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We consider a manufacturer that produces in response to a stochastic demand and emits pollution during the production process. Industrial pollutants released into the air are characterised by spatial variability and heterogeneity, and the precision of instruments for measuring stochastic pollution stocks varies widely across pollutants. Consequently, commitment (open-loop) strategies to control production and associated emissions are considered more practical than contingent (feedback) approaches when manufacturers are concerned about environmental consequences. In this article, we derive an optimal, open-loop control policy over an infinite time horizon and show a condition that ensures that such a policy induces asymptotic convergence of expected inventory stock and pollution stock trajectories to unique steady states. Moreover, we find that the variance in the pollution stock also converges asymptotically to a unique steady state, which is critical for prevention of irreversible environmental consequences. We show that environmental uncertainty affects inventories, leading, in the long run, to lower expected steady-state inventory stocks. We compare feedback and open-loop production policies and show that though there are losses associated with the inability to track inventory and pollution stocks accurately and thus applying the open-loop control instead of feedback, the expected long-term inventory and pollution stocks are not affected. [ABSTRACT FROM AUTHOR]

This paper studies the security issues for cyber–physical systems, aimed at countering potential malicious cyber-attacks. The main focus is on solving the problem of extracting the most vulnerable attack path in a known attack graph, where an attack path is a sequence of steps that an attacker can take to compromise the underlying network. Determining an attacker's possible attack path is critical to cyber defenders as it helps identify threats, harden the network, and thwart attacker's intentions. We formulate this problem as a path-finding optimization problem with logical constraints represented by AND and OR nodes. We propose a new Dijkstra-type algorithm that combines elements from Dijkstra's shortest path algorithm and the critical path method. Although the path extraction problem is generally NP-hard, for the studied special case, the proposed algorithm determines the optimal attack path in polynomial time, O (n m) , where n is the number of nodes and m is the number of edges in the attack graph. To our knowledge this is the first exact polynomial algorithm that can solve the path extraction problem for different attack graphs, both cycle-containing and cycle-free. Computational experiments with real and synthetic data have shown that the proposed algorithm consistently and quickly finds optimal solutions to the problem. [ABSTRACT FROM AUTHOR]

Despite legislation and price controls by the government and state agencies that typically assume responsibility for providing water services, water bills continue to rise. However, this has not prevented growth in overall water consumption, nor an increase in water scarcity, both of which are fueled by worldwide population growth, urbanization, and demand for a higher quality of life. Market-based competition is thought to be a promising approach to controlling water charges and managing water scarcity. We compare a spot-market-based competitive supply model for water, which determines the equilibrium price, with a supply chain approach, in which a non-profit public entity encourages competition between private water providers within the framework of a regulated, time-invariant price. We derive dynamic equilibrium replenishment and inventory policies and show that, contrary to expectations, spot-market competition does not necessarily result in greater levels of supply, nor in a lower price, than does a regulated supply chain. Furthermore, the public-private partnership can have an additional advantage in the form of both higher consumption and higher consumer welfare. However, increasing the distribution cost, and hence, the regulated price is likely to diminish the differences between the two market types. [ABSTRACT FROM AUTHOR]

The management of a military supply chain (SC) involves the integration of production, packaging, warehousing, repair, maintenance and transportation of army supplies. In this paper, we focus on the integrated demand-responsive scheduling of maintenance and transportation operations within the SC. We consider a modular representation of a SC, in which the maintenance and transportation operations constitute the corresponding modules. An efficient integration of these operations is carried out by an additional controlling (commanding) module. We describe and analyse the optimisation problems arising in each module. The main contribution of the paper is that the analysis and optimisation of the controlling module permit to enhance the performance of the entire SC. Computational experiments prove the validity and effectiveness of the suggested models. [ABSTRACT FROM PUBLISHER]

We focus on planning transportation operations within a blood sample supply chain, which comprises clinics and a laboratory. Specifically, the main goal of this study is to obtain the optimal number of vehicles to be deployed and the scheduling of the pickup process. First, we formulate a mixed-integer programming (MIP) problem. Next, we develop a heuristic scheme composed of two heuristic algorithms and numerical search, and a new genetic algorithm. In an extensive numerical study, based on the data from a real-life blood sample collection process, we illustrate the potential of the new heuristic scheme. [ABSTRACT FROM AUTHOR]