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.
From airplanes to electric vehicles and trains, modern transportation systems require large quantities of energy. These vast amounts of energy have to be produced somewhere—ideally by using sustainable sources—and then brought to the transportation system. Energy is a scarce and costly resource, which cannot always be produced from renewable sources. Therefore, it is critical to consume energy as efficiently as possible, that is, transportation activities need to be carried out with an optimal intake of energetic means. This paper reviews existing work on the optimization of energy consumption in the area of transportation, including road freight, passenger rail, maritime, and air transportation modes. The paper also analyzes how optimization methods—of both exact and approximate nature—have been used to deal with these energy-optimization problems. Finally, it provides insights and discusses open research opportunities regarding the use of new intelligent algorithms—combining metaheuristics with simulation and machine learning—to improve the efficiency of energy consumption in transportation.
Cataloged from PDF version of article.This paper considers large-scale stochastic simulations with correlated inputs having normal-to-anything (NORTA) distributions\ud
with arbitrary continuous marginal distributions. Examples of correlated inputs include processing times of workpieces\ud
across several workcenters in manufacturing facilities and product demands and exchange rates in global supply chains.\ud
Our goal is to obtain mean performance measures and confidence intervals for simulations with such correlated inputs\ud
by accounting for the uncertainty around the NORTA distribution parameters estimated from finite historical input data.\ud
This type of uncertainty is known as the parameter uncertainty in the discrete-event stochastic simulation literature. We\ud
demonstrate how to capture parameter uncertainty with a Bayesian model that uses Sklar’s marginal-copula representation\ud
and Cooke’s copula-vine specification for sampling the parameters of the NORTA distribution. The development of such a\ud
Bayesian model well suited for handling many correlated inputs is the primary contribution of this paper. We incorporate\ud
the Bayesian model into the simulation replication algorithm for the joint representation of stochastic uncertainty and\ud
parameter uncertainty in the mean performance estimate and the confidence interval. We show that our model improves\ud
both the consistency of the mean line-item fill-rate estimates and the coverage of the confidence intervals in multiproduct\ud
inventory simulations with correlated demands
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