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Fresh produce has stricter requirements for supply and distribution due to their short preservation period. Investigating the optimal supply and distribution route of fresh produce in the online to offline (O2O) mode can help the advancement of the fresh industry in the e-commerce sector. This paper established a model of the optimal supply and distribution path for fresh produce in the O2O mode and used a genetic algorithm for solution. By contrasting the distribution distance, time, total cost, and satisfaction before and after optimization, the distribution cost-minimizing path solution was obtained. It was found through simulation experiments that the minimum total distribution cost was 357.57 yuan after the computation by the genetic algorithm; the optimized path reduced the total distribution distance by 47.4 kilometers, the time by 0.95 hours, and the cost by 133.75 yuan, and raised the average consumer satisfaction by 0.12. The results further confirm the correctness of the model constructed in the study and the selected algorithm. [ABSTRACT FROM AUTHOR]

The increase in computer numerical control machine efficiency highly contributes to environmental emission reduction and energy-savings. Path and trajectory optimizations are used to improve machine efficiency in a coverage motion such as pocket milling, polishing, inspection, gluing, and additive manufacturing. Several studies have proposed coverage motion optimization in improving machine efficiency for time and energy consumption. Ensuring the smoothness and satisfaction of the machine constraints in coverage motion is necessary. This paper proposes a multi-objective path and trajectory optimization to obtain a trade-off between time and energy consumption for coverage motion. Jerk limited acceleration profiles describe the trajectory where velocity profiles generated for each linear segment attain desirable velocities. The energy model of an industrial two-axis feed drive system is used in finding solutions to the optimization problem. The non-dominated sorting genetic algorithm II generates a Pareto front for trade-off time and energy consumption solutions. Simulation results of the proposed method are validated through experiments using the industrial two-axis feed drive system. Experimental results show the effectiveness of the proposed approach where time reduction and energy savings are 10.05% and 2.10%, respectively. In addition, the optimized path has a lower maximum error of 76.6% compared to the constantly commanded velocity optimized path. • A method of simultaneous trajectory generation and path optimization is proposed. • Jerk limited acceleration profiles describe the trajectory. • The NSGA II is used to find the trade-off between time and energy consumption. • The best trade-off solution achieves time reduction and energy savings of 10.05% and 2.10%. • Optimized path motion with variable velocities reduces maximum error by 76.6%. [ABSTRACT FROM AUTHOR]

Aiming at the current problems of duplicated paths and low work efficiency in machine punching of warp knitted vamp marker points, this paper proposes a punching path planning method of machine vision combined with intelligent algorithms. The method can improve the timeliness of visual recognition of punching location by limiting the search area and similarity function threshold, and improve the ability of global search and adaptive adjustment in punching path planning by combining with the improved ant colony algorithm to calculate a more accurate and optimized path more efficiently. Through the visual recognition test and the simulation test of the improved ant colony algorithm, the results show that the template matching can correctly identify the positioning hole marker points for different styles, rotation directions and lighting conditions, and the recognition accuracy is 0.43 mm and the repeat positioning accuracy is 0.09 mm; meanwhile, the improved ant colony algorithm can effectively avoid the local optimal solution, which can improve the optimal rate of the result by about 38% and the algorithm can reduce the number of iterations of the optimal solution within 60 times, which greatly saves the calculation time of path planning. The method can be used to improve the efficiency of punching in the actual warp knit vamp punching. [ABSTRACT FROM AUTHOR]

The rapid development of shipping trade pushes automated container terminals toward the direction of intelligence, safety and efficiency. In particular, the formulation of AGV scheduling tasks and the safety and stability of transportation path is an important part of port operation and management, and it is one of the basic tasks to build an intelligent port. Existing research mainly focuses on collaborative operation between port equipment and path optimization under environmental perception, while there is relatively little research on optimization of path smoothness and safety. Therefore, we propose a path optimization model based on the artificial potential field and twin delayed deep deterministic policy gradient (APF-TD3) framework for the port environment. Firstly, we obtain the scheduling task plan of a single AGV by enumeration. Secondly, according to the artificial potential field (APF) algorithm to generate repulsion for obstacles in the harbor and attraction for container storage at the target point with the position information of the AGV as the input data of the reinforcement learning algorithm is inputted into the twin delayed deep deterministic policy gradient algorithm (TD3). Then TD3 selects the optimal action strategy for the AGV according to the input AGV state information and the designed reward mechanism as well as executes the action. Through repeated execution, the optimal action for the next step is selected at each point to generate a path with start and end points. We validate the model by simulating the scale of containerized cargo in the port i.e. small scale, medium scale and large scale scenes. The experimental results show that the method has the shortest path length of 27.519 m, 270.847 m, and 496.389 m compared to artificial potential field and deep deterministic policy gradient (APF-DDPG), APF, and rapidly-exploring random tree (RRT) algorithms, which also have significant advantages in terms of path security and path smoothness. This framework could respond to the scheduling and transportation tasks of single AGV in different environmental layouts and guarantee the smoothness and safety of the path based on the optimization of the path, which promotes the efficient operation and management of ports. [ABSTRACT FROM AUTHOR]

