Food waste has potential to be recycled and converted to energy and other valuable products. In China, the food waste is partially collected and processed by illegal organizations to produce ‘gutter oil’, which is a serious public health and safety issue. Therefore, the city government plans to develop a central management system where the food waste from large number of restaurants and food vendors will be collected, pre-processed at existing facilities, and then converted into bioenergy and other usable products at a central treatment facility whose location is to be determined. A mixed integer linear programming (MILP) approach is present in this thesis to determine an optimal supply chain and processing network. We first develop a p-median model and determine an optimal grouping of the waste sources in multiple clusters where each cluster is served by a single preprocessing facility. This is considered as a proxy to the aggregate cost of delivering food waste to intermediate and final processing locations. The true minimum delivery cost is then determined by routing the delivery vehicles optimally within each cluster where the waste at all sources is collected by multiple delivery vehicles. This approach is a heuristic procedure. The difference between exact optimum and heuristic solution is about 12 percent. The empirical application of the MILP model is presented with a real data set involving a large number of food waste sources in City of Shenzhen China, and evaluate the economic viability of the centralized collection and precession system. The results show that such system is profitable and environmentally beneficial.
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Optimization of a supply chain network for bioenergy production from food waste
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