336 research outputs found
The application of discrete event simulation and system dynamics in the logistics and supply chain context
Discrete event simulation (DES) and system dynamics (SD) are two modelling approaches widely used as decision support tools in logistics and supply chain management (LSCM). A widely held belief exists that SD is mostly used to model problems at a strategic level, whereas DES is used at an operational/tactical level. This paper explores the application of DES and SD as decision support systems (DSS) for LSCM by looking at the nature and level of issues modelled. Peer reviewed journal papers that use these modelling approaches to study supply chains, published between 1996 and 2006 are reviewed. A total of 127 journal articles are analysed to identify the frequency with which the two simulation approaches are used as modelling tools for DSS in LSCM. Our findings suggest that DES has been used more frequently to model supply chains, with the exception of the bullwhip effect, which is mostly modelled using SD. Based on the most commonly used modelling approach, issues in LSCM are categorised into four groups: the DES domain, the SD domain, the common domain and the less common domain. The study furthermore suggests that in terms of the level of decision making involved, strategic or operational/tactical, there is no difference in the use of either DES or SD. The results of this study inform the existing literature about the use of DES and SD as DSS tools in LSCM
Optimisation of vendor-managed inventory systems
Imperial Users onl
Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold:
Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction
The development of a methodology for the evaluation of installed CAPM systemâs effectiveness and efficiency
The objective of this work was to design, develop and evaluate an audit for a Computer Aided Production Management (CAPM) system. Such systems, despite their costs of purchase and implementation, find wide application in industry but there is still considerable debate as to their contribution to the overall performance of a company. A variety of possible methodologies were explored. However, it was found that most of the existing analytical techniques tended to focus on a comparison of systems with respect to best practice or to require data that a company was unlikely to have. Best practice is not an absolute measure, nor does it take account of different company types and their individual requirements. A flexible methodology, 'the CAPM Audit', designed to establish the effectiveness and efficiency of any installed CAPM system, has been developed. The audit is a development of the Delphi approach and is designed to establish the contribution of the CAPM system to the company's overall competitive position. In its development, a generic model for any CAPM system was devised to facilitate analysis without reference to any particular technology, management mode, or manufacturing control system. The audit developed (in the form of a workbook) consists of four stages: stage one establishes the context; stage two determines the underlying architecture of the system; stage three quantifies the contribution to the company's competitive position; and stage four identifies the causes of any failure of the CAPM system. The design of the audit is such that: it enables a systematic investigation of the effectiveness and efficiency of an installed CAPM system to be completed; it enables the CAPM system's contribution to the company to be identified; and it also enables any inadequacies to be determined
The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems
Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three âpillarsâ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a âboomerangâ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems
The dynamic bowser routing problem
We investigate opportunities offered by telematics and analytics to enable
better informed, and more integrated, collaborative management decisions on
construction sites. We focus on efficient refuelling of assets across
construction sites. More specifically, we develop decision support models that,
by leveraging data supplied by different assets, schedule refuelling operations
by minimising the distance travelled by the bowser truck as well as fuel
shortages. Motivated by a practical case study elicited in the context of a
project we recently conducted at Crossrail, we introduce the Dynamic Bowser
Routing Problem. In this problem the decision maker aims to dynamically refuel,
by dispatching a bowser truck, a set of assets which consume fuel and whose
location changes over time; the goal is to ensure that assets do not run out of
fuel and that the bowser covers the minimum possible distance. We investigate
deterministic and stochastic variants of this problem and introduce effective
and scalable mathematical programming models to tackle these cases. We
demonstrate the effectiveness of our approaches in the context of an extensive
computational study designed around data collected on site as well as supplied
by our project partners.
Keywords: Routing; Dynamic Bowser Routing Problem; Stochastic Bowser Routing
Problem; Mixed-Integer Linear Programming; Construction
- âŠ