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• An integrated location and routing approach is proposed for Hazmat transportation. • A two-stage stochastic programming model is used. • Three heuristics based on statistical sampling are presented to solve the model. • The heuristics' performance are conducted by numerical simulations. This article considers how best to strategically transport and route hazardous materials through a bi-modal transportation network (MMTS) consisting of road and rail links. The placement of transfer yards is also considered as well as the possibility of disruptions at those facilities. For this decision problem a two-stage stochastic programming model is formulated. The objective of the model is to minimize transportation costs and risk. As the model is intractable to solve using traditional techniques, three algorithms are proposed, namely Maximum Likelihood Sampling (MLS), Sample Average Approximation (SAA), and a combination of these two algorithms (MLSAA). Those optimization approaches have been applied to various instances to demonstrate their viability and effectiveness. Numerical testing shows that the MLSAA algorithm is superior and can solve large-scale instances. [ABSTRACT FROM AUTHOR]

One main reason for food scarcity is its improper and uneven distribution amongst those who require aid. To overcome this issue, charities and food banks serve as the connection between beneficiaries and donors. They are mostly nonprofit organizations but they incur operational costs for storage and delivery of the donated food items. The donated food items are either canned and cold, or hot meals from over production of businesses; and therefore their freshness, inventory and shelf-life bring about additional operational challenges in distribution and logistics. The uncertainty of demand and supply is another challenge to overcome, which necessitates a robust plan. This article proposes a multi-objective mathematical programming model for a food bank network design to optimize the cost, food freshness and its nutritional value. A robust fuzzy counterpart of the model is developed together with three solution methods including ϵ -constraint, MOGWO and NSGA II. The MOGWO algorithm shows a better performance in our numerical experiment with large instances. Its application on a case study resulted in a supply network with lower cost, smaller fleet size and higher food quality, although less fresh distributed foods compared to the benchmark network. The trade-off between the cost and freshness of food is depicted here by examining shelf-life of products and vehicle capacity. The long lasting products incur less transportation cost due to compactness of packaging, and similarly, higher capacity vehicles lead to more cost efficient dispatch with longer routes which decrease the freshness of food. According to our numerical results, higher uncertainty rate in the network increases total cost, but also overall nutritional value of the distributed food over the network due to greater supply of food. • Modeling a food bank network design problem under uncertainty is studied. • The proposed model simultaneously adopted all strategic and tactical decisions. • A fuzzy robust optimization method was used to control the uncertain parameters. • Three solving methods: epsilon-constraint, MOGWO and NSGA II, were implemented. [ABSTRACT FROM AUTHOR]

Recently, integrated models of facility location and network design (FLND) problems have attracted many research attentions. This class of NP-hard problems, as the name suggests, simultaneously optimizes facility locations and the design of the underlying network. They have been a central and useful planning tool in many contexts, e.g., regional planning, distribution, energy management, etc. However, FLND problems are extremely hard to solve and computational difficulties has limited their way into the practice. In this paper, we propose three variants of a simple and efficient meta-heuristic based on variable neighborhood search method to solve the capacitated FLND problem effectively. We particularly improve the neighborhood exploration mechanism by taking advantage of exact methods within the solution framework. Computational experiments on a wide-range of test instances with up to 100 nodes and 600 links show that our approaches consistently return high quality solutions in short CPU times in comparison with the state-of-the-art of commercial solvers. [ABSTRACT FROM AUTHOR]

Abstract: This article presents a mixed-integer model to optimize the location of facilities and the underlying transportation network at the same time to minimize the total transportation and operating costs. In this problem, it is assumed that for connecting two nodes, there are several types of links in which their capacity, transportation and construction costs are different. The developed model has various applications in telecommunication, emergency, regional planning, pipeline network, energy management, distribution, to just name a few. To solve the model effectively, this paper also proposes a fix-and-optimize heuristic based on the evolutionary fire-fly algorithm. Finally, to validate the model and evaluate the algorithm’s performance, a series of test instances with up to 100 nodes and 600 candidate links with three different levels of quality are reported. [Copyright &y& Elsevier]

