Production planning systems for cellular manufacturing

New product development is one of the most powerful but difficult activities in business. It is also a very important factor affecting final product quality. There are many techniques available for new product development. Experimental design is now regarded as one of the most significant techniques. In this article, we will discuss how to use the technique of experimental design in developing a new product - an extrusion press. In order to provide a better understanding of this specific process, a brief description of the extrusion press is presented. To ensure the successful development of the extrusion press, customer requirements and expectations were obtained by detailed market research. The critical and non-critical factors affecting the performance of the extrusion press were identified in preliminary experiments. Through conducting single factorial experiments, the critical factorial levels were determined. The relationships between the performance indexes of the extrusion press and the four critical factors were determined on the basis of multi-factorial experiments. The mathematical models for the performance of the extrusion press were established according to a central composite rotatable design. The best combination of the four critical factors and the optimum performance indexes were determined by optimum design. The results were verified by conducting a confirmatory experiment. Finally, a number of conclusions became evident.

Autonomous navigation in interaction-based environments - a case of non-signalised roundabouts

To reduce the number of collision fatalities at crossroads intersections many countries have started replacing intersections with non-signalised roundabouts, forcing the drivers to be more situationally aware and to adapt their behaviours according to the scenario. A non-signalised roundabout adds to the autonomous vehicle planning challenge, as navigating such interaction dependent scenarios safely, efficiently and comfortably has been a challenge even for human drivers. Unlike traffic signal controlled roundabouts where the merging order is centrally controlled, driving a non-signalised roundabout requires the individual actor to make the decision to merge based on the movement of other interacting actors. Most traditional autonomous planning approaches use rule-based speed assignment for generating admissible motion trajectories, which work successfully in non-interaction-based driving scenarios. They, however, are less effective in interaction-based scenarios as they lack the necessary ability to adapt the vehicle's motion according to the evolving driving scenario. In this paper, we demonstrate an Adaptive Tactical Behaviour Planner (ATBP) for an autonomous vehicle that is capable of planning human-like motion behaviours for navigating a non-signalised roundabout, combining naturalistic behaviour planning and tactical decision-making algorithm. The human driving simulator experiment used to learn the behaviour planning approach and ATBP design are described in the paper

Surveying the sense of urgency of the tactical-level management to adopt industry 4.0 technologies: Ranking of three sister plants based on BWM-CRITIC-TOPSIS

Purpose:Although the decision to adopt Industry 4.0 technologies is commonly strategical, the selection and implementation of technology are the responsibilities of the tactical level management. The tactical level management will also directly experience the impact of adopting the technology towards the organizational performances in their functional areas. The comparative survey study aims to measure the tactical level management’s sense of urgency of the nine pillars in three plants of a single manufacturing organization. Design/methodology/approach: The research methodology starts with a literature review to collect the criteria appertaining to the pillars. Based on the 95 constituting criteria, the second step prepares and conducts a questionnaire survey with 32 participants on three sister plants. Next, rough BWM-CRITIC-TOPSIS ranks these plants at the pillar and criteria levels. The ranking method integrates Best-Worst Method (BWM), Criteria Importance Through Intercriteria Correlation (CRITIC), and technique for order performance by similarity to ideal solution (TOPSIS). The top management discussed and rendered insights into the results. Findings: Results show that the high-mix and labor-intensive plant (Plant 1) has the highest urgency, whereas the largely automated plant (Plant 3) has the lowest urgency to adopt the nine pillars. The findings provide empirical evidence of the effect of the recent Industry 4.0 awareness programs in Plant 1 and advanced infrastructure would lead to organization inertia (Plant 3) to aggressively pursue technological change. The most urgent pillar is cybersecurity, and the least urgent pillar is additive manufacturing (AM), outlining the concern over cyber threats when product information is increasingly integrated into the supply chain and technology immaturity of AM in production. Research limitations/implications: A limitation of this study is that the comparative survey only focused on three plants and the tactical level management of an organization. Originality/value: This study contributes to the knowledge of Industry 4.0 readiness by being the first to show different levels in the sense of urgency of the tactical level managements on the relevant technologies, which potentially affect the direction and the pace of Industry 4.0 adoptionPeer Reviewe

Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.dynamic pricing;machine learning;market forecasting;Trading agents

Food supply chain network robustness : a literature review and research agenda

Today’s business environment is characterized by challenges of strong global competition where companies tend to achieve leanness and maximum responsiveness. However, lean supply chain networks (SCNs) become more vulnerable to all kind of disruptions. Food SCNs have to become robust, i.e. they should be able to continue to function in the event of disruption as well as in normal business environment. Current literature provides no explicit clarification related to robustness issue in food SCN context. This paper explores the meaning of SCN robustness and highlights further research direction

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Developing and testing of control software framework for autonomous ground vehicle

Automation in ground vehicles has been gaining momentum in recent years highlighted by the significant number of public demonstrations in the last two decades. This momentum has created an urgency within research organizations, vehicle manufacturers and academia to solve existing problems with autonomous vehicle technology to make it usable in the real world. As autonomous ground vehicles operate in close proximity to one another, the margin of error for navigation is smaller than in other domains such as aerospace and marine application. The real-world driving scenarios for the autonomous ground vehicle can sometimes be predictable and unpredictable at other times, demanding different behaviours from the autonomous vehicle for successful navigation. To satisfy such as requirement, the autonomous vehicle should exhibit the capability to adapt to through deliberative planning in predictable environments and reactive planning in unpredictable environments. In this paper, we describe a hybrid control software framework designed to incorporate behaviour planning algorithms that are capable of both deliberative and reactive planning. The paper describes the development of this novel adaptive autonomous control software framework and validates it through both virtual testing and real world testing environments