A joint inventory–finance model for coordinating a capital‑constrained supply chain with financing limitations

Faced with economic recession,firms struggle to find ways to stay competitive and maintain market share.Effective coordination of the supply chain can solve this problem,but this may fail if existing capital constraints and financial flows are ignored.This study addresses the challenge by exploiting coordination through joint decision-making on the physical and financial flows of a capital-constrained supply chain.We also consider the capital-constrained member’s financing limitations that lead to lost sales.Two scenarios based on non-coordinated and coordinated structures are modeled in the form of bi-objective optimization problems that simultaneously optimize system costs and service levels.The models are solved using the -constraint method.The results indicate that the non-coordinated model cannot satisfy more than about 50%of the demand due to capital shortage and financing limitations,while the coordinated model can satisfy all of the demand via internal financing.Furthermore,the proposed coordination scheme leads to cost reduction for the members and the total system.To motivate all members to accept the proposed coordination scheme,a cost-sharing mechanism is applied to the coordination procedure.Finally,a sensitivity analysis concerning financial parameters is provided for validating the coordination model.

Bi-objective optimization models for mitigating traffic congestion in urban road networks

Traffic congestion in road transportation networks is a persistent problem in major metropolitan cities around the world.In this context,this paper deals with exploiting underutilized road capacities in a network to lower the congestion on overutilized links while simultaneously satisfying the system optimal flow assignment for sustainable transportation.Four congestion mitigation strategies are identified based on deviation and relative deviation of link volume from the corresponding capacity.Consequently,four biobjective mathematical programming optimal flow distribution(OFD)models are proposed.The case study results demonstrate that all the proposed models improve system performance and reduce congestion on high volume links by shifting flows to low volumeto-capacity links compared to UE and SO models.Among the models,the system optimality with minimal sum and maximum absolute relative-deviation models(SO-SAR and SO-MAR)showed superior results for different performance measures.The SO-SAR model yielded 50%and 30%fewer links at higher link utilization factors than UE and SO models,respectively.Also,it showed more than 25%improvement in path travel times compared to UE travel time for about 100 paths and resulted in the least network congestion index of1.04 compared to the other OFD and UE models.Conversely,the SO-MAR model yielded the least total distance and total system travel time,resulting in lower fuel consumption and emissions,thus contributing to sustainability.The proposed models contribute towards efficient transportation infrastructure management and will be of interest to transportation planners and traffic managers.