The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

Semiconductor manufacturing simulation design and analysis with limited data

This paper discusses simulation design and analysis for Silicon Carbide (SiC) manufacturing operations management at New York Power Electronics Manufacturing Consortium (PEMC) facility. Prior work has addressed the development of manufacturing system simulation as the decision support to solve the strategic equipment portfolio selection problem for the SiC fab design [1]. As we move into the phase of collecting data from the equipment purchased for the PEMC facility, we discuss how to redesign our manufacturing simulations and analyze their outputs to overcome the challenges that naturally arise in the presence of limited fab data. We conclude with insights on how an approach aimed to reflect learning from data can enable our discrete-event stochastic simulation to accurately estimate the performance measures for SiC manufacturing at the PEMC facility

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

Agri-food supply chains with stochastic demands: a multi-period inventory routing problem with perishable products

This paper considers an agri-food supply chain with a single fresh food supplier, who owns a central warehouse that serves several retail centers. Retail centers carry a certain amount of inventory of the fresh product, which is prone to deterioration. The supplier makes both inventory and routing decisions to minimize the inventory, transportation, food-waste, and stock-out costs in the face of stochastic customer demand and perishable products that need to be delivered to each retail center. This inventory routing problem is known as perishable inventory routing problem (PIRP) with stochastic demands in the literature. We model it using a mixed integer program and propose a simheuristic algorithm, which integrates Monte Carlo simulation within an iterated local search, to solve it. Our experiments show that the proposed algorithm can improve the initial solution with reasonable computational times. The resulting procedure is easy to implement and is applicable to other domains where a multi-period PIRP with stochastic demands may appear

Solving facility location problems for disaster response using simheuristics and survival analysis: a hybrid modeling approach

One of the important decisions for mitigating the risk from a sudden onset disaster is to determine the optimal location of relevant facilities (e.g., warehouses), because this affects the subsequent humanitarian operations. Researchers have proposed several methods to solve the facility location problem (FLP) in disaster management. This paper considers a stochastic FLP where the goal is to minimize the expected time required to provide service to all affected regions when travel times are stochastic due to uncertain road conditions. The number of facilities to open is constrained by a certain maximum budget. To solve this stochastic optimization problem, we propose a hybrid simulation optimization model that combines a simheuristic algorithm with a survival analysis method to evaluate the probability of meeting the demand of all affected areas within a time target. An experiment using a benchmark set shows our model outperforms deterministic solutions by about 8.9%. </p

Agent-based simheuristics: Extending simulation-optimization algorithms via distributed and parallel computing

This paper presents a novel agent-based simheuristic (ABSH) approach that combines simheuristic and multi-agent system to efficiently solve stochastic combinatorial optimization problems. In an ABSH approach, multiple agents cooperate in searching a near-optimal solution to a stochastic combinatorial optimization problem inside a vast space of feasible solutions. Each of these agents is a simheuristic algorithm integrating simulation within a metaheuristic optimization framework. Each agent follows a different pattern while exploring the solution space. However, all simheuristic agents cooperate in the search of a near-optimal solution by sharing critical information among them. The distributed nature of the multi-agent system makes it easy for ABSH to make use of parallel and distributed computing technology. This paper discusses the potential of this novel simulation-optimization approach and illustrates, with a computational experiment, the advantages that ABSH approaches offer over traditional simheuristic ones.</p

Solving an inventory-routing problem with stochastic demand using simheuristic

Supply chain operations have become more complex. Hence, in order to optimise supply chain operations, we often need to simplify the optimisation problem in such a way that it can be solved efficiently using either exact methods or metaheuristics. One common simplification is to assume all model inputs are deterministic. However, for some management decisions, considering the uncertainty in model inputs (e.g. demands, travel times, processing times) is essential. Otherwise, the results may be misleading and might lead to a wrong decision. This paper considers an example of a complex supply chain operation that can be viewed as an Inventory-Routing Problem with stochastic demands. We demonstrate how a simheuristic framework can be employed to solve the problem. Further, we illustrate the risks of not considering input uncertainty. The results show that simheuristic can produce a good result and ignoring the uncertainty in the model input may lead to sub-optimal results