Logistic optimization frequently involves complex routing decisions bound by tight numerical constraints such as vehicle capacities. This paper addresses a real-world industrial multi-batching problem where products must be routed between distributed sites. The objective is to determine optimal routes, travel frequencies, and packing configurations at minimum cost. The problem corresponds to a minimum cost flow problem coupled a bin packing problem. We investigate direct formalizations, decompositions, and scalable sequential approaches across three base technologies: Mixed-Integer Linear Programming, Constraint Programming, and Constraint Answer Set Programming. Our contributions are threefold: we propose a direct formalization of the problem, additional distinct approaches that scale for an industrial use case, and finally an empirical evaluation. By comparing these approaches we highlight the most effective configurations. Results suggests that a three-step approach provides the best results: combining MILP for flow routing, a greedy bin packing and CP for refinement.

The aeronautical industry transitioned in the 90s to takt-paced, product-specific assembly lines. The current trend of increased customization and demand variability is pushing for a transition to flexible mixed-model assembly lines. We address a mid-term planning problem for an airframe assembly plant, modeled as a Resource-Constrained Project Scheduling Problem (RCPSP) with complex industrial constraints. This formulation serves a dual purpose: facilitating high-level production planning and validating plant designs, particularly during ramp-up scenarios. We specifically tackle challenges involving calendar-based preemption, variable resource capacity, and resource blocking between task groups. We propose a Constraint Programming (CP) formulation that optimizes conflicting objectives, including Tardiness and Just-in-Time costs. To ensure scalability for large industrial instances, we introduce a sequential solving method based on topological decomposition. The proposed approach proves effective in handling complex scenarios, acting as a foundation for more realistic models.

Multi skill resource-constrained project scheduling Problems (MS-RCPSP) have been object of studies from many years. Also, preemption is an important feature of real-life scheduling models. However, very little research has been investigated concerning MS-RCPSPs including preemption, and even less research moving out from academic benchmarks to real problem solving. In this paper we present a solution to those problems based on a hybrid method derived from large neighborhood search incorporating constraint programming components tailored to deal with complex scheduling constraints. We also present a constraint programming model adapted to preemption. The methods are implemented in a new open source python library allowing to easily reuse existing modeling languages and solvers. We evaluate the methods on an industrial case study from aircraft manufacturing including additional complicating constraints such as generalized precedence relations, resource calendars and partial preemption on which the standard CP Optimizer solver, even with the preemption-specific model, is unable to provide solutions in reasonable times. The large neighborhood search method is also able to find new best solutions on standard multi-skill project scheduling instances, performing better than a reference method from the literature.

Mission planning for Earth Observation Satellite operators typically implies dynamically altering how requests from different customers are prioritised in order to meet expected deadlines. A request corresponds to a given area of interest to capture on Earth. This exercise is challenging for different reasons. First, satellites are limited by maneuvers and power consumption constraints resulting in a limited surface that can be covered at each orbit. Consequently, many requests are in competition with each other and so all of them cannot be treated at each orbit. When a request priority is boosted, it may incidentally penalise surrounding requests. Second, there are several uncertain factors such as weather (in particular cloud cover) and future incoming requests that can impact the completion progress of the requests. With order books of increasing size and the planned operations of a growing number of satellites in a close future, there is a clear need for a decision support method.