By using the literature, logical analysis, field investigations and in-depth interview, this paper analyzes the current situation of the implementation of physical education entrance examination policy for senior high school from the five dimensions itself, namely policy implementation mechanism, policy subject and object, policy resources and policy environment. Researches believe that the lack of policy programs, the imperfect policy system, the obstruction of policy implementation mechanisms, the divergence of interests between policy subjects and objects, the shortage and imbalance of policy resources, and the increased competitiveness of senior high school entrance examinations are all the restrictive factors in the implementation of physical education entrance examination policy for senior high school. Based on these above, in the continuous advancement of the physical education entrance examination, we should establish a policy goal with the development of students' physical literacy as the core, and continuously improve the physical education entrance examination policy system, and improve the quality of the policy itself; promoting from a high level, implement it at various levels, as well as the linkage of the education department mechanism; strengthening the propaganda and guidance of policies, seeking policy recognition from target groups, and forming a unified "objective function" among society, schools, and families; highlighting the dominant position of teachers, and increasing the importance of physical education teachers, along with the continuous investment for school physical education equipment construction. [ABSTRACT FROM AUTHOR]

Based on experiences with common additive manufacturing processes, the application in the construction industry opens up new design freedoms and cost-effective production of complex structures. However, the time-dependent yield strength of fresh concrete leads to deformations of underlying layers during the printing process, especially when using conventional path planning methods in combination with material extrusion or jetting methods. This paper presents a finite element model based approach to minimize the resulting deviations from the target geometry by iteratively adjusting the process parameters according to simulated deformations. To achieve more detailed modelling, the utilized finite element model is derived from the printing path instead of the CAD data. [ABSTRACT FROM AUTHOR]

Based on the concept of green logistics development, in order for companies to seek a win-win situation between the economy and the environment, this article integrates the cold chain logistics cost of fresh agricultural products from a low-carbon perspective into the path optimization problem and establishes a mathematical model of cold chain logistics path optimization of agricultural products Internet of Things with the minimum total cost as the research goal. First, a cold chain logistics vehicle path model including the fixed costs of cold chain distribution services, transportation costs, cargo damage costs, refrigeration costs, time penalty costs, carbon emissions costs, and IoT usage costs is established. Secondly, this paper uses the global convergence of the A* algorithm to combine with the ant colony algorithm, the initial pheromone, and uses the wolf colony algorithm principle to improve the pheromone in the ant colony algorithm to avoid falling into the local optimum. he improved ant colony algorithm is used to solve the optimal vehicle path of cold chain logistics. Finally, the simulation results show that the total cost of the cold chain logistics vehicle distribution without joint distribution is 6085.5 yuan, and the total transportation cost under the common distribution mode is 5740.7 yuan, and the total transportation cost is reduced by 6.01%. Compared with the genetic algorithm and the standard ant colony algorithm, the algorithm in this paper can improve the quality of the solution and the convergence speed of the algorithm, and can be applied to cold chain logistics and distribution. [ABSTRACT FROM AUTHOR]

This paper aims to optimize the largescale complex multimodal transport path in an efficient and accurate manner. To this end, the genetic algorithm (GA) was embedded into the ant colony optimization (ACO), creating a dual pheromone hybrid algorithm. The GA guides and speeds up the ACO to avoid the local optimum trap and enhance the optimization ability. Considering the scale effect of transport cost, a combinatorial optimization model was established to minimize the cost under the constraints of delivery time and path capacity, and tested in largescale multimodal transport networks. The test results show that the hybrid algorithm converged to the optimal solution through 46.17% and 45.25% fewer iterations, and lowered the transport cost by 4.23% and 2.39% than the GA and ACO, respectively. In addition, the hybrid algorithm was relied on to explore the factors affecting path selection. The research findings lay a solid basis for decision-making on multimodal transport. [ABSTRACT FROM AUTHOR]

