Due to the nature of the agricultural and food industry, the management of production, storage, transportation, waste disposal and environmental effects of their production, are of great importance. To deal with the sustainability issues linked to their supply chains, we propose in this study a mathematical model to design a sustainable supply chain of highly perishable agricultural product (strawberry). The model is a multiperiod, multiproduct multiobjective MINLP mathematical program that takes into consideration economic, social and environmental objectives to cover all aspects of sustainability. In addition, a G/M/S/M queuing system is developed for the transportation of harvested products between facilities for the first time. Since real‐world problems related to industries such as food and agriculture are inherently uncertain, in this model, the important parameters of the problem are considered uncertain using fuzzy sets theory and a hybrid robust possibilistic programming model is developed. In addition, the Epsilon constraint approach converts the multiobjective mathematical model into a single‐objective one and the Lagrangian relaxation method is used to effectively solve the model on a large scale. A case study in Iran is provided to investigate the results and discuss the solutions. Finally, a sensitivity analysis is performed to identify the impacts of important parameters on the solution. According to the analysis, equipping greenhouses with drip irrigation system and using solar panels in greenhouses, respectively, have the greatest impact on improving all target functions. Recommendations for Resource Managers Multiobjective optimization shows trade‐offs among conflicting objective function and assists decision‐making to enhance sustainable agriculture industry. Focus on transportation system in fresh product will lead to less waste. The use of solar panels and drip irrigation helps to minimize water and energy consumption and CO2 emission.

In recent decades, the forest industry has been growingly expanded due to economic conditions, climate changes, environmental and energy policies, and intense demand changes. Thus, appropriate planning is required to improve this industry. To achieve economic, social and environmental goals, a supply chain network is designed based on a multi-period and multi-product Mixed-Integer Non-Linear Programming (MINLP) model in which the objective is to maximize the profit, minimize detrimental environmental effects, improve social effects, and minimize the number of lost demands. In addition, to improve forest industry planning, strategic and tactical decisions have been implemented throughout the supply chain for all facilities, suppliers and machinery. These decisions significantly help to improve processes and product flows and to meet customers’ needs. In addition, because of the presence of uncertainty in some parameters, the proposed model was formulated and optimized under uncertainty using the hybrid robust possibilistic programming (HRPP-II) approach. The -constraint technique was used to solve the multi-objective model, and the Lagrangian relaxation (LR) method was utilized to solve the model of more complex dimensions. A case study in Northern Iran was conducted to assess the efficiency of the suggested approach. Finally, a sensitivity analysis was performed to determine the impact of important parameters on objective functions. The results of this study show that increasing the working hours of machines instead of increasing their number, increasing the capacity of some facilities instead of establishing new facilities and expanding the transport fleet has a significant impact on achieving predetermined goals.

Today, with the daily increase in population, the demand for agricultural and/or, in general, food products continuously raises and so does the natural resources consumption which induces challenges to the supply chain management of important food products. In the present study, drivers as well as barriers of the agriculture sustainable supply chain are identified and ranked with the help of multicriteria decision-making (MCDM) techniques. We identify six drivers and seven barriers with the help of experts’ opinions in the field and apply ranking methods including TOPSIS-AHP, AHP, and COPRAS-AHP as well as Borda rule and Copeland method to merge the ratings. The results highlight that the economic dimension of sustainability is more important than the environmental and social dimension. Distrust of consumers, lack of understanding and awareness of managers, and performance appraisal problems were found to be the most important barriers. Furthermore, informing the community by the media, entering the global market, exporting products, and producing sustainable products as a competitive advantage were identified to be the most important drivers. The study also shows that public awareness and demands can push the food supply chains toward sustainability goals with the cooperation of governments and suppliers.

In this study, we present a multiobjective mixed-integer nonlinear programming (MINLP) model to design a closed-loop supply chain (CLSC) from production stage to distribution as well as recycling for reproduction. The given network includes production centers, potential points for establishing of distribution centers, retrieval centers, collecting and recycling centers, and the demand points. The presented model seeks to find optimal locations for distribution centers, second-hand product collection centers, and recycling centers under the uncertainty situation alongside the factory’s fixed points. The purpose of the presented model is to minimize overall network costs including processing, establishing, and transportation of products and return flows as well as environmental impacts while maximizing social scales and network flexibility according to the presence of uncertainty parameters in the problem. To solve the proposed model with fuzzy uncertainty, first, the improved epsilon (ε)-constraints approach is used to transform a multiobjective to a single-objective problem. Afterward, the Lagrangian relaxation approach is applied to effectively solve the problem. A real-world case study is used to evaluate the performance of the proposed model. Finally, sensitivity analysis is performed to study the effects of important parameters on the optimal solution.

Due to the unexpected breakdowns that can happen in various components of a production system, failure to reach production targets and interruptions in the process of production are not surprising. Since this issue remains for manufactured products, this halting results in the loss of profitability or demand. In this study, to address a number of challenges associated with the management of crucial spare parts inventory, a mathematical model is suggested for the determination of the optimal quantity of orders, in the case of an unpredicted supplier failure. Hence, a production system that has various types of equipment with crucial components is assumed, in which the crucial components are substituted with spare parts in the event of a breakdown. This study’s inventory model was developed for crucial spare parts based on the Markov chain process model for the case of supplier disruption. Moreover, for optimum ordering policies, re-ordering points, and cost values of the system, four metaheuristic algorithms were utilized that include Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Moth–Flame Optimization (MFO) Algorithm, and Differential Evolution (DE) Algorithm. Based on the results, reliable suppliers cannot meet all of the demands; therefore, we should sometimes count on unreliable suppliers to reduce unmet demand.