Toward an Engineering Discipline of Warehouse Design

Warehouses today are complex dynamic engineered systems, incorporating automation, mechanization, equipment, fixtures, computers, networks, products and people, and they can support the flow of tens or hundreds of thousands of different items to enable fulfilling thousands or tens of thousands of orders daily. In that sense, they represent a design challenge that is not terribly different from the design of other complex dynamic engineered systems, such as a modern passenger airplane, an automobile, or a unique building. What is different is that the design of these other complex dynamic engineered systems typically follows some engineering design discipline. Here, we argue for the development of a corresponding engineering discipline of warehouse design

Investigating into the Prevalence of Complex Event Processing and Predictive Analytics in the Transportation and Logistics Sector: Initial Findings From Scientific Literature

As ever new sensor solutions are invading people’s everyday lives and business processes, the use of the signals and events provided by the devices poses a challenge. Innovative ways of handling the large amount of data promise an effective and efficient means to overcome that challenge. With the help of complex event processing and predictive techniques, added value can be created. While complex event processing is able to process the multitude of signals coming from the sensors in a continuous manner, predictive analytics addresses the likelihood of a certain future state or behavior by detecting patterns from the signal database and predicting the future according to the detections. As to the transportation and logistics domain, processing the signal stream and predicting the future promises a big impact on the operations because the transportation and logistics sector is known as a very complex one. The complexity of the sector is linked with the many stakeholders taking part in a variety of operations and the partly high level of automation often being accompanied by manual processes. Hence, predictions help to prepare better for upcoming situations and challenges and, thus, to save resources and cost. The present paper is to investigate the prevalence of complex event processing and predictive analytics in logistics and transportation cases in the research literature in order to motivate a subsequent systematic literature view as the next step in the research endeavor

Development of Integrated Logistics Paradigm in Solving Complex Multi-Criteria Problems of Material Flow Management

The paper considers the historical retrospective of the development of logistics paradigms and analyzes the apparatus used to solve applied logistics problems. Obstacles to the implementation of the integrated paradigm of logistics, its analytical approach and simulation tools are investigated, which allowed to determine the structure of the decision support system in the management of material flow and formulate the concept of logistics coordination. As a result, it was possible to develop a conceptual scheme of the economic mechanism of logistics coordination of the production enterprise and to form a General algorithm for mathematical modeling of simulation and analytical models of logistics coordination and solving complex problems of material flow management in complex organizational and technical systems

How to Make the Most Productive Intervention in a Complex Economic System

Information about supply and demand propagates through supply chains in a queueing network with people and computers as batch information processors. As each batch processor delays propagation of information whilst pursuing optimal local decisions, the effect is delay and distortion of the information that is used to commit resources to actions in the supply chain. This thesis investigates the effect of delay and imperfect information as a source of error, to establish the case for change in research focus from optimal exploitation of physical constraints to optimal exploitation of information. In the context of real world supply chains, the thesis asks "How does one make the most productive intervention in a complex economic system?" and pursues a meta-intervention which perpetually minimises the discovered error-term. Evidence from literature indicates that agent-based modelling permits real-time peer-to-peer communication and distributed optimisation. Based on the literature the research project designs and develops an agent-based model which operates in real-time without batch-processes and can perform incremental multi-objective optimisation under realistic (chronologically progressive) conditions for decision making. The agent based model is then used to investigate two real-world supply chains, as case studies, which reveals a significant improvement of profitability and order-fulfilment. The thesis concludes that agent-based modelling is a very promising direction for "making the most productive intervention" as it reduces delay to a minimum. Finally it recommends that continuous improvement of decision making methods is a role better suited for humans, rather than operational decision making where computers cope much better with the high amount of detailed information

Selection of simulation tools for improving supply chain performance

Simulation is an effective method for improving supply chain performance. However, there is limited advice available to assist practitioners in selecting the most appropriate method for a given problem. Much of the advice that does exist relies on custom and practice rather than a rigorous conceptual or empirical analysis. An analysis of the different modelling techniques applied in the supply chain domain was conducted, and the three main approaches to simulation used were identified; these are System Dynamics (SD), Discrete Event Simulation (DES) and Agent Based Modelling (ABM). This research has examined these approaches in two stages. Firstly, a first principles analysis was carried out in order to challenge the received wisdom about their strengths and weaknesses and a series of propositions were developed from this initial analysis. The second stage was to use the case study approach to test these propositions and to provide further empirical evidence to support their comparison. The contributions of this research are both in terms of knowledge and practice. In terms of knowledge, this research is the first holistic cross paradigm comparison of the three main approaches in the supply chain domain. Case studies have involved building ‘back to back’ models of the same supply chain problem using SD and a discrete approach (either DES or ABM). This has led to contributions concerning the limitations of applying SD to operational problem types. SD has also been found to have risks when applied to strategic and policy problems. Discrete methods have been found to have potential for exploring strategic problem types. It has been found that discrete simulation methods can model material and information feedback successfully. Further insights have been gained into the relationship between modelling purpose and modelling approach. In terms of practice, the findings have been summarised in the form of a framework linking modelling purpose, problem characteristics and simulation approach