In order to strengthen the logistics distribution ability at the end of e-commerce, it is necessary to study the co-delivery collaborative delivery method of e-commerce. When the current delivery method is used to deliver the courier at the end of the e-commerce, the distribution takes a long time to meet the distribution requirements of the user, and there is a problem of low distribution efficiency and low customer satisfaction. On the basis of the big data platform, an e-commerce end express co-delivery distribution method is proposed. By calculating the inventory cost, fixed investment cost, storage cost and operation cost, the total cost of the distribution center is coordinated, and the lowest total cost is selected as the distribution center. The distribution time range is analyzed by the concept of time window to obtain a penalty function. According to the penalty function, the path optimization model of e-commerce end express delivery is established. According to the path optimization model, the optimal route of collaborative delivery is obtained, and the coordinated delivery of the e-commerce end express is completed. The experimental results show that the proposed method has high distribution efficiency and high customer satisfaction. [ABSTRACT FROM AUTHOR]

Abstract Due to the complexity of multi-objective optimization problems (MOOPs) in general, it is crucial to test MOOP methods on some benchmark test problems. Many benchmark test problem toolkits have been developed for continuous parameter/numerical optimization, but fewer toolkits reported for discrete combinational optimization. This paper reports a benchmark test problem toolkit especially for multi-objective path optimization problem (MOPOP), which is a typical category of discrete combinational optimization. With the reported toolkit, the complete Pareto front of a generated test problem of MOPOP can be deduced and found out manually, and the problem scale and complexity are controllable and adjustable. Many methods for discrete combinational MOOPs often only output a partial or approximated Pareto front. With the reported benchmark test problem toolkit for MOPOP, we can now precisely tell how many true Pareto points are missed by a partial Pareto front, or how large the gap is between an approximated Pareto front and the complete one. [ABSTRACT FROM AUTHOR]

The research on optimization of cold link path of agricultural products operation oriented to environmental protection is of great significance for controlling the finished products of distribution and enhancing the quality of distribution service. Considering the fixed cost, transportation cost, fresh agricultural product damage cost, refrigeration cost, carbon emission cost and penalty cost of distribution vehicles as objective functions, the distribution route optimization model of fresh agricultural products considering environmental protection and carbon emission was constructed. The improved genetic algorithm was used to solve the path optimization model, and the solution space was converted to genetic space by chromosome coding. The initial population is generated, the fitness function suitable for the model is selected, and the crossover and mutation operations of the model are carried out through the selection strategy until the optimal path is obtained. The experimental results show that the total transportation cost, carbon emission cost, total calculation time and robustness of the proposed method are better than those of the comparative method. The computational time is as low as 0.1s and the robustness is as high as 100%. The surface method has a high performance of cold link path optimization for agricultural products. [ABSTRACT FROM AUTHOR]

