Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling

• URL: http://arxiv.org/abs/2212.09077v1
• Date: Sun, 18 Dec 2022 12:43:24 GMT
• Title: Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling
• Authors: Thomas Eiter, Tobias Geibinger, Nysret Musliu, Johannes Oetsch, Peter Skocovsky, Daria Stepanova
• Abstract summary: We deal with a challenging scheduling problem on parallel machines with sequence-dependent setup times and release dates. We put the individual machine spans in non-ascending order and lexicographically minimise the resulting robustnesss. Our experimental results show that ASP is indeed a promising KRR paradigm for this problem and is competitive with state-of-the-art CP and MIP solvers.
• Score: 18.286430978487388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We deal with a challenging scheduling problem on parallel machines with sequence-dependent setup times and release dates from a real-world application of semiconductor work-shop production. There, jobs can only be processed by dedicated machines, thus few machines can determine the makespan almost regardless of how jobs are scheduled on the remaining ones. This causes problems when machines fail and jobs need to be rescheduled. Instead of optimising only the makespan, we put the individual machine spans in non-ascending order and lexicographically minimise the resulting tuples. This achieves that all machines complete as early as possible and increases the robustness of the schedule. We study the application of Answer-Set Programming (ASP) to solve this problem. While ASP eases modelling, the combination of timing constraints and the considered objective function challenges current solving technology. The former issue is addressed by using an extension of ASP by difference logic. For the latter, we devise different algorithms that use multi-shot solving. To tackle industrial-sized instances, we study different approximations and heuristics. Our experimental results show that ASP is indeed a promising KRR paradigm for this problem and is competitive with state-of-the-art CP and MIP solvers. Under consideration in Theory and Practice of Logic Programming (TPLP).

Related papers

• Is the GPU Half-Empty or Half-Full? Practical Scheduling Techniques for LLMs [3.7758841366694353]
  We survey scheduling techniques from the literature and from practical serving systems. We find that schedulers from the literature often achieve good performance but introduce significant complexity. In contrast, schedulers in practical deployments often leave easy performance gains on the table but are easy to implement, deploy and configure.
  arXiv  Detail & Related papers  (2024-10-23T13:05:46Z)
• A mathematical model for simultaneous personnel shift planning and unrelated parallel machine scheduling [3.0477617036157136]
  This paper addresses a production scheduling problem derived from an industrial use case. It focuses on unrelated parallel machine scheduling with the personnel availability constraint. It assumes shared personnel among machines, with one personnel required per machine for setup and supervision during job processing.
  arXiv  Detail & Related papers  (2024-02-24T01:04:04Z)
• Scalable Mechanism Design for Multi-Agent Path Finding [87.40027406028425]
  Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. Finding an optimal solution is often computationally infeasible, making the use of approximate, suboptimal algorithms essential. We introduce the problem of scalable mechanism design for MAPF and propose three strategyproof mechanisms, two of which even use approximate MAPF algorithms.
  arXiv  Detail & Related papers  (2024-01-30T14:26:04Z)
• SatLM: Satisfiability-Aided Language Models Using Declarative Prompting [68.40726892904286]
  We propose a new satisfiability-aided language modeling (SatLM) approach for improving the reasoning capabilities of large language models (LLMs) We use an LLM to generate a declarative task specification rather than an imperative program and leverage an off-the-shelf automated theorem prover to derive the final answer. We evaluate SATLM on 8 different datasets and show that it consistently outperforms program-aided LMs in the imperative paradigm.
  arXiv  Detail & Related papers  (2023-05-16T17:55:51Z)
• Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning [73.19312285906891]
  In flexible job shop scheduling problem (FJSP), operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. This paper presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making.
  arXiv  Detail & Related papers  (2023-05-09T01:35:48Z)
• ART: Automatic multi-step reasoning and tool-use for large language models [105.57550426609396]
  Large language models (LLMs) can perform complex reasoning in few- and zero-shot settings. Each reasoning step can rely on external tools to support computation beyond the core LLM capabilities. We introduce Automatic Reasoning and Tool-use (ART), a framework that uses frozen LLMs to automatically generate intermediate reasoning steps as a program.
  arXiv  Detail & Related papers  (2023-03-16T01:04:45Z)
• Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks [58.720142291102135]
  Large-scale machine learning models are bringing advances to a broad range of fields. Many of these models are too large to be trained on a single machine, and must be distributed across multiple devices. We show that maximum parallelisation is sub-optimal in relation to user-critical metrics such as throughput and blocking rate.
  arXiv  Detail & Related papers  (2023-01-31T17:41:07Z)
• Single and Parallel Machine Scheduling with Variable Release Dates [0.0]
  We study a simple extension of the total weighted flowtime problem for single and identical parallel machines. Our main contribution is that we show the NP- completeness of the problem even for the single machine case.
  arXiv  Detail & Related papers  (2021-03-02T14:52:28Z)
• Exact and heuristic methods for the discrete parallel machine scheduling location problem [0.0]
  The problem consists in choosing the locations of $p$ machines among a finite set of candidates and scheduling a set of jobs on these machines. It is proposed a new arc-flow formulation, a column generation and three procedures that are evaluated through extensive computational experiments.
  arXiv  Detail & Related papers  (2020-06-09T00:10:18Z)
• Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints [52.58352707495122]
  We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
  arXiv  Detail & Related papers  (2020-05-27T01:10:41Z)
• Train Scheduling with Hybrid Answer Set Programming [1.4823899140444556]
  We present a solution to real-world train scheduling problems, involving routing, scheduling, and optimization, based on Answer Set Programming (ASP) We exemplarily show how the hybrid ASP system clingo[DL] can be used to tackle demanding planning-and-scheduling problems.
  arXiv  Detail & Related papers  (2020-03-19T06:50:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.