Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.     

Sustainable cold supply chain management under demand uncertainty and carbon tax regulation

Increasing awareness of sustainability in supply chain management has prompted organizations and individuals to consider environmental impacts when managing supply chains. The issues concerning environmental impacts are significant in cold supply chains due to substantial carbon emissions from storage and distribution of temperature-sensitive product. This paper investigates the impact of carbon emissions arising from storage and transportation in the cold supply chain in the presence of carbon tax regulation, and under uncertain demand. A two-stage stochastic programming model is developed to determine optimal replenishment policies and transportation schedules to minimize both operational and emissions costs. A matheuristic algorithm based on the Iterated Local Search (ILS) algorithm and a mixed integer programming is developed to solve the problem in realistic sizes. The performance and robustness of the matheuristic algorithm are analyzed using test instances in various sizes. A real-world case study in Queensland, Australia is used to demonstrate the application of the model. The results highlight that higher emissions price does not always contribute to the efficiency of the cold supply chain system. Furthermore, the analyses indicate that using heterogeneous fleet including light duty and medium duty vehicles can lead to further cost saving and emissions reduction.     

Development of control models for the planning of sustainable transportation systems

Planning of sustainable transportation systems requires integration of multiple systems while considering a holistic approach. A limited amount of research has been conducted that simultaneously considers all the transportation, economic activity, environmental and social effects. The proposed research envisages incorporating considerations related to sustainability and providing solutions to stakeholders in policy making. In this paper, a dynamic model for planning and development of sustainable transportation systems is presented. This is given by a system of three nonlinear differential equations representing the dynamics of the three independent states, namely, transportation, activity, and environmental systems. A policy scenario considering investment in energy efficient technologies and its effects on the states is discussed to assist making investment decisions. Optimal control techniques are used to design the controls. The results show that it is possible to formulate an optimal control to achieve the desired target. Numerical results, based on actual parameters, are presented to illustrate the long-term trends of the states. The methodology discussed in this paper will be helpful to decision makers in making optimal decisions. The contribution of this research work is the introduction of a systems and controls methodology to develop optimal policies for the design of sustainable systems.     

A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: A case study

The management of products’ end-of-life and the recovery of used products has gained significant importance in recent years. In this paper, we address the carbon footprint-based problem that arises in a closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed of or be remanufactured to be resold as new ones. Given this environment, an optimization model for a closed-loop supply chain (CLSC) in which carbon emission is expressed in terms of environmental constraints, i.e., carbon emission constraints, is developed. These constraints aim to limit the carbon emission per unit of product supplied with different transportation modes. Here, we design a closed-loop network where capacity limits, single-item management and uncertainty on product demands and returns are considered. First, fuzzy mathematical programming is introduced for uncertain modeling. Then, the statistical approach to the possibility to synthesize fuzzy information is utilized. Therefore, using a defined possibilistic mean and variance, we transform the proposed fuzzy mathematical model into a crisp form to facilitate efficient computation and analysis. Finally, the risk caused by violating the estimated resource constraints is analyzed so that decision makers (DMs) can trade off between the expected cost savings and the expected risk. We utilize data from a company located in Iran.     

Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data

Assessing sustainability of supply chains is a critical and increasingly complex problem. In recent years sustainability has received more attention in supply chain management (SCM) literature with triple bottom lines including social, environmental, and economic factors. Conventional data envelopment analysis (DEA) models consider decision making units (DMUs) as black boxes that consume a set of inputs to produce a set of outputs and do not take into consideration internal interactions of DMUs. Two-stage DEA models deal with such DMUs. However, existing two-stage DEA models are applicable only in technologies characterized by positive inputs/outputs. This paper aims to build and present a new two-stage DEA model considering negative input-intermediate-output data. Some numerical examples along with some theorems and properties are given to show capability of proposed method. The proposed ideas are used in a case study where 29 Iranian supply chains producing equipment of expendable medical devices are evaluated in terms of sustainability.     

