A solution method for a two-layer sustainable supply chain distribution model

This article presents an effective solution method for a two-layer, NP-hard sustainable supply chain distribution model. A DoE-guided MOGA-II optimiser based solution method is proposed for locating a set of non-dominated solutions distributed along the Pareto frontier. The solution method allows decision-makers to prioritise the realistic solutions, while focusing on alternate transportation scenarios. The solution method has been implemented for the case of an Irish dairy processing industry׳s two-layer supply chain network. The DoE generates 6100 real feasible solutions after 100 generations of the MOGA-II optimiser which are then refined using statistical experimentation. As the decision-maker is presented with a choice of several distribution routes on the demand side of the two-layer network, TOPSIS is applied to rank the set of non-dominated solutions thus facilitating the selection of the best sustainable distribution route. The solution method characterises the Pareto solutions from disparate scenarios through numerical and statistical experimentations. A set of realistic routes from plants to consumers is derived and mapped which minimises total CO2 emissions and costs where it can be seen that the solution method outperforms existing solution methods

Green logistic network design : intermodal transportation planning and vehicle routing problems.

Due to earth\u27s climate change and global warming, environmental consideration in the design of logistic systems is accelerating in recent years. In this research we aim to design an efficient and environmentally friendly logistical system to satisfy both government and carriers. In particular, we considered three problems in this dissertation: intermodal network design, deterministic green vehicle routing problem and stochastic green vehicle routing problem. The first problem aims to design an economic and efficient intermodal network including three transportation modes: railway, highway and inland waterway. The intent of this problem is to increase the utilization percentage of waterway system in the intermodal transportation network without increasing the cost to the consumer. In particular, we develop a real world coal transportation intermodal network across 15 states in the United States including highway, railway and inland waterway. The demand data were obtained from the Bureau of Transportation Statistics (BTS) under the US Department of Transportation (DOT). Four boundary models are built to evaluate the potential improvement of the network. The first boundary model is a typical minimum cost problem, where the total transportation cost is minimized while the flow balance and capacity restrictions are satisfied. An additional constraint that help obtain an upper bound on carbon emission is added in the second boundary model. Boundary model 3 minimizes the total emission with flow balance and capacity restrictions the same as boundary model 1. Boundary model 4 minimizes the total emission with an additional current cost restriction to achieve a less-aggressive lower bound for carbon emission. With a motivation to minimize the transportation and environmental costs simultaneously, we propose multi-objective optimization models to analyze intermodal transportation with economic, time performance and environmental considerations. Using data from fifteen selected states, the model determines the tonnage of coal to be transported on roadways, railways and waterways across these states. A time penalty parameter is introduced so that a penalty is incurred for not using the fastest transportation mode. Our analysis provides authorities with a potential carbon emission tax policy while minimizing the total transportation cost. In addition, sensitivity analysis allows authorities to vary waterway, railway and highway capacities, respectively, and study their impact on the total transportation cost. Furthermore, the sensitivity analysis demonstrates that an intermodal transportation policy that uses all the three modes can reduce the total transportation cost when compared to one that uses just two modes. In contrast with traditional vehicle routing problems, the second problem intends to find the most energy efficient vehicle route with minimum pollution by optimization of travel speed. A mixed integer nonlinear programming model is introduced and a heuristic algorithm based on a savings heuristic and Tabu Search is developed to solve the large case for this problem. Numerical experiments are conducted through comparison with a solution obtained by BONMIN in GAMS on randomly generated small problem instances to evaluate the performance of the proposed heuristic algorithm. To illustrate the impact of a time window constraint, travel speed and travel speed limit on total carbon emission, sensitivity analysis is conducted based on several scenarios. In the end, real world instances are examined to further investigate the impact of these parameters. Based on the analysis from the second problem, travel speed is an important decision factor in green vehicle routing problems to minimize the fuel cost. However, the actual speed limit on a road may have variance due to congestion. To further investigate the impact of congestion on carbon emission in the real world, we proposed a stochastic green vehicle routing problem as our third problem. We consider a green vehicle problem with stochastic speed limits, which aims to find the robust route with the minimum expected fuel cost. A two-stage heuristic with sample average approximation is developed to obtain the solution of the stochastic model. Computational study compares the solutions of robust and traditional mean-value green vehicle routing problems with various settings

