Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles

IEEE This article demonstrates the application of a novel multiagent modeling approach to support supply network configuration (SNC) decisions toward addressing several challenges reported in the literature. These challenges include: enhancing supply network (SN)-level performance in alignment with the goals of individual SN entities; addressing the issue of limited information sharing between SN entities; and sustaining competitiveness of SNs in dynamic business environments. To this end, a multistage, multiechelon SN consisting of geographically dispersed SN entities catering to distinct product-market profiles was modeled. In modeling the SNC decision problem, two types of agents, each having distinct attributes and functions, were used. The modeling approach incorporated a reverse-auctioning process to simulate the behavior of SN entities with differing individual goals collectively contributing to enhance SN-level performance, by means of setting reserve values generated through the application of a genetic algorithm. A set of Pareto-optimal SNCs catering to distinct product-market profiles was generated using Nondominated Sorting Genetic Algorithm II. Further evaluation of these SNCs against additional criteria, using a rule-based approach, allowed the selection of the most appropriate SNC to meet a broader set of conditions. The model was tested using a refrigerator SN case study drawn from the literature. The results reveal that a number of SNC decisions can be supported by the proposed model, in particular, identifying and evaluating robust SNs to suit varied product-market profiles, enhancing SC capabilities to withstand disruptions and developing contingencies to recover from disruptions

Modelling sustainable supply networks with adaptive agents

© 2018 IEEE. This paper proposes a multi-agent modelling approach that supports supply network configuration decisions towards sustaining operations excellence in terms of economic, business continuity and environmental performance. Two types of agents are employed, namely, physical agents to represent supply entities and auxiliary agents to deal with supply network configuration decisions. While using the evolutionary algorithm, Non-dominated Sorting Genetic Algorithm-II to optimize both cost and lead time at the supply network level, agents are modelled with an architecture which consists of decision-making, learning and communication modules. The physical agents make decisions considering varying situations to suit specific product-market profiles thereby generating alternative supply network configurations. These supply network configurations are then evaluated against a set of performance metrics, including the energy consumption of the supply chain processes concerned and the transportation distances between supply entities. Simulation results generated through the application of this approach to a refrigerator production network show that the selected supply network configurations are capable of meeting intended sustainable goals while catering to the respective product-market profiles

Optimization of multi-objective outbound logistics operation

Distribution, one of major outbound logistics functions draws attention due to high cost incurred. This study investigates the planning of a real time distribution operation achieving three objectives simultaneously. Route optimization, truck utilization and equal delivery make-span have been accompanied in order to address the problem giving financial benefits to company and satisfying the stake-holders. It is a real challenge to fulfill these three objectives concurrently; however, this research provides promising solution for the problem combining both exact and heuristics techniques. Heuristics techniques exploits to cluster the customers ensuring equal delivery make-span and Dijikstra algorithm has been modified to generate optimal route in terms of distance and delivery quantity. Algorithm was developed in C++. Results reveal that proposed route planning reduces the cost by 11.5 % included with 50% reduction of fleet size and 37% saving of travel distance

Modeling supply network configuration problems with varying demand profiles

© 2018 IEEE. In this paper, we develop a novel multi-objective modeling approach to support supply network configuration decisions, while considering varying demand profiles. In so doing, we illustrate how such an approach could contribute to building supply network robustness and resilience. The proposed model entails two key objectives; minimizing lead time and cost across the supply network. The solution approach first employs a bidding mechanism to select a set of supply network entities that match with a given demand profile from a candidate pool of entities. It then applies the popular technique known as N on-dominated Sorting Genetic Algorithm-II to generate a set of Pareto-optimal solutions representing alternative supply network configurations. The proposed model is tested on a case study of a refrigerator supply network to draw delivery time and cost comparisons under static and dynamic demand profiles

Accurate attitude estimation of low-accelerating vehicles by the use of multiple low cost MEMS-Based IMUs

Inertial sensors and inertial measurement units (IMUs) are widely used in navigation support systems, autonomous vehicle navigation, and vehicle motion control process. Inertial measurement based attitude determination and navigation systems are capable of sustaining their accurate operation with high resolution without subjecting to external jamming and noise conditions. The most vital equipment in inertial measurement system is the IMU. The required degree of accuracy, resolution and repeatability in angular velocities and accelerations mainly depend on the implementation technology of the gyroscopes and accelerometers. The paper presents a methodical approach to obtain better attitude determination for vehicles with low-cost, multiple MEMS-based IMU. The simulated results are based on the mathematical models of MEMS-based gyroscopes and accelerometers

An Active monocular platform for intelligent vehicles: Design and simulations

Intelligent vehicles have to perceive their environment in order to navigate and avoid collision. This capability is also important in other applications such as mobile robot navigation, Automated Guided Vehicles (AGVs) for automated material handling, intelligent transportation systems, rescue operations in natural disasters, and wildlife observation. Since angular rotations of the moving vehicle deteriorate the quality of the image, cameras have to be mounted on a stabilized platform. Further control is required if the cameras are expected to keep looking at an object of interest irrespective of the vehicle's translational motion. This paper describes designing a monocular vision, system kinematics, and dynamics analysis. Simulation results are also present

Neoproterozoic crustal evolution in Sri Lanka: insights from petrologic, geochemical and zircon U-Pb and Lu-Hf isotopic data and implications for Gondwana assembly

Abstract not availableM. Santosh, T. Tsunogae, Sanjeewa P.K. Malaviarachchi, Zeming Zhang, Huixia Ding, Li Tang, P.L. Dharmapriy