Air transportation in a carbon constrained world: Long-term dynamics of policies and strategies for mitigating the carbon footprint of commercial aviation

With increasing demand for air transportation worldwide and decreasing marginal fuel efficiency improvements, the contribution of aviation to climate change relative to other sectors is projected to increase in the future. As a result, growing public and political pressures are likely to further target air transportation to reduce its greenhouse gas emissions. The key challenges faced by policy makers and air transportation industry stakeholders is to reduce aviation greenhouse gas emissions while sustaining mobility for passengers and time-sensitive cargo as well as meeting future demand for air transportation in developing and emerging countries. This paper examines five generic policies for reducing the emissions of commercial aviation; (1) technological efficiency improvements, (2) operational efficiency improvements, (3) use of alternative fuels, (4) demand shift and (5) carbon pricing (i.e. market-based incentives). In order to evaluate the impacts of these policies on total emissions, air transport mobility, airfares and airline profitability, a system dynamics modeling approach was used. The Global Aviation Industry Dynamics (GAID) model captures the systemic interactions and the delayed feedbacks in the air transportation system and allows scenarios testing through simulations. For this analysis, a set of 34 scenarios with various levels of aggressiveness along the five generic policies were simulated and tested. It was found that no single policy can maintain emissions levels steady while increasing projected demand for air transportation. Simulation results suggest that a combination of the proposed policies does produce results that are close to a “weak” sustainability definition of increasing supply to meet new demand needs while maintaining constant or increasing slightly emissions levels. A combination of policies that includes aggressive levels of technological and operations efficiency improvements, use of biofuels along with moderate levels of carbon pricing and short-haul demand shifts efforts achieves a 140% increase in capacity in 2024 over 2004 while only increasing emissions by 20% over 2004. In addition, airline profitability is moderately impacted (10% reduction) compared to other scenarios where profitability is reduced by over 50% which pose a threat to necessary investments and the implementation of mitigating measures to reduce CO2 emissions. This study has shown that an approach based on a portfolio of mitigating measures and policies spanning across technology and operational improvements, use of biofuels, demand shift and carbon pricing is required to transition the air transportation industry close to an operating point of environmental and mobility sustainability.     

Closed-loop Inventory Routing Problem for returnable transport items

Increasing concerns on supply chain sustainability have given birth to the concept of closed-loop supply chain. Closed-loop supply chains include the return processes besides forward flows to recover the value from the customers or end-users. Vendor Managed Inventory (VMI) systems ensure collaborative relationships between a vendor and a set of customers. In such systems, the vendor takes on the responsibility of product deliveries and inventory management at customers. Product deliveries also include reverse flows of returnable transport items. The execution of the VMI policy requires vendor to deal with a Closed-loop Inventory Routing Problem (CIRP) consisting of its own forward and backward routing decisions, and inventory decisions of customers. In CIRP literature, traditional assumptions of disregarding reverse logistic operations, knowing beforehand distribution costs between nodes and customers demand, and managing single product restrict the usage of the proposed models in current food logistics systems. From this point of view, the aim of this research is to enhance the traditional models for the CIRP to make them more useful for the decision makers in closed-loop supply chains. Therefore, we propose a probabilistic mixed-integer linear programming model for the CIRP that accounts for forward and reverse logistics operations, explicit fuel consumption, demand uncertainty and multiple products. A case study on the distribution operations of a soft drink company shows the applicability of the model to a real-life problem. The results suggest that the proposed model can achieve significant savings in total cost and thus offers better support to decision makers.     

Green supply chain network design to reduce carbon emissions

We consider a supply chain network design problem that takes CO₂ emissions into account. Emission costs are considered alongside fixed and variable location and production costs. The relationship between CO₂ emissions and vehicle weight is modeled using a concave function leading to a concave minimization problem. As the direct solution of the resulting model is not possible, Lagrangian relaxation is used to decompose the problem into a capacitated facility location problem with single sourcing and a concave knapsack problem that can be solved easily. A Lagrangian heuristic based on the solution of the subproblem is proposed. When evaluated on a number of problems with varying capacity and cost characteristics, the proposed algorithm achieves solutions within 1% of the optimal. The test results indicate that considering emission costs can change the optimal configuration of the supply chain, confirming that emission costs should be considered when designing supply chains in jurisdictions with carbon costs.     

