Heuristic Procedures to Solve Sequencing and Scheduling Problems in Automobile Industry

With the growing trend for greater product variety, mixed-model assembly nowadays is commonly employed in many industries, which can enable just-in-time production for a production system with high variety. Efficient production scheduling and sequencing is important to achieve the overall material supply, production, and distribution efficiency around the mixed-model assembly line. This research addresses production scheduling and sequencing on a mixed-model assembly line for products with multiple product options, considering multiple objectives with regard to material supply, manufacturing, and product distribution. This research also addresses plant assignment for a product with multiple product options as a prior step to scheduling and sequencing for a mixed-model assembly line. This dissertation is organized into three parts based on three papers. Introduction and literature review Part 1. In an automobile assembly plant many product options often need to be considered in sequencing an assembly line with which multiple objectives often need to be considered. A general heuristic procedure is developed for sequencing automobile assembly lines considering multiple options. The procedure uses the construction, swapping, and re-sequencing steps, and a limited search for sequencing automobile assembly lines considering multiple options. Part 2. In a supply chain, production scheduling and finished goods distribution have been increasingly considered in an integrated manner to achieve an overall best efficiency. This research presents a heuristic procedure to achieve an integrated consideration of production scheduling and product distribution with production smoothing for the automobile just-in-time production assembly line. A meta-heuristic procedure is also developed for improving the heuristic solution. Part 3. For a product that can be manufactured in multiple facilities, assigning orders to the facility is a common problem faced by industry considering production, material constraints, and other supply-chain related constraints. This paper addresses products with multiple product options for plant assignment with regard to multiple constraints at individual plants in order to minimize transportation costs and costs of assignment infeasibility. A series of binary- and mixed-integer programming models are presented, and a decision support tool based on optimization models is presented with a case study. Summary and conclusion

Development and demonstration of an on-board mission planner for helicopters

Mission management tasks can be distributed within a planning hierarchy, where each level of the hierarchy addresses a scope of action, and associated time scale or planning horizon, and requirements for plan generation response time. The current work is focused on the far-field planning subproblem, with a scope and planning horizon encompassing the entire mission and with a response time required to be about two minutes. The far-feld planning problem is posed as a constrained optimization problem and algorithms and structural organizations are proposed for the solution. Algorithms are implemented in a developmental environment, and performance is assessed with respect to optimality and feasibility for the intended application and in comparison with alternative algorithms. This is done for the three major components of far-field planning: goal planning, waypoint path planning, and timeline management. It appears feasible to meet performance requirements on a 10 Mips flyable processor (dedicated to far-field planning) using a heuristically-guided simulated annealing technique for the goal planner, a modified A* search for the waypoint path planner, and a speed scheduling technique developed for this project

A New Planning to Forecasting Fuel Consumption in Iran Transportation Using a Hybrid Algorithm and Artificial Neural Network

Forecasting fuel demand is one of the preconditions for energy planning and management. Fossil fuels, a major part of which is consumed in transportation, are one of the most important energies. Therefore, forecasting fuel demand in transportation is of particular importance. An MLP perceptron neural network has been proposed in this study, which is trained using a Hybrid algorithm. The hybrid algorithm is based on an imperialist competitive algorithm (ICA), in which the specifications of simulated annealing (SA) algorithm have been used. To perform the forecasting, the effective parameters on road transportation including the amounts of ton kilometer of transported goods, person kilometer of passengers, the average age of cargo fleet, and the average age of passenger fleet were identified. The results of the investigation indicate the effective performance of the proposed algorithm in ANN training

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Optimisation of scheduling and routing for offshore wind farm maintenance

The growing increase in the size and scope of offshore wind farms motivates the need for industry to have access to mathematical tools that reduce costs by efficiently performing daily operations and maintenance activities. Key offshore activities require the transportation of technicians to and within offshore wind farms to complete corrective and preventive maintenance tasks to keep turbines operating efficiently. We provide a new deterministic mixed integer linear programming formulation for deciding the optimal vessel routes for transporting technicians around a wind farm and the scheduling of crew transfers, by minimising downtime, travel and technician costs. The model contains sufficient flexibility to account for multiple vessels, shifts and task profiles, whilst being able to prioritise and omit tasks in environments containing limited resources. Computational experiments are performed which quantify and confirm the impact of key instance characteristics such as technician availability, task profiles and weather conditions. We implement and evaluate the impact of a novel industry safety constraint. The complexity of larger instances motivates a second continuous time formulation, in which preventive maintenance again requires no minimum duration of work before it can provide benefit. We employ a specific decomposition structure to take advantage of variable preventive maintenance and utilise an adaptive large neighbourhood search procedure to solve instances. We evaluate several distinct acceptance criteria in conjunction with random and adaptive operator selection to determine the best option for our model. We produce a statistical model of offshore weather conditions to help quantify the likelihood of limited vessel accessibility to offshore wind farms. We model the joint distribution of key meteorological and oceanographic variables over time whilst accounting for seasonal trends using multivariate kernel density estimation. Our method generates alternative metocean realisations from historical data and reproduces the important long term persistence statistics of good and adverse offshore conditions