Stochastic user equilibrium model with a tradable credit scheme and application in maximizing network reserve capacity

The tradable credit scheme (TCS) outperforms congestion pricing in terms of social equity and revenue neutrality, apart from the same perfect performance on congestion mitigation. This article investigates the effectiveness and efficiency of TCS on enhancing transportation network capacity in a stochastic user equilibrium (SUE) modelling framework. First, the SUE and credit market equilibrium conditions are presented; then an equivalent general SUE model with TCS is established by virtue of two constructed functions, which can be further simplified under a specific probability distribution. To enhance the network capacity by utilizing TCS, a bi-level mathematical programming model is established for the optimal TCS design problem, with the upper level optimization objective maximizing network reserve capacity and lower level being the proposed SUE model. The heuristic sensitivity analysis-based algorithm is developed to solve the bi-level model. Three numerical examples are provided to illustrate the improvement effect of TCS on the network in different scenarios.     

Planning for a bus-based evacuation

Planning for a bus-based regional evacuation is essential for emergency preparedness, especially for regions threatened by hurricanes that have large numbers of transit-dependent people. While this difficult planning problem is a variant of the vehicle routing problem, it differs in some key aspects, including the objective and the network structure (e.g., capacitated shelters). This problem is not well studied. In this paper we introduce a model specifically designed for bus-based evacuation planning, along with two mathematical programming formulations, which are used to develop a heuristic algorithm. Using these models, we analyze the differences in the structural properties of optimal solutions between this problem and traditional vehicle routing problems.     

Transmission system expansion planning by a branch-and-bound algorithm

A branch and bound (B&B) algorithm using the DC model, to solve the power system transmission expansion planning by incorporating the electrical losses in network modelling problem is presented. This is a mixed integer nonlinear programming (MINLP) problem, and in this approach, the so-called fathoming tests in the B&B algorithm were redefined and a nonlinear programming (NLP) problem is solved in each node of the B&B tree, using an interior-point method. Pseudocosts were used to manage the development of the B&B tree and to decrease its size and the processing time. There is no guarantee of convergence towards global optimisation for the MINLP problem. However, preliminary tests show that the algorithm easily converges towards the best-known solutions or to the optimal solutions for all the tested systems neglecting the electrical losses. When the electrical losses are taken into account, the solution obtained using the Garver system is better than the best one known in the literature.     

Reliability assessment of restructured power systems using optimal load shedding technique

Power systemrestructuring and deregulation has changed the strategy of reliabilitymanagement of a power system. Load shedding, and generation and reserve re-dispatch methods used in the existing reliability evaluation techniques have to be improved to incorporate these changes. An optimisation technique, incorporating those changes, is proposed in this study to determine load curtailment and generation re-dispatch for each contingency state in the reliability evaluation of restructured power systems with the Poolco market structure. The problem is formulated using the optimal power flow (OPF) technique. The objective of the problem is to minimise the total system cost, which includes generation, reserve and interruption costs, subject to market and network constraints. A model for the contingency management of a Poolco power market is presented to include generation and reserve biddings, reliability considerations and transmission network constraints in reliability evaluation. Both supply side reliability for a generation company (Genco) and demand-side reliability for a customer can be calculated using the technique. The proposed technique can be used to evaluate both conventional and restructured power systems, and can provide both economic and reliability information for the independent system operator to manage system reliability, for Gencos to enhance their reliability, and for customer to select suppliers. The modified IEEE-RTS with the Poolco market has been analysed to illustrate the techniques. The results obtained using the proposed technique have been compared with those from the existing load shedding techniques.     

