A three-phase heuristic approach for reverse logistics network design incorporating carbon footprint

Reverse logistics (RL) is emerging as a significant area of activity for business and industry, motivated by both commercial profitability and wider environmental sustainability factors. However, planning and implementing an appropriate RL network within existing supply chains for product recovery that increases customer satisfaction, decreases overall costs, and provides a competitive advantage over other companies is complex. In the current study, we developed a mixed integer linear programming (MILP) model for a reverse logistics network design (RLND) in a multi-period setting. The RL network consists of collection centres, capacitated inspection and remanufacturing centres and customer zones to serve. Moreover, the model incorporates significant characteristics such as vehicle type selection and carbon emissions (through transportation and operations). Since the network design problems are NP-hard, we first propose a solution approach based on Benders decomposition (BD). Then, based on the structure of the problem we propose a three-phase heuristic approach. Finally, to establish the performance and robustness of the proposed solution approach, the results are compared with benchmark results obtained using CPLEX in terms of both solution quality and computational time. From the computational results, we validated that the three-phase heuristic approach performs superior to the BD and Branch &Cut approach

Strategic emergency preparedness network design integrating supply and demand sides in a multi-objective approach

<p>We consider integration of fast evacuation and cost-effective relief distribution objectives, the two critical aspects of emergency management, to design a strategic emergency preparedness network for foreseen disasters, such as hurricanes. To this end, we introduce the design of a three-tier system, involving evacuation source, shelters, and distribution centers, that integrates the relief (supply) and evacuation (demand) sides of an emergency preparedness network. This is motivated by the realization that the shelters are shared facilities at the interface of the supply and demand sides. Although primarily intended for strategic decision making, our model can also make tactical decisions, thus spanning two separate time frames before a disaster’s occurrence.</p> <p> To solve models for large-scale instances, we adopt a Benders Decomposition approach with an implementation that solves only one instance of the master problem. We also determine that, in this framework, tuning of master tree search parameters along with the strengthening of Benders cuts significantly impact convergence.</p> <p> We conduct an extensive computational study to examine the impact of algorithmic improvements and further consider a realistic case study based on geographic information system (GIS) data from coastal Texas and examine the effects of changing problem parameters. By comparing our approach with current practice, we illustrate that a pro-active strategic integration of evacuation and distribution can relieve the resource-constrained large urban areas, traditionally considered as shelter locations.</p