A cyclic scheduling approach to maintaining production flow robustness

The organization of flow production, which is typical found in assembly processes, involves a repetitive, fixed-takt time flow of same-size production batches. The cyclic nature of the production flow, which ensures a steady production rhythm, enables just-in-time planning and organization of the associated supply chains. Disruptions in the operation of machinery and equipment, which occur in practice, lead to deviations from nominal operation times. These types of local disturbances lead to changes in production takt time, making it necessary to adjust previously created schedules for delivery/reception of materials and products. Assuming that a control action can be taken to adjust transport operation times within a specified time range, the problem of cyclic scheduling of production flows boils down to seeking conditions the satisfaction of which will guarantee robustness to this kind of disruptions. Satisfaction of robustness conditions allows a return to the nominal production takt time and appropriate adjustment of the production flow trajectory (which makes it possible for the system to return to the previously scheduled delivery times). Numerous examples are included to illustrate the principles of the proposed research methodology aimed at finding solutions for robust scheduling of fixed-takt time production flow. </jats:p

Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization

This paper presents a novel fuzzy particle swarm optimization with cross-mutated (FPSOCM) operation, where a fuzzy logic system developed based on the knowledge of swarm intelligence is proposed to determine the inertia weight for the swarm movement of particle swarm optimization (PSO) and the control parameter of a newly introduced cross-mutated operation. Hence, the inertia weight of the PSO can be adaptive with respect to the search progress. The new cross-mutated operation intends to drive the solution to escape from local optima. A suite of benchmark test functions are employed to evaluate the performance of the proposed FPSOCM. Experimental results show empirically that the FPSOCM performs better than the existing hybrid PSO methods in terms of solution quality, robustness, and convergence rate. The proposed FPSOCM is evaluated by improving the quality and robustness of two real world industrial systems namely economic load dispatch system and self-provisioning systems for communication network services. These two systems are employed to evaluate the effectiveness of the proposed FPSOCM as they are multi-optima and non-convex problems. The performance of FPSOCM is found to be significantly better than that of the existing hybrid PSO methods in a statistical sense. These results demonstrate that the proposed FPSOCM is a good candidate for solving product or service engineering problems which have multi-optima or non-convex natures

Unpacking the Role of Artificial Intelligence for a Multimodal Service System Design

Since requirements of service demands are becoming increasingly complex and diversified, one of the success factors of a multimodal service system is its capability to design a specific service instance satisfying a specific set of requirements. This capability is further highlighted in Ad Hoc Multimodal Service Systems (AHMSSs), where service instances rarely follow a standard form of service delivery and exist only for a limited time. However, due to the increasing scale and frequency of services in many business and public sectors, meeting the desired level of capability has become troublesome. A well-designed Artificial Intelligence (AI) approach can be a solution to the difficulty by addressing the underlying complexity and uncertainty of the AHMSS design process. To conceptualize and foster AI applications to an AHMSS, this study identifies key decision-making problems in the AHMSS design process and discusses the role of AI in the process. The results will form the basis for AI development and implementation for an AHMSS and relevant service systems

Delivery-flow routing and scheduling subject to constraints imposed by vehicle flows in fractal-like networks

The problems of designing supply networks and traffic flow routing and scheduling are the subject of intensive research. The problems encompass the management of the supply of a variety of goods using multi-modal transportation. This research also takes into account the various constraints related to route topology, the parameters of the available fleet of vehicles, order values, delivery due dates, etc. Assuming that the structure of a supply network, constrained by a transport network topology that determines its behavior, we develop a declarative model which would enable the analysis of the relationships between the structure of a supply network and its potential behavior resulting in a set of desired delivery-flows. The problem in question can be reduced to determining sufficient conditions that ensure smooth flow in a transport network with a fractal structure. The proposed approach, which assumes a recursive, fractal network structure, enables the assessment of alternative delivery routes and associated schedules in polynomial time. An illustrative example showing the quantitative and qualitative relationships between the morphological characteristics of the investigated supply networks and the functional parameters of the assumed delivery-flows is provided