Nodal congestion price estimation in spot power market using artificial neural network

In the world wide increasing trend of restructured power system, open access in transmission system and competition in generation and distribution have introduced a frequently occurring problem of congestion. To establish a proficient price-based congestion management procedure, the nodal pricing strategy is found to be appropriate. From congestion management point of view, the optimal nodal prices are comprised of two basic components. First component is locational marginal price, that is marginal cost of generation to supply load and transmission losses both. Second component is nodal congestion price (NCP), that is the charges to maintain network security. Levenberg-Marquardt algorithm based neural network (LMANN) for estimating NCPs in spot power market by dividing the whole power system into various congestion zones is presented. Euclidian distance based clustering technique has been applied for feature selection before employing LMANN. The purpose of using artificial neural network (ANN) based approach for NCP estimation in spot power market is to exploit the tolerance for any missing or partially corrupted data to achieve tractability, robustness and very fast solution. The proposed ANN method also handles the congestion price volatility by taking continuously varying load and constrained transmission into account. The information provided by the proposed method regarding the formation of different congestion zones and the severity of congestion within a zone instructs both the market participants as well as independent system operator in making effective decisions. The proposed method has been examined for an RTS 24-bus system and is found to be quite promising.     

Determining the number of kanbans in a multiproduct,multistage production system

Significant reductions in setup and lead times reported by the Japanese have led Western manufacturers to rethink the need for complex computer-based MRP systems. Consequently, many of these firms have adopted some form of kanban for shop floor control. In this paper, a planning model is developed to assist line managers in determining an optimal kanban policy at each workstation. The objective is to work within the capacity of the system to balance cost and service over the planning horizon. The model takes the form of a mixed integer linear program, and is solved with standard techniques. When no shortages are permitted, a number of alternative formulations are introduced that sharply reduce the computational burden. Results are presented for an example based on the assembly of printed wire boards at Texas Instruments' Austin plant.     

Transmission Control under Multi-Service Disciplines in Wireless Sensor Networks

The wireless sensor network (WSN), as the terminal data acquisition system of the 5G network, has attracted attention due to advantages such as low cost and easy deployment. Its development is mainly restricted by energy. The traditional transmission control scheme is not suitable for WSNs due to the significant information interaction. A switchable transmission control scheme for WSNs based on a queuing game (SQGTC) is proposed to improve network performance. Considering that sensor nodes compete for the resources of sink nodes to realize data transmission, the competitive relationship between nodes is described from the perspective of a game. Different types of sensor node requests require a sink node to provide different service disciplines. Mathematical models of social welfare are established for a sink node under the service disciplines of first-come, first-served (FCFS), egalitarian processor sharing (EPS), and shortest service first (SSF). The optimal service strategies are obtained by maximizing social welfare. The sensor nodes provide the expected benefits and satisfy the service requirements of the requests, and the sink node switches the transmission control strategy for the service. Simulation results show that the proposed scheme improves the data transmission efficiency of WSNs and achieves the optimal allocation of resources.     

Econo-techno dynamic model for promotion of maritime industries in the developing countries

The computer simulation model proposed in this study enables a policy planner in a developing country to assess the contribution of new marine transportation technology to the national economy, and to find an optimal scheme for introducing the new technology. The simulation model consists of the national economic cycle submodel (econo-submodel) and the maritime industry technology submodel (techno-submodel). The econo-submodel, which utilizes input-output analysis, can evaluate the multiplying effects on the national economy of preferential investments in the marine transportation system. The techno-submodel may be characterized as a production planning model for the marine transportation system. The techno-submodel is also furnished with several functions such as (1) forecasting the demand for marine transportation, (2) optimizing the merchant marine fleet, (3) planning the development of port facilities, (4) planning the service of ship repairing, and (5) deciding on feasible investment under several constraints. The two submodels include several parameters with respect to economics and technology as exogenous variables and are mutually linked to trade and investment

The new technologies introduced into marine industry are categorized from the viewpoints of labour-saving and energy-saving technology. The degree of containerization of general cargo in liner services and ‘Segregated Ballast Tank (SBT)’ in tanker services are taken into consideration. Port facilities are reinforced corresponding to new shipping technology. The optimal allocation of investment between ships and ports depends on the trade-off of the capital efficiency

The uniqueness of this study is in the potential for the quantitative evaluation, by means of national economic indicators, of the effect of introducing the new technology     

Application and comparison of metaheuristic techniques to generation expansion planning in the partially deregulated environment

