Metaheuristic approach to solving U-shaped assembly line balancing problems using a rule-base coded genetic algorithm

Includes bibliographical references.2015 Summer.The need to achieve line balancing for a U-shaped production line to minimize production time and cost is a problem frequently encountered in industry. This research presents an efficient and quick algorithm to solve the U-shape line-balancing problem. Heuristic rules used to solve a straight line-balancing problem (LBP) were modified and adapted so they could be applied in a U-shape line-balancing problem model. By themselves, the heuristic rules, which were adapted from straight-line systems, can produce good solutions for the U-shape LBP, however, there is nothing that guarantees that this will be the case. One way to achieve improved solutions using heuristic rules can be accomplished by using a number of rules simultaneously to break ties during the task assignment process. In addition to the use of heuristic and simultaneous heuristic rules, basic genetic operations were used to further improve the performance of the assignment process and thus obtain better solutions. Two genetic algorithms are introduced in this research: a direct-coded and an indirect-coded model. The newly introduced algorithms were compared with well-known problems from literature and their performance as compared to other heuristic approaches showed that they perform well. The indirect-coded genetic algorithm uses the adapted heuristic rules from the LBP as genes to find the solutions to the problem. In the direct-coded algorithm, each gene represents an operation in the LBP and the position of the gene in the chromosome represents the order in which an operation, or task, will be assigned to a workstation. The indirect-coded genetic algorithm introduces sixteen heuristic rules adapted from the straight LBP for use in a U-shape LBP. Each heuristic rule was represented inside the chromosome as a gene. The rules were implemented in a way that precedence is preserved and at the same time, facilitate the use of genetic operations. Comparing the algorithm’s results with known results from literature, it obtained better solutions in 26% of the cases; it obtained an equivalent solution in 62% of the cases (not better, not worse); and a worse solution the remaining 12%. The direct-coded genetic algorithm introduces a new way to construct an ordered arrangement of the task assignation without violating any precedence. This method consists of creating a diagram that is isomorphic to the original precedence diagram to facilitate the construction of the chromosome. Also, crossover and mutation operations are conducted in a way that precedence relations are not violated. The direct-coded genetic algorithm was tested with the same set of problems as the indirect-coded algorithm. It obtained better solutions than the known solutions from literature in 22% of the cases; 72% of the problems had an equivalent solution; and 6% of the time it generated a solution less successful than the solution from literature. Something that had not been used in other genetic algorithm studies is a response surface methodology to optimize the levels for the parameters that are involved in the response model. The response surface methodology is used to find the best values for the parameters (% of children, % of mutations, number of genes, number of chromosomes) to produce good solutions for problems of different sizes (large, medium, small). This allows for the best solution to be obtained in a minimum amount of time, thus saving computational effort. Even though both algorithms produce good solutions, the direct-coded genetic algorithm option requires less computational effort. Knowing the capabilities of genetic algorithms, they were then tested in two real industry problems to improve assembly-line functions. This resulted in increased efficiency in both production lines

Project network models with discounted cash flows. A guided tour through recent developments.

The vast majority of the project scheduling methodologies presented in the literature have been developed with the objective of minimizing the project duration subject to precedence and other constraints. In doing so, the financial aspects of project management are largely ignored. Recent efforts have taken into account discounted cash flow and have focused on the maximalization of the net present value (npv) of the project as the more appropriate objective. In this paper we offer a guided tour through the important recent developments in the expanding field of research on deterministic and stochastic project network models with discounted cash flows. Subsequent to a close examination of the rationale behind the npv objective, we offer a taxonomy of the problems studied in the literature and critically review the major contributions. Proper attention is given to npv maximization models for the unconstrained scheduling problem with known cash flows, optimal and suboptimal scheduling procedures with various types of resource constraints, and the problem of determining both the timing and amount of payments.Scheduling; Models; Model; Discounted cash flow; Cash flow; Project scheduling; Project management; Management; Net present value; Value; Problems; Maximization; Optimal;

Modeling and Solution Methodologies for Mixed-Model Sequencing in Automobile Industry

The global competitive environment leads companies to consider how to produce high-quality products at a lower cost. Mixed-model assembly lines are often designed such that average station work satisfies the time allocated to each station, but some models with work-intensive options require more than the allocated time. Sequencing varying models in a mixed-model assembly line, mixed-model sequencing (MMS), is a short-term decision problem that has the objective of preventing line stoppage resulting from a station work overload. Accordingly, a good allocation of models is necessary to avoid work overload. The car sequencing problem (CSP) is a specific version of the MMS that minimizes work overload by controlling the sequence of models. In order to do that, CSP restricts the number of work-intensive options by applying capacity rules. Consequently, the objective is to find the sequence with the minimum number of capacity rule violations. In this dissertation, we provide exact and heuristic solution approaches to solve different variants of MMS and CSP. First, we provide five improved lower bounds for benchmark CSP instances by solving problems optimally with a subset of options. We present four local search metaheuristics adapting efficient transformation operators to solve CSP. The computational experiments show that the Adaptive Local Search provides a significant advantage by not requiring tuning on the operator weights due to its adaptive control mechanism. Additionally, we propose a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provide improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. We also provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high-quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances. To the best of our knowledge, this is the first study that considers stochastic failures of products in MMS. Moreover, we propose a two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. We present a bi-objective evolutionary optimization algorithm, a two-stage bi-objective local search algorithm, and a hybrid local search integrated evolutionary optimization algorithm to tackle the proposed problem. Numerical experiments over a case study show that while the hybrid algorithm provides a better exploration of the Pareto front representation and more reliable solutions in terms of waiting time of failed vehicles, the local search algorithm provides more reliable solutions in terms of work overload objective. Finally, dynamic reinsertion simulations are executed over industry-inspired instances to assess the quality of the solutions. The results show that integrating the reinsertion process in addition to considering vehicle failures can keep reducing the work overload by around 20\% while significantly decreasing the waiting time of the failed vehicles

