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Optimising order picking efficiency in a warehouse and distribution centre: A flexible flow shop scheduling approach

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Procedia Computer Science 2023年 225卷 832-841页

作者： Kateryna Czerniachowska Krzysztof Lutosławski Wroclaw University of Economics and Business Komandorska 118/120 53-345 Wroclaw Poland

The scheduling of order-picking problems is a critical aspect of warehouse and distribution centre operations. The efficient execution of order picking requires determining the sequence in which it is needed to pick items from storage locations, as well as the assignment of employees to different picking tasks. In this paper, we present an approach to optimise the process using the Flexible Flow Shop Scheduling Problem (FFSSP), which encompasses multiple order fulfilment stages performing a set of operations on various machines (groups of employees) assigned to the picking stages (e.g., order creating, product collection, quality control, packing, shipment). The aim of the study was to determine the sequence of operations on the machines to minimise the order completion time while taking into account the available resources. We used IBM'sCP Optimizer. The computational experiments showed, on the one hand, the advantages of solver usage for relatively small instances providing optimal solutions, while on the other hand, it demonstrated its disadvantages as for 11 large instances from 24, optimal solutions were not found. Moreover, in 6 cases from 24 ones, the solution which was found was 94% worse than the optimal one.

关键词： flexible flow shop scheduling constraint programming warehouse distribution centre

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Certifying Bounds Propagation for Integer Multiplication constraints 39

Certifying Bounds Propagation for Integer Multiplication Con...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： McIlree, Matthew J. McCreesh, Ciaran University of Glasgow Scotland United Kingdom

ISBN: (纸本)157735897X

A constraint programming (CP) solver that implements proof logging will output a machine-checkable certificate of correctness alongside any result it obtains. This is useful for trusting claims of unsatisfiability or optimality, as well as for debugging and auditing solver implementations. Proofs can be constructed by having the solver log justifications for each inference it makes, and previous work has shown that many standard CP reasoning techniques can be efficiently justified using a pseudo-Boolean (PB) proof format. This paper extends PB justifications to propagators enforcing bounds-consistency on multiplication and division constraints. We show that even though the proof system and checker operate only on linear inequalities over 0-1 variables, non-linear reasoning over bounded domains can be efficiently expressed as a sequence of PB proof steps. Additionally, we demonstrate that bespoke proof logging for bounds-consistency algorithms offers a clear advantage over constructing justifications by brute force. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： constraint programming

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Packing-Inspired Algorithms for Periodic Scheduling Problems with Harmonic Periods

arXiv

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arXiv 2024年

作者： Grus, Josef Hanen, Claire Hanzálek, Zdeněk DCE FEE Czech Technical University in Prague Czech Republic Sorbonne Université CNRS LIP6 ParisF-75005 France UPL Université Paris Nanterre NanterreF-92000 France IID CIIRC Czech Technical University in Prague Czech Republic

We tackle the problem of non-preemptive periodic scheduling with a harmonic set of periods. Problems of this kind arise within domains of periodic manufacturing and maintenance, and also during the design of industrial, automotive, and avionics communication protocols, where efficient scheduling of messages is crucial for the performance of a time-triggered network. We consider the decision variant of the periodic scheduling problem on a single highly-utilized machine. We first prove a bijection between periodic scheduling and a particular (so-called height-divisible) 2D packing of rectangles. We formulate the problem using constraint programming and compare it with equivalent state-of-the-art Integer Linear programming formulation, showing the former’s superiority on difficult instances. Furthermore, we develop a packing-inspired first fit heuristic, which we compare with methods described in the literature. We justify our proposed methods on synthetically generated problem instances inspired by the communication of messages on one channel. © 2024, CC BY-NC-ND.

关键词： constraint programming

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To Be or Not To Be: Adding Integrity constraints to stableKanren to Make a Decision

arXiv

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arXiv 2024年

作者： Guo, Xiangyu Bansal, Ajay Arizona State University United States

We integrate integrity constraints to stableKanren to enable a new problem-solving paradigm in combinatorial search problems. stableKanren extends miniKanren to reasoning about contradictions under stable model semantics. However, writing programs to solve combinatorial search problems in stableKanren did not fully utilize the contradiction reasoning. This is mainly due to the lack of control over the predicate (goal function) outcome during resolution. Integrity constraints defined by answer set programming (ASP) provide the ability to constrain the predicate outcome. However, integrity constraints are headless normal clauses, and stableKanren cannot create a goal function without a valid head. There are two approaches to handling integrity constraints, but they do not fit stableKanren. Therefore, we design a new approach to integrate integrity constraints into stableKanren. We show a uniform framework to solve combinatorial search problems using integrity constraints in extended *** Codes 03B70, 68T27, 68T30 © 2024, CC BY-NC-ND.