The deregulation of the electric utility industry is an on-going process in many developing countries. A novel generation expansion planning (GEP) model is proposed that is suitable for developing countries such as India in a partially deregulated environment. In a partially deregulated/restructured environment, both utilities and independent power producers (IPPs) participate in the generation market. In this model, the utility purchases electric power from the IPPs and sells it to the consumer. The utility maximises its profit and ensures profits for all the participating IPPs. In addition, the utility checks under/over investment and considers system security, national security (fuel-mix ratio), social welfare and reliability simultaneously. The budget constraints of the utility are to be taken into consideration during the expansion plan. Metaheuristic techniques, such as genetic algorithms, differential evolution, evolutionary programming, evolutionary strategy, particle swarm optimisation, tabu search, simulated annealing, and the hybrid approach are used to solve the restructured GEP problem, and their performances are evaluated and validated against the dynamic programming (DP) method for a synthetic test system having five types of candidate plant for the utility and three types of candidate plant for IPP, with a 6 year planning horizon. The effectiveness of the proposed modifications and techniques is also addressed.     

Impact of the implementation of rest days in live bird markets on the dynamics of H5N1 highly pathogenic avian influenza

Live bird markets (LBMs) act as a network ‘hub’ and potential reservoir of infection for domestic poultry. They may therefore be responsible for sustaining H5N1 highly pathogenic avian influenza (HPAI) virus circulation within the poultry sector, and thus a suitable target for implementing control strategies. We developed a stochastic transmission model to understand how market functioning impacts on the transmission dynamics. We then investigated the potential for rest days—periods during which markets are emptied and disinfected—to modulate the dynamics of H5N1 HPAI within the poultry sector using a stochastic meta-population model. Our results suggest that under plausible parameter scenarios, HPAI H5N1 could be sustained silently within LBMs with the time spent by poultry in markets and the frequency of introduction of new susceptible birds'' dominant factors determining sustained silent spread. Compared with interventions applied in farms (i.e. stamping out, vaccination), our model shows that frequent rest days are an effective means to reduce HPAI transmission. Furthermore, our model predicts that full market closure would be only slightly more effective than rest days to reduce transmission. Strategies applied within markets could thus help to control transmission of the disease.     

Path synthesis of four-bar mechanisms using synergy of polynomial neural network and Stackelberg game theory

In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms.     

Cascaded cuckoo search optimization of router placement in signal attenuation minimization for a wireless sensor network in an indoor environment

Optimization of the energy required during data transmission in a wireless indoor area network can be achieved through intelligent router placements to keep the network active for longer and improve the packet delivery ratio. In this work, a cascaded cuckoo search algorithm (C-CSA) approach is implemented for optimal router placement in a wireless indoor area network based on minimization of signal attenuation during data packet transmission through a novel mathematical formulation. The transmission energy for each packet, signal-to-noise ratio and packet error ratio are studied over 50 independent runs of the algorithm. The results are presented with statistical confidence to prove the efficiency of the algorithm. C-CSA provides superior results for data transmission energy and the packet delivery ratio compared to existing algorithms. Physical placement of wireless nodes in a building further establishes the reduction in energy requirement and data packet loss through this optimal router placement strategy.     

Differential evolution algorithm for static and multistage transmission expansion planning

A novel differential evolution algorithm (DEA) is applied directly to the DC power flow-based model in order to efficiently solve the problems of static and multistage transmission expansion planning (TEP). The purpose of TEP is to minimise the transmission investment cost associated with the technical operation and economical constraints. Mathematically, long-term TEP using the DC model is a mixed integer nonlinear programming problem that is difficult to solve for large-scale real-world transmission networks. In addition, the static TEP problem is considered both with and without the resizing of power generation in this research. The efficiency of the proposed method is initially demonstrated via the analysis of low, medium and high complexity transmission network test cases. The analysis is performed within the mathematical programming environment of MATLAB using both DEA and conventional genetic algorithm and a detailed comparative study is presented.     

MATHEMATICAL PROGRAMMING IN SEWER NETWORK DESIGN

This paper addresses itself to the question of obtaining the minimum cost design for a wastewater collection network. It first presents an empirical cost function for a single sewer line. The concept of optimization is then explored with respect lo sewer networks. The design problems of selecting an optimal mix of pipe diameters and slopes, given a set of economic and technological inputs as well as a network layout is then presented in three alternative mathematical programming formulations: separable convex, dynamic and geometric programming. These formulations guarantee the generation of global optimal solutions. Numerical solutions can be obtained using presently available computer capabilities. The paper concludes with an evaluation of the proposed formulations and their implications for both design practice and overall cost reduction in the area of wastewater collection.     

