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.     

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.     

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.     

Product substitution and dual sourcing under random supply failures

Product substitution can mitigate supply chain disruptions. However, it may not be very effective without multiple sourcing. In this paper, we consider a supply chain with two downward substitutable products. The products can be ordered from an unreliable supplier or a reliable but more expensive supplier. It is found that in an optimal sourcing policy the higher-grade product should be preferred over the lower-grade product. A sufficient condition is given for an optimal policy where only the higher-grade product is dual-sourced. The effect of substitution is contrasted with the non-substitution case. Numerical study shows the impact of demand variability and correlation on the effect of product substitution and the corresponding optimal sourcing policy.     

Achieving sustainable development of supply chain by incorporating various carbon regulatory mechanisms

Nowadays, sustainability issues have received considerable attention in supply chain management because of the governmental requirements as well as expectations of the people. This paper introduces a novel supply chain network design problem to cover three dimensions of sustainability, namely economic, environmental, and social. The advantage of the presented model stems from considering the booming development aligned with reduction in environmental impact. In this paper, to achieve the mentioned benefits and to derive a more sustainable supply chain, a novel model in the presence of the most commonly used carbon policies is proposed. This paper, addresses sustainable development through imposing proper carbon regulatory mechanisms. Main contribution of this study is to consider the effect of imposing carbon policies on environmental advantages as well as improving the regional development level in a supply chain network design problem. Moreover, the shipment consolidation decisions are utilized to reduce cost as well as environmental impact. In addition, a novel mixed uncertainty approach is proposed to capture the uncertain emission parameters. The numerical examples and a case study are analyzed to evaluate the performance of the proposed models. It is concluded that, a high-growth economy with low-carbon can be made and also almost global well-being of people is ensured by applying the proposed model. Some managerial insights are provided for the enterprises of supply chains to make the most appropriate sustainable decisions. Finally, proper carbon emission policies are suggested based on the region sustainability characteristics.     

A coordinated location-inventory problem in closed-loop supply chain

This paper considers a coordinated location-inventory model under uncertain demands for a closed loop supply chain comprising of one plant, forward and reverse distribution centers, and retailers. The inventory of new and returned products is managed at forward and reverse distribution centers respectively through a periodic review policy. The proposed model determines the location of forward and reverse distribution centers and the associated capacities, the review intervals of the inventory policy at distribution centers, and the assignments of retailers to the distribution centers. We model six different coordination strategies. All the models are formulated as nonlinear integer programs with chance constraints and transformed to conic quadratic mixed-integer programs that can be efficiently solved by CPLEX. An outer approximation based solution algorithm is developed to solve the conic quadratic mixed-integer program. The benefit of different types of coordination strategies is shown through extensive computational testing.     

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.     

Synchronized Production and Transportation Planning Using Subcontracted Vehicles in a Production-Distribution Network

Abstract

Effective management of interfaces between procurement, supply, production and distribution for higher efficiency in the supply chain is an important issue in global manufacturing, where the synchronization of production and transportation planning represents important savings in operational costs. This paper focuses on the synchronization of production planning and transportation planning in a production distribution network, where transportation is subcontracted to a professional transportation enterprise (PTE) for vehicle-hiring. Dynamic and flexible numbers of vehicles are used to cater for fast changing market demands. Thus, the number of vehicles to be hired is viewed from the planning point of view as an operational decision considered simultaneously with production and transportation planning. A mathematical model – SPTP/MTDS – for synchronized production and transportation planning under multiple times and direct shipping strategy (MTDS) is discussed, and a Lagrange relaxation decomposition-based two layer decision procedure (LRD-TLDP) is developed. By introducing artificial decision variables and Lagrange multipliers, SPTP/MTDS is decomposed into a production decision sub-problem (SPTP-PD), and a distribution decision sub-problem (SPTP-DD). A priority-based assignment heuristic and a partial chain-based genetic algorithm are developed to solve SPTP-PD and SPTP-DD, respectively. An illustration of the application of the model in an electronic appliance manufacturing enterprise in China is presented.     

