Green vehicle routing optimization based on dynamic constraint selection co-evolutionary algorithm

The accuracy of carbon emission and fuel consumption computations in green vehicle routing

PurposeThis paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total costs, which include vehicle operating cost, quality loss cost, product freshness cost, penalty cost, energy cost and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle maximum load in relation to cost and GHG emissions.Design/methodology/approachThis study develops a mathematical optimization model, considering the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the routing problem of a real case.FindingsThe results of this study show the extend of the proposed MPSO performing better in achieving green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis. The research outputs also evaluated the effect of different enterprises’ conditions (e.g. customers’ locations and demand patterns) for better distribution routes planning.Research limitations/implicationsThere are some limitations in the proposed model. This study assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather conditions and road conditions.Originality/valuePrior studies, particularly in green cold chain logistics vehicle routing problem, are fairly limited. The prior works revolved around GHG emissions problem have not considered methane and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set of GHGs.

SummaryWith the development of Internet of Things (IoT)‐related technologies and artificial intelligence (AI) technologies, various IoT services are becoming more intelligent, and their use range is increasing and diversifying. IoT hardware and IoT software must support AI‐related functions to provide an intelligent IoT service. In general, IoT devices powered by batteries have limited computing performance when compared to general computing environments. Therefore, it is essential to provide AI‐related functions at low power in IoT devices to implement and offer various intelligent services. Neuromorphic computing devices or neuromorphic computing architectures can operate with low power energy consumption. If applied to IoT devices, AI‐related functions can be implemented in a resource‐constrained IoT device environment. The proposed neuromorphic architecture abstraction (NAA) model dynamically selects the proper neuromorphic architecture by comparing the parameter size of a given SNN model. It also considers the specifications and error probability of the available neuromorphic architecture. We also implement the proposed model in a real IoT computing environment and show that the proposed NAA model and dynamic selection scheme can reduce the execution time for training and inferencing. It reduces the training and inferencing time of a given model compared with the method of randomly specifying the neuromorphic architecture.

With the proposal of the "double-carbon" development goal, all walks of life are actively implementing the national green and low-carbon development requirements. As an important branch of the logistics industry, cold chain logistics is also a key area to achieve the goal of "double carbon". If the concept of low carbon is introduced into cold chain logistics and the distribution route is rationally planned, it can not only reduce logistics costs, but also reduce carbon emissions. Therefore, the minimization of the total distribution cost is taken as the optimization goal, Under the constraints of vehicle load and time window, the cold chain logistics distribution path optimization model is constructed with the goal of minimizing the total distribution cost. Then designed the genetic algorithm to solve, respectively from considering carbon emissions and carbon emissions two cases planning distribution route, and optimization before single temperature distribution scheme comparative analysis, the results show that the carbon emissions considering the cold chain distribution path optimization, and choose cold storage incubator type in distribution mode, not only can reduce the enterprise distribution cost, improve the distribution efficiency of distribution, but also can reduce the carbon emissions.

Green vehicle routing and scheduling problem with heterogeneous fleet including reverse logistics in the form of collecting returned goods

Expressways in mountainous plateau areas exhibit complex driving conditions and harsh climatic characteristics that continuously impact vehicle carbon emissions throughout their entire lifecycle and determine the carbon emission levels of expressways during the operational period. To study the carbon emission characteristics of expressways in the western Sichuan Plateau mountainous area, a coupling simulation analysis method combining CarSim/TruckSim simulation software and the Motor Vehicle Emissions Simulator (MOVES) was employed to analyze the spatial distribution characteristics of vehicle-specific power (VSP). This analysis was based on the alignment data and operational environment of the Wenchuan‒Barkam Expressway in the mountainous area of the western Sichuan Plateau. A conversion formula was established to calculate the cumulative VSP per second and total carbon emissions of each unit. Statistical analysis was conducted on the distribution of carbon emissions equivalent per kilometer across the different alignment units. Distribution ranges of carbon emissions equivalent per kilometer for light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) at each alignment unit were 60‒240 g and 100‒1600 g, respectively. The grey relational analysis method was used to quantify the relationship between the carbon emissions equivalent per kilometer of alignment units, circular curves radius, and average grade. Based on the vertical variations in climatic changes with altitude in the mountainous plateau area, a comparative analysis was performed on the trends in the effects of altitude, season, and vehicle starting frequency on carbon emissions. Frequent vehicle starts significantly impacted carbon emissions, and this impact was significantly higher in winter than in summer. Carbon emissions equivalent of the LDV and HDV starting twice on average in summer were approximately 1.09- and 1.04-times higher, respectively, than that when vehicles were started 0.5 times; whereas, in winter, they were 1.17- and 1.07-times higher, respectively.