Electricity market equilibrium analysis based on nonlinear interior point algorithm with complementarity constraints

This paper presents an interior point algorithm based on a.c. network model for determining the Nash supply function equilibrium (SFE) of bid-based electricity markets. The SFE problem is considered as a bi-level game. At the first level, the problem begins with the formulation of an optimal power flow (OPF) interior point-based algorithm to handle the independent system operator (ISO) problem for maximising social welfare. This algorithm is based on the OPF with a.c. network transmission model taking into account all the operating aspects such as the generation capacity limits, bus voltage limits, transmission line constraints, network losses and especially the effect of the reactive power. The resulting Karush-Kuhn-Tucker (KKT) conditions of the problem at the first level are then reformulated using nonlinear complementarity constraints and incorporated as equality constraints in the second-level formulation for maximising the individual profit for each strategic generating firm. By employing a special nonlinear complementarity function, the complementarity constraints are then transformed into nonlinear algebraic expressions, thus the KKT conditions of the resulting combined problem can be derived. The final problem is then solved iteratively based on the solution techniques of the interior point algorithm. Numerical examples of a three-bus system, the IEEE 14-bus system and the IEEE 30-bus system, show that the algorithm can successfully determine the electricity market equilibrium with the a.c. network model.     

Power system transmission network expansion planning using AC model

An optimisation technique to solve transmission network expansion planning problem, using the AC model, is presented. This is a very complex mixed integer nonlinear programming problem. A constructive heuristic algorithm aimed at obtaining an excellent quality solution for this problem is presented. An interior point method is employed to solve nonlinear programming problems during the solution steps of the algorithm. Results of the tests, carried out with three electrical energy systems, show the capabilities of the method and also the viability of using the AC model to solve the problem.     

A Genetic Algorithm to Solve Capacity Assignment Problem in a Flow Network

Computer networks and power transmission networks are treated as capacitated flow networks. A capacitated flow network may partially fail due to maintenance. Therefore, the capacity of each edge should be optimally assigned to face critical situations—i.e., to keep the network functioning normally in the case of failure at one or more edges. The robust design problem (RDP) in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s failure. The RDP is known as NP-hard. Thus, capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper. The problem is formulated mathematically, and a genetic algorithm is proposed to determine the optimal solution. The optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total capacity. Some numerical examples are presented to illustrate the efficiency of the proposed approach.     

A genetic algorithm-based model for solving multi-period supplier selection problem with assembly sequence

Under fierce market competition, only products that can meet market demands timely and are competitive can enjoy advantages in the market. Production planning is important in enhancing product competitiveness by effectively reducing both production cost and time. To complete the planning task, a better assembly sequence that includes selecting suitable part suppliers and satisfying the multi-period demands should be designed. In this paper, a mathematical model is presented for dealing with this planning problem, and its objective is to minimise the value of integrated criteria. A hybrid heuristic algorithm, which involves guided genetic algorithm combined with Pareto genetic algorithm, known as Guided-Pareto genetic algorithm (Gu-PGA), is developed for solving the addressed problem. Finally, experiments are conducted to validate the proposed algorithm. The results demonstrate that the Gu-PGA is more effective in solving the multi-period supplier selection problem.     

A parallel machine lot-sizing and scheduling problem with a secondary resource and cumulative demand

We investigate a parallel machine multi-item lot-sizing and scheduling problem with a secondary resource, in which demands are given for the entire planning horizon rather than for every single period. All-or-nothing assumption of the discrete lot-sizing and scheduling problem is valid so that a machine is either idle or works at full capacity in a period. The objective is to minimise the number of setups and teardowns. We prove that the problem is NP-hard and present two equivalent formulations. We show some properties of the optimal objective value, give optimality conditions and suggest a heuristic algorithm. We discuss and formulate two possible extensions related to real-life applications. Finally, we carry out computational experiments to compare the two formulations, to determine the effect of our proposed modeling improvements on solution performance, and to test the quality of our heuristic.