Commercial methanol catalysts based on Cu/ZnO/Al 2 O 3 are less effective applied to direct hydrogenation of CO 2 to methanol. The main reason is that the catalyst deactivation increases with the water pressure and temperature, and from stoichiometry, water formation is equal to the CO 2 consumption. Here, the focus is on how the process can be designed to reduce this problem. Multi-stage reactor designs with inter-condensation of water and methanol will reduce the water pressure. Several optimal designs are generated with the use of a path optimization method to maximize the methanol production per pass with the use of the least possible reaction volume and hydrogen. Based on a published kinetic model, the optimal volume stage distribution, coolant temperature, and fluid mixing are found. Two configurations of the tail gas treatment are investigated, a once-though and a recycle configuration. A three-stage reactor design with recycling of the tail gas is found to be the better configuration. High CO 2 -conversion per pass and a low recycle ratio are obtained. Rigorous process simulations of the most promising designs are made to verify that the pressure drop, temperature peaks, and water pressure are good. The maximum water pressure is low. A shell and tube boiling water type reactor design is selected. For a 10 t h− 1 plant, all tubes of all three stages can be located in the same shell. • Systematic staging of hydrogenation of CO 2 to methanol. Objective to maximize the methanol production with use of least possible reaction volume and hydrogen. • Reduced sintering effect with a commercial catalyst. • Intensified process with three stages in one reactor shell. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Physical Education / Tiyu Xuekan is the property of Journal of Physical Education Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To overcome the low accuracy and high energy consumption of positioning algorithm in wireless sensor network, we proposed a optimization positioning based on algorithm neighborhood model. Based on the characteristics of the neighborhood model, the algorithm selects the best beacon node and calculates the proximity distance to transmit the distance information to the base station. The base station uses the MDS-MAP algorithm to determine the location of the unknown node. The simulation was conducted on NS-2 platform. The results show that the performance of the proposed algorithm was better than traditional optimization algorithms. Significant enhancement is obtained with the proposed algorithm in terms of node distance estimation error and position error. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southern Yangtze University: Humanities & Social Sciences Edition is the property of Jiangnan Daxue and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, the urbanization and energy consumption complex system enters the stage of ultimate development. So it is not certain about synergistic development. From the perspective of causal feedback mechanism system and based on the mechanism of various elements of urbanization and energy consumption, this paper explores the path of synergistic development of urbanization and energy consumption, simulates the baseline scenario from 2005 to 2020 through establishment of the SD model of urbanization and energy consumption complex system, predicts the future behavior of the state variable when the control variable changes in the system through simulation, seeks for interaction and interrelationships between various elements in the process of evolution and development, and finally puts forward three paths to optimize the synergistic development of urbanization and energy consumption which are population transfer, industrial upgrading and technological improvement, which provides some references and foundation for making decisions of synergistic development of new urbanization and energy consumption. [ABSTRACT FROM AUTHOR]

The cement industry is a highly CO 2 emission-intensive sector and is facing significant pressure to reduce emissions and many available emissions abatement options. This study establishes a long-term, bottom-up integrated assessment model to analyse optimal technological development pathways, CO 2 abatement potentials and costs in the cement industry. The model incorporates five main types of low-carbon technologies which are clearly characterized: energy efficiency improvement technology, waste heat recovery (WHR), carbon capture and storage (CCS), alternative fuels and clinker substitution. The path of technological development is endogenously determined by cost functions, energy savings and emissions reduction targets. Using the above model, this study uses data from Chinese cement industry to evaluate three policy scenarios: business as usual, single emissions reduction target and multiple emissions reduction targets. The results show the need to combine mid-term and long-term emissions reduction targets. WHR and clinker substitution are always cost-effective over the planning horizon (2011–2050). Implementing energy efficiency improvement technology and alternative fuels are keys for achieving the mid-term target around 2030. The technology lock-in effect is an important factor that limits the diffusion of energy efficiency improvement technology after 2030. CCS technology is necessary for the cement industry to achieve stringent emissions reduction targets. [ABSTRACT FROM AUTHOR]

The development of autonomous unmanned vehicles is of high interest to many organizations around the world and path planning is the key point of the navigation for the autonomous unmanned vehicle. Intelligent algorithms have been applied in this field and an essential aspect of unmanned vehicles autonomy is the ability for automatic path planning. In this paper, particle swarm optimization algorithm as one of new swarm intelligent optimization methods is introduced into a path planning for autonomous vehicle, which is constructed of a particle representation methods for vehicle routing problem with fast convergence speed. The results show that the particle swarm optimization algorithm can obtain the solution of the vehicle routing problem quickly and effectively. It is a good method for solving the vehicle routing problem. [ABSTRACT FROM AUTHOR]