THE PARTIALLY RECHARGEABLE ELECTRIC VEHICLE ROUTING PROBLEM WITH TIME WINDOWS AND CAPACITATED CHARGING STATIONS

Electric vehicles are potentially beneficial for both the environment and an organization\u27s bottom line. These benefits include, but are not limited to, reduced fuel costs, government tax incentives, reduced greenhouse gas emissions, and the ability to promote a company\u27s green image. In order to decide whether or not to convert or purchase electric trucks and install charging facilities, decision makers need to consider many factors including onboard battery capacity, delivery or service assignments, scheduling and routes, as well as weather and traffic conditions in a well-defined modeling framework. We develop a model to solve the partially rechargeable electric vehicle routing problem with time windows and capacitated charging stations. Given destination data and vehicle properties, our model determines the optimal number of vehicles or charging stations needed to meet the network\u27s requirements. Analyzing the model shows the relationships between vehicle range, battery recharge time, and fleet size

Integrating passenger and freight transportation : model formulation and insights

Integrating passenger and freight flows creates attractive business opportunities because the same transportation needs can be met with fewer vehicles and emissions. This paper seeks an integrated solution for the transportation of passenger and freight simultaneously, so that fewer vehicles are required. The newly introduced problem concerns scheduling a set of vehicles to serve the requests such that a part of the journey can be carried out on a scheduled passenger transportation service. We propose an arc-based mixed integer programming formulation for the integrated transportation system. Computational results on a set of instances provide a clear understanding on the benefits of integrating passenger and freight transportation in the current networks, considering multi-modality of traditional passenger-oriented transportation modes, such as taxi, bus, train or tram

Toward sustainable express deliveries for online shopping: reusing packaging materials through reverse logistics

The COVID-19 pandemic has led to an increase in online purchases, which has inevitably raised the demand for express delivery packaging materials (EDPMs). This study proposes a reverse logistics reuse framework that extends the EDPM life cycle by drawing on insights and conclusions from a review of the literature on supply chain management and materials science to achieve a sustainable e-commerce system. A key benefit of reverse logistics is its effectiveness in exploiting opportunities for resource reuse, which is preferred to recycling. By extending service life through resource optimization, recycling, and recovery processes, the novel reuse framework based on reverse logistics can be implemented with minimal changes to existing forward logistics systems, potentially leading to more sustainable online shopping. This study proposes a novel combination of reusable packaging materials and reverse logistics as a viable and more environmentally friendly practice, in line with circular economy goals

Freight transportation planning in platform service supply chain considering carbon emissions

The online-to-offline (O2O) platform is becoming increasingly popular in today's market. This study investigates the decision-making problem of an O2O freight platform facing uncertain demand and carbon emission constraints to achieve sustainable development and establish an environment-friendly image. By receiving customer orders online and then delivering them offline, the platform chooses the optimal dispatching time and pricing level to maximize its profit under a limited carbon cap. We consider a stochastic model with two kinds of demand functions, i.e., additive and multiplicative cases, and solve the optimization problems. By adopting a data set from a leading O2O freight platform in China, we find that the proposed model can effectively increase the revenue of the platform by more than 20 % if the platform increases the price appropriately. The results further show that market parameters such as potential market size and price elasticity have a more significant influence on the price decision than logistics parameters such as the fixed shipping cost and holding cost per unit volume per unit time, while logistics parameters affect the dispatching time decision more significantly than market parameters. With respect to the profit, as the carbon cap increases, the profit of the platform first climbs up to a peak point and then decreases with the further increase of the cap due to rising inventory holding costs. Furthermore, the O2O platform can benefit from a large-scale market and thus might suffer a loss at its start-up stage. Our study contributes to the literature on the O2O platform and the freight transportation planning with the consideration of sustainable development

