In this paper, we present a model to support decision-makers about where to locate safety barriers and mitigate the consequences of an accident triggering cascade effects. Based on the features of an industrial area that may be affected by domino accidents, and knowing the characteristics of the safety barriers that can be installed to stall the fire propagation between installations, the decision model can help practitioners in their decision-making. The model can be effectively used to decide how to allocate a limited budget in terms of safety barriers. The goal is to maximize the time-to-failure of a chemical installation ensuring a worst case scenario approach. The model is mathematically stated and a flexible and effective solution approach, based on metaheuristics, is developed and tested on an illustrative case study representing a tank storage area of a chemical company. We show that a myopic optimization approach, which does not take into account knock-on effects possibly triggered by an accident, can lead to a distribution of safety barriers that are not effective in mitigating the consequences of a domino accident. Moreover, the optimal allocation of safety barriers, when domino effects are considered, may depend on the so-called cardinality of the domino effects.

Distribution companies that serve a very large number of customers, courier companies for example, o en partition the geographical region served by a depot into zones. Each zone is assigned to a single vehicle and each vehicle serves a single zone. An alternative approach is to partition the distribution region into smaller microzones that are assigned to a preferred vehicle in a so-called tactical plan. When the workload in each microzone is known, the microzones can be reassigned to vehicles in such a way that the total distance traveled is minimized, the workload of the di erent vehicles is balanced, and as many microzones as possible are assigned to their preferred vehicle.In this paper we model the resulting microzone-based vehicle routing problem as a multi-objective optimization problem and develop a simple yet e ective algorithm to solve it. We analyze this algorithm and discuss the results that it obtains.

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A method is proposed to analyse the e ect of the algorithmic parameters and instance characteristics on the quality of a Pareto front produced by a multi-objective algorithm. is method is applied to a variable neighborhood tabu search that is used to solve a multi-objective microzone-based vehicle routing problem. Our method can accommodate many di erent performance indices for Pareto fronts and uses the P multicriteria decision analysis method to select the best con guration based on the characteristics of the instance being solved.

Real-life utility networks such as smart grids, pipelines, and water networks can be exposed to safety-and security-related risk. To mitigate the risks that might result in service interruptions for the users of these networks, countermeasures can be applied. In this paper, a decision model is proposed that assumes that all edges (e.g., pipes, cables) and nodes (e.g., switching or connection stations, substations in an electricity network) have a certain probability of failing, which can be reduced by applying appropriate security strategies. An optimization model is developed that determines the optimal security strategy to be applied to each node and each arc so as to minimize the probability for disconnected node pairs to arise in the network, subject to a budget constraint. A metaheuristic approach to solve this problem has been proposed. The metaheuristic is calibrated in a statistical experiment and detailed experiments on realistic instances confirm that it performs well.