A model based method for evaluation of crop operation scenarios in greenhouses

Abstract This research initiated a model-based method to analyse labour in crop production systems and to quantify effects of system changes in order to contribute to effective greenhouse crop cultivation systems with efficient use of human labour and technology. This method was gradually given shape in the discrete event simulation model GWorkS, acronym for Greenhouse Work Simulation. Model based evaluation of labour in crop operations is relatively new in greenhouse horticulture and could allow for quantitative evaluation of existing greenhouse crop production systems, analysis of improvements, and identification of bottlenecks in crop operations. The modelling objective was a flexible and generic approach to quantify effects of production system changes. Cut-rose was selected as a case-study representative for many cut-flowers and fruit vegetables. The first focus was a queueing network model of the actions of a worker harvesting roses in a mobile cultivation system. Data and observations from a state-of-art mobile rose production system were used to validate and test the harvesting model. Model experiments addressed target values of operational parameters for best system performance. The model exposed effects of internal parameters not visible in acquired data. This was illustrated for operator and gutter speed as a function of crop yield. The structure and setup of the GWorkS model was generic where possible and system specific where inevitable. The generic concept was tested by transferring GWorkS to harvesting a greenhouse section in a static growing system for cut-roses and extending it with navigation in the greenhouse, product handling, and multiple operator activity (up to 3 workers). Also for rose harvesting in a static growing system, the model reproduced harvesting accurately. A seven workday validation for an average skilled harvester showed a relative root mean squared error (RRMSE) under 5% for both labour time and harvest rate. A validation for 96 days with various harvesters showed a higher RRMSE, 15.2% and 13.6% for labour time and harvest rate respectively. This increase was mainly caused by the absence of model parameters for individual harvesters. Work scenarios were simulated to examine effects of skill, equipment, and harvest management. For rose yields of 0.5 and 3 harvested roses per m2, harvest rate was 346 and 615 stems h-1 for average skilled harvesters, 207 and 339 stems h-1 for new harvesters and 407 and 767 stems h-1 for highly skilled harvesters. Economic effects of trolley choice are small, 0-2 € per 1000 stems and two harvest cycles per day was only feasible if yield quality effects compensate for extra costs of 0.2-1.1 eurocents per stem. In a sensitivity analysis and uncertainty analysis, parameters with strong influence on labour performance in harvesting roses in a static system were identified as well as effects of parameter uncertainty on key performance indicators. Differential sensitivity was analysed, and results were tested for linearity and superposability and verified using the robust Monte Carlo method. The model was not extremely sensitive for any of the 22 tested input parameters. Individual sensitivities changed with crop yield. Labour performance was most affected by greenhouse section dimensions, single rose cut time, and yield. Throughput was most affected by cut time of a single rose, yield, number of harvest cycles, greenhouse length and operator transport velocity. In uncertainty analysis the coefficient of variation for the most important outputs labour time and throughput is around 5%. The main sources of model uncertainty were in parallel execution of actions and trolley speed. The uncertainty effect of these parameters in labour time, throughput and utilisation of the operator is acceptably small with CV less than 5%. The combination of differential sensitivity analysis and Monte Carlo analysis gave full insight in both individual and total sensitivity of key performance indicators. To realise the objective of model based improvement of the operation of horticultural production systems in resources constrained system, the GWorkS-model was extended for simultaneous crop operations by multiple workers analysis. This objective was narrowed down to ranking eight scenarios with worker skill as a central theme including a labour management scenario applied in practise. The crop operations harvest, disbudding and bending were considered, which represent over 90% of crop-bound labour time. New sub-models on disbudding and bending were verified using measured data. The integrated scenario study on harvest, disbudding and bending showed differences between scenarios of up to 5 s per harvested rose in simulated labour time and up to 7.1 € m-2 per year in labour costs. The simulated practice of the grower and the scenario with minimum costs indicated possible savings of 4 € m-2 per year, which equals 15% of labour cost for harvest, disbudding and bending. Multi-factorial assessment of scenarios pointed out that working with low skilled, low paid workers is not effective. Specialised workers were most time effective with -17.5% compared to the reference, but overall a permanent team of skilled generalists ranked best. Reduced diversity in crop operations per day improved labour organisational outputs but ranked almost indifferent. The reference scenario was outranked by 5 scenarios. Discrete event simulation, as applied in the GWorkS-model, described greenhouse crop operations mechanistically correct and predicts labour use accurately. This model-based method was developed and validated by means of data sets originating from commercial growers. The model provided clear answers to research questions related to operations management and labour organisation using the full complexity of crop operations and a multi-factorial criterion. To the best of our knowledge, the GWorkS-model is the first model that is able to simulate multiple crop operations with constraints on available staff and resources. The model potentially supports analysis and evaluation of design concepts for system innovation.</p