Multihoist cyclic scheduling with fixed processing and transfer times

Cataloged from PDF version of article.In this paper, we study the no-wait multihoist cyclic scheduling problem, in which the processing times in the tanks and the transfer times between tanks are constant parameters, and develop a polynomial optimal solution to minimize the production cycle length.We first analyze the problem with a fixed cycle length and identify a group of hoist assignment constraints based on the positions of and the relationships among the part moves in the cycle.We show that the feasibility of the hoist scheduling problem with fixed cycle length is consistent with the feasibility of this group of constraints which can be solved efficiently. We then identify all of the special values of the cycle length at which the feasibility property of the problem may change. Finally, the whole problem is solved optimally by considering the fixed-cycle-length problems at these special values

A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist Scheduling Problems

Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices. Although there are a few approaches proposed to target at the challenging problem, they generally cannot scale to large-scale scheduling problems. In this paper, we formulate the hoist scheduling problem as a new temporal planning problem in the form of adapted PDDL, and propose a novel hierarchical temporal planning approach to efficiently solve the scheduling problem. Additionally, we provide a collection of real-life benchmark instances that can be used to evaluate solution methods for the problem. We exhibit that the proposed approach is able to efficiently find solutions of high quality for large-scale real-life benchmark instances, with comparison to state-of-the-art baselines

Model and heuristic solutions for the multiple double-load crane scheduling problem in slab yards

This article studies a multiple double-load crane scheduling problem in steel slab yards. Consideration of multiple cranes and their double-load capability makes the scheduling problem more complex. This problem has not been studied previously. We first formulate the problem as a mixed-integer linear programming (MILP) model. A two-phase model-based heuristic is then proposed. To solve large problems, a pointer-based discrete differential evolution (PDDE) algorithm was developed with a dynamic programming (DP) algorithm embedded to solve the one-crane subproblem for a fixed sequence of tasks. Instances of real problems are collected from a steel company to test the performance of the solution methods. The experiment results show that the model can solve small problems optimally, and the solution greatly improves the schedule currently used in practice. The two-phase heuristic generates near-optimal solutions, but it can still only solve comparatively modest problems within reasonable (4 h) computational timeframes. The PDDE algorithm can solve large practical problems relatively quickly and provides better results than the two-phase heuristic solution, demonstrating its effectiveness and efficiency and therefore its suitability for practical use

Future value based single assignment program representations and optimizations

An optimizing compiler internal representation fundamentally affects the clarity, efficiency and feasibility of optimization algorithms employed by the compiler. Static Single Assignment (SSA) as a state-of-the-art program representation has great advantages though still can be improved. This dissertation explores the domain of single assignment beyond SSA, and presents two novel program representations: Future Gated Single Assignment (FGSA) and Recursive Future Predicated Form (RFPF). Both FGSA and RFPF embed control flow and data flow information, enabling efficient traversal program information and thus leading to better and simpler optimizations. We introduce future value concept, the designing base of both FGSA and RFPF, which permits a consumer instruction to be encountered before the producer of its source operand(s) in a control flow setting. We show that FGSA is efficiently computable by using a series T1/T2/TR transformation, yielding an expected linear time algorithm for combining together the construction of the pruned single assignment form and live analysis for both reducible and irreducible graphs. As a result, the approach results in an average reduction of 7.7%, with a maximum of 67% in the number of gating functions compared to the pruned SSA form on the SPEC2000 benchmark suite. We present a solid and near optimal framework to perform inverse transformation from single assignment programs. We demonstrate the importance of unrestricted code motion and present RFPF. We develop algorithms which enable instruction movement in acyclic, as well as cyclic regions, and show the ease to perform optimizations such as Partial Redundancy Elimination on RFPF

A study of alternative forms of flexible manufacturing systems

The study relates to manufacturing conditions in a particular company. Flow synthesis and cell formation analysis have been conducted. A modification of methodology proposed by other researchers has assisted analysis. The main investigation is concerned with modelling, simulation and evaluation of seven alternative FMS configurations conceived for the machining cell manufacturing prismatic parts. The alternative systems encompass 6 CNC machines with (i) manual transport of materials and tools, (ii) with two and four station automated pallet changers, (iii) conveyor system, (iv) stacker crane, (v) rail guided shuttle, and (vi) AGV transport. [Continues.

