Determining the Efficiency of the Government of Ghana’s Network of Grain Storage Facilities

Governments in developing countries design networks of grain storage facilities to help farmers store excess agricultural produce to prepare for climate induced crop failures. The efficiency of such networks has serious economic and food security implications on respective countries. Periodic review of the efficiency of such networks is necessary to identify lapses and opportunities for optimization. Past studies on efficiency of networksof facilities, which usually assume scenarios peculiar to the developed world used data that are usually unavailable or unreliable in developing countries. This work therefore developed an integrated approach that relies solely on readily available and reliable governmental and open source data to compute the short and long-term efficiencies of networks of grain storage facilities. This approach was used to analyze the efficiency of the government of Ghana’s network of forty-eight grain storage facilities. A transportation model was used to compute the total transportation cost within the existing network. A P-median model was then used to develop and compute the transportation cost of a theoretically optimal network. Outputs from a forecasting model were used with the transportation andP-median models to study the short and long-term efficiencies of the existing and optimal networks. The average short and long term efficiencies of the existing network were 66% and 26% respectively. The study also investigated the efficiencies of a rank network which is created by siting GSF’s in only high grain production districts. The short and long-term efficiencies of this network were 87% and 72% respectively. The study showed that Ghana’s GSFs were sub-optimally sited hence farmers would have to travel excessively longer distances than necessary to use it. This offers some explanation for its low patronage. Furthermore, the study shows that a rank network was not as efficient as the optimal network. This study therefore demonstrates the use of this integrated approach coupledwith readily available data to analyze networks of grain storage facilities in developing countries

Green supply chain quantitative models for sustainable inventory management: A review

[EN] This paper provides a systematic and up-to-date review and classification of 91 studies on quantitative methods of green supply chains for sustainable inventory management. It particularly identifies the main study areas, findings and quantitative models by setting a point for future research opportunities in sustainable inventory management. It seeks to review the quantitative methods that can better contribute to deal with the environmental impact challenge. More specifically, it focuses on different supply chain designs (green supply chain, sustainable supply chain, reverse logistics, closed-loop supply chain) in a broader application context. It also identifies the most important variables and parameters in inventory modelling from a sustainable perspective. The paper also includes a comparative analysis of the different mathematical programming, simulation and statistical models, and their solution approach, with exact methods, simulation, heuristic or meta-heuristic solution algorithms, the last of which indicate the increasing attention paid by researchers in recent years. The main findings recognise mixed integer linear programming models supported by heuristic and metaheuristic algorithms as the most widely used modelling approach. Minimisation of costs and greenhouse gas emissions are the main objectives of the reviewed approaches, while social aspects are hardly addressed. The main contemplated inventory management parameters are holding costs, quantity to order, safety stock and backorders. Demand is the most frequently shared information. Finally, tactical decisions, as opposed to strategical and operational decisions, are the main ones.The research leading to these results received funding from the Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". It was also funded by the National Agency for Research and Development (ANID) / Scholarship Program/Doctorado Becas en el Extranjero/2020 72210174.Becerra, P.; Mula, J.; Sanchis, R. (2021). Green supply chain quantitative models for sustainable inventory management: A review. Journal of Cleaner Production. 328:1-16. https://doi.org/10.1016/j.jclepro.2021.129544S11632

Using simulated annealing to improve the information dissemination network structure of a foreign animal disease outbreak response

Master of ScienceDepartment of Industrial & Manufacturing Systems EngineeringJessica L. Heier StammCommunication is an integral part of emergency response, and improving the information dissemination network for crisis communication can save time, resources, and lives. This thesis focuses specifically on emergency response to a foreign animal disease (FAD) outbreak, an incident in which an animal disease that is not active domestically is introduced and being spreading in the U.S. In a FAD outbreak, timeliness of detection and response are critical. An outbreak of foot-and-mouth disease, a particularly significant FAD, could cripple the agriculture economy and every hour of poor communication could result in the loss of thousands of animals. Improving this and other such crisis communication networks is of high importance. There is a comparatively large amount of prior research that critiques past catastrophic events but very little that aims to quantitatively improve such networks. This research uses communication data from a FAD response exercise in Kansas to develop a reliable network model, contributing a general method for creating an information dissemination network from empirical communication data. The thesis then introduces a simulated annealing heuristic to alter the network structure, reducing the overall information transmission time by almost 90%. Both the application of simulated annealing in network design and the use of discrete event simulation to calculate the heuristic objective function are new contributions to the field of crisis communication and emergency response. This work begins by extracting data from communication logs, grouping the large numbers of stakeholders into more manageable clusters, and developing a simulation model framework that accurately depicts the flow of information in the actual network. Then a simulated annealing heuristic is used to alter the network structure. The goal is to identify an alternative network structure in which the time for information to reach all response participants is minimized. The resultant network structures are analyzed to reveal observations and recommendations for FAD response communication. This research finds that not only can such a network be improved significantly, but the quantitative results support the qualitative observations from early in the data extraction process. This paper adds original methods to the literature and opens the door for future quantitative work in the area of crisis communication and emergency response