Abstract
The production and supply chain management of biofuels from organic waste as raw material has been identified as a promising strategy in the field of renewable energies and circular economy initiatives. This industry involves complex tasks such as strategic land use, feedstock purchasing, production plant location, production capacity strategy, and material flows, which can be solved by mathematical modeling. The study proposed a multi-objective mixed-integer linear programming model to design a sustainable supply chain of biofuels with forest residues from its triple function: economic, environmental, and social. The trade-offs between the proposed objectives were determined with computational results. The proposed objectives were profit maximization, CO2 minimization, and employment generation maximization. Thus, the proposed model serves as a tool for decision-making, allowing the projection of a long-term structure of the biofuel supply chains and contribute to the United Nations Sustainable Development Goals.

Abstract
Supplier development contributes to supply chain integration and performance, thus playing an essential role in any industry. In the retail market, no formal procedures exist for the selection-assignment of supplier development programs, meaning that respective management is complex and unclear given actor and requirement heterogeneities. Multi-criteria techniques are currently used for selection and evaluation processes. However, no models exist that integrate clustering and multi-criteria techniques together to efficiently select-assign supplier development programs. This study proposes a four-phase methodology - diagnosis, program design, assignment, and proposal - that considers supplier clustering through K-means and multi-criteria fuzzification. Additionally, case-study analysis of 149 retail suppliers in Colombia clustered businesses into high (8), medium (32), and low (109) risk sets, each of which was assigned tailored development programs. The obtained results support use of the proposed methodology to improve supply chain performance for organisations with many suppliers requiring development programs.

Abstract
A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation. However, smart grids require that energy managers become more concerned about the reliability and security of power systems. Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. This paper deals with short-term load forecasting (STLF), which has become an active area of research over the last few years, with a handful of studies. STLF deals with predicting demand one hour to 24 h in advance. We extensively experimented with several methodologies from machine learning and a complex case study in Panama. Deep learning is a more advanced learning paradigm in the machine learning field that continues to have significant breakthroughs in domain areas such as electricity forecasting, object detection, speech recognition, etc. We identified that the main predictors of electricity demand in the short term: the previous week's load, the previous day's load, and temperature. We found that the deep learning regression model achieved the best performance, which yielded an R squared (R-2) of 0.93 and a mean absolute percentage error (MAPE) of 2.9%, while the AdaBoost model obtained the worst performance with an R-2 of 0.75 and MAPE of 5.70%.

Abstract
Sustainability and resilience in Agri-Food Supply Chains is a challenging topic of current interest in the research community. Resilience for Agri-Food Supply Chain (AFSC) is the capability of the supply network to manage and mitigate disruptions due to global warming and natural phenomena such as landslides and floods of crops, among others caused by humans. A significant challenge is to design efficient and resilient AFSCs in emerging countries while perishability constraints are considered. A methodology to design an AFSC for emerging countries is addressed in this research. The phenomena that aid in identifying critical aspects of the AFSC affecting their resilience are identified. The former approach combines optimization and simulation schemes by considering resilience metrics related to availability and connectivity. Indeed, the solution approach addresses the uncer-tainty by using simulation of disruptive events and finding resilient designs using mathematical programming. The proposed framework has been evaluated in a Colombian coffee supply chain. The obtained results show the efficiency of the proposed scheme to design AFSCs and allow the practitioners to measure, predict, compare, and improve the level of resilience of their supply chains (SCs).

Abstract
Guided by a real case, this paper efficiently proposes a new metaheuristic algorithm based on Simulated Annealing to solve the Heterogeneous Vehicle Routing Problem with Time Windows to deliver fresh meat in urban environments. Our proposal generates an initial feasible solution using a hybrid heuristic based on the well-known Travelling Salesman Problem (TSP) solution and, subsequently, refining it through a Simulated Annealing (SA). We have tested the efficiency of the proposed approach in a company case study related to the planning of the transportation of a regional distribution center meat company to customers within the urban and rural perimeter of Bogota, Colombia. The main goal is to reach a service level of 97% while reducing operational costs and several routes (used vehicles). The results show that the proposed approach finds better routes than the current ones regarding costs and service level within short computing times. The proposed scheme promises to solve the refrigerated vehicle routing problem. (c) 2022 by the authors; licensee Growing Science, Canada

Abstract
This paper presents the problem of redesigning a supply network of large scale by considering variability of the demand. The central problematic takes root in determining strategic decisions of closing and adjusting of capacity of some network echelons and the tactical decisions concerning to the distribution channels used for transporting products. We have formulated a deterministic Mixed Integer Linear Programming Model (MILP) and a stochastic MILP model (SMILP) whose objective functions are the maximization of the EBITDA (Earnings before Interest, Taxes, Depreciation and Amortization). The decisions of Network Design on stochastic model as capacities, number of warehouses in operation, material and product flows between echelons, are determined in a single stage by defining an objective function that penalizes unsatisfied demand and surplus of demand due to demand changes. The solution strategy adopted for the stochastic model is a scheme denominated as Sample Average Approximation (SAA). The model is based on the case of a Colombian company dedicated to production and marketing of foodstuffs and supplies for the bakery industry. The results show that the proposed methodology was a solid reference for decision support regarding to the supply networks redesign by considering the expected economic contribution of products and variability of the demand.