Abstract: This paper presents a model for the budget-constrained dynamic (multi-period) uncapacitated facility location–network design problem (DUFLNDP). This problem is concerned with the determination of the optimal locations of facilities and the design of the underlying network simultaneously in which there is a budget constraint on investment for opening the facilities and constructing (activating) links for each time period during the planning horizon. The objective is to minimize the total travel costs for customers and operating costs for facilities and network links. Furthermore, a greedy heuristic and a fix-and-optimize heuristic based on simulated annealing and exact methods (Branch & Bound and cutting methods) are proposed to solve the model. The performance of the proposed algorithms were tested on extensive randomly generated instances and compared with the CPLEX solver. The budget-constrained DU FLNDP has a lot of important applications which as one of them, the accessibility of health care facilities in Illam Province of Iran is investigated. [Copyright &y& Elsevier]

Uncertainties of renewable power generation, load, and electricity price imposes serious challenges in optimal scheduling of microgrids (MGs). To cope with this issue, advanced models should be developed to optimize scheduling problem of MGs under uncertainties. However, the optimality and robustness of solutions as well as the computational burden are still a matter of concern when applied to scheduling of a practical MG. To provide a model which is adjustably robust and computationally tractable and also financial effective at the same time, this paper proposes a novel uncertainty handling framework based on stochastic p -robust optimization method for day-ahead scheduling of MGs including renewable and non-renewable distributed generations, combined heat and power systems, energy-storage systems, as well as demand response providers. The objective is to find optimal scheduling of MG in a way that the expected profit is maximized while the regret in each scenario is bounded. The effectiveness of the proposed model is tested on a MG test system thorough different case studies. Simulation results demonstrate that the robustness of the scheduling results is enhanced significantly without substantial decrease in the expected profit of MG. • A comprehensive day-ahead energy management model is developed for CHP-based microgrids. • The model is transformed to an MILP one to ensure near-optimal solutions. • All uncertainties including market price, load and renewable intermittencies are modeled. • A new stochastic p-robust optimization framework is proposed to properly cover uncertainty risks. [ABSTRACT FROM AUTHOR]

The location-pricing problem aims to solve pricing and facility location problems at the same time. While the demand of customers is known in the classical facility location problem, it is a function of price in the location-pricing problem. Moreover, some other factors, such as production, transportation and inventory costs, and competition, can affect the final price of products, making the location-pricing problem more complicated. Considering the necessity of incorporating environmental aspects into supply chain design and the importance of considering the behavioral characteristics of decision-makers, this study addresses the location-pricing problem based on these two significant perspectives. In this paper, there is a lead company responsible for locating and planning a set of manufacturers in the first echelon. On the other hand, the focal manager controls the total emissions and sets the wholesale price. In the second echelon, there are two retailers that base their pricing strategies on social preferences in order to gain a larger market share. The competition between the retailers determines the demands of the customers. The modeling approach is based on the Stackelberg-behavioral Nash game and is formulated as a bi-level mixed-integer nonlinear programming model. The problem is solved using a combination of an analytical solution method and the utilization of a commercial optimization solver. The proposed model is assessed using a real case study in the energy sector. The results indicate that deviating from purely selfish behavior in the pursuit of profit can lead to beneficial outcomes. Generally speaking, price competition between status-seeking retailers results in lower prices for the leader and followers compared to the standard game theory approach. This leads to an increase in average demand in the market, which results in increased profit for members and consequently improves the performance of the whole system. Also, it is worth mentioning that the status-seeking behavior of retailers results in a decrease in retail prices, which improves social welfare. [Display omitted] • Considering behavioral aspects of competitive decision-makers in sustainable LPPs. • Applying BGT approach to capture the players' real behaviors. • The study aims to model retailers' social preferences in a competitive two-tier SC. • The proposed model is presented in the form of a Stackelberg-behavioral Nash game. • Status-seeking retailers increase profits, system performance, and social welfare. [ABSTRACT FROM AUTHOR]