Optimization of emergency supplies paths based on dynamic real-time split deliver

A multi-objective dynamic demand split delivery emergency material distribution model is developed to enhance the efficiency of emergency material distribution and facilitate the smooth progress of safety rescue operations during unconventional emergencies. This model incorporates the psychological view of those affected by disasters. The issue of dynamic demand may be transformed into a static demand problem by dividing the distribution time window into time domains of equal length. The optimization process is thereafter executed in real-time with the timed batch methodology. A refined ant colony method has been developed to address the model by integrating the attributes of the mathematical model, followed by doing an arithmetic case analysis. The findings indicate that the algorithm and mathematical model suggested in this study are efficacious in addressing the emergency material distribution issue, offering valuable decision-making advice and reference

Simulating The Impact of Emissions Control on Economic Productivity Using Particle Systems and Puff Dispersion Model

A simulation platform is developed for quantifying the change in productivity of an economy under passive and active emission control mechanisms. The program uses object-oriented programming to code a collection of objects resembling typical stakeholders in an economy. These objects include firms, markets, transportation hubs, and boids which are distributed over a 2D surface. Firms are connected using a modified Prim’s Minimum spanning tree algorithm, followed by implementation of an all-pair shortest path Floyd Warshall algorithm for navigation purposes. Firms use a non-linear production function for transformation of land, labor, and capital inputs to finished product. A GA-Vehicle Routing Problem with multiple pickups and drop-offs is implemented for efficient delivery of commodities across multiple nodes in the economy. Boids are autonomous agents which perform several functions in the economy including labor, consumption, renting, saving, and investing. Each boid is programmed with several microeconomic functions including intertemporal choice models, Hicksian and Marshallian demand function, and labor-leisure model. The simulation uses a Puff Dispersion model to simulate the advection and diffusion of emissions from point and mobile sources in the economy. A dose-response function is implemented to quantify depreciation of a Boid’s health upon contact with these emissions. The impact of emissions control on productivity and air quality is examined through a series of passive and active emission control scenarios. Passive control examines the impact of various shutdown times on economic productivity and rate of emissions exposure experienced by boids. The active control strategy examines the effects of acceptable levels of emissions exposure on economic productivity. The key findings on 7 different scenarios of passive and active emissions controls indicate that rate of productivity and consumption in an economy declines with increased scrutiny of emissions from point sources. In terms of exposure rates, the point sources may not be the primary source of average exposure rates, however they significantly impact the maximum exposure rate experienced by a boid. Tightening of emissions control also negatively impacts the transportation sector by reducing the asset utilization rate as well as reducing the total volume of goods transported across the economy

Hybrid genetic algorithm for inventory routing problem with carbon emission consideration

Inventory Routing Problem (IRP) has been continuously developed and improved due to pressure from global warming issue particularly related to greenhouse gases (GHGs) emission. The burning of fossil fuel for transportations such as cars, trucks, ships, trains, and planes primarily emits GHGs. Carbon dioxide (CO2) from burning of fossil fuel to power transportation and industrial process is the largest contributor to global GHGs emission. Therefore, the focus of this study is on solving a multi-period inventory routing problem (MIRP) involving carbon emission consideration based on carbon cap and offset policy. Hybrid genetic algorithm (HGA) based on allocation first and routing second is used to compute a solution for the MIRP in this study. The objective of this study is to solve the proposed MIRP model with HGA then validate the effectiveness of the proposed HGA on data of different sizes. Upon validation, the proposed MIRP model and HGA is applied on real data and parameter sensitivity analysis is performed on the MIRP model. The HGA is found to be able to solve small size and large size instances effectively by providing near optimal solution in relatively short CPU execution time. In addition, the increase in unit carbon price results in the increase of the supply chain’s total cost while the increase in carbon cap results in the decrease of supply chain’s total cost. The results from the analysis gave an indication that the unit carbon price and carbon cap need to be thoroughly designed so that it will not burden the participating companies of carbon emission regulation and environment