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

A combined simulated annealing and TABU search strategy to solve network design problem with two classes of users

A methodology to solve a transportation network design problem (TCNDP) with two classes of users (passenger cars and trucks) is developed. Given an existing highway system, with a capital investment budget constraint, the methodology selects the best links to be expanded by an extra lane by considering one of three types of traffic operations: exclusive for passenger cars, exclusive for trucks, and for both passenger cars and trucks such that the network total user equilibrium (UE) travel time is minimized. The problem is formulated as an NP-hard combinatorial nonlinear integer programming problem. The classical branch and bound methodology for the integer programming problem is very inefficient in solving this computationally hard problem. A combined simulated annealing and tabu search strategy (SA-TABU), was developed which is shown to perform in a robust and efficient manner in solving five networks ranging from 36 to 332 links. A comprehensive heuristic evaluation function (HEF), a core for the heuristic search strategy, was developed which can be adjusted to the characteristics of the problem and the search strategy used. It is composed of three elements: the link volume to capacity ratio, the historical contribution of the link to the objective function, and a random variable which resembles the error term of the HEF. The principal characteristics of the SA-TABU are the following: HEF, Markov chain length, “temperature” dropping rate and the tabu list length. Sensitivity analysis was conducted in identifying the best parameter values of the main components of the SA-TABU. Sufficiently “good” solutions were found in all the problems within a rather short computational time. The solution results suggest that in most of the scenarios, the shared lane option, passenger cars and trucks, was found to be the most favored selection. Expanding approximately 10% of the links, results in a very high percentage improvement ranging from 73% to 97% for the five test networks

The Use of Persistent Explorer Artificial Ants to Solve the Car Sequencing Problem

Ant Colony Optimisation is a widely researched meta-heuristic which uses the behaviour and pheromone laying activities of foraging ants to find paths through graphs. Since the early 1990’s this approach has been applied to problems such as the Travelling Salesman Problem, Quadratic Assignment Problem and Car Sequencing Problem to name a few. The ACO is not without its problems it tends to find good local optima and not good global optima. To solve this problem modifications have been made to the original ACO such as the Max Min ant system. Other solutions involve combining it with Evolutionary Algorithms to improve results. These improvements focused on the pheromone structures. Inspired by other swarm intelligence algorithms this work attempts to develop a new type of ant to explore different problem paths and thus improve the algorithm. The exploring ant would persist throughout the running time of the algorithm and explore unused paths. The Car Sequencing problem was chosen as a method to test the Exploring Ants. An existing algorithm was modified to implement the explorers. The results show that for the car sequencing problem the exploring ants did not have any positive impact, as the paths they chose were always sub-optimal

Applications of simulation and optimization techniques in optimizing room and pillar mining systems

The goal of this research was to apply simulation and optimization techniques in solving mine design and production sequencing problems in room and pillar mines (R&P). The specific objectives were to: (1) apply Discrete Event Simulation (DES) to determine the optimal width of coal R&P panels under specific mining conditions; (2) investigate if the shuttle car fleet size used to mine a particular panel width is optimal in different segments of the panel; (3) test the hypothesis that binary integer linear programming (BILP) can be used to account for mining risk in R&P long range mine production sequencing; and (4) test the hypothesis that heuristic pre-processing can be used to increase the computational efficiency of branch and cut solutions to the BILP problem of R&P mine sequencing. A DES model of an existing R&P mine was built, that is capable of evaluating the effect of variable panel width on the unit cost and productivity of the mining system. For the system and operating conditions evaluated, the result showed that a 17-entry panel is optimal. The result also showed that, for the 17-entry panel studied, four shuttle cars per continuous miner is optimal for 80% of the defined mining segments with three shuttle cars optimal for the other 20%. The research successfully incorporated risk management into the R&P production sequencing problem, modeling the problem as BILP with block aggregation to minimize computational complexity. Three pre-processing algorithms based on generating problem-specific cutting planes were developed and used to investigate whether heuristic pre-processing can increase computational efficiency. Although, in some instances, the implemented pre-processing algorithms improved computational efficiency, the overall computational times were higher due to the high cost of generating the cutting planes --Abstract, page iii