An integrated risk assessment model: A case of sustainable freight transportation systems

The major challenge in the development of sustainable freight transportation systems (SFTSs) is due to the involvement of numerous dynamic uncertainties and intrinsic sustainability risks. Sustainability risks are potential threats that can have undesirable impacts on the sustainability of a system. The main objective of this study is to identify and evaluate the sustainability risks associated with freight transportation systems (FTSs). Accordingly, a risk analysis approach is developed by innovatively integrating the intuitionistic fuzzy set theory and D-number theory to quantitatively model the sustainability risks. Intuitionistic fuzzy numbers can examine both the membership and non-membership degrees of an element while the D-number theory increases the objectivity of assessments by fusing multiple expert judgments. The proposed risk assessment model facilitates the managers in the development of SFTSs by ensuring visibility, predictability and measurability in freight operations. Unlike the conventional perception, the findings indicate that most of the high priority sustainability risks in FTSs are socially induced rather than financially driven and consciousness in people’s conduct is must to attain the positive results. The analysis alerts the freight managers toward the high priority sustainability risks and guides in pro-active strategy formulation and optimum allocation of mitigation resources to minimize disruptions in SFTSs.     

A dynamic modeling approach to highway sustainability: Strategies to reduce overall impact

The need for sustainable development is increasing as the industrial and service activities keep putting such a strain on the natural functions of the Earth, thus the ability of the planet’s to sustain future generations. Since most of the industrial and service activities are provided via transportation, it is one of the most crucial elements of sustainable development. In this paper, US highway system sustainability problem is studied. System dynamics modeling approach is employed due to the causal relationships and feedback loops that are observed in the problem structure. The reference mode is considered as the increasing CO₂ emission trend. The objective is to meet the Liberman and Warner Climate Act’s targets by 2050. Three potential strategies for policy making are tested with the developed dynamic simulation: fuel efficiency, public transportation and electric vehicle usage. The results indicate that hybrid implementation of individual policies has a crucial impact on the success of policy making.     

Eco-design of transportation in sustainable supply chain management: A DEA-like method

This study addresses the issue of eco-design for transportation in sustainable supply chain management (SSCM). Data envelopment analysis (DEA) is adopted and extended to construct a model for this application. This proposed model, together with the tractable algorithm developed in this research, can provide stakeholders with a Pareto Optimal transportation strategy. This derived transportation strategy can help stakeholders realize certain transportation goals with less resource consumption and pollution emission. The discussion presented leads to a heuristic Joint Transportation Policy and concludes with two useful suggestions for putting the strategy into practice. The proposed model was used in an empirical study of design sustainable transportation mechanism for one air-condition manufacturer in China to transport its products as well, the analysis further demonstrating the theoretical and practical value of this research.     

Designing a supply chain resilient to major disruptions and supply/demand interruptions

Global supply chains are more than ever under threat of major disruptions caused by devastating natural and man-made disasters as well as recurrent interruptions caused by variations in supply and demand. This paper presents a hybrid robust-stochastic optimization model and a Lagrangian relaxation solution method for designing a supply chain resilient to (1) supply/demand interruptions and (2) facility disruptions whose risk of occurrence and magnitude of impact can be mitigated through fortification investments. We study a realistic problem where a disruption can cause either a complete facility shutdown or a reduced supply capacity. The probability of disruption occurrence is expressed as a function of facility fortification investment for hedging against potential disruptions in the presence of certain budgetary constraints. Computational experiments and thorough sensitivity analyses are completed using some of the existing widely-used datasets. The performance of the proposed model is also examined using a Monte Carlo simulation method. To explore the practical application of the proposed model and methodology, a real world case example is discussed which addresses mitigating the risk of facility fires in an actual oil production company. Our analysis and investigation focuses on exploring the extent to which supply chain design decisions are influenced by factors such as facility fortification strategies, a decision maker's conservatism degree, demand fluctuations, supply capacity variations, and budgetary constraints.     

