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In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

Inventory–production coordination for customer orders is becoming increasingly important for companies to increase customer responsiveness and achieve economic purposes. In this paper, the joint optimization of inventory and production is considered for stochastic customer orders to maximize the throughput. Demands of customer orders dynamically arrive at the inventory department, and each incoming order consists of multiple product types with random workloads. To process the workloads, certain amounts of a common raw material are required and need to be drawn from the inventory department. A customer order will be lost if there do not exist enough raw materials in the inventory department. With the necessary materials, workloads of accepted orders will be assigned to a set of unrelated parallel servers to be processed in the production department. This paper intends to maximize the effective throughput through proper coordination of the inventory and the production departments. For this problem, system bottlenecks are identified and analyzed, and mathematical programming models are developed to determine the optimal throughput and the corresponding inventory and production policies. Several special cases are also explored to provide intuitive insights into the relationship between the system parameters and optimal throughput. Relationships between key model parameters and effective throughput are identified through sensitivity analysis and further validated by the results of computational experiments. Note to Practitioners—Coordination of production and inventory is crucial for system efficiency improvement. In order to better operate the system and respond to customer demands, this paper explores such coordination for stochastic customer orders to achieve the maximum effective throughput. Particularly, we consider the case where production is constrained by material inventory. System bottlenecks for various cases are identified, which can facilitate system diagnosis and performance evaluation. Based on the bottleneck analysis, an optimization problem is formulated to provide the optimal throughput and policy. Several important practical cases are also discussed, including single product type, identical/uniform servers, and speed-identical/uniform product types. Besides, sensitivity analysis is conducted to show how the changes of parameters such as expected workload and server speed will affect throughput performance. In the future research, more complex production environments and cost-related constraints will be further considered to help practitioners achieve desirable system outputs. [ABSTRACT FROM AUTHOR]

In this paper, a new group of optimization algorithms named Human Strategy Algorithm (HS) is proposed which is inspired by human strategies to problem solving. The main idea of HS is based on human actions to find the problem’s optima by means of accessible instruments. As the environment of an unknown problem assumed to be a black box, it is supposed that the environment of our problem is a dark room occupied by several men named blind men. The main mission of these men is to look for the optimum solution. Each man has at least one instrument as his assistance. Like real life, the instrument might be any tool such as stick, billy, rope, stone, yoyo, sweep. Any instrument by its unique features is suitable in some situations. In fact, this algorithm maps problem space and searches agents to dark room and people, respectively. In this paper, one sample algorithm of the group of human strategy, YOYO Blind Man Algorithm (YOYO-BMA), is introduced which uses yoyos as men’s accessible instruments. The performance of the YOYO-BMA is evaluated on a set of benchmark problems provided for CEC’2010 Special Session and Competition on Large-Scale Global Optimization (Tang et al. 2010). The results show superior performance of proposed algorithm in comparison with others. Moreover, the problem of designing urban traffic network is solve to evaluate the algorithm using a real complex problem. [ABSTRACT FROM AUTHOR]

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively. [ABSTRACT FROM AUTHOR]

This paper considers the design of an input signal for minimizing the time and energy required to detect and isolate faults in the outputs of a system. Faults are represented by discrete switches between affine models with bounded disturbances and bounded measurement errors. Within this framework, previous work has demonstrated that a minimally harmful input guaranteeing fault diagnosis can be obtained by solving a Mixed Integer Quadratic Program (MIQP). A closed-loop approach allows to reduce the length and/or norm of this input by solving an MIQP at each time instant with the newly available measurements. However, solving such programs online can be computationally demanding. In this paper, we employ multi-parametric (mp) programming to move most of the computation offline, thus allowing the application of the closed-loop approach to fast processes. Still, the mp-MIQP complexity becomes quickly prohibitive as the number of faulty models increases. In order to overcome this problem, we propose a strategy based on mp-optimization and graph theory that takes into account only two models at a time. While this approach is suboptimal compared to the case in which all the models are considered simultaneously, simulations show that, in practice, the performance is comparable. [ABSTRACT FROM AUTHOR]

