A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach

In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version

Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach

In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines

Why simheuristics? : Benefits, limitations, and best practices when combining metaheuristics with simulation

Many decision-making processes in our society involve NP-hard optimization problems. The largescale, dynamism, and uncertainty of these problems constrain the potential use of stand-alone optimization methods. The same applies for isolated simulation models, which do not have the potential to find optimal solutions in a combinatorial environment. This paper discusses the utilization of modelling and solving approaches based on the integration of simulation with metaheuristics. These 'simheuristic' algorithms, which constitute a natural extension of both metaheuristics and simulation techniques, should be used as a 'first-resort' method when addressing large-scale and NP-hard optimization problems under uncertainty -which is a frequent case in real-life applications. We outline the benefits and limitations of simheuristic algorithms, provide numerical experiments that validate our arguments, review some recent publications, and outline the best practices to consider during their design and implementation stages

A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages

In this paper, we study the scheduling problem for a customized production system consisting of a flow shop production line with a parallel assembly stage that produces various products in two stages. In the first stage of the production line, parts are produced using a flow shop production line, and in the second stage, products are assembled on one of the parallel assembly lines. The objective is to minimize the time required to complete all goods (makespan) using efficient scheduling. A mathematical model is developed; however, the model is NP-hard and cannot be solved in a reasonable amount of time. To solve this NP-hard problem, we propose two well-known metaheuristics and a hybrid algorithm. To calibrate and improve the performance of our algorithms, we employ the Taguchi method. We evaluate the performance of our hybrid algorithm with the two well-known methods of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and demonstrate that our hybrid algorithm outperforms both the GA and PSO approaches in terms of efficiency

Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this recordA symbiotic simulation system (S3) enables interactions between a physical system and its computational model representation. To support operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors and saved in an enterprise data storage system (EDSS). Both real-time and historical data are then used as inputs to the different components of an S3. This paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. The paper also reviews the literature on S3 and analyses how these systems can be used for real-time decision-making.Erasmus

Job shop estocástico con minimización del valor esperado del maximum lateness

The drawbacks that programming in job -shop environment imply, refer to a notorious difficulty for its resolution due to its NP-hard nature. However, the research has grown in the late years because of its constant use in manufacturing industries. According to studies, most of the research has approached the job shop scheduling through a deterministic approach. Nevertheless, real industrial environments are subject to random events as: machinery faults, maintenance duration, processing duration, enlistment times, availability times, among many others. In this project, a stochastic job shop that minimizes the expected maximum lateness is addressed. The problem consider sequence dependent setup times, and the stochastic events are machine breakdowns. To solve the problem a simheuristic approach is proposed. The simheuristic Hybridizes a tabu search algorithm with a Monte Carlo simulation. The problem was solved in three phases: Firstly, a mixed integer linear programming model was designed for the deterministic counterpart of the JSSP studied. Secondly, the meta-heuristic tabu search was designed to solving large instances of the deterministic problem. Thirdly, the simheuristic was designed and implemented to minimize the expected maximum lateness value, considering stochastic machine breakdowns. For the simheuristic designing, stochastic variables were generated: times between failures and repair times, following exponential and log-normal distributions. To generate their respective parameters [expected value (μ) and standard deviation (σ)], the mean time to repair was found (MTTR Mean Time to Repair), out of the total mean time between breakdowns. Four different variation coefficient values were proposed (0%, 5%, 10% and 15%), them being: 0% for the deterministic case and 5%, 10% and 15% for stochastic events, to calculate the (σ) in log-normal distribution. On the other hand, a simulation was performed to calculate the expected objective function. The simheuristic was firstly parametrized through an experimental design considering different tabu list sizes and number of iterations without improvement. With the generated parametrization, another computational experiment was executed for a total of 554 instances of different sizes. First, the performance of the simheuristic, for small instances, was evaluated in comparison with the simulation of optimal solutions obtained with the mathematical model. Results show that the simheuristic improves the results of simulations of the model in a 37% for 4x4 instances and in an 11% for 6x6 instances, demonstrating that the simheuristic is better than a deterministic mathematical model simulated. Additionally, the simheuristic performance was evaluated, for large instances, in comparison with the simulation of EDD dispatching rule sequences. Results show that the average improvement is 28% in log-normal distribution and 10% for exponential distribution.Ingeniero (a) IndustrialAdministrador (a) de EmpresasPregrad

Diseño de una metaheurística GRASP hibridizada con la metodología PAES y la simulación de Monte Carlo en un ambiente Flexible Flow Shop estocástico multi-objetivo

El propósito de este proyecto es estudiar un problema de programación de la producción multiobjetivo en un ambiente Flexible Flow Shop (FFS) estocástico. Los objetivos a minimizar son el valor esperado de la tardanza, la desviación estándar de la tardanza, el valor esperado del tiempo total de terminación y la desviación estándar del tiempo total de terminación. Los parámetros estocásticos son los tiempos entre fallas de las máquinas y los tiempos de reparación de las máquinas. Como método de solución, se propone una simheurística, la cual hibridiza la metaheurística GRASP con la simulación de Monte Carlo y el algoritmo PAES para obtener la frontera de Pareto. Inicialmente, se realiza un diseño experimental de la versión determinística del problema para evaluar el desempeño de la simheurística, comparando los resultados de la simheurística con el tiempo total de terminación obtenido en la programación de los trabajos con la regla de despacho FL, y la tardanza con la regla de despacho ENS2. Un segundo diseño de experimentos es diseñado para evaluar los efectos de los diferentes coeficientes de variación y la distribución de probabilidad para ambos parámetros estocásticos en las cuatro funciones objetivo del caso estocástico. Para el caso estocástico, los resultados arrojaron que ambas distribuciones de probabilidad y coeficientes de variación tienen un efecto significativo en las variables, lo que demuestra la importancia de un ajuste preciso de las distribuciones de probabilidad para obtener soluciones adecuadas.To achieve a higher level of efficiency within a manufacturing industry, the production scheduling is essential, because this process is crucial for the maximization of the business value. Currently, a big part of literature in scheduling is focused on solving a deterministic problem to minimize the makespan. Given that, realistically, the industry is exposed to random events that can affect its performance, the aim of this project is to study a multi-objective stochastic Flexible Flow Shop (FFS) environment. The objectives to minimize are expected value of tardiness, standard deviation of tardiness, expected value of total completion time (equal to flowtime due to release times are zero) and standard deviation of total completion time. The stochastics parameters are the times between failures and times to repair the machines (duration of machine breakdowns). As solution method, a simheuristic is proposed, which hybridizes the metaheuristic Greedy Randomized Adaptive Search Procedures (GRASP) with the Monte Carlo simulation and Pareto Archived Evolution Strategy (PAES) algorithm to obtain the Pareto frontier (see illustration 2). A first experimental design is done to test the simheuristic performance for the deterministic version (see illustration 1) of the problem by comparing the results of the simheuristic with the flowtime obtained by scheduling the jobs with FL dispatching rule, and the tardiness with the ENS2 dispatching rule. A second design of experiments is designed to evaluate the effects of different coefficients of variation and probability distribution of both stochastic parameters in the four objective functions of the stochastic case. To do both experimental designs 324 benchmark instances were evaluated in both cases. Results show, that for the deterministic case, the metaheuristic presents an average improvement of 3% in flowtime against FL rule, 2% in tardiness against ENS2 rule. For the stochastic case, results show that both probability distributions and coefficient of variation have a significant effect in the four response variables, which shows the importance of an accurate fitting of probability distributions to obtain adequate solutions.Ingeniero (a) IndustrialPregrad