ABSTRACTIn urban highway networks, some factors may influence the amount of average kilometric vehicle carbon emission (AKVC). Especially in highway networks of a large metropolitan area, the factors could be more diversified. In the present paper, the particular factors in the statistics of the districts in Seoul City that affect the AKVCs were investigated. Individual and combined factors were considered. The individual factors in the present study included the number of intersections, the number of intersections per kilometre, the road portion to the total area, the number of registered vehicles, the number of commuters, the population, the population density, the total land area and the road length. The combined factors considered included the number of intersections per kilometre over the number of registered vehicles, the number of intersections per kilometre over the road portion to the total area, and the road portion to the total area over the number of registered vehicles. Using the stepwise regression, it was demonstrated that the number of intersections per kilometre is the only significant factor affecting the AKVC in the road network of the district. All other factors are regarded as insignificant in affecting the AKVC. Additionally, correlation analysis confirms the result. From the study, as ‘the number of intersections per kilometre’ is the most influencing factor on reduction of vehicle carbon emissions in the road network, it may be implied that the number of intersections should be controlled or reduced in the road network of a city.

Carbon-emission calculation method during operation period based on asphalt pavement performance

Transportation accounts for nearly one quarter of global greenhouse gas (GHG) emissions. A significant proportion of transportation emissions can be attributed to supply chain transport, which also represents the fastest-growing sector of emissions. As a way of addressing this challenge in the effort to combat global climate change, many local and national governments have leveraged public policy in the form of carbon taxes, emissions trading systems (ETSs), and subsidies for heavy goods electric vehicles (HGEVs). Firms affected by these policies are thus faced with higher costs for more emissions-intensive supply networks and a lower barrier to entry towards adopting HGEVs. However, the exact policy conditions under which firms would be most motivated to change their behaviors remains unclear. In this paper, we develop a novel methodology to address this obstacle in the form of a bi-objective green vehicle routing problem. The first objective is the minimization of the total cost of transportation over a set of vertices comprised of a depot, customers, and charging stations; the second objective is the minimization of total GHGs emitted during transportation. The proposed approach considers the three policy instruments and their effects on both fleet mix decisions (i.e., the conditions under which a firm is most motivated to adopt HGEVs) and cost- and GHG-minimizing routing options. Via an analysis of the change of the Pareto frontier given increasingly stringent carbon pricing and/or increasingly generous HGEV subsidies, firms may consider routing options that yield the most significant GHG emissions reduction at the lowest cost. To this end, we provide a survey of current and forecasted global trends related to carbon tax rates, ETS carbon allowance prices, and HGEV subsidy amounts.

Multi-echelon distribution mechanism is common in supply chain design and logistics systems in which freight is delivered to the customers through intermediate depots (IDs), instead of using direct shipments. This primarily decreases the cost of the chain and consequences of environmental (energy consumption) and social (traffic, air pollution, etc.) logistic operations. This paper develops a novel multi-objective mixed-integer linear programming model (MOMILP) for a two-echelon green capacitated vehicle routing problem (2E-CVRP) in which environmental issues and time windows constraints are considered for perishable products delivery phase. To validate the proposed mathematical model, several numerical examples are generated randomly and solved using CPLEX solver of GAMS software. The e-constraint method is applied to the model to deal with the multi-objectiveness of the proposed model. Finally, the best Pareto solution for each problem is determined based on the reference point approach (RPA) as one of the most effective techniques to help the decision-makers.

