Supply chain coordination of fresh agricultural products based on consumer behavior

Seafood products are sought-after among communities all over the globe and are the main sources of essential nutrition for humans. Recently, the seafood supply chain networks have encountered obstacles that new sustainability regulations and the pandemic have brought forward. In this study, a novel supply chain network is developed for fresh seafood, considering sustainability aspects, to ideally balance the financial aspect of the network while enhancing the recycling of waste products. Moreover, four metaheuristics are employed to conquer the computational complexity of exact solution methods. To evaluate the performance of the algorithms in addressing the complexity of the proposed seafood supply chain model, some numerical examples in three different scales are designed. The obtained results from metaheuristic optimizers are assessed based on five effective measures. To facilitate the statistical analysis process, each measure is normalized using the relative deviation index indicator. According to the results obtained from the implementation of metaheuristic algorithms, it can be concluded that the multi-objective grey wolf and multi-objective golden eagle optimizers outperform the other two solution methods in terms of quality of solutions. Therefore, they can be applied efficiently in solving real-world seafood supply chain network problems.

In recent decades, the rapid growth of the global population has caused a significant increase in agricultural and food product demands. Thereby, the production of various items in the agricultural food supply chain network has increased to diminish food security concerns. On the other hand, the excessive production of products has led to various issues, such as greenhouse gas emissions and increased water consumption in farmlands, followed by supply chain-related challenges affecting the intermediaries in the next network levels. In this study, an agricultural food supply chain network under marketing practices is firstly probed by developing a stochastic multi-objective programming model to effectively improve three main pillars of sustainability. A convex robust optimization approach addresses the uncertainty of the farm production capacity and the saffron demands in the supply network. The effectiveness of the proposed mathematical model is certified by a case study on saffron business using the LP-metric method. A metaheuristic-oriented methodology comprising a modified Keshtel Algorithm is adapted to deal with the NP-hardness of the problems. The performance of the proposed solution methods is evaluated by two strategies, a statistical comparison and a supportive tool developed based on multi-criteria decision-making (MCDM) methods. The results validate the capability of the applied algorithms to solve the problem in different dimensions. Moreover, the MCDM method approves that MOKASEO outclassed in small, medium, and large-sized problems compared to other algorithms.

Optimization techniques in reverse logistics and closed-loop supply chains (CLSC) may effectively address food waste problem in urban areas. Motivated by recent advances in collecting/recycling food wastes through recycling supply chains, this study contributes to optimizing such a recycling channel in urban areas. We examine if renting waste-recycling facilities by a food trading company located in urban areas, where production/recycling processes are prohibited, is able to mitigate the problem. This paper examines a three-tier CLSC, including a trading company located in urban areas, a production plant located in peri-urban zones, and a food-waste-collector, in which food-waste is collected from restaurants/chain-stores for recycling purposes. The trading company rents waste-recycling facilities from the production plant in the peri-urban zones to carry out the relevant processes. To coordinate the channel, a novel Quantity Flexibility (QF) contract is developed. Results, based on a real case study in the meat products supply chain, show that profitability of the supply chain is increased using the proposed QF contract significantly (e.g., 30 percent increase in the food-waste-collector's expected profit). The findings from our developed models can help food supply chain managers to find cost-effective solutions for cooperating with other supply chain members. Our results show a Pareto-improvement outcome.

Recently, the closed-loop supply chain (CLSC) and its application to various fields have been an area of great interest. Despite the importance of CLSC, there remains a paucity of evidence on agriculture in this area. In this work, a CLSC network for the avocado industry is firstly designed by developing a bi-objective model considering the costs of the avocado industry and the social factor of job employment opportunities. The two objectives are the total costs minimization and job employment maximization in various opened locations. To validate the proposed model, a real case study in Puebla, Mexico, is addressed. The GAMS software and its CPLEX solver are utilized to find the best optimum solutions and determine the best locations to open different centers. The applicability of the proposed network is verified by conducting several sensitivity analyses on the important parameters of the problem. According to the obtained results, demand has the most effect on this network in which that a 25 percent decrease in demand can increase the total cost (the first objective) up to 40 percent and improve employment efficiency (the second objective) up to around 30 percent, simultaneously.

Given the social, environmental, and economic advantages of organic products compared to their conventional counterparts, there has been a growing interest in improving agribusiness sustainability through organic farming technology. However, less attention has been paid to developing analytical models to resolve challenges observed in organic supply chains. This study analytically explores competition- and coordination-based scenarios driving conventional and organic markets under contract farming mechanisms. Motivated by real-world practices, we investigate a three-party agricultural supply chain consisting of an agribusiness enterprise, an organic farmer, and a conventional farmer, where the farmers’ products are imperfect substitutes. First, we develop analytical models to derive closed-form optimal solutions under the traditional contract farming, where the agribusiness enterprise as the Stackelberg leader offers the guaranteed wholesale price and the farmers, as the followers, set their production quantity. We examine the problem by incorporating the specific features of organic farming, including organic price premium, crop yield gap, and organic cost factor into the model. We also conduct sensitivity analyses to explore the effects of competition and main characteristics of organic farming on the farmers’ and agribusiness enterprise’s optimal strategies. Meanwhile, we analytically compare and contrast the optimal traditional contract farming strategies with those under the centralized setting as a benchmark. Finally, we propose a coordinated version of contract farming named synergistic trilateral contract farming to convince the members to move towards centralization. The proposed contract not only benefits all the members by resolving both horizontal and vertical channel conflicts but also motivates the consumption of organic produce in the market. The societal measures in the investigated framework, including consumer surplus and social welfare, also improve when the supply chain members move towards coordination. Additionally, our results highlight the impact of organic farming parameters on quantity- and pricing-based decisions across the network.