Despite the utility of Maximum Satisfiability (MaxSAT) in discrete optimization, developing iterative workflows remains cumbersome due to fragmented, low-level solver APIs. We present Hermax, a unified Python library and modelling compiler for MaxSAT. Hermax provides an IPAMIR interface that exposes incremental solving, assumptions, and weight updates through a single API across many incremental and non-incremental backends. Furthermore, it introduces a compiler with Constraint Programming primitives that translates high-level models directly into optimized CNF/WCNF through eager evaluation. This compiler allows automatic optimizations like integer ladder graph encoding that bypasses Pseudo-Boolean formulation when possible. Together, these features enable rapid prototyping and production grade optimization directly from Python across major platforms and hardware architectures.

One of the most challenging problems on modern urban planning and one of the goals to be solved for smart city design is that of urban waste disposal. Given urban population growth, and that the amount of waste generated by each of us citizens is also growing, the total amount of waste to be collected and treated is growing dramatically (EPA 2011), becoming one sensitive issue for local governments. A modern technique for waste collection that is steadily being adopted is automated vacuum waste collection. This technology uses air suction on a closed network of underground pipes to move waste from the collection points to the processing station, reducing greenhouse gas emissions as well as inconveniences to citizens (odors, noise, . . . ) and allowing better waste reuse and recycling. This technique is open to optimize energy consumption because moving huge amounts of waste by air impulsion requires a lot of electric power. The described problem challenge here is, precisely, that of organizing and scheduling waste collection to minimize the amount of energy per ton of collected waste in such a system via the use of Artificial Intelligence techniques. This kind of problems are an inviting opportunity to showcase the possibilities that AI for Computational Sustainability offers.

Automated vacuum waste collection (AVWC) uses air suction on a closed network of underground pipes to transport waste from the drop off points scattered throughout the city to a central collection point, reducing greenhouse gas emissions and the inconveniences of conventional methods (odors, noise). Since a significant part of the cost of operating AVWC systems is energy consumption, we have started a project, together with a company that builds and installs such systems, with the aim of applying constraint programming technology to schedule the daily emptying sequences of the drop off points in such a way that energy consumption is minimized. In this paper we describe how the problem of deciding the drop off points that should be emptied at a given time can be modeled as a constraint integer programming (CIP) problem. Moreover, we report on experiments using real data from AVWC systems installed in different cities that provide empirical evidence that CIP offers a suitable technology for reducing energy consumption in AVWC.

This paper introduces new solving strategies for the resolution of Pseudo-Boolean Modularity (PBMod) constraints. In particular, we deal with modular arithmetic constraints on Boolean variables. On the one hand, we analyze translations to Pseudo-Boolean (PB) constraints and apply PB solvers. We also look at those PB solvers that have shown that a transformation to the SAT problem can be an effective solving strategy for PB problems. Among the existing translation techniques we focus on the encoding based on a network of sorters. We extend this encoding technique to generate directly a SAT formula from the PBMod constraints. We compare our approach to other standard techniques such as Satisfiability Modulo Theories (SMT) solvers with support for the Quantifier Free Linear Integer Arithmetic (QF_LIA) theory and the GLPK package for Mixed Integer Programming. In order to conduct our experimental investigation we present a generator of random PBMod constraints and study the impact of the several parameters on the hardness of the instances.