Ordonnancement cyclique robuste appliqué à la gestion des conteneurs dans les ports maritimes de taille moyenne

This PhD thesis is dedicated to propose a robust cyclic scheduling methodology applied to container management of medium sized seaport which faces ever changing terminal conditions and the limited predictability of future events and their timing. The robust cyclic scheduling can be seen not just a predictable scheduling to compute a container transportation schedule, but also a reactive scheduling to eliminate the disturbances in real time. In this work, the automated intelligent vehicles (AIV) are used to transport the containers, and the P-time strongly connected event graph (PTSCEG) is used as a graphical tool to model the container transit procedures. Before the arrival of the container vessel, a cyclic container transit schedule can be given by the mixed integer programming (MIP) method in short time. The robustness margins on the nodes of the system can be computed by robustness algorithms in polynomial computing time. After the stevedoring begins, this robust cyclic schedule is used. When a disturbance is observed in system, it should be compared with the known robustness margin. If the disturbance belongs to the robustness margin, the robustness algorithm is used to eliminate the disturbance in a few cycle times. If not, the MIP method is used to compute a new cyclic schedule in short timeCette thèse présente une méthodologie d’ordonnancement cyclique robuste appliquée à la gestion des conteneurs dans les ports maritimes de taille moyenne. Ces derniers sont sujet constamment à des variations des conditions des terminaux, la visibilité réduite sur des évènements futurs ne permet pas de proposer une planification précise des tâches à accomplir. L’ordonnancement cyclique robuste peut jouer un rôle primordial. Il permettra non seulement de proposer un ordonnancement prédictif pour le transport des conteneurs, mais aussi, il proposera également une planification robuste permettant d’éliminer les perturbations éventuelles en temps réel. Dans ce travail nous utilisons les Véhicules Intelligents Automatisés (AIV) pour transporter les conteneurs et nous modélisons les procédures de transit de ces derniers par des graphes d’évènements P-temporels fortement connexes (PTSCEG). Avant l’arrivée d’un porte conteneur au port, un plan (planning) de transport des conteneurs est proposé en un temps court par la programmation linéaire mixte (MIP). Des algorithmes polynomiaux de calcul de robustesse permettent de calculer sur les différents nœuds du système les marges de robustesse. Une fois le navire à quai, l’ordonnancement cyclique robuste est appliqué. Lorsqu’une perturbation est observée (localisée) dans le système, une comparaison avec la marge de robustesse connue est effectuée. Si cette perturbation est incluse dans la marge de robustesse, l’algorithme robuste est utilisé pour éliminer ces perturbations en quelques cycles. Dans le cas où la perturbation est trop importante, la méthode MIP est utilisée pour calculer un nouvel ordonnancement cyclique en un temps rédui