Nowadays, with the rapid development of intelligent logistics and Automated Guided Vehicles (AGV), the application scenario areas of AGVs are becoming more and more widely. This research takes the application of AGV in airport baggage loading scenarios as the research direction. Most of the airport AGV path planning does not capitalize on the time window of the early arrival of baggage, which leads to the prolonged running time of AGVs. To reduce the operating cost and path cost of AGV, this research proposed an airport AGV path optimization model based on the Dijkstra algorithm of ant colony optimization (ACO-DA). The model considered the environment with obstacles, first of all, the baggage pickup sequencing by ant colony optimization, and in the second place, the AGV path planning by integrating with Dijkstra's algorithm. Last, the model was simulated and analyzed, and its proposed fusion algorithm performed better than the path planning models of the other three algorithms in airport baggage check-in, and its paths were respectively shortened by 2.3%, 2.64%, and 6.06%. • Conditions for using early baggage intervals, • Improve the efficiency of airport baggage sorting, • The simulation model is established according to the actual sorting area, • Add obstacles and plan the obstacle avoidance path of AGV trolley. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose an optimal path for a circular economy strategy of Aluminum Corporation of China (CHINALCO) by constructing a circular economy evaluation model. Based on CHINALCO's resource circulation and the regularity of material balance, and by depicting the value information in the process of resource input, consumption, output and waste, we build a comprehensive evaluation model for the enterprise circular economy - a resource value flow equation based on the integration of resource efficiency, value cycle efficiency and environmental efficiency. This model and its additional deformation form act as analytical tools that can be used to research of the development of a circular economy of CHINALCO at three different levels of the enterprise's production systems, branch office, and head office. With this research, we then analyze the periodic characteristics of the circular economy of CHINALCO. By analyzing the single factor static and multi-factor linkage, this paper illustrates the optimal path for CHINALCO to achieve its circular economy objectives. This research can provide the basis for the future development of both a circular economy and also the enhanced use of technology in China's aluminum industry. In the meantime, the data resulting from the case study are likely to provide guidance for a technical and economic practice for China's aluminum recycling economy. [ABSTRACT FROM AUTHOR]

The traditional e-commerce logistics distribution path optimization algorithm has the problem of a long time to find the optimal path. To solve this problem, this paper designs an e-commerce logistics distribution path optimization algorithm in the context of big data, introduces NSGA-II evolutionary algorithm for solving multi-objective optimization problems, combines improved genetic algorithm to solve multi-objective terminal distribution path optimization model in new retail mode to obtain the Pareto optimal solution set of the research problem in this paper, and then establishes a multi-objective function for logistics distribution path optimization through five aspects: weight index, time efficiency index, customer importance index. In this paper, in the process of using the ant colony algorithm to solve the optimization of the end-delivery path of e-commerce logistics, global pheromone update rules such as the ant-perimeter model will be used for the pheromone update in the ant pathfinding process. Then, the multi-objective function of logistics distribution path optimization is established by five aspects: weight index, timeliness index, customer importance index, time window index, total path index, and finally, the distribution target weights are set to find the better distribution path in the objective function according to the different demands of e-commerce logistics, to complete the e-commerce logistics distribution path optimization. We were able to save 6.6% on the route optimized by the ant colony algorithm over the empirical route under the condition of path optimization with the ant colony algorithm. The experimental comparison results show that the designed e-commerce logistics distribution path optimization algorithm in the context of big data is shorter than the traditional algorithm to find the optimal path, which can reduce the e-commerce logistics distribution time and has certain practical application significance. [ABSTRACT FROM AUTHOR]

Highlights: [•] We consider path planning in leader–follower formation with the obstacle avoidance using the potential field method. [•] We compare the performances of 4 methods by simulations and real experiment on e-Puck robots. [•] Experimental results, higher performance and fast convergence time to the best solution of the ARO demonstrate that this method is more appropriate for real time control application. [Copyright &y& Elsevier]

Hull markline marking, which is done with the CNC operations for hull parts at the same time, is one of the important parts in modern shipbuilding. According to the features of the marking, taking the marking order and the marking direction as the parameters and taking the minimum idle marking path as the objective, a mathematical model of marking optimization is established on the basis of hybrid encoding in genetic algorithm. The different genetic strategies are used for evolution of the marking order and the marking direction. The random mutation operator maintains the diversity of the population and the self-evolution of elite operator accelerates the evolution process. The use of "greedy strategy" for initial population improves the fitness of the population. The simulation results show that the model is effective, which can effectively reduce the idle marking path and improve the efficiency of shipbuilding. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a research center due to the general application like military and scrutinizing circumstance of environment. The recent approaches showed that the mobility of sink in a controlled path could enhance the efficiency of energy in WSN, but the constraints of the path make the routing more complicated. Thus, the sink with fixed speed poses less communication time for collecting data via sensor nodes that are arbitrarily deployed. This limitation has significant issues in enhancing data collected and minimizing energy consumption. Moreover, these research constraints led to the design proficient path optimization technique in WSN. This work presents a survey based on path optimization strategies. Also, a thorough investigation is done based on various parameters Also, we have analyzed the merits and demerits of conventional path optimization strategies. Finally, future research directions in obtaining efficient path optimization in Wireless Sensor Networks and mobile sink are elaborated. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired method, so-called ripple-spreading algorithm (RSA) for multi-objective path optimization problem (MOPOP). Unlike most existing methods mainly capable of finding partial or approximated Pareto front, this paper focuses on calculating the complete Pareto front. This is achieved by taking advantage of the optimality principle in natural ripple-spreading phenomenon. Basically, the proposed RSA carries out a one-off ripple relay race in the route network, and then the complete Pareto front will be identified with guaranteed optimality by backtracking those Pareto non-dominated ripples (PNDRs) which reached the destination node. Theoretical analyses and comprehensive experiments show that all complete Pareto fronts of a one-to-all MOPOP can also be found in just a single run of ripple relay race, and the reported method can be further extended to calculate all Pareto optimal paths in dynamical networks, which have rarely been touched by existing MOPOP methods. Since many real-world application problems can be converted into MOPOP, the reported method has a great potential of applications. [ABSTRACT FROM AUTHOR]

