Customer order fulfillment based on a rolling horizon available-to-promise mechanism: solution by fuzzy approach and genetic algorithm

[[abstract]]This study attempts to solve a dynamic order promising problem, where customer requests arrive in a random fashion, and the producer processes customer orders on a batch basis. This decision process repeated for every predefined batching interval, and the current decision-making must take into account the previously committed orders. The problem is formulated as a mixed integer programming model with fuzzy constraints, which express the decision-maker’s subjective judgment regarding customer’s price tolerance. The proposed model embeds the advanced available-to-promise (AATP) concept to support accurate computation of profit and customer order promising. A genetic algorithm is developed to solve the problem. Experiments by computer simulations are carried out to demonstrate the proposed approach.[[notice]]補正完

Available-to-promise (ATP) systems: a classification and framework for analysis

Available-to-promise (ATP) systems deal with a number of managerial decisions related to order capture activities in a company, including order acceptance/rejection, due date setting, and resource scheduling. These different but interrelated decisions have often been studied in an isolated manner, and, to the best of our knowledge, no framework has been presented to integrate them into the broader perspective of order capture. This paper attempts to provide a general framework for ATP-related decisions. By doing so, we: (1) identify the different decision problems to be addressed; (2) present the different literature-based models supporting related decisions into a coherent framework; and (3) review the main contributions in the literature for each one of these. We first describe different approaches for order capture available in the literature, depending on two parameters related to the application context of ATP systems, namely the inclusion of explicit information about due dates in the decision model, and the level of integration among decisions. According to these parameters, up to six approaches for ATP-related decisions are identified. Secondly, we show the subsequent decision problems derived from the different approaches, and describe the main issues and key references involving each one of these decision problems. Finally, a number of conclusions and future research lines are discussed.Ministerio de Ciencia e Innovación DPI2007-6134

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

Modular product development for mass customization

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Reinforcement Learning, Intelligent Control and their Applications in Connected and Autonomous Vehicles

Reinforcement learning (RL) has attracted large attention over the past few years. Recently, we developed a data-driven algorithm to solve predictive cruise control (PCC) and games output regulation problems. This work integrates our recent contributions to the application of RL in game theory, output regulation problems, robust control, small-gain theory and PCC. The algorithm was developed for $H_\infty$ adaptive optimal output regulation of uncertain linear systems, and uncertain partially linear systems to reject disturbance and also force the output of the systems to asymptotically track a reference. In the PCC problem, we determined the reference velocity for each autonomous vehicle in the platoon using the traffic information broadcasted from the lights to reduce the vehicles\u27 trip time. Then we employed the algorithm to design an approximate optimal controller for the vehicles. This controller is able to regulate the headway, velocity and acceleration of each vehicle to the desired values. Simulation results validate the effectiveness of the algorithms

Multiagent autonomous energy management

The objective of this thesis is to design distributed software agents for reliable operation of integrated electric power systems of modern electric warships. The automatic reconfiguration of electric shipboard power systems is an important step toward improved fight-through and self-healing capabilities of naval warships. The improvements are conceptualized by redesigning the electric power system and its controls. This research focuses on a new scheme for an energy management system in the form of distributed control/software agents. Multiagent systems provide an ideal level of abstraction for modeling complex applications where distributed and heterogeneous entities need to cooperate to achieve a common goal. The agents\u27 task is to ensure supply of the various load demands while taking into consideration system constraints and load and supply path priorities. A self-stabilizing maximum flow algorithm is investigated to allow implementation of the agents\u27 strategies and find a global solution by only considering local information and a minimum amount of communication. (Abstract shortened by UMI.)