Ant Colony Optimization For Multiobjective Buffers Sizing Problems

International audienc

The Effect of Learning on Assembly Line Balancing: A Review

Classical assembly line balancing (ALB) models assume constant cycle times during production. However, this assumption oversimplifies the actual situation, especially in small batch production of up to a few hundred units, since employees can significantly improve their performance thanks to the learning effect, causing task times to decrease. Several researchers have realised the importance of the effect of learning in ALB. However, only a limited number of papers have so far addressed this issue. This is problematic, since ignoring the learning effect in ALB may lead to inaccurate results and by extension misleading conclusions. This study summarises the main contributions in the field of ALB that focus on the learning effect. First, assembly lines (ALs) and ALB problems are characterised. Next, the importance of the learning effect in ALB is highlighted, and the main learning curve (LC) models are introduced. Finally, an exhaustive review of the main contributions in the field of ALB and learning effect is provided. The results highlight that many problems in this area need to be investigated further, in relation to both conceptual model building and the development of algorithms for solving practical size problems

Analysis and improvement of a bottling line using a simulation modelling approach

This project thesis is focused on the study of a bottling production line using a modelling simulation method, through which we analyse the inefficiencies and then improve their performance. Moreover, the line is also analysed thorugh an analytic approach applying a formula to optimize the buffer sizing. The two approachs are compared to highlight the differences

Optimal Configuration of Inspection and Rework Stations in a Multistage Flexible Flowline

Inspection and rework are two important issues of quality control. In this research, an N-stage flowline is considered to make decisions on these two issues. When defective items are detected at the inspection station the items are either scrapped or reworked. A reworkable item may be repaired at the regular defect-creating workstation or at a dedicated off-line rework station. Two problems (end-of-line and multistage inspections) are considered here to deal with this situation. The end-of-line inspection (ELI) problem considers an inspection station located at the end of the line while the multistage inspection (MSI) problem deals with multiple in-line inspection stations that partition the flowline into multiple flexible lines. Models for unit cost of production are developed for both problems. The ELI problem is formulated for determining the best decision among alternative policies for dealing with defective items. For an MSI problem a unit cost function is developed for determining the number and locations of in-line inspection stations along with the alternative decisions on each type of defects. Both of the problems are formulated as fractional mixed-integer nonlinear programming (f-MINLP) to minimize the unit cost of production. After several transformations the f-MINLP becomes a mixed-integer linear programming (MILP) problem. A construction heuristic, coined as Inspection Station Assignment (ISA) heuristic is developed to determine a sub-optimal location of inspection and rework stations in order to achieve minimum unit cost of production. A hybrid of Ant-Colony Optimization-based metaheuristic (ACOR) and ISA is devised to efficiently solve large instances of MSI problems. Numerical examples are presented to show the solution procedure of ELI problems with branch and bound (B&B) method. Empirical studies on a production line with large number of workstations are presented to show the quality and efficiency of the solution processes involved in both ELI and MSI problems. Computational results present that the hybrid heuristic ISA+ACOR shows better performance in terms of solution quality and efficiency. These approaches are applicable to many discrete product manufacturing systems including garments industry

Enhancing productivity through simulation and layout planning : a case study of a manufacturing company in South Africa

Abstract: This paper aims to optimize a manufacturing process through creation of a simulation model that will be used to identify bottlenecks, restructure the layout and improve productivity. The paper also highlights the significance of process optimization in a manufacturing set up. Process optimizations strive to find the best solution for a process within the available constraints. Simulation is a collection of methodologies used to mimic the characteristics and behaviour of real system using computer software. Literature review was carried out to understand system dynamics and simulation. A case study was conducted at a manufacturing company. An Arena simulation model representing the process under study was developed and analysed. Various models were run and the results compared. The best model was developed that improved productivity through restructuring of the layout and minimization of the cycle times on the identified bottleneck stations. The simulation results showed that there was a vast difference on the amount of material input and the ATMs and Safes produced. The limitation of this study was that it only focused on the production of two products in the case studied company

Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing

Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in energy harvesting mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to the centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning. We prove the convergence of the proposed algorithm and analytically show that the learned policy has a simple monotone structure amenable to practical implementation. Our simulation results validate the efficacy of our algorithm, which significantly improves the edge computing performance compared to fixed or myopic optimization schemes and conventional reinforcement learning algorithms.Comment: arXiv admin note: text overlap with arXiv:1701.01090 by other author

Optimization and Integration of Electric Vehicle Charging System in Coupled Transportation and Distribution Networks

With the development of the EV market, the demand for charging facilities is growing rapidly. The rapid increase in Electric Vehicle and different market factors bring challenges to the prediction of the penetration rate of EV number. The estimates of the uptake rate of EVs for light passenger use vary widely with some scenarios gradual and others aggressive. And there have been many effects on EV penetration rate from incentives, tax breaks, and market price. Given this background, this research is devoted to addressing a stochastic joint planning framework for both EV charging system and distribution network where the EV behaviours in both transportation network and electrical system are considered. And the planning issue is formulated as a multi-objective model with both the capital investment cost and service convenience optimized. The optimal planning of EV charging system in the urban area is the target geographical planning area in this work where the service radius and driving distance is relatively limited. The mathematical modelling of EV driving and charging behaviour in the urban area is developed

Just-In-Time in high variety / low volume manufacturing environments.

Available from British Library Document Supply Centre-DSC:DXN049763 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Improving Performance for CSMA/CA Based Wireless Networks

Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) based wireless networks are becoming increasingly ubiquitous. With the aim of supporting rich multimedia applications such as high-definition television (HDTV, 20Mbps) and DVD (9.8Mbps), one of the technology trends is towards increasingly higher bandwidth. Some recent IEEE 802.11n proposals seek to provide PHY rates of up to 600 Mbps. In addition to increasing bandwidth, there is also strong interest in extending the coverage of CSMA/CA based wireless networks. One solution is to relay traffic via multiple intermediate stations if the sender and the receiver are far apart. The so called “mesh” networks based on this relay-based approach, if properly designed, may feature both “high speed” and “large coverage” at the same time. This thesis focusses on MAC layer performance enhancements in CSMA/CA based networks in this context. Firstly, we observe that higher PHY rates do not necessarily translate into corresponding increases in MAC layer throughput due to the overhead of the CSMA/CA based MAC/PHY layers. To mitigate the overhead, we propose a novel MAC scheme whereby transported information is partially acknowledged and retransmitted. Theoretical analysis and extensive simulations show that the proposed MAC approach can achieve high efficiency (low MAC overhead) for a wide range of channel variations and realistic traffic types. Secondly, we investigate the close interaction between the MAC layer and the buffer above it to improve performance for real world traffic such as TCP. Surprisingly, the issue of buffer sizing in 802.11 wireless networks has received little attention in the literature yet it poses fundamentally new challenges compared to buffer sizing in wired networks. We propose a new adaptive buffer sizing approach for 802.11e WLANs that maintains a high level of link utilisation, while minimising queueing delay. Thirdly, we highlight that gross unfairness can exist between competing flows in multihop mesh networks even if we assume that orthogonal channels are used in neighbouring hops. That is, even without inter-channel interference and hidden terminals, multi-hop mesh networks which aim to offer a both “high speed” and “large coverage” are not achieved. We propose the use of 802.11e’s TXOP mechanism to restore/enfore fairness. The proposed approach is implementable using off-the-shelf devices and fully decentralised (requires no message passing)

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research