关键词： constraint programming

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PLANNING ANYTHING WITH RIGOR: GENERAL-PURPOSE ZERO-SHOT PLANNING WITH LLM-BASED FORMALIZED programming

arXiv

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arXiv 2024年

作者： Hao, Yilun Zhang, Yang Fan, Chuchu MIT United States MIT-IBM Watson AI Lab United States

While large language models (LLMs) have recently demonstrated strong potential in solving planning problems, there is a trade-off between flexibility and complexity. LLMs, as zero-shot planners themselves, are still not capable of directly generating valid plans for complex planning problems such as multi-constraint or long-horizon tasks. On the other hand, many frameworks aiming to solve complex planning problems often rely on task-specific preparatory efforts, such as task-specific in-context examples and pre-defined critics/verifiers, which limits their cross-task generalization capability. In this paper, we tackle these challenges by observing that the core of many planning problems lies in optimization problems: searching for the optimal solution (best plan) with goals subject to constraints (preconditions and effects of decisions). With LLMs’ commonsense, reasoning, and programming capabilities, this opens up the possibilities of a universal LLM-based approach to planning problems. Inspired by this observation, we propose LLMFP, a general-purpose framework that leverages LLMs to capture key information from planning problems and formally formulate and solve them as optimization problems from scratch, with no task-specific examples needed. We apply LLMFP to 9 planning problems, ranging from multi-constraint decision making to multi-step planning problems, and demonstrate that LLMFP achieves on average 83.7% and 86.8% optimal rate across 9 tasks for GPT-4o and Claude 3.5 Sonnet, significantly outperforming the best baseline (direct planning with OpenAI o1-preview) with 37.6% and 40.7% improvements. We also validate components of LLMFP with ablation experiments and analyzed the underlying success and failure reasons. Project page: https://***/view/llmfp. Copyright © 2024, The Authors. All rights reserved.

关键词： constraint programming

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Composable constraint Models for Permutation Enumeration

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Discrete Mathematics and Theoretical Computer Science 2023年第1期26卷

作者： Hoffmann, Ruth Akgün, Özgür Jefferson, Christopher School of Computer Science University of St Andrews St Andrews United Kingdom School of Computer Science and Engineering Central South University Changsha China School of Science & Engineering University of Dundee Dundee United States

constraint programming (CP) is a powerful tool for modeling mathematical concepts and objects and finding both solutions or counterexamples. One of the major strengths of CP is that problems can easily be combined or expanded. In this paper, we illustrate that this versatility makes CP an ideal tool for exploring problems in permutation patterns. We declaratively define permutation properties, permutation pattern avoidance and containment constraints using CP and show how this allows us to solve a wide range of problems. We show how this approach enables the arbitrary composition of these conditions, and also allows the easy addition of extra conditions. We demonstrate the effectiveness of our techniques by modelling the containment and avoidance of six permutation patterns, eight permutation properties and measuring five statistics on the resulting permutations. In addition to calculating properties and statistics for the generated permutations, we show that arbitrary additional constraints can also be easily and efficiently added. This approach enables mathematicians to investigate permutation pattern problems in a quick and efficient manner. We demonstrate the utility of constraint programming for permutation patterns by showing how we can easily and efficiently extend the known permutation counts for a conjecture involving the class of 1324 avoiding permutations. For this problem, we expand the enumeration of 1324-avoiding permutations with a fixed number of inversions to permutations of length 16 and show for the first time that in the enumeration there is a pattern occurring which follows a unique sequence on the Online Encyclopedia of Integer Sequences. © 2025 by the author(s)

关键词： constraint programming

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Cost Projections for the Product Life Cycle at the Early Stages of Product Development

Cost Projections for the Product Life Cycle at the Early Sta...

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International-Federation-of-Information-Processing-Working-Group-5.7 (IFIP WG 5.7) International Conference on Advances in Production Management Systems (APMS)

作者： Relich, Marcin Bocewicz, Grzegorz Banaszak, Zbigniew Univ Zielona Gora Fac Econ & Management Zielona Gora Poland Koszalin Univ Technol Fac Elect & Comp Sci Koszalin Poland

ISBN: (纸本)9783030859107;9783030859091

The paper is concerned with predicting the total cost of a new product and searching for cost reduction at the early stages of product development. The costs of a new product development project, product promotion, production and after-sales service are predicted using parametric models. The identified relationships are also used to searching for possibilities to reduce the cost of faulty products and after-sales service through increasing prototype tests. As a result, the tradeoff between the cost of a product development project and costs of production and after-sales service are sought. Company resources and product specification are formulated in terms of variables and constraints that constitute the systems approach for a problem related to cost optimization. This problem is described in the form of a constraint satisfaction problem and implemented using constraint programming techniques. An example shows the applicability of the proposed approach in the context of searching for the desirable level of the cost related to prototype tests, faulty products and after-sales service. This study develops previous research in the context of adding the cost of after-sales service to a model of total costs of a new product. Moreover, the proposed method of predicting cost has been developed towards using the similarity value to data selection.