Distributed multi-agent pathfinding in horizontal transportation

Horizontal transportation in maritime container terminals plays a crucial role in ensuring safe, efficient, and cost-effective operations. Heavy working machines, such as straddle carriers, trucks, and automated guided vehicles, transport containers between cranes, creating complex routing problems known as multi-agent pathfinding (MAPF) problems. Existing solutions may not adequately address the unique challenges presented by container terminals, necessitating the development of new algorithms. This thesis aims to develop and demonstrate a distributed MAPF algorithm for horizontal transportation in container terminals. The MAPF problem is first formulated as a binary linear programming (BLP) model by expressing the actions in the container terminal using a directed pseudograph. Optimal solutions are obtained using PYOMO, an open-source Python-based optimization software. The Augmented Lagrangian, a graph pathfinding algorithm, and a stochastic element are then employed to create a sub-optimal, distributed algorithm. The developed algorithm is evaluated against an optimal solution and a reference method that prioritizes calculating the path for one agent at a time while taking into account previously calculated paths. A simulator is set up to emulate horizontal transportation in a maritime container terminal, by modeling the terminal as a graph in MATLAB. In the simulator, MAPF algorithms are applied in combination with a high-level coordinator assigning destinations. The experimental part of this thesis investigates the trade-off between solution time (iterations) and solution quality by tuning algorithm parameters and evaluating the performance of the distributed algorithm in comparison to the priority-based method under two different map layouts, particularly addressing the presence of a bottleneck. The results demonstrate the need to adapt the algorithm's parameters and strategies according to specific environments and map layouts, to ensure good performance across various scenarios. The main contribution of this thesis lies in the development of a adaptable, distributed MAPF solution that can ultimately address diverse scenarios and environments. Merikonttiterminaaleissa konttien vaakatasoinen kuljettaminen on keskeinen tekijä turvallisen, tehokkaan ja kustannustehokkaan toiminnan varmistamisessa. Raskaat työkoneet, kuten konttilukit, kuorma-autot ja automaattisti ohjatut ajoneuvot (AGV:t), kuljettavat kontteja nosturien välillä, luoden monimutkaisia reititysongelmia, joita kutsutaan monitoimija-reitinhaku (MAPF) -ongelmiksi. Olemassa olevat ratkaisut eivät riittävästi käsittele konttiterminaaleille asetettuja erityisiä haasteita, mikä edellyttää uusien hajautettujen MAPF-algoritmien kehittämistä. Tämän diplomityön tavoitteena on kehittää ja esitellä hajautettu MAPF-algoritmi vaakasuuntaiseen kuljetukseen konttiterminaaleissa. MAPF-ongelma mallinnetaan ensin binääriseksi lineaariseksi ohjelmointimalliksi (BLP), jossa konttiterminaalissa liikkuminen mallinnetaan suunnattuna pseudograafina. Optimaalisia ratkaisuja tutkitaan käyttämällä PYOMO-ohjelmistoa, joka on avoimen lähdekoodin Python-pohjainen optimointiohjelmisto. Tämän jälkeen laajennettua Lagrangen kertoimien menetelmää, graafin polunetsintä algoritmia ja stokastistista elementtiä käytetään luomaan lähes optimaalinen, hajautettavissa oleva algoritmi. Kehitettyä algoritmia arvioidaan vertaamalla sitä optimaaliseen ratkaisuun ja viite-menetelmään, joka priorisoi polkujen laskemista yhden agentin kerrallaan ottaen huomioon aiemmin lasketut polut. Simulaattori rakennetaan jäljittelemään vaakasuuntaista kuljetusta merikonttiterminaaleissa, mallintamalla terminaali graafina MATLABissa. Simulaattorissa MAPF-algoritmeja sovelletaan yhdessä korkean tason koordinaattorin kanssa, joka määrittelee määränpäät. Diplomityön kokeellisessa osuudessa tarkastellaan ratkaisuajan (iteraatioiden) ja ratkaisun laadun välistä tasapainoa kokeilemalla erilaisia parametreja ja arvioimalla hajautetun algoritmin suorituskykyä verrattuna prioriteettipohjaiseen menetelmään kahden erilaisen karttapohjan päällä, keskittyen erityisesti pullonkaulan lisäämisen tuomiin vaikutuksiin. Tulokset osoittavat, että algoritmin parametrien ja strategioiden mukauttaminen erityisiin ympäristöihin ja karttapohjiin on tarpeen, jos halutaan varmistua tulosten olevan mahdollisimman lähellä optimaalisuutta erilaisissa skenaarioissa. Tämän diplomityön pääasiallinen kontribuutio on joustavan, hajautetun MAPF-ratkaisun kehittäminen, jolla voidaan tulevaisuudessa käsitellä erilaisia tilanteita ja ympäristöjä

Matlab

This book is a collection of 19 excellent works presenting different applications of several MATLAB tools that can be used for educational, scientific and engineering purposes. Chapters include tips and tricks for programming and developing Graphical User Interfaces (GUIs), power system analysis, control systems design, system modelling and simulations, parallel processing, optimization, signal and image processing, finite different solutions, geosciences and portfolio insurance. Thus, readers from a range of professional fields will benefit from its content