The sustainable development of mankind is a matter of concern to the whole world. Environmental pollution and haze diffusion have greatly affected the sustainable development of mankind. According to previous research, vehicle exhaust emissions are an important source of environmental pollution and haze diffusion. The sharp increase in the number of cars has also made the supply of energy increasingly tight. In this paper, we have explored the use of intelligent navigation technology based on data analysis to reduce the overall carbon emissions of vehicles on road networks. We have implemented a traffic flow prediction method using a genetic algorithm and particle-swarm-optimization-enhanced support vector regression, constructed a model for predicting vehicle exhaust emissions based on predicted road conditions and vehicle fuel consumption, and built our low-carbon-emission-oriented navigation algorithm based on a spatially optimized dynamic path planning algorithm. The results show that our method could help to significantly reduce the overall carbon emissions of vehicles on the road network, which means that our method could contribute to the construction of low-carbon-emission intelligent transportation systems and smart cities.

Today, serious environmental problems arise with overconsumption. The most effective way of protecting the environment is recycling. To obtain maximum benefit from the recycling activities depends on the effective and robust design of the disassembly lines. In addition, the distribution plan of products to be recycled is as important as the disassembly process. Therefore, this study includes the integrated optimization of the partial disassembly line balancing problem and the multi-objective green vehicle routing problem for the first time. The integrated problem is formulated as a Multi-Objective Mixed Integer Linear Programming. The model’s objectives consist of minimization of total CO2 emission and cost. The Pareto optimal solution set is found by solving this problem with the Augmented ℇ-Constraint Method. The model respectively determines (i) the total number of opened workstations in the disassembly center, (ii) optimal task assignments to workstations in disassembly centers and non-disassembled tasks, (iii) total workstation times and idle times of each workstation, (iv) optimal distribution routes for all vehicles. This study examines a case study in Turkey. To determine the best Pareto optimal solution for homogeneous and heterogeneous fleets, the integrated multi-criteria decision-making method consisting of EWM (Entropy Weight Method) and COPRAS (Complex Proportional Assessment) is used.

Vehicle carbon emission models are essential for monitoring and managing transportation emissions. Considering the numerous vehicle types and diverse driving conditions, modeling each vehicle individually is not possible. A generalized carbon emission module (GCEM) is proposed to realize fast model transfer across both light-duty gasoline and heavy-duty diesel vehicles. It has five main components: normalized torque function, category-specified fuel consumption rate function, denormalized function, emission-standard correction, and fuel-specified conversion. GCEM estimates carbon emissions based on the engine bench results of a reference vehicle, avoiding time-consuming and costly experiments. By normalizing engine torque, the significant power disparities within the same vehicle category can be effectively addressed. Integrating GCEM within the parallel transportation system is a feasible solution for monitoring complex traffic emissions. Through comprehensive comparisons, GCEM demonstrates a superior generalized ability in this data-limited scenario than three baseline models, both for various test vehicles and diverse driving conditions. As real-world traffic is a typical data-limited scenario, GCEM is a promising and practical transfer method for estimating vehicle carbon emissions.

This paper, based on traditional vehicle routing model with time window constraints, analysis the distribution costs according to the cold-chain logistics characteristics of frozen food. Considering the actual situation of variety frozen foods being loaded together, the paper constructs the cold chain distribution routing optimization model of frozen food with the main constraints of the load volume, load quality and soft time window. At last take a food distribution Co., Ltd as an analysis sample which is solved by heuristic algorithm to verify the rationality and the feasibility of the model.

Vessel routing and optimization for marine debris collection with consideration of carbon cap