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
PurposeNon-profit organizations (NPO) contribute significantly to the welfare of citizens and communities. Engagement in volunteering is crucial for sustaining volunteer motivation and for the effective and efficient functioning of NPO, with significant implications for society at large. Yet, literature on volunteer engagement (VE) is limited to date. Grounded on service-dominant logic, self-congruity theory and self-determination theory, this study aims to understand what motivates VE and how it may evolve into a co-creation process valuable to NPO and its stakeholders.Design/methodology/approachBased on survey data collected from 450 volunteers, working with a diverse set of NPO, a comprehensive model of drivers and outcomes of VE was empirically tested using PLS-SEM, considering the mediating role of volunteers' congruence with the core values of the NPO.FindingsThe impact of volunteers' perceived autonomy, competence and relatedness on VE and its subsequent role in volunteers' loyalty and extra-role engagement behaviors (i.e. co-development, influencing and mobilizing behaviors) were validated. Moreover, the study validates value congruence as an internalizing mediating mechanism in the engagement process, a role that has been implied but not empirically tested.Originality/valueThe study contributes to the engagement and volunteering literature, which despite an unprecedented parallel have developed almost independently, with limited reference to one another. As the nomological network of VE is still underexplored, the study extends the engagement literature to the volunteering sector, validating the key (but underexplored) role of self-determination needs and value congruence in driving VE and value co-creation behaviors. The study further adds to engagement research while addressing other actors' engagement beyond the customer-brand dyad. While adopting a seldom explored marketing perspective of VE, this study provides NPO valuable insights on how to manage and engage volunteers.

Abstract
The problem of routing and scheduling of technicians is a problem that technical assistance and maintenance companies face nowadays, market competitiveness requires quick response, service diversification, and customer satisfaction. The relationship between competitiveness and profitability of companies involves the effective management of their resources. The work developed addresses a real problem of a major Portuguese company providing technical assistance to the home, a varied set of services (need for specific skills and execution times) must be scheduled for a set of technicians with heterogeneous skills and geographical locations (start and end of the route) based on their different places of residence. The results show a considerable increase in the efficiency levels of the solution obtained when compared to the company's current solution and reveals that the lack of homogeneity of skills among technicians and the variation in service flows are factors that should be considered in the operational management of resources and the contracting of work, and that the increase in working hours can also contribute to improving the efficiency of the process.

Abstract
Tackling uncertainty is becoming increasingly relevant for decision-support across fields due to its critical impact on real-world problems. Uncertainty is often modelled using scenarios, which are combinations of possible outcomes of the uncertain parameters in a problem. Alongside expected value methods, decisions under uncertainty may also be tackled using methods that do not rely on probability distributions and model different decision-maker risk profiles. Scenarios are at the core of these approaches. Therefore, we propose a scenario generation methodology that seizes the structure and concepts of genetic algorithms. This methodology aims to obtain a diverse set of scenarios, evolving a scenario population with a diversity goal. Diversity is here expressed as the difference in the impact that scenarios have on the value of potential solutions to the problem. Moreover, this method does not require a priori knowledge of probability distributions or statistical moments of uncertain parameters, as it is based on their range. We adapt the available code for Biased-Random Key Genetic Algorithms to apply the methodology to a packing problem under demand uncertainty as a proof of concept, also extending its use to a multiobjective setting. We make available these code adaptations to allow the straightforward application of this scenario generation method to other problems. With this, the decision-maker obtains scenarios with a distinct impact on potential solutions, enabling the use of different criteria based on their profile and preferences.

Abstract
With the advent of mass customization and product proliferation, the appearance of hybrid Make-toStock(MTS)/Make-to-Order(MTO) policies arise as a strategy to cope with high product variety maintaining satisfactory lead times. In companies operating under this reality, Sales and Operations Planning (S&OP) practices must be adapted accordingly during the coordinated planning of procurement, production, logistics, and sales activities. This paper proposes a novel S&OP decision-making framework for a flow shop/batch company that produces standard products under an MTS strategy and customized products under an MTO strategy. First, a multi-objective mixed-integer programming model is formulated to characterize the problem. Then, a matrix containing the different strategies a firm in this context may adopt is proposed. This rationale provides a business-oriented approach towards the analysis of different plans and helps to frame the different Pareto-optimal solutions given the priority on MTS or MTO segments and the management positioning regarding cost minimization or service level orientation. The research is based on a real case faced by an electric cable manufacturer. The computational experiments demonstrate the applicability of the proposed methodology. Our approach brings a practical, supply chain-oriented, and mid-term perspective on the study of operations planning policies in MTS/MTO contexts.

Abstract
This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application.