Abstract Inter-plant hydrogen network with reuse/recycle optimization is important for saving hydrogen resource. Thus, it's necessary to optimize the inter-plant hydrogen network. In this paper, a novel two-step method that combines the pinch insight with mathematical programming is developed to optimize the inter-plant hydrogen network with purification reuse/recycle. A new transhipment model for targeting inter-plant hydrogen network is built to target the hydrogen utility consumption for each refinery. After the hydrogen consumption is determined, individual plant hydrogen networks are designed separately. The mathematical model is linear to guarantee global optimal solution. Furthermore, the model can also optimize the number of inter-plant connections of the hydrogen network. Case study indicates the effectiveness of model. Highlights • A two-step method to target and design inter-plant hydrogen network with purification reuse/recycle is proposed. • The hydrogen utilities for inter-plant network can be reduced further by inter-plant streams. • The number of inter-plant connections can be optimized. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents new definitions for process intensification identifying two main categories called unit intensification and plant intensification. This paper also proposes general mathematical programming formulations for the different categories of intensification through a disjunctive formulation to consider the use of existing units or to include additional units. The ideas of process integration (energy, mass and property integration) are very useful for process intensification helping in maximizing throughput for a given process, minimizing process inventory for a given throughput or in minimizing utilities and feedstock for a given throughput (this means that process intensification is broader than process integration). This paper identifies that process intensification is a wider approach than single unit intensification as it was considered by most of the previous works. A case study is presented to show the applicability of the proposed models and definitions. [Copyright &y& Elsevier]

In this paper, we present a fuzzy semi-infinite optimization problem. Moreover, we will deduce the Fritz-John and Kuhn-Tucker necessary conditions of this problem. Finally, a numerical example is given to illustrate the results. [ABSTRACT FROM AUTHOR]

Safety and performance are important factors in the design and operation of chemical plants. This paper describes the formulation of a mixed integer nonlinear programming model for the optimization of plant layout with safety considerations. The model considers a quantitative risk analysis to take safety into account, and a bowtie analysis is used to identify possible catastrophic outcomes. These effects are quantified through consequence analyses and probit models. The model allows the location of facilities at any available point, an advantage over grid-based models. Two case studies are solved to show the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: The optimal placement of turbines in a wind farm is critical to the maximization of power production. In this paper, we develop a new mathematical programming approach for wind farm layout optimization. We use Jensen's wake decay model to represent multi-turbine wake effects. We develop mixed integer linear and quadratic optimization formulations and apply them to several example layout cases in the literature. Compared to previous approaches, our models produce layouts that tend to be more symmetric and that generate slightly more power. Our formulations solve quickly, allowing a decision maker to efficiently explore the impact of different turbine densities in a wind farm. [Copyright &y& Elsevier]

Abstract: This paper presents a disjunctive mathematical model for the optimal design of air cooled heat exchangers. The model involves seven discrete decisions which are related to the selection of the type of the finned tube, number of tube rows, number of tube per row, number of passes, fins per unit length, mean fin thickness and the type of the flow regime. Each discrete decision is modeled using disjunctions, boolean variables and logical propositions. The main continuous decisions are: fan diameter, bundle width, tube length, pressure drops and velocities in both sides of the ACHE, heat transfer area, fan power consumption. Then, the resulting generalized disjunctive programming model is reformulated as a mixed integer non-linear programming, implemented in GAMS (general algebraic modeling system) and solved using a branch-and-bound method. The proposed model was successfully verified by comparing the obtained output results with different designs taken from the literature. Then, the model is solved to obtain the optimal designs corresponding to the following optimization criteria: a) minimization the total annual cost which includes investment (heat transfer area) and operating cost (fan power consumption), b) minimization the heat transfer area and c) minimization the fan power consumption. Obtained optimal and sub-optimal designs are compared in detail. [Copyright &y& Elsevier]

Abstract: This research deals with the utilization of analytic programming for a synthesis of control law for three selected discrete chaotic systems. The novelty of the approach is that a tool for symbolic regression–analytic programming–is used for such kinds of difficult problems. The paper consists of the descriptions of analytic programming as well as chaotic systems and used cost functions. For simulations, evolutionary algorithm SOMA (Self-Organizing Migrating Algorithm) and analytic programming were used. For each case study, repeated simulations were conducted to outline the effectiveness and robustness of used methods. All repeated simulations have given satisfactory results. [Copyright &y& Elsevier]