Parking permits management and optimal parking supply considering traffic emission cost

Given a many-to-one bi-modal transportation network where each origin is connected to the destination by a bottleneck-constrained highway and a parallel transit line, we investigate the parking permit management methods to minimize traffic time cost and traffic emission cost simultaneously. More importantly, the optimal supply of parking spots is also discussed in the policies of parking permit. First, we derive the total travel costs and emission costs for the two cases of sufficient and insufficient parking spot provisions at the destination. Second, we propose a bi-objective model and solve the Pareto optimal parking permit distribution, given a certain level of parking supply. Third, we investigate the optimal parking supply in the policy of parking permit distribution, with the objectives of minimizing both total travel cost and traffic emission. Fourth, we provide a model of optimizing parking supply, in the policy of free trading of parking permits. Finally, the numerical examples are presented to illustrate the effectiveness of these schemes, and the numerical results show that restricting parking supply at the city center could be efficient to reduce traffic emission.     

Active or passive? Sustainable manufacturing in the direct-channel green supply chain: A perspective of two types of green product designs

This paper considers a supply chain in which a manufacturer produces and sells two types of green products in its direct channel under government interventions aiming to improve social welfare. There is a Stackelberg game between the two stakeholders. Our research derives the closed form solutions to the optimal tax, green degree and price for the products, and obtains the strategies for government and manufacturer under different environmental policies. By considering six scenarios, our models obtain the best choice from the comparison between the two cases in which the manufacturer determines its product green degree actively or accepts the green standard set by the government passively. Then, we conduct some sensitivity analyses and explore the scenario in which there is competition between green products and non-green products. The results of this research indicate that the government setting the product green standard and providing the manufacturer with a subsidy is significant.     

Optimal pricing for build-to-order supply chain design under price-dependent stochastic demand

Build to order (BTO) is a supply chain disruption mitigation strategy. Whereas cost minimization is an operational objective, the goal of the BTO manufacturer is to maximize its profit by using pricing as its competitive decision-making strategy. In this paper, we study a BTO manufacturer who simultaneously determines its product prices and designs its supply chain network to maximize its expected profit under price-dependent stochastic demand. We propose an L-shaped decomposition with complete enumeration to solve for optimality and show that the expanded master problem remains convex programming, although the optimality cuts are quadratic inequalities. The computational results demonstrate that stocking up on differentiated components and allocating modules appropriately to meet realized demand is a resilient policy that sustains variations in demand. Furthermore, the pricing decision balances the expected revenue and expected operating cost with an increase in expected profit. The integration of pricing and operational planning results in a higher expected profit than by individual decisions. We also demonstrate that cost minimization may not provide the same level of profit if the manufacturer overestimates or underestimates its most profitable demand.     

Designing Emission Charging Schemes for Transportation Conformity

This paper designs emission charging schemes to ensure traffic emissions not exceeding the emission budgets in emission regulation areas. In a network where all links are tollable, we prove that, under mild conditions, there exists a non‐negative link‐based toll scheme that achieves the transportation conformity requirement while minimizing total emissions. We further consider only tolling in the emission regulation areas and formulate a model to determine optimal area‐based emission charges to ensure the conformity. The model is formulated as a mathematical program with complementarity constraints. A solution framework is developed to solve the model, leveraging on a derivative‐free pattern search algorithm and integrating MOVES to estimate vehicular emissions more accurately. The proposed models are demonstrated on two numerical examples. Copyright © 2013 John Wiley & Sons, Ltd.     

A bi-criteria indicator to assess supply chain network performance for critical needs under capacity and demand disruptions

In this paper, we develop a supply chain/logistics network model for critical needs in the case of disruptions. The objective is to minimize the total network costs, which are generalized costs that may include the monetary, risk, time, and social costs. The model assumes that disruptions may have an impact on both the network link capacities as well as on the product demands. Two different cases of disruption scenarios are considered. In the first case, we assume that the impacts of the disruptions are mild and that the demands can be met. In the second case, the demands cannot all be satisfied. For these two cases, we propose two individual performance indicators. We then construct a bi-criteria indicator to assess the supply chain network performance for critical needs. An algorithm is described which is applied to solve a spectrum of numerical examples in order to illustrate the new concepts.     

