Optimization of a Low-Carbon Two-Echelon Heterogeneous-Fleet Vehicle Routing for Cold Chain Logistics under Mixed Time Window

Research on different modes of energy conservation and emission reduction: A differential game model based on carbon trading perspective

In recent years, due to global climate change, increasing resource scarcity, and environmental constraints, countries have prioritized energy conservation and emissions reduction. However, enterprises are primarily responsible for energy saving and emissions reduction. To encourage industrial enterprises to engage in energy conservation and emissions reduction, high-carbon enterprises must purchase carbon emission rights from low-carbon counterparts. Common modes of energy conservation and emission reduction of industrial enterprises include reducing production scale, improving energy utilization efficiency, and expanding renewable energy. This article constructs three differential game models to identify the applicable scope of various energy conservation and emission reduction strategies, comparing and analyzing the equilibrium results. The study concludes that when the cost of changing the production mode and the income obtained from the production of unit product is large, the low-carbon enterprise can obtain the maximum benefit by reducing the production scale mode. Otherwise, low carbon enterprises can be maximized through improving energy efficiency mode. For both low-carbon and high-carbon enterprises, reducing production scale is the fastest way to enhance efficiency when the costs of energy conservation and emission reduction are substantial.

Application of a Cold-Chain Logistics Distribution System Based on Cloud Computing and Web Delivery Date Management

The cold chain maintains and transports fresh food in the correct temperature range for slow biological decay processes and delivers safe, high-quality food to customers. Ensuring that quality and efficiency are not affected by the supply chain of cold chain products is a goal. Therefore, this paper proposes the intelligent time scheduling management model (ITSMM) based on cloud computing and a web-based platform for cold-chain logistics and distribution systems. This paper establishes a time scheduling model to reduce the overall order operation cost, diminish the variance among the expected and actual time of finalizing the service orders, and improve useful logistics service providers' satisfaction. Data, including all cold chain phases (distributors, industry, consumers, and retailers), have been gathered. This paper examines the distribution cost and time refrigerated vehicles, thus instituting a cold chain distribution vehicle path optimization.

Reducing Carbon Emissions for the Vehicle Routing Problem by Utilizing Multiple Depots

Emission reductions could be achieved by replacing the single-depot mode with a multi-depot mode of vehicle routing. In our study, we identified situations under which multiple depots could be used to effectively reduce carbon emissions. We proposed a branch-and-price (BAP) algorithm to obtain an optimal solution for the multi-depot green vehicle routing problem. Based on the BAP algorithm, we accurately quantified the carbon emission reduction potential of the multi-depot mode over the single-depot mode. Factors such as the number of depots, vehicle speed, customer demand, and service time were considered and analyzed. Computational tests were conducted, and the results showed that using multiple depots in a vehicle routing problem can reduce carbon emissions by at most 37.6%. In sensitivity analyses, we show relationships between these factors, and several managerial insights that can be used to successfully reduce carbon emissions were summarized.

Simplified Swarm Optimization for the Heterogeneous Fleet Vehicle Routing Problem with Time-Varying Continuous Speed Function

Transportation planning has been established as a key topic in the literature and practices of social production, especially in urban contexts. To consider traffic environment factors, more and more researchers are taking time-varying factors into account when scheduling their logistic activities. The time-dependent vehicle routing problem (TDVRP) is an extension of the classical Vehicle Routing Problem with Time Windows (VRPTW) by determining a set of optimal routes serving a set of customers within specific time windows. However, few of them use the continuous speed function to express the time-varying. In practice, many vehicle routing problems are addressed by a fleet of heterogeneous vehicles with different capacities and travel costs including fix costs and variable costs. In this study, a Heterogeneous Fleet Vehicle Routing Problem (HFPRP) Time-Varying Continuous Speed Function has been proposed. The objective is to minimize distribution costs, which contained fixed costs of acquiring and variable fuel costs. To address this problem, our research developed a mathematical model and proposed a Simplified Swarm Optimization (SSO) heuristic for HFVRP with Time-Varying Continuous Speed Function.

Heterogeneous fleet recyclables collection routing optimization in a two-echelon collaborative reverse logistics network from circular economic and environmental perspective

In recent years, production and consumption activities carried out in accordance with the "take-make-dispose" process have caused environmental damage and resource waste. Reverse logistics based on the concept of "sustainable development and circular economy" has gained public attention, including the recycling and reuse of recyclable wastes. In order to explore the impact of recyclable wastes transportation on cost, environment and resources, this paper focuses on a vehicle routing problem considering electric heterogeneous fleet for a two-echelon recycling network, recycling stations and recycling centers. With the goal of minimizing the total cost, a recycling heterogeneous fleet electric vehicle routing model with time windows is established under the consideration of vehicle load constraints, vehicle type constraints on the weight of loaded recyclable wastes and customer's allowable service time constraints. For carbon emissions reduction and environment protection, vehicles used in recycling stations are designated as electric vehicles. Then, a hybrid genetic algorithm with a large-scale neighborhood search algorithm is proposed. Finally, numerical experiments are conducted on vehicle types in recycling stations, and the computational results show the use of heterogeneous fleets could reduce costs. By analyzing the relationship between customer service time and total cost, the best service time rule could be obtained in the case of customer satisfaction. Meanwhile, by analyzing the relationship between the number and the loading capacity of electric vehicles in recycling stations and total cost, the best number and the loading capacity of electric vehicles could be obtained. This heterogeneous fleet recyclables collection routing model can greatly support the construction of recycling centers and the planning of recycling tasks, and provide a basis for recycling companies to deal with the relationship between economy, environment and resources.

Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

Public transportation is a vital service provided to enable a community to carry out daily activities. One of the mass transportations used in an area is a bus. Moreover, the smart transportation concept is an integrated application of technology and strategy in the transportation system. Using smart idea is the key to the application of the Internet of Things. The ways to improve the management transportation system become a bottleneck for the traditional data analytics solution, one of the answers used in machine learning. This paper uses the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithm for the best prediction of travel time with a lower error rate on a case study of a university shuttle bus. Apart from predicting the travel time, this study also considers the fuel cost and gas emission from transportation. The analysis of the experiment shows that the ANN outperformed the SVM. Furthermore, a recommender system is used to recommend suitable routes for the chosen scenario. The experiments extend the discussion with a range of future directions on the stipulated field of study.

A Sustainable Distribution Design for Multi-Quality Multiple-Cold-Chain Products: An Integrated Inspection Strategies Approach

Cold-chain products are time-sensitive and perishable and pose the risk of failure if they are transported to a distant location. Thus, there is a need to analyze their quality during distribution so that the customers may receive optimal-quality products. To address this issue, this study integrates inspection strategies with the sustainable distribution system of multi-quality multiple-cold-chain products. A bi-objective model of cost and emission is proposed under the constraints of heterogeneous vehicle and time window. Furthermore, this study intends to address the following questions: which inspection strategy helps to ensure the potency of delivered products, and what is the impact of quality differentiation on the value of objective functions? A set of meta-heuristics is used for implementing the model using a rich panel of experiments. The results reveal that the quality conditions of different products impact the solutions of cost and emissions. Moreover, the conformity strategy is more viable, as it results in less cost and ensures that the quantity of delivered products meets the level of demand. Finally, the study provides implications for managers and practitioners to develop a sustainable distribution system to maintain the quality of cold-chain products.

Sustainable dynamic lot sizing models for cold products under carbon cap policy

Amid the ever growing interest in operational supply chain models that incorporate environmental aspects as an integral part of the decision making process, this paper addresses the dynamic lot sizing problem of a cold product while accounting for carbon emissions generated during temperature-controlled storage and transportation activities. We present two mixed integer programming models to tackle the two cases where the carbon cap is imposed over the whole planning horizon versus the more stringent version of a cap per period. For the first model, a Lagrangian relaxation approach is proposed which provides a mean for comparing the operational cost and carbon footprint performance of the carbon tax and the carbon cap policies. Subsequently, a Bisection based algorithm is developed to solve the relaxed model and generate the optimal ordering policy. The second model, however, is solved via a dynamic programming based algorithm while respecting two established lower and upper bounds on the periodic carbon cap. The results of the computational experiments for the first model display a stepwise increase (decrease) in the total carbon emissions (operational cost) as the preset cap value is increased. A similar behavior is also observed for the second model with the exception that paradoxical increases in the total emissions are sometimes realized with slightly tighter values of the periodic cap.

Transit-Based Evacuation for Urban Rail Transit Line Emergency

Urban rail systems are the backbone of urban transit networks and are characterized by large passenger volumes, high speeds, punctuality, and low environmental impacts. However, unforeseen events such as rail transit line emergencies can lead to unexpected costs and delays. As a means of disruption management, we divide the decision support system for urban rail transit line emergency situations into two stages—transit-based evacuation and bus bridging management. This paper focuses on the transit-based evacuation under emergency scenarios on a single rail line. The model determines the vehicles and routes within traditional transit systems required to evacuate stranded passengers within a given time window. In addition, the proposed method ensures the reliability of traditional transit systems by considering the operating fleet and reserve fleet in the traditional transit systems. Therefore, the proposed optimization model is established with the objective of maximizing the total number of stranded passengers transferred within the given time window and headway constraint. Herein, we present the optimization model and solution method, and the proposed method is validated. The effectiveness of the proposed control method is evaluated in the Changchun urban transit network. By analyzing stranded passengers at stations under different numbers of vehicles and time periods, the results show that the proposed model can significantly provide routing arrangements to maximize the number of passengers evacuated from stations. The results are useful in the development of emergency evacuation plans to prevent secondary accidents and evacuate stranded passengers during a rail transit line emergency.