Abstract: Optimal allocation of emergency resources is a crucial content of emergency management. It is a key step in emergency rescue and assistance. Multiple resources and potential secondary disasters are often neglected in the existing methods, which desperately need to be improved. In this paper, we formulate the emergency resource allocation problem with constraints of multiple resources and possible secondary disasters, and model the multiple resources and multiple emergency response depots problem considering multiple secondary disasters by an integer mathematical programming. For the complexity, a heuristic algorithm is designed to efficiently solve it based on linear programming and network optimization. The algorithm modifies the solutions of the linear programming by setting a priority of preference for each location where the secondary disasters will take place with certain possibilities. The numerical simulation provides evidence for its effectiveness and efficiency. Our method and algorithm can also be implemented in the practical applications with large-scale scenario. [Copyright &y& Elsevier]

Abstract: Optimization of industrial water networks made up of several water-using processes can reduce freshwater usage and wastewater production. A common feature of previous water network studies is that each water-using operation is assumed to have a single outlet stream. In this paper, a superstructure and a mathematical programing model are proposed for optimization of a water network with both single and two outflow water-using processes. A two outflow water-using process has two outlet streams with different impurity concentrations, in which one has a lower concentration and the other a higher concentration. Outlet stream with a high concentration can be reused in other water-using processes, regenerated in a regeneration system or discharged, while the outlet stream with a low concentration can only be reused in other water-using processes. These intricacies are accounted for in the mathematical model. For a two outflow water-using process, the main parameters are the allocation rates of flowrate and mass load which may be determined through consideration of the process’s performance characteristics. The optimization methodology is demonstrated through a case study of a water network consisting of four single outflow water-using units, one two outflow water-using unit and a regeneration unit. [Copyright &y& Elsevier]

Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modeled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system and an experiment on an industrial robot are presented. They show that there exists a real possibility for a significant reduction of the energy consumption in comparison to state-of-the-art scheduling approaches. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents a new mathematical programming model for the optimal synthesis of recycle and reuse networks considering simultaneously the integration of the water network system and the surrounding watershed to satisfy process and environmental constraints. The model considers the optimal location of the new industrial facility to integrate its wastewater discharge to the environment with the surrounding watershed through a disjunctive formulation. The pollutants discharged for the new plant are tracked simultaneously with the other discharges to the watershed (i.e., residential, sanitary, industrial and extractions), and the natural phenomena that affect the composition of the watershed (i.e., evaporation, filtration, etc.), in addition to the chemical reactions that are carried out in the rivers. The objective function consists in minimizing the total annual cost that is constituted by the installation of the new plant cost (including the transportation for raw materials, products and services, as well as the land cost), the wastewater treatment costs (including the piping cost) and the fresh sources cost. Two example problems were used to show the applicability of the proposed methodology. [Copyright &y& Elsevier]

Solution approaches to chance constrained programming (CCP) have been recently developed and applied in many areas for optimization under uncertainty. Due to the nonlinear model with multiple uncertain variables as well as multiple output constraints, CCP has not been directly applied to optimal power flow (OPF) under uncertainty. The objective of this paper is twofold. First, we introduce the CCP approach to OPF under uncertainty and analyze the computational complexity of the chance constrained OPF. Second, the effectiveness of implementing a back-mapping approach and a linear approximation of the nonlinear model equations to solve the formulated CCP problem is investigated. Load power uncertainties are considered as multivariate random variables with correlated normal distribution. Based on both the nonlinear and the linearized model, results of a five-bus system and the IEEE 30-bus test system are presented to demonstrate the scope of chance constrained OPF. [ABSTRACT FROM PUBLISHER]