A methodology to control urban traffic noise under the constraint of environmental capacity: A case study of a double-decision optimization model

This paper presents a novel methodology to control urban traffic noise under the constraint of environmental capacity. Considering the upper limits of noise control zones as the major bottleneck to control the maximum traffic flow is a new idea. The urban road network traffic is the mutual or joint behavior of public self-selection and management decisions, so is a typical double decision optimization problem.The proposed methodology incorporates theoretically model specifications. Traffic noise calculation model and traffic assignment model for O–D matrix are integrated based on bi-level programming method which follows an iterated process to obtain the optimal solution. The upper level resolves the question of how to sustain the maximum traffic flow with noise capacity threshold in a feasible road network. The user equilibrium method is adopted in the lower layer to resolve the O–D traffic assignment.The methodology has been applied to study area of QingDao, China. In this illustrative case, the noise pollution level values of optimal solution could satisfy the urban environmental noise capacity constraints. Moreover, the optimal solution was intelligently adjusted rather than simply reducing the value below a certain threshold. The results indicate that the proposed methodology is feasible and effective, and it can provide a reference for a sustainable development and noise control management of the urban traffic.     

A ‘sustainability window’ of urban form

With global environmental change and the rise of global megacities, environmental and social externalities of urban systems, and especially of urban form, become increasingly prevalent. The question of optimal urban form has been debated and investigated by different disciplines in numerous contexts, including those of transport costs, land consumption and congestion. Here we elucidate theoretically how urban form and the urban transport system systematically modifies sustainability concerns, such as greenhouse gas emissions, local air pollution and congestion. We illustrate our analytical considerations with empirical analysis. Denser urban form would almost unambiguously mitigate climate change, but it would also lead to undesired effects, such as a higher proportion of urban dwellers affected by air pollution. Our study presents a ‘sustainability window’ by highlighting trade-offs between these sustainability concerns as a function of urban form. Only a combination of transportation policies, infrastructure investments and progressive public finance enables the development of cities that perform well in several sustainability dimensions. We estimate that a residential population density between 50 and 150 persons/ha and a modal share of environmental modes above at least 50% corresponds to the sustainability window of urban form. The parameters of the sustainability window of urban form is subject to policy changes and technological progress.     

The multi-port berth allocation problem with speed optimization and emission considerations

The container shipping industry faces many interrelated challenges and opportunities, as its role in the global trading system has become increasingly important over the last decades. On the one side, collaboration between port terminals and shipping liners can lead to costs savings and help achieve a sustainable supply chain, and on the other side, the optimization of operations and sailing times leads to reductions in bunker consumption and, thus, to fuel cost and air emissions reductions. To that effect, there is an increasing need to address the integration opportunities and environmental issues related to container shipping through optimization. This paper focuses on the well known Berth Allocation Problem (BAP), an optimization problem assigning berthing times and positions to vessels in container terminals. We introduce a novel mathematical formulation that extends the classical BAP to cover multiple ports in a shipping network under the assumption of strong cooperation between shipping lines and terminals. Speed is optimized on all sailing legs between ports, demonstrating the effect of speed optimization in reducing the total time of the operation, as well as total fuel consumption and emissions. Furthermore, the model implementation shows that an accurate speed discretization can result in far better economic and environmental results.     

Combining pickups and deliveries in vehicle routing – An assessment of carbon emission effects

This paper studies the effect on carbon emissions of consolidation of shipments on trucks. New positioning and communication technologies, as well as decision support systems for vehicle routing, enable better utilization of vehicle capacity, reduced travel distance, and thereby carbon emission reductions. We present a novel carbon emission analysis method that determines the emission savings obtained by an individual transport provider, who receives customer orders for outbound deliveries as well as pickup orders from supply locations. The transport provider can improve vehicle utilization by performing pickups and deliveries jointly instead of using separate trucks. In our model we assume that the transport provider minimizes costs by use of a tool that calculates detailed vehicle routing plans, i.e., an assignment of each transport order to a specific vehicle in the fleet, and the sequence of customer visit for each vehicle. We compare a basic set-up, in which pickups and deliveries are segregated and performed with separate vehicles, with two consolidation set-ups where pickups and deliveries may be mixed more or less freely on a single vehicle. By allowing mixing, the average vehicle load will increase and the total driven distance will decrease. To compare carbon emissions for the three set-ups, we use a carbon assessment method that uses the distance driven and the average load factor. An increase in the load factor can reduce part of the emission savings from consolidation. We find that emission savings are relatively large in case of small vehicles and for delivery and pickup locations that are relatively far from the depot. However, if a truck visits many demand and supply locations before returning to the depot, we observe negligible carbon emission decreases or even emission increases for consolidation set-ups, meaning that in such cases investing in consolidation through joint pickups and deliveries may not be effective. The results of our study will be useful for transport users and providers, policymakers, as well as vehicle routing technology vendors.     

Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem

The traditional distribution planning problem in a supply chain has often been studied mainly with a focus on economic benefits. The growing concern about the effects of anthropogenic pollutions has forced researchers and supply chain practitioners to address the socio-environmental concerns. This research study focuses on incorporating the environmental impact on route design problem. In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing problem. The proposed models are a significant contribution to the field of research in green vehicle routing problem at the operational level. The formulated integer linear programming model is solved for a set of small scale instances using LINGO solver. A computationally efficient Ant Colony Optimization (ACO) based meta-heuristic is developed for solving both small scale and large scale problem instances in reasonable amount of time. For solving large scale instances, the performance of the proposed ACO based meta-heuristic is improved by integrating it with a variable neighbourhood search.     

A fuzzy-stochastic optimization model for the intermodal fleet management problem of an international transportation company

ABSTRACT

In this paper, a fuzzy-stochastic optimization model is developed for an intermodal fleet management system of a large international transportation company. The proposed model integrates various strategic, tactical and operational level decisions simultaneously. Since real-life fleet planning problems may involve different types of uncertainty jointly such as randomness and fuzziness, a hybrid chance-constrained programming and fuzzy interactive resolution-based approach is employed. Therefore, stochastic import/export freight demand and fuzzy transit times, truck/trailer availabilities, the transport capacity of Ro-Ro vessels, bounds on block train services, etc. can also be taken into account concurrently. In addition to minimize overall transportation costs, optimization of total transit times and CO₂ emission values are also incorporated in order to provide sustainable fleet plans by maximizing customer satisfaction and environmental considerations. Computational results show that effective and efficient fleet plans can be produced by making use of the proposed optimization model.     

Low-carbon futures for Shenzhen’s urban passenger transport: A human-based approach

Shenzhen, one of China’s leading cities, has the potential to be a model for achieving China’s ambitious CO₂ emission reduction targets. Using data from a travel diary survey in Shenzhen in 2014, we develop a human-based agent model to conduct a scenario study of future urban passenger transport energy consumption and CO₂ emissions from 2014 to 2050. Responses to different policy interventions at the individual level are taken into account. We find that with current policies, the carbon emissions of the urban passenger transport sector in Shenzhen will continuously increase without a peak before 2050. Strengthening 21 transport policies will help Shenzhen to peak the carbon emissions by 2030 for passenger transport. Among these policies, the car quota policy and the fuel economy standard are essential for achieving a carbon peak by 2030. In addition, a package of seven policies, including fewer car quotas, a stricter fuel economy standard, raising parking fees, limiting parking supply, increasing EV charging facilities and subway lines, and improving public transport services, is sufficient to peak carbon emissions by 2030, although at an emissions level higher than for the 21 policies.     

The impact of airport noise as part of a Strategic Environmental Assessment,case study: The Tivat (Montenegro) Airport expansion plan

Strategic Environmental Assessment (SEA) is one of the most important instruments for making relevant decisions on the basis of which spatial planning is aligned with the tenets and principles of sustainable spatial development. Its primary application is in spatial (strategic) and urban planning, as well as in the planning and design of sectoral policies in the areas of energy, water management, waste management, transport, etc. The implementation of SEA allows developers to establish the benefits and implications of the proposed spatial changes, taking into account the capacity of the space to sustain the planned development, and to determine the degree of acceptability of the proposed spatial changes. This paper presents a specific method of assessing the impact of airport noise as part of a particular SEA. The particularity of this method is that it integrates the objectives, indicators and criteria for assessing the impact of airport noise on the population using the method of multi-criteria evaluation, applied in the preparation of the SEA for the Urban development plan regarding the expansion of Tivat Airport, Montenegro. The changes in noise intensity within the planning horizon to 2030 were predicted taking into account the following factors: physical, geographic and demographic characteristics of the space, the projected increase in the number and nature of the flights operated within the planning period, as well as the types of aircraft. On the basis of these data noise dispersion modelling was carried out using the IMMI model, and the results obtained were used in a multi-criteria evaluation as part of the SEA. The results of the research do not indicate any significant increase in noise intensity within the planning period to 2030 and they represent a good basis for making relevant decisions regarding the future development of Tivat Airport.