manufacturing operations planners

Abstract: We study the classical single-machine scheduling problem of minimizing the sum of completion times of jobs in a non-clairvoyant setting, where the processing time of each job remains unknown until its completion. This is a hard problem for which no constant competitive algorithm is possible. Inspired by robust optimization and learning-augmented algorithms, we introduce a novel robustness framework that leverages structural information provided by a classification model to overcome this limitation. Specifically, we assume that jobs are partitioned into classes and we have access to the confusion matrix of the classifier, whose entry $(k,\ell)$ indicates the number of jobs predicted to belong to class~$k$ but that actually belong to class~$\ell$. In this manner, we are able to characterize uncertainty as a set of permutations within each predicted class, rather than as a collection of discrete numerical scenarios, avoiding the computational difficulty of classical robust metrics, such as Min-Max and Min-Max Regret. In addition to these worst-case metrics, we also consider the expected objective over all scenarios. We first propose an optimal non-adaptive strategy that is oblivious with respect to all three robust criteria. We then investigate adaptive and randomized algorithms, showing that they can outperform the optimal non-adaptive strategy when the matrix exhibits particular structural properties.

Abstract: Dynamic multi-mode resource-constrained project scheduling requires decisions to be made under precedence constraints, limited resources, multiple execution modes, and uncertain activity durations. Genetic programming (GP) can automatically evolve heuristic rules for such problems, but its simulation-based fitness evaluation is computationally expensive. This study investigates phenotypic characterisation (PC) in surrogate-assisted GP to evolve higher-quality scheduling heuristics under a fixed budget of full simulation-based fitness evaluations. A key question is how GP individuals should be encoded into phenotypic characterisations to support effective fitness estimation. To answer this question, three PC encoding schemes with different levels of information richness are designed: priority-value encoding, which preserves raw rule outputs; rank encoding, which captures candidate ordering; and binary encoding, which represents final scheduling decisions. These encodings are combined with different distance metrics to measure behavioural similarity between GP individuals. The experimental results show that binary encoding with Euclidean distance provides the most effective and robust surrogate guidance. Further analyses show that surrogate estimation accuracy alone does not fully explain the performance differences. The PC representation also determines how effectively phenotypically redundant offspring are removed and how much behavioural diversity is retained after preselection. Ablation experiments further demonstrate that duplicate removal and surrogate preselection provide complementary benefits, with their combination producing the largest improvement. These findings highlight that effective surrogate-assisted GP depends not only on identifying promising offspring, but also on controlling redundancy and preserving useful diversity during evolutionary search.

Abstract: We study strategyproof scheduling on \(n\) unrelated machines with predictions. Each machine is controlled by an agent with privately known processing times, while the mechanism receives a public prediction of the processing-time matrix before the agents report. The objective is to minimize the makespan subject to strategyproofness. We measure performance by consistency, the approximation guarantee for correct predictions, and robustness, the worst-case guarantee for arbitrary predictions. We introduce \textsc{EdgeSkip}, a deterministic strategyproof member of the class of job-wise weighted mechanisms. Such mechanisms allocate each job independently using prediction-dependent weights. Using a polynomial-time \(2\)-approximate reference schedule computed from the prediction and a standard tradeoff parameter, \textsc{EdgeSkip} is \(4\)-consistent and \((2n-2)\)-robust, improving on the \((6,2n)\) guarantee of Balkanski, Gkatzelis, and Tan. Without computational restrictions, \textsc{EdgeSkip} with an optimal reference schedule is \(C\)-consistent and \(\max\{n,(n-1)C/(C-1)\}\)-robust for every \(C>1\). We prove a matching information-theoretic lower bound for all job-wise weighted mechanisms, thereby determining the exact consistency--robustness tradeoff for this class. For arbitrary deterministic strategyproof mechanisms, we establish the robustness lower bound \(\max\{n,C/(C-1)\}\) for every \(C>1\). We also study an error-tolerant variant and improve upon prior guarantees. Experiments demonstrate that our mechanisms achieve good empirical performance.