Simheuristic Approach Research Articles

Simheuristic-based decision support system for efficiency improvement of an iron ore crusher circuit

The production rate of an ore crushing circuit depends on the amount of equipment in operation. If the amount of active equipment is less than the optimum level, the reduced ore flow paths restrict the production rate. However, if the amount of active equipment is greater than the optimum level, the excess circulating load ore and extra energy consumption reduce the circuit efficiency. In addition, the optimum amount of active equipment can change over time due to changes in the ore characteristics, such as hardness and particle size. In this paper, a decision support system is proposed for optimizing the amount of active equipment for maximum crushing production considering changes in the circuit feed rate. The proposed solution is based on a simheuristic approach in which a simulated plant model is used to evaluate the production rate. Real production scenarios at a Brazilian mining plant are used in computational experiments. The results show that the simheuristic solutions generate a higher production rate and result in less energy consumption. Production is increased by up to 9%, and energy consumption is reduced by up to 59%, demonstrating the efficacy of the proposal.

Enhancing evacuation response to extreme weather disasters using public transportation systems: a novel simheuristic approach

Abstract In recent years, there have been an increasing number of extreme weather events that have had major impacts on the built environment and particularly on people living in urban areas. As the frequency and intensity of such events are predicted to increase in the future, innovative response strategies to cope with potential emergency conditions, particularly evacuation planning and management, are becoming more important. Although mass transit evacuation of populations at risk is recognized to play a potentially important role in reducing injury and mortality rates, there is relatively little research in this area. In answering the need for more research in this increasingly important and relatively new field of research, this study proposes a hybrid simulation–optimization approach to maximize the number of evacuees moved from disaster-affected zones to safe locations. In order to improve the efficiency of the proposed optimization approach, a novel multipopulation differential evolution approach based on an opposition-based learning concept is developed. The results indicate that even for large populations the proposed approach can produce high-quality options for decision makers in reasonable computational times. The proposed approach enables emergency decision makers to apply the procedure in practice to find the best strategies for evacuation, even when the time for decision making is severely limited.

Maximising reward from a team of surveillance drones: a simheuristic approach to the stochastic team orienteering problem

We consider the problem of routing a team of unmanned aerial vehicles (drones) being used to take surveillance observations of target locations, where the value of information at each location is different and not all locations need be visited. As a result, this problem can be described as a stochastic team orienteering problem (STOP), in which travel times are modelled as random variables following generic probability distributions. The orienteering problem is a vehicle-routing problem in which each of a set of customers can be visited either just once or not at all within a limited time period. In order to solve this STOP, a simheuristic algorithm based on an original and fast heuristic is developed. This heuristic is then extended into a variable neighbourhood search (VNS) metaheuristic. Finally, simulation is incorporated into the VNS framework to transform it into a simheuristic algorithm, which is then employed to solve the STOP. [Received 5 January 2019; Revised 15 June 2019; Accepted 13 October 2019]

A simheuristic approach for throughput maximization of asynchronous buffered stochastic mixed-model assembly lines

Mixed-model assembly lines are large scale production layouts that often operate under uncertainties such as stochastic product sequences. Balancing such lines can be particularly challenging as throughput estimation can be difficult to determine, especially when asynchronous pace and buffers are considered. Recent works have addressed problem variants with a given target throughput, but few authors consider a variant of the throughput maximization of mixed-model assembly line balancing problem. This paper addresses the balancing optimization problem for an assembly line with a given number of workstations and buffers between them. A make-to-order environment is considered, modeled as stochastic sequence of products with known demand rates. A novel specialized cycle time simulator (CTS) is introduced, as well as a simheuristic approach (PSH) that exploits CTS to assess the cycle time of an assembly line and provide good balancing solutions. The proposed simheuristic PSH is applied to a dataset with several buffer layouts, and its solutions are then compared to those of literature benchmarks. Performance comparisons show that PSH’s solutions outperform the benchmarks’ ones, with statistically significant differences. Furthermore, the solution quality difference was greater for instances with more buffers, highlighting PSH capacity to conveniently exploit buffers in assembly lines. Lastly, analyses on the average processing times of stations, obtained for each buffer layout, partially verifies and question established results of the “bowl phenomenon” on unpaced assembly lines.

Robust solutions in multi-objective stochastic permutation flow shop problem

The aim of this paper is to present a simheuristic approach that obtains robust schedules for a multi-objective permutation flow shop problem with stochastic processing times. In fact, this approach minimizes the expected tardiness and standard deviation of tardiness, as a efficiency and a robustness measure for the stated problem. The simheuristic algorithm hybridize the Tabu Search metaheuristic and the Pareto Archived Evolution Strategy algorithm with a Monte Carlo Simulation process. At first, this approach is tested in 540 benchmarked instances for the deterministic case. It uses a zero-standard-deviation strategy to show the competitiveness compared with other implemented tabu search algorithms. Afterwards, two experimental designs are carried out, with the same 540 instances, where two factors of interest are considered, such as the probability distribution and coefficient of variation of processing times. The probability distributions used were the lognormal and uniform distributions, and three coefficients of variation (0.3, 0.4, and 0.5). Results show that both probability distributions and coefficients of variation have a significant effect in the objective functions, showing the importance of an accurate fitting of probability distributions of the parameter under uncertainty. In addition, these results evidence that the usage of deterministic methods in presence of random events are not desirable or recommended. Finally, the simheuristic was implemented to solve the scheduling problem in an optical laboratory showing better results for expected tardiness and standard deviation of tardiness in comparison with company schedules.