Quantum Optimization Algorithms in Operations Research: Methods, Applications, and Implications

Quantum optimization algorithms (QOAs) have the potential to fundamentally transform the application of optimization methods in decision making. For certain classes of optimization problems, it is widely believed that QOA enables significant run-time performance benefits over current state-of-the-art solutions. With the latest progress on building quantum computers entering the industrialization stage, quantum-based optimization algorithms have become more relevant. The recent extreme increase in the number of publications in the field of QOA demonstrates the growing importance of the topic in both the academia and the industry. The objectives of this paper are as follows: (1) First, we provide insight into the main techniques of quantum-based optimization algorithms for decision making. (2) We describe and compare the two basic classes of adiabatic and gate-based optimization algorithms and argue their potentials and limitations. (3) Herein, we also investigate the key operations research application areas that are expected to be considerably impacted by the use of QOA in decision making in the future. (4) Finally, current implications arising from the future use of QOA from an operations research perspective are discussed.Comment: 16 pages, 6 figure

Computer aided process planning for multi-axis CNC machining using feature free polygonal CAD models

This dissertation provides new methods for the general area of Computer Aided Process Planning, often referred to as CAPP. It specifically focuses on 3 challenging problems in the area of multi-axis CNC machining process using feature free polygonal CAD models. The first research problem involves a new method for the rapid machining of Multi-Surface Parts. These types of parts typically have different requirements for each surface, for example, surface finish, accuracy, or functionality. The CAPP algorithms developed for this problem ensure the complete rapid machining of multi surface parts by providing better setup orientations to machine each surface. The second research problem is related to a new method for discrete multi-axis CNC machining of part models using feature free polygonal CAD models. This problem specifically considers a generic 3-axis CNC machining process for which CAPP algorithms are developed. These algorithms allow the rapid machining of a wide variety of parts with higher geometric accuracy by enabling access to visible surfaces through the choice of appropriate machine tool configurations (i.e. number of axes). The third research problem addresses challenges with geometric singularities that can occur when 2D slice models are used in process planning. The conversion from CAD to slice model results in the loss of model surface information, the consequence of which could be suboptimal or incorrect process planning. The algorithms developed here facilitate transfer of complete surface geometry information from CAD to slice models. The work of this dissertation will aid in developing the next generation of CAPP tools and result in lower cost and more accurately machined components