This paper proposes an active-reactive power dispatch procedure that is expressed via a bilevel optimization problem whose upper and lower level criteria are, respectively, the minimum opportunity cost and minimum offered price of active power. It analyzes the impact of minimizing such criteria on system operation, and it investigates the behavior of marginal prices and opportunity costs and their use in mechanisms to compensate generators for power provision. The bilevel problem is solved using a version of interior point methods derived for mathematical programs with complementarity constraints. Results obtained with three test systems are analyzed. [ABSTRACT FROM PUBLISHER]

This paper provides a methodology to assist a strategic producer in making informed decisions on generation investment. A single target year is considered with demand variations modeled through blocks. The strategic behavior of the producer is represented through a bilevel model: the upper-level considers both investment decisions and strategic production actions and the lower-level corresponds to market clearing. Prices are obtained as dual variables of power balance equations. Rival uncertainties (on offering and investment) are characterized through scenarios. The resulting model is a large-scale mixed-integer LP problem solvable using currently available branch-and-cut techniques. Results pertaining to an illustrative example and a case study are reported and discussed. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper a systematic methodology is presented for the simultaneous optimal selection of raw materials, fossil fuels and alternative fuels in cement production. The aim is to offer a generic mathematical formulation that can be used as the basis for developing case specific mathematical formulations that can assist the strategic decision-making process. The mathematical model presented takes into consideration the essential elements of a cement plant operation. The final formulation is a mixed integer linear programming problem that aims at minimizing the overall operating cost. A realistic case study is presented which demonstrates the usefulness of the proposed mathematical programming methodology. [Copyright &y& Elsevier]

Abstract: This paper presents a mathematical programming model for the optimal design of mass and property integration networks that include property interceptors within the structure of the network, as opposed to the end-of-pipe use of such interceptors. The model is based on a recycle and reuse scheme that simultaneously satisfies process and environmental constraints. The properties considered in this work are composition, toxicity, theoretical oxygen demand, pH, density and viscosity. The property mixing rules included in the model give rise to bilinear terms for the property operators, and a global optimization algorithm is used for the solution of the model. The model minimizes the total annual cost of the network, which includes the fresh sources cost and the annualized property treatment system and the piping costs. Three examples are included to show the applicability and advantages of the proposed model. [Copyright &y& Elsevier]

Abstract: In this paper, the optimization techniques for solving a class of non-differentiable optimization problems are investigated. The non-differentiable programming is transformed into an equivalent or approximating differentiable programming. Based on Karush–Kuhn–Tucker optimality conditions and projection method, a neural network model is constructed. The proposed neural network is proved to be globally stable in the sense of Lyapunov and can obtain an exact or approximating optimal solution of the original optimization problem. An example shows the effectiveness of the proposed optimization techniques. [Copyright &y& Elsevier]

Dynamic site layout planning requires identifying and updating the positions of all temporary construction facilities such as offices, storage areas, and workshops over the entire project duration. Existing models do not guarantee global optimal solutions because they focus on optimizing the planning and layout of successive construction stages in a chronological order, without considering the future implications of layout decisions made in early stages. This paper presents the development of an approximate dynamic programming model that is capable of searching for and identifying global optimal dynamic site layout plans. The model applies the concepts of approximate dynamic programming to estimate the future effects of layout decisions in early stages on future decisions in later stages. The model is developed in three main phases: (1) formulating the decision variables, geometric constraints, and objective function of the dynamic site layout planning problem; (2) modeling the problem using approximate dynamic programming; and (3) implementing and evaluating the performance of the model. An evaluation example is analyzed to illustrate the use of the model and demonstrate its capabilities in generating global optimal solution for dynamic site layout planning of construction projects. [ABSTRACT FROM AUTHOR]

This paper discusses a method for scheduling Berth and Quay cranes, which are critical resources in port container terminals. An integer programming model is formulated by considering various practical constraints. A two-phase solution procedure is suggested for solving the mathematical model. The first phase determines the Berthing position and time of each vessel as well as the number of cranes assigned to each vessel at each time segment. The subgradient optimization technique is applied to obtain a near-optimal solution of the first phase. In the second phase, a detailed schedule for each Quay crane is constructed based on the solution found from the first phase. The dynamic programming technique is applied to solve the problem of the second phase. A numerical experiment was conducted to test the performance of the suggested algorithms. [ABSTRACT FROM AUTHOR]