In fact, optimizing path within short computation time still remains a major challenge for mobile robotics applications. In path planning and obstacles avoidance, Q-Learning (QL) algorithm has been widely used as a computational method of learning through environment interaction. However, less emphasis is placed on path optimization using QL because of its slow and weak convergence toward optimal solutions. Therefore, this paper proposes an Efficient Q-Learning (EQL) algorithm to overcome these limitations and ensure an optimal collision-free path in less possible time. In the QL algorithm, successful learning is closely dependent on the design of an effective reward function and an efficient selection strategy for an optimal action that ensures exploration and exploitation. In this regard, a new reward function is proposed to initialize the Q-table and provide the robot with prior knowledge of the environment, followed by a new efficient selection strategy proposal to accelerate the learning process through search space reduction while ensuring a rapid convergence toward an optimized solution. The main idea is to intensify research at each learning stage, around the straight-line segment linking the current position of the robot to T a r g e t (optimal path in terms of length). During the learning process, the proposed strategy favors promising actions that not only lead to an optimized path but also accelerate the convergence of the learning process. The proposed EQL algorithm is first validated using benchmarks from the literature, followed by a comparison with other existing QL -based algorithms. The achieved results showed that the proposed EQL gained good learning proficiency; besides, the training performance is significantly improved compared to the state-of-the-art. Concluded, EQL improves the quality of the paths in terms of length, computation time and robot safety, furthermore outperforms other optimization algorithms. • The mobile robot path optimization problem is handled and modeled. • An Efficient Q-Learning (EQL) algorithm is proposed. • In EQL , a new definition of states space and actions space is proposed. • New reward function is proposed to initialize Q-table. • Learning process is sped up by exploiting a new efficient selection strategy. • Results on benchmarks from literature demonstrate EQL efficiency and superiority. [ABSTRACT FROM AUTHOR]

a) Methanol synthesis process from CO 2 and H 2. b) Conventional methanol production route from natural gas where natural gas is first converted to syngas and then to methanol. • Conventional and green methanol production are compared. • Maximization of reactor productivity, CO 2 utilization, and hydrogen conversion were considered. • The higher the H 2 /CO to the methanol reactor, the higher gas consumption in the conventional route. • The optimal path configuration depends on the objective function chosen. In this paper, CO 2 hydrogenation to methanol is investigated with three objective functions, i.e. maximization of reactor productivity, CO 2 utilization, and hydrogen conversion. Key performance indicators of methanol production via conventional and CO 2 hydrogenation routes are compared. The results suggest that the methanol production rate with an inlet H 2 /CO 2 of 3 is ca. 59 % greater than the case with the inlet H 2 /CO 2 composition of 2. The overall hydrogen and CO 2 conversion of the cases with the inlet H 2 /CO 2 of 3 is greater than the reactor system with the inlet H 2 /CO 2 of 2. For each objective function criteria, the analysis shows that increasing the H 2 /CO 2 to the methanol reactor increases the natural gas consumption in the conventional route and the higher the inlet H 2 /CO 2 ratio, the higher the CO 2 emission is. If the inlet hydrogen is produced via water electrolysis, the CO 2 emission of the CO 2 hydrogenation process route will be negative. [ABSTRACT FROM AUTHOR]