关键词： Product data management New product development Production cost Decision support system constraint programming

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A Hybrid CP/MOLS Approach for Multi-Objective Imbalanced Classification

A Hybrid CP/MOLS Approach for Multi-Objective Imbalanced Cla...

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2nd Genetic and Evolutionary Computation Conference (GECCO)

作者： Szczepanski, Nicolas Audemard, Gilles Jourdan, Laetitia Lecoutre, Christophe Mousin, Lucien Veerapen, Nadarajen Univ Lille CNRS Cent Lille UMR 9189CRIStAL F-59000 Lille France Univ Artois CRIL CNRS UMR 8188 Lens France Lille Catholic Univ Fac Gest Econ & Sci Lille France

ISBN: (纸本)9781450383509

In the domain of partial classification, recent studies about multi-objective local search (MOLS) have led to new algorithms offering high performance, particularly when the data are imbalanced. In the presence of such data, the class distribution is highly skewed and the user is often interested in the least frequent class. Making further improvements certainly requires exploiting complementary solving techniques (notably, for the rule mining problem). As constraint programming (CP) has been shown to be effective on various combinatorial problems, it is one such promising complementary approach. In this paper, we propose a new hybrid combination, based on MOLS and CP that are quite orthogonal. Indeed, CP is a complete approach based on powerful filtering techniques whereas MOLS is an incomplete approach based on Pareto dominance. Experimental results on real imbalanced datasets show that our hybrid approach is statistically more efficient than a simple MOLS algorithm on both training and tests instances, in particular, on partial classification problems containing many attributes.

关键词： Partial Classification Imbalanced Data Multi-objective Optimization Local Search constraint programming

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Short-Term Scheduling of Production Fleets in Underground Mines Using CP-Based LNS 18th

Short-Term Scheduling of Production Fleets in Underground Mi...

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18th International Conference on the Integration of constraint programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Astrand, Max Johansson, Mikael Feyzmahdavian, Hamid Reza KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Decis & Control Syst Stockholm Sweden ABB Corp Res Vasteras Sweden

ISBN: (纸本)9783030782306;9783030782290

Coordinating the mobile production fleet in underground mines becomes increasingly important as the machines are more and more automated. We present a scheduling approach that applies to several of the most important production methods used in underground mines. Our algorithm combines constraint programming with a large neighborhood search strategy that dynamically adjusts the neighborhood size. The resulting algorithm is complete and able to rapidly improve constructed schedules in practice. In addition, it has important benefits when it comes to the acceptance of the approach in real-life operations. Our approach is evaluated on public and private industrial problem instances representing different mines and production methods. We find significant improvements over the current industrial practice.

关键词： Scheduling Underground mining constraint programming Large neighborhood search

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Agile and Autonomous Production Control for Remanufacturing 9

Agile and Autonomous Production Control for Remanufacturing

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9th International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Gros, Sebastian Gerke, Wolfgang Plapper, Peter Vette-Steinkamp, Matthias Trier Univ Appl Sci Sustainable Prod Proc & Ind Robot Environm Campus Birkenfeld Birkenfeld Germany Univ Luxembourg Dept Engn Luxembourg Luxembourg

ISBN: (纸本)9781665410731

Motivated by high ecological and economical potentials, remanufacturing is receiving increasing attention as a process that puts used products into an "as-good-as-new-or-better" condition. Here, many challenges occur, which are unseen in manufacturing, resulting from unknown conditions of used products. One example is the stochastic routing of the products through the remanufacturing system (RS), which makes a flexible material transport by e.g. automated guided vehicles (AGVs) necessary. This places special demands on the control of the RS. To handle these uncertainties a decentralized-hierarchical control architecture is presented. The scheduling of machines and AGVs is optimized simultaneously using constraint programming (CP). All the participants within the RS will be networked as a cyber-physical system and controlled by decentralized components. The approach is validated through simulation and the implementation in a model factory. Results show a 35.6% makespan reduction, short computing times and the ability to react adequately to unforeseen events.

关键词： simultaneous scheduling AGV constraint programming remanufacturing production control

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