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The Technical Debt (TD) metaphor describes development shortcuts taken for expediency that cause the degradation of internal software quality. It has served the discourse between engineers and management regarding how to invest resources in maintenance and extend into scientific software (both the tools, the algorithms and the analysis conducted with it). Mathematical programming has been considered ‘special purpose programming’, meant to program and simulate particular problem types (e.g., symbolic mathematics through Matlab). Likewise, more traditional mathematical programming has been considered ‘modelling programming’ to program models by providing programming structures required for mathematical formulations (e.g., GAMS, AMPL, AIMMS). Because of this, other authors have argued the need to consider mathematical programming as closely related to software development. As a result, this paper presents a novel exploration of TD in mathematical programming by assessing self-reported practices through a survey, which gathered 168 complete responses. This study discovered potential debts manifested through smells and attitudinal causes towards them. Results uncovered a trend to refactor and polish the final mathematical model and use version control and detailed comments. Nonetheless, we uncovered traces of negative practices regarding Code Debt and Documentation Debt, alongside hints indicating that most TD is deliberately introduced (i.e., modellers are aware that their practices are not the best). We aim to discuss the idea that TD is also present in mathematical programming and that it may hamper the reproducibility and maintainability of the models created. The overall goal is to outline future areas of work that can lead to changing current modellers’ habits and assist in extending existing mathematical programming (both practice and research) to eventually manage TD in mathematical programming.

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Fossil sources scarcity and environmental contamination are the major factors that put the energy industry into focus. Considering the lack of accuracy in fossil reserves, we propose an optimization model to plan investments in the Argentinean energy structure taking into account uncertainty in the fossil source availability. Tactical decisions, such as the amount of primary and secondary sources produced, are included and the emission of greenhouse gases is penalized in the objective function to improve the environmental impact of the energy structure. Also, two different methods are used to model uncertainty. Fuzzy set theory is applied to generate scenarios that show the random behavior of resource availability parameters. In addition, a two-stage stochastic model is proposed to integrate the different decision levels involved in the problem and represent resource availability uncertainty in fossil fuels. Both approaches complement each other to obtain a comprehensive solution of the energy planning problem.

This paper proposes a FMIP (fuzzy mixed integer program) to model the procurement process of a manufacturing company which contemplates uncertainty in the delivery of raw materials. The focus of this article lies on the use of fuzzy sets to represent the percentages of failure in the delivery of the amount of materials requested and include it in a mathematical model as an evaluation measure in the performance of each supplier. The main objective of the proposed model is to select the most promising suppliers in order to optimize the quantitative and qualitative performance of the company, by maximizing the net present value (NPV) and providing a better customer service, respectively, in relation with the commitment of delivery of the company’s suppliers. To solve the problem raised, the FMIP model proposed is transformed into an equivalent MILP (mixed integer linear program), and then, several scenarios are solved. An illustrative example is presented to show the utility of the model.