Carbon and energy footprints of electric delivery trucks: A hybrid multi-regional input-output life cycle assessment

Due to frequent stop-and-go operation and long idling periods when driving in congested urban areas, the electrification of commercial delivery trucks has become an interesting topic nationwide. In this study, environmental impacts of various alternative delivery trucks including battery electric, diesel, diesel-electric hybrid, and compressed natural gas trucks are analyzed. A novel life cycle assessment method, an environmentally-extended multi-region input-output analysis, is utilized to calculate energy and carbon footprints throughout the supply chain of alternative delivery trucks. The uncertainties due to fuel consumption or other key parameter variations in real life, data ranges are taken into consideration using a Monte Carlo simulation. Furthermore, variations in regional electricity mix greenhouse gas emission are also considered to present a region-specific assessment for each vehicle type. According to the analysis results, although the battery electric delivery trucks have zero tailpipe emission, electric trucks are not expected to have lower environmental impacts compared to other alternatives. On average, the electric trucks have slightly more greenhouse emissions and energy consumption than those of other trucks. The regional analysis also indicates that the percentage of cleaner power sources in the electricity mix plays an important role in the life cycle greenhouse gas emission impacts of electric trucks.     

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.     

Freight Transport Deceleration: Its Possible Contribution to the Decarbonisation of Logistics

The paper challenges the conventional view that the movement of goods through supply chains must continue to accelerate. The compression of freight transit times has been one of the most enduring logistics trends but may not be compatible with governmental climate change policies to cut greenhouse gas emissions by 60–80% by 2050. Opportunities for cutting CO₂ emissions by ‘despeeding' are explored within a freight decarbonisation framework and split into three categories: direct, indirect and consequential. Discussion of the direct carbon savings focuses on the trucking and deep-sea container sectors, where there is clear evidence that slower operation cuts cost, energy and emissions and can be accommodated within current supply chain requirements. Indirect emission reductions could accrue from more localised sourcing and a relaxation of just-in-time (JIT) replenishment. Acceleration of logistical activities other than transport could offset increases in freight transit times, allowing the overall carbon intensity of supply chains to reduce with minimal loss of performance. Consequential deceleration results from other decarbonisation initiatives such as freight modal split and a shift to lower carbon fuels. Having reviewed evidence drawn from a broad range of sources, the paper concludes that freight deceleration is a promising decarbonisation option, but raises a number of important issues that will require new empirical research.     

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.     

Build-to-order supply chain network design under supply and demand uncertainties

Supply chain disruptions are unintended, unwanted situations resulting in a negative supply chain performance. We study the supply chain network design under supply and demand uncertainty with embedded supply chain disruption mitigation strategies, postponement with downward substitution, centralized stocking and supplier sourcing base. We designed an integrated supply-side, manufacturing and demand-side operations network in such that the total expected operating cost is minimized. We modeled it in a deterministic equivalent formulation. An L-shaped decomposition with an additional decomposition step in the master problem is proposed. The computational results showed that parallel sourcing has a cost advantage against single sourcing under supply disruptions. In addition, the build-to-order (BTO) manufacturing mitigation process has its greatest impact with high variations on demands and is integrated with the component downward substitution. Lastly, the manufacturer needs to order differentiated components to cover its requirement for maximal product demand to prevent the loss of sale, even with fewer modules in stock.     

A Fuzzy Set Approach for a Location-Inventory Model

Abstract

In the supply chain, production and logistics facilities are positioned between the customer and supply markets. As a consequence, any changes in these markets should cause the enterprise to re-evaluate its location and the capacity of its activities at that particular location in the logistics network, which also means re-evaluating the parameters of input-output matrices for the entire supply chain. Different analytical approaches have been developed to describe the influence of production on the hierarchical spatial pattern. Our article presents a fuzzy set approach to the model of spatial hierarchy as a result of spatial interactions. We attempt to present the difference between net present value estimated by a fuzzy approach on the one hand and report on an analytical approach to the problem on the other.     

Measuring supply chain efficiency based on a hybrid approach

Supply chain management has a tremendous impact on the success of a company. One of the critical issues for gaining competitive advantages for companies is improving supply chain performance. Most studies about the application of Data Envelopment Analysis (DEA) Supply chain models do not identify the benchmarking units for inefficient supply chains. On the other, measuring the short run and long run of the supply chain efficiency is another challenge for decision makers in supply chain management. Hence, we propose a methodology of DEA for measuring of the supply chain. We integrated two approaches as special cases of the hybrid model and compare the short and long run strategies of supply chain and can be identified benchmarking.