Closed-loop sustainable food supply chain management

Abstract

As the food supply chain (FSC) is facing new challenges such as climate change, fair trade, food waste (FW) and food security, increasing consumer awareness, and governmental regulations, it is becoming a necessity to consider ways to produce, process, distribute and consume food more sustainably. These changes in FSC have led to the development of the trending concept: Sustainable Food Supply Chain Management (SFSCM). In this study, we develop integrated FW assessment and network models that improve the FSC responsiveness and leverage the social, environmental, and economic implications of sustainability, simultaneously. First, while existing literature reviews in SFSCM only consider the forward FSC, we present in this study a comprehensive analysis of recent research directions in the closed-loop SFSCM with a focus on the design and planning of sustainable models in the food sector. We first develop a general structure of a closed-loop SFSC that demonstrates the reverse logistics operations and FW recovery options. Then, we conducted a thorough literature review in the areas of reverse logistics, closed-loop, and sustainability in the FSC. Second, the animal and plant-based FW valorization alternatives are evaluated from a sustainability perspective. Using FW characteristics, we estimate the sustainable benefits such as food security, reduced human toxicity, energy utilization, and GHG emission reduction for each FW processing technique. We formulate the FW network as a strategic linear programming (LP) and weighted goal programming models. We test the efficiency of the proposed framework by designing a sustainable FW treatment network for the state of Massachusetts, USA. Results show that with a marginal increase in the treatment cost of FW, the model achieved zero net emissions, zero net energy use, and a competitive sustainability impact. Lastly, we study the sustainable impact of animal and plant-based food surplus donation activities through food banks. The goal is to determine the optimal number and location of the distribution centers. A Mixed Integer Linear Programming (MILP) model is formulated and by solving the model for different budget constraints, we identify the distribution centers by which the responsiveness of the system is increased. Finally, we propose future research trends.--Author's abstract