem (NPFSP) is a more general type of flow-shop scheduling problem than the permutation flow-shop scheduling problem (PFSP). It features a large solution space and is highl...
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The non-permutation flow-shop scheduling problem (NPFSP) is a more general type of flow-shop scheduling problem than the permutation flow-shop scheduling problem (PFSP). It features a large solution space and is highly complex. In this study, a novel metaheuristicalgorithm is proposed for the NPFSP, where makespan is the scheduling objective. The specific implementation process is as follows. First, a mathematical model of the NPFSP is constructed. Secondly, the encoding and decoding rules are designed to establish the association between the solutions for PFSP and NPFSP. Subsequently, a metaheuristichybrid simulated annealing-slime mould algorithm is proposed. Finally, a series of control experiments is conducted based on the Demirkol benchmark. The statistical results show the effectiveness of the proposed algorithm in solving the NPFSP.
Tournamenting process plays an important role in the selection of best alternative in a knockout system. This concept is applied in the development of a hybridalgorithm based on differential evolution known as tourna...
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Tournamenting process plays an important role in the selection of best alternative in a knockout system. This concept is applied in the development of a hybridalgorithm based on differential evolution known as tournament differential evolution. In this work, a hybrid tournament differential evolution algorithm is applied on a two-warehouse inventory control problem of deteriorating item with the objective for determining the lot-size, maximum shortage level and cycle length of the concerned system. The corresponding inventory model is formulated with two separate warehouse facilities, partially backlogged shortages and all-unit discount. Here the demand of the product increases with time. The corresponding optimization problem of the developed inventory model is highly non-linear constrained optimization problem which cannot be solved analytically. In this context, to solve the optimization problem, the said hybridalgorithm is developed with the help of six different options of tournamenting procedure and differential evolution. Then, to examine the validity of the proposed model and also to test the performance of the hybridalgorithm, numerical experiments are performed by solving a numerical example and nonparametric statistical test. Finally, to investigate the effects of changes of different parameters, sensitivity analyses are carried out on the best found policy and a fruitful conclusion is drawn along with the future scope of research. (C) 2021 Elsevier B.V. All rights reserved.
Planning of an islanded hybrid system (IHS) with different sources and storages to supply clean, flexible, and highly reliable energy at consumption sites is of high importance. To this end, this paper presents the de...
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Planning of an islanded hybrid system (IHS) with different sources and storages to supply clean, flexible, and highly reliable energy at consumption sites is of high importance. To this end, this paper presents the design of an IHS with a wind turbine, photovoltaic, diesel generator, and stationary (battery) and mobile (electrical vehicles) energy storage systems (ESS). The proposed method includes a multi-objective optimization to minimize the total cost of construction, maintenance, and operation of sources and ESSs within the IHS and the emission level of the system using two separate objective functions. The problem is subject to operational and planning constraints of sources and ESSs and power. Employing the Pareto optimization technique based on the epsilon-constraint method forms a single-objective optimization problem for the proposed design. The problem involves uncertainties of load, renewable energy, and energy demand of mobile ESSs and has a nonlinear form. Adaptive robust optimization based on a hybrid meta-heuristic algorithm that utilizes a combination of the sine-cosine algorithm (SCA) and crow search algorithm (CSA) is proposed to achieve an optimal robust structure for the suggested scheme. In this scheme, operation model of the mobile storage systems in the IHS considering the uncertainties prediction errors and its model using HMA-based ARO besides adopting the HMA to achieve a unique optimal solution are among the novelties of this research. Eventually, considering the climate data and energy consumption of a region in Rafsanjan, Iran, capabilities of the method in extracting a robust IHS for sources and ESSs are validated depending on optimal economic and environmental conditions. The HMA succeeds to reach an optimal solution with an SD of 0.92% in the final response and this underlines its capability in achieving approximate conditions of unique responsiveness. The proposed scheme with proper planning and operation of sources and storages in
One of the drawbacks of glowworm swarm optimisation (GSO) is its premature convergence, which leaves it often ineffective for solving complex practical problems. This paper proposes a new hybridmetaheuristic algorith...
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One of the drawbacks of glowworm swarm optimisation (GSO) is its premature convergence, which leaves it often ineffective for solving complex practical problems. This paper proposes a new hybrid metaheuristic algorithm, that is, moth-flame glowworm swarm optimisation (MFGSO). The main idea of the hybridalgorithm is to combine the exploration ability in moth-flame optimisation (MFO) with the exploitation ability in GSO. Performance evaluations are conducted on benchmarking test functions in comparison with the basic GSO and other metaheuristicalgorithms. The results show that MFGSO outperforms the basic GSO and other metaheuristicalgorithms on most test functions in terms of local optima avoidance and convergence speed.
metaheuristicalgorithms have been used to solve scheduling problems in grid computing. However, stand-alone metaheuristicalgorithms do not always show good performance in every problem instance. This study proposes ...
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metaheuristicalgorithms have been used to solve scheduling problems in grid computing. However, stand-alone metaheuristicalgorithms do not always show good performance in every problem instance. This study proposes a high level hybrid approach between ant colony system and genetic algorithm for job scheduling in grid computing. The proposed approach is based on a high level hybridization. The proposed hybrid approach is evaluated using the static benchmark problems known as ETC matrix. Experimental results show that the proposed hybridization between the two algorithms outperforms the stand-alone algorithms in terms of best and average makespan values.
This article proposed adaptive hybrid dwarf mongoose optimization (DMO) with whale optimization algorithm (DMOWOA) to extract solar cell model parameters. In DMOWOA, the whale optimization algorithm (WOA) is used to e...
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This article proposed adaptive hybrid dwarf mongoose optimization (DMO) with whale optimization algorithm (DMOWOA) to extract solar cell model parameters. In DMOWOA, the whale optimization algorithm (WOA) is used to enhance the capability of DMO in escaping local optima, while introducing inertial weights to achieve a balance between exploration and exploitation. The DMOWOA performances are tested through the solving of the single diode model, double diode model, and photovoltaic (PV) modules. Finally, the DMOWOA is compared with six well-known algorithms and other optimization methods. The experimental results demonstrate that the proposed DMOWOA exhibits remarkable competitiveness in convergence speed, robustness, and accuracy.
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