Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/15247
Authors: Kumar, Manoj
Keywords: Greenhouse Gas;Global Warming;Transportation;Heterogeneous Fleet
Issue Date: Jan-2018
Publisher: IIT Roorkee
Abstract: Logistics activities especially transportation are significant sources of greenhouse gas (GHG) emissions. It is widely agreed that GHG emissions contribute to global warming and is a great threat to the human health and safety. There is a need to implement sustainability measures to reduce dependence on non-renewable resources along with the need to reduce large amount of waste generation and emissions. As the new sustainability paradigm gains momentum, the need to switch from our use-and-dispose model (one-way economy) to a closed-loop economic model, where packaging or durable articles can have multiple lives, will become increasingly evident. During last decade the role of green logistics in the distribution management has played a significant role to focus on environmental protection, consumer environmental concern, green legislation, and sustainability. Green logistics is broadly concerned with the management of production; distribution of goods; and the collection of re-usable packaging/Returnable Transport Items (RTIs) and goods for recycling or remanufacturing in a sustainable way while considering environmental, social, and traditional economic factors. Supply chain management of perishable products typically poses unique challenges at the strategic, tactical, and operational levels of an organization's decision-making hierarchy. These products demand specialized packaging for careful handling and transportation. The high value perishable products like blood, vaccines, and other bio materials use reusable packaging for transportation. A breakdown in the supply of packaging would impact the overall flow of products; for instance, such a breakdown would lead to increased delivery times to customers, induced backlogging and storage costs. Thus, it is advised to jointly manage the schedule and quantity of products shipped to customers, and empty packaging collected from customers for reuse. This thesis proposed environmental friendly models for distribution of perishable products for three cases. In the first case a green logistics decision support system (DSS) for blood distribution in time window is proposed. The blood in reusable packaging is delivered to the hospitals and pickup of empty packaging is done simultaneously. The distribution structure “one- to-many” is served via a fleet of homogeneous vehicle. A mixed integer linear programming formulation of four of vehicle routing problem with simultaneous delivery and pickup (full and iii partial) and time window (hard and, soft) is provided and solved by Genetic Algorithm (GA). The real-life data of blood distribution is used for comparing Vehicle Routing Problem for Simultaneous Delivery and Pickup in Time Window (VRPSDPTW) variants on the basis of their economic and environmental value. For the second case, the model is generalized for fixed shelf life perishable product in “one-to-many” distribution structure. Unlike the first case, in second case the demand and pickup are uncertain; delivery is performed via heterogeneous fleet; pickup of empty RTIs along with perished products; explicit fuel consumption and demand for a fixed planning horizon is considered. The comprehensive objective function proposed is the sum of distribution cost, inventory holding cost, product wastage cost and RTIs related costs. The case is modelled as a chance constrained closed loop inventory routing problem. For the solution of the model first the deterministic approximation of the model is derived and then the model is linearized. The model is solved by Cplex. A set of valid inequalities is also proposed and computational experiments shows that the some of the valid inequalities reduce the solution time. The parametric analysis of the model is also done to study the impact of heterogeneous fleet, unit emission cost, time window, shelf life, coefficient of variation and service level. Unlike the second case the distribution structure selected is “many to many” in the third case. The case is formulated as chance constrained model for closed loop inventory routing problem and model is solved as in case 2. A set of valid inequalities are also modified for the “many to many” distribution structure and reduction in computational time is observed. The parametric analysis of the model is also done. The results show that the combination of soft time window with full pick is the most beneficial routing option in terms of economic and environmental cost. Due to increase in recycling empty insulated containers in full pickup mode the total economic cost decreases. Although transportation cost (vehicle cost + labour cost + waiting cost + penalty cost + fuel cost) increases due to greater dependence on vehicle capacity, this percentage increase of transportation cost is very small Thus, full pickup in soft time windows is recommended for enforcing reverse logistics strategies because it permits the recycling of more end-of-life products as compared to other strategies. The results show that the comprehensive objective function reduces the total cost by 11.34%. The effect of variation in demand and service level is also analysed. It is found that the wastage cost increases by 17 times i.e. from 121 to 2074.6 with 4 times increase in coefficient of iv variation from 0.1 to 0.4 at 99.9 service level. The increase in service level from 98% to 99.9% leads to an increase of 2.5 folds in waste cost and 1.12 folds increase in total cost. The total cost increases with an increase in unit emission cost but the Co2 emission does not change if the unit emission cost is increased from 0.248 to 0.496 and 0.992 to 1.488. Therefore as normally perceived that emission can be reduced by increasing the unit emission is not true but it is found to increase the total cost.
URI: http://localhost:8081/xmlui/handle/123456789/15247
Research Supervisor/ Guide: Kumar, Dinesh.
metadata.dc.type: Thesis
Appears in Collections:DOCTORAL THESES (MIED)

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