Balancing and lot-sizing mixed-model lines in the footwear industry

This report describes the full research proposal for the project \Balancing and lot-sizing mixed-model lines in the footwear industry", to be developed as part of the master program in Engenharia Electrotécnica e de Computadores - Sistemas de Planeamento Industrial of the Instituto Superior de Engenharia do Porto. The Portuguese footwear industry is undergoing a period of great development and innovation. The numbers speak for themselves, Portugal footwear exported 71 million pairs of shoes to over 130 countries in 2012. It is a diverse sector, which covers different categories of women, men and children shoes, each of them with various models. New and technologically advanced mixed-model assembly lines are being projected and installed to replace traditional mass assembly lines. Obviously there is a need to manage them conveniently and to improve their operations. This work focuses on balancing and lot-sizing stitching mixed-model lines in a real world environment. For that purpose it will be fundamental to develop and evaluate adequate effective solution methods. Different objectives may be considered, which are relevant for the companies, such as minimizing the number of workstations, and minimizing the makespan, while taking into account a lot of practical restrictions. The solution approaches will be based on approximate methods, namely by resorting to metaheuristics. To show the impact of having different lots in production the initial maximum amount for each lot is changed and a Tabu Search based procedure is used to improve the solutions. The developed approaches will be evaluated and tested. A special attention will be given to the solution of real applied problems. Future work may include the study of other neighbourhood structures related to Tabu Search and the development of ways to speed up the evaluation of neighbours, as well as improving the balancing solution method

Mixed-model Sequencing with Stochastic Failures: A Case Study for Automobile Industry

In the automotive industry, the sequence of vehicles to be produced is determined ahead of the production day. However, there are some vehicles, failed vehicles, that cannot be produced due to some reasons such as material shortage or paint failure. These vehicles are pulled out of the sequence, and the vehicles in the succeeding positions are moved forward, potentially resulting in challenges for logistics or other scheduling concerns. This paper proposes a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provides improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. Moreover, we provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances.Comment: 30 pages, 9 figure

Meta-Heuristics for Dynamic Lot Sizing: a review and comparison of solution approaches

Proofs from complexity theory as well as computational experiments indicate that most lot sizing problems are hard to solve. Because these problems are so difficult, various solution techniques have been proposed to solve them. In the past decade, meta-heuristics such as tabu search, genetic algorithms and simulated annealing, have become popular and efficient tools for solving hard combinational optimization problems. We review the various meta-heuristics that have been specifically developed to solve lot sizing problems, discussing their main components such as representation, evaluation neighborhood definition and genetic operators. Further, we briefly review other solution approaches, such as dynamic programming, cutting planes, Dantzig-Wolfe decomposition, Lagrange relaxation and dedicated heuristics. This allows us to compare these techniques. Understanding their respective advantages and disadvantages gives insight into how we can integrate elements from several solution approaches into more powerful hybrid algorithms. Finally, we discuss general guidelines for computational experiments and illustrate these with several examples

Preventive maintenance task balancing with spare parts optimisation via big-bang big-crunch algorithm

Work balancing increasingly plays an important role in both the production and maintenance functions. However, the literature on work balancing problems in transfer line manufacturing systems provides little information on the contributions of maintenance technicians and spare parts with a focus on penalty, technicians’ costs and incentives for staff. Unlike existing reports, the current investigation attempts to solve the maintenance task balancing problem. It combines preventive maintenance technicians’ assignments with product demand and spares utilisation in a transfer line manufacturing system. It uses an optimisation framework that measures the success of post-line balancing solution performance in a system from a holistic perspective. The novelty of the approach lies in the integration of technicians and spare parts theory and the introduction of penalty, technicians’ costs and incentive for staff. The proposed optimisation method was applied to a case study for detergent manufacturing system as a means of testing the effectiveness and robustness of the approach. The results show that the proposed model appears to be effective. Some simulations were also carried out to complement practical result

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

SURROGATE SEARCH: A SIMULATION OPTIMIZATION METHODOLOGY FOR LARGE-SCALE SYSTEMS

For certain settings in which system performance cannot be evaluated by analytical methods, simulation models are widely utilized. This is especially for complex systems. To try to optimize these models, simulation optimization techniques have been developed. These attempt to identify the system designs and parameters that result in (near) optimal system performance. Although more realistic results can be provided by simulation, the computational time for simulator execution, and consequently, simulation optimization may be very long. Hence, the major challenge in determining improved system designs by incorporating simulation and search methodologies is to develop more efficient simulation optimization heuristics or algorithms. This dissertation develops a new approach, Surrogate Search, to determine near optimal system designs for large-scale simulation problems that contain combinatorial decision variables. First, surrogate objective functions are identified by analyzing simulation results to observe system behavior. Multiple linear regression is utilized to examine simulation results and construct surrogate objective functions. The identified surrogate objective functions, which can be quickly executed, are then utilized as simulator replacements in the search methodologies. For multiple problems containing different settings of the same simulation model, only one surrogate objective function needs to be identified. The development of surrogate objective functions benefits the optimization process by reducing the number of simulation iterations. Surrogate Search approaches are developed for two combinatorial problems, operator assignment and task sequencing, using a large-scale sortation system simulation model. The experimental results demonstrate that Surrogate Search can be applied to such large-scale simulation problems and outperform recognized simulation optimization methodology, Scatter Search (SS). This dissertation provides a systematic methodology to perform simulation optimization for complex operations research problems and contributes to the simulation optimization field