Análise de Performance de Técnicas de Optimização

Real-world complex optimization problems are one of the most complex challenges faced by scientific community. Achieving the best solution for a complex problem in an acceptable time interval is not always possible. In order to solve this problem, metaheuristics are one of the available resources. Having this in mind, finding a technique among others that presents better results in most executions would allow solution choosing to be more directive and assertive. Most used techniques comprise metaheuristics. These allow to find an acceptable solution in an acceptable time interval, even if the achieved solution was not the optimal possible. In this sense, this thesis intends to analyse four optimization techniques. Two population based techniques, one of them based in the behaviour of the bees in colony (Bee Colony) and another based in computational evolution (Genetic Algorithms). And, two single solution techniques, one based in memory structures (Tabu Search) and another based in the metallurgy industry (Simulated Annealing). These techniques were applied to two different optimization problems and computational results were registered and analysed. A prototype was built and used to obtain the results of applying metaheuristics to the Travelling Salesman problem (TSP) and the Knapsack Problem (KP). Evaluating the results, it was not possible to prove either that all algorithms are equivalent or that one of them is better in the majority of the cases.A resolução de problemas de otimização reais complexos constitui um dos grandes desafios científicos atuais. A possibilidade de obter as melhores soluções para os problemas nem sempre é possível em tempo útil e o recurso a técnicas de otimização para os resolver de forma eficaz e eficiente é constante. Neste sentido, encontrar uma técnica que sobressaia por entre as demais permitiria usar essas técnicas de forma mais direcionada e assertiva. Algumas das técnicas de otimização mais usadas são as meta-heurísticas. Estas permitem encontrar uma solução em tempo útil, mesmo não sendo a melhor solução possível. Neste contexto, a presente dissertação tem por vista a análise de quatro técnicas de otimização. Duas populacionais, sendo que uma técnica é baseada no comportamento dos enxames de abelhas (Bee Colony) e outra baseada na computação evolucionária, algoritmos genéticos (Genetic Algorithms). E, por posição, duas de solução única, a pesquisa tabu (Tabu Search), que se baseia nas estruturas de memória e uma técnica baseada na indústria metalúrgica, o arrefecimento simulado (Simulated Anealing). Estas técnicas foram aplicadas a dois problemas de otimização e os resultados computacionais, eficiência e eficácia das técnicas, foram registados e analisados. Um protótipo foi construído e utilizado para obter os resultados da aplicação das metaheurísticas ao problema de caixeiro viajante (TSP) e ao problema da mochila (KP). Após avaliação dos resultados, não foi possível provar que existia um algoritmo que se destacava entre os demais ou que os algoritmos eram equivalentes

Heuristic Approach to Job Scheduling in a Small Scale Groundnut Oil Processing Firm in Nigeria

Groundnut is an important legume cash crop for tropical farmers and its seeds contain high amounts of edible oil (43-55%) and protein (25-28%). This paper developed a framework for the scheduling of activities (jobs) in small scale groundnut oil processing firm in Nigeria. The research problem is addressed using makespan as a measure of performance with CDS, A1 and Usual Serial Order (USO) heuristics solution methods. Findings reveal that A1 and CDS heuristics are preferred to the traditional USO methods. Also, the mean of A1 (27.11) heuristic, followed by CDS (27.22) heuristics, gives the best makespan results while the USO (31.52) gives the worst result. This paper thus presents a framework that could be beneficial to stakeholders in the Groundnut oil processing industry towards improved customer’s satisfaction, less idle time, and profit optimization. Keywords: Groundnut, small enterprises, scheduling of orders, makespans, optimum results

Aggregate assembly process planning for concurrent engineering

In today's consumer and economic climate, manufacturers are finding it increasingly difficult to produce finished products with increased functionality whilst fulfilling the aesthetic requirements of the consumer. To remain competitive, manufacturers must always look for ways to meet the faster, better, and cheaper mantra of today's economy. The ability for any industry to mirror the ideal world, where the design, manufacturing, and assembly process of a product would be perfected before it is put mto production, will undoubtedly save a great deal of time and money. This thesis introduces the concept of aggregate assembly process planning for the conceptual stages of design, with the aim of providing the methodology behind such an environment. The methodology is based on an aggregate product model and a connectivity model. Together, they encompass all the requirements needed to fully describe a product in terms of its assembly processes, providing a suitable means for generating assembly sequences. Two general-purpose heuristics methods namely, simulated annealing and genetic algorithms are used for the optimisation of assembly sequences generated, and the loading of the optimal assembly sequences on to workstations, generating an optimal assembly process plan for any given product. The main novelty of this work is in the mapping of the optimisation methods to the issue of assembly sequence generation and line balancing. This includes the formulation of the objective functions for optimismg assembly sequences and resource loading. Also novel to this work is the derivation of standard part assembly methodologies, used to establish and estimate functional tunes for standard assembly operations. The method is demonstrated using CAPABLEAssembly; a suite of interlinked modules that generates a pool of optimised assembly process plans using the concepts above. A total of nine industrial products have been modelled, four of which are the conceptual product models. The process plans generated to date have been tested on industrial assembly lines and in some cases yield an increase in the production rate