A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics

The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task. Unlike traditional optimisation techniques such as hill-climbing or derivatives-based methods, Genetic Algorithms are robust for addressing highly multi-modal and discontinuous search landscapes. In this paper, we outline a simheuristic GA-based approach for automatic generation of finite state machine based behavioural models of intelligent agents, where the aim is the identification of novel combat tactics. Rather than evolving states, the proposed approach evolves a sequence of transitions. We also discuss workable starting points for the use of Genetic Algorithms for such scenarios, shedding some light on the associated design and implementation difficulties.

A simheuristic approach for the two-dimensional vehicle routing problem with stochastic travel times

The two-dimensional vehicle routing problem (2L-VRP) is a realistic extension of the classical vehicle routing problem in which customers’ demands are composed by sets of non-stackable items. Examples can be found in real-life applications such as the transportation of furniture or industrial machinery. Often, it is necessary to consider stochastic travel times due to traffic conditions or customers availability. However, there is a lack of works discussing stochastic versions of the 2L-VRP. This paper offers a model of the 2L-VRP with stochastic travel times that also includes penalty costs generated by overtime. To solve this stochastic and non-smooth version of the 2L-VRP, a hybrid simheuristic algorithm is proposed. Our approach combines Monte Carlo simulation, an iterated local search framework, and biased-randomised routing and packing heuristics. Our algorithm is tested on an extensive benchmark, which extends the deterministic one for the 2L-VRP with unrestricted and non-oriented loading.

A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and Monte Carlo simulation

The present study extends a multi-objective mathematical model in the context of industrial hazardous waste management, which covers the integrated decisions of three levels with locating, vehicle routing, and inventory control. Analyzing these decisions simultaneously not only may lead to the most effective structure in the waste management network, but also may reduce the potential risk of managing the hazardous waste. Furthermore, because of the inherent complexity of the waste management system, uncertainty is inevitable and should be acknowledged to guarantee reliability in the decision-making process. From this perspective, the proposed model is novel in the following three aspects: (1) shifting from a deterministic to stochastic environment; (2) considering a multi-period planning horizon; and (3) incorporating the inventory decisions into the problem. The problem is formulated as a multi-objective stochastic Mixed-Integer Nonlinear Programming (MINLP) model, which can be easily converted into a MILP one. In terms of methodological contribution, a new simheuristic approach that is an integration of Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and Monte Carlo simulation is developed to overcome the stochastic combinatorial optimization problem of this study. Our findings verify the efficiency of the proposed approach as it is able to find a high-quality solution within a relatively reasonable computational time.

Combining variable neighborhood search with simulation for the inventory routing problem with stochastic demands and stock-outs

Abstract Vendor managed inventory aims at reducing supply chain costs by centralizing inventory management and vehicle routing decisions. This integrated supply chain approach results in a complex combinatorial optimization problem known as the inventory routing problem (IRP). This paper presents a variable neighborhood search metaheuristic hybridized with simulation to solve the IRP under demand uncertainty. Our simheuristic approach is able to solve large sized instances for the single period IRP with stochastic demands and stock-outs in very short computing times. A range of experiments underline the algorithm’s competitiveness compared to previously used heuristic approaches. The results are analyzed in order to provide closer managerial insights.

A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands

AbstractThe inventory routing problem (IRP) combines inventory management and delivery route‐planning decisions. This work presents a simheuristic approach that integrates Monte Carlo simulation within a variable neighborhood search (VNS) framework to solve the multiperiod IRP with stochastic customer demands. In this realistic variant of the problem, our goal is to establish the optimal refill policies for each customer–period combination, that is, those individual refill policies that minimize the total expected cost over the periods. This cost is the aggregation of both expected inventory and routing costs. Our simheuristic algorithm allows to consider the inventory changes between periods generated by the realization of the random demands in each period, which have an impact on the quantities to be delivered in the next period and, therefore, on the associated routing plans. A range of computational experiments are carried out in order to illustrate the potential of our simulation–optimization approach.

A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times

This paper describes a simulation–optimization algorithm for the Permutation Flow shop Problem with Stochastic processing Times (PFSPST). The proposed algorithm combines Monte Carlo simulation with an Iterated Local Search metaheuristic in order to deal with the stochastic behavior of the problem. Using the expected makespan as initial minimization criterion, our simheuristic approach is based on the assumption that high-quality solutions (permutations of jobs) for the deterministic version of the problem are likely to be high-quality solutions for the stochastic version – i.e., a correlation will exist between both sets of solutions, at least for moderate levels of variability in the stochastic processing times. No particular assumption is made on the probability distributions modeling each job-machine processing times. Our approach is able to solve, in just a few minutes or even less, PFSPST instances with hundreds of jobs and dozens of machines. Also, the paper proposes the use of reliability analysis techniques to analyze simulation outcomes or historical observations on the random variable representing the makespan associated with a given solution. This way, criteria other than the expected makespan can be considered by the decision maker when comparing different alternative solutions. A set of classical benchmarks for the deterministic version of the problem are adapted and tested under several scenarios, each of them characterized by a different level of uncertainty – variance level of job-machine processing times.