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Batch processing machine (BPM) scheduling problem is a NP hard problem for it includes machine allocation, job grouping, and batch scheduling. In this paper, to address the BPM scheduling problem with unrelated parallel machine, a multiobjective algorithm based on multipopulation coevolution is proposed to minimize the total energy consumption and the completion time simultaneously. Firstly, the mixed integer programming model of the problem is established, and four heuristic decoding rules are proposed. Secondly, to improve the diversity and convergence of the algorithm, the population is divided into two populations: each of the populations evolves independently by using different decoding rules, and the two populations will communicate through a common external archive set every certain number of generations. Thirdly, an initialization strategy and a variable neighborhood search algorithm (VNS) are proposed to improve the overall performance of the algorithm. Finally, in order to evaluate the proposed algorithm, a large number of comparative experiments with the state-of-the-art multiobjective algorithms are carried out, and the experimental results proved the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

An airport gate is the core resource of an airport operation, which is an important place for passengers to get on and off the aircraft and for maintaining aircraft. It is the prerequisite for other related dispatch. Effective and reasonable allocation of gates can reduce airport operating costs and increase passenger satisfaction. Therefore, an airport gate assignment problem (AGAP) needs to be urgently solved in the actual operation of the airport. In this paper, considering the actual operation of the airport, we formulate an integer programming model for AGAP by considering multiple constraints. The model aims to maximize the number of passengers on flights parked at the gate. A tabu search-based algorithm is designed to solve the problem. In the process of algorithm design, an effective initial solution is obtained. A unique neighborhood structure and search strategy for tabu search are designed. The algorithm can adapt to the dynamic scheduling of airports. Finally, tests are performed using actual airport data selected from Kunming Changshui International Airport in China. The experimental results indicate that the proposed method can enhance the local search ability and global search ability and get satisfactory results in a limited time. These results provide an effective support for the actual gate assignment in airport operations. [ABSTRACT FROM AUTHOR]

Urban metro trains have the characteristics of short running distance between stations and frequent starting and braking. A large amount of regenerative braking energy is generated during the braking process. The effective utilization of the regenerative braking energy can substantially reduce the total energy consumption of train operation. In this paper, we establish two integer programming models of train operation that maximize the overlap time between train traction and braking in peak hours and nonpeak hours. On this basis, an improved differential evolution (IDE) algorithm is developed for solving the two integer programming models. The results demonstrate that the overlap time increases by 51.44% after optimization using the IDE algorithm when the headway is set to 154 s in peak hours. The overlap time is further increased by 14.87% by optimizing the headway. In nonpeak hours, the overlap time of traction and braking of the trains in opposite directions at the same station is increased by optimizing the bidirectional departure interval, thereby reducing the total energy consumption of the system. [ABSTRACT FROM AUTHOR]

Clustering algorithms have been explored in recent years to solve hotspot clustering problems in integrated circuit design. With various applications in design for manufacturability flow such as hotspot library generation, systematic yield optimization, and design space exploration, generating good quality clusters along with their representative clips is of utmost importance. With several generic clustering algorithms at our disposal, hotspots can be clustered based on the distance metric defined while satisfying some tolerance conditions. However, the clusters generated from generic clustering algorithms need not achieve optimal results. In this paper, we introduce two optimal integer linear programming formulations based on triangle inequality to solve the problem of minimizing cluster count while satisfying given constraints. Apart from minimizing cluster count, we generate representative clips that best represent the clusters formed. We achieve a better cluster count for both formulations in most test cases as compared to the results published in the literature in the ICCAD 2016 contest benchmarks as well as the reference results reported in the ICCAD 2016 contest website. [ABSTRACT FROM AUTHOR]

This paper presents mixed integer programming for a transportation service procurement bid construction problem from a less than full truckload perspective, in which the bidders (carriers) generate their best bid (package) using a bundled price to maximize their utility and increase the chance of winning the business. The models are developed from both the carriers and shippers perspectives to establish a relationship between the quoted price and the likelihood of winning to assist the carriers in balancing the potential benefits and the possibility of winning the bid. An intelligent algorithm based on Particle Swarm Optimization is then designed to solve the proposed model and hypothetical data sets are used to test the effectiveness and efficiency of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the scheduling of Electrical Multiple Units (EMUs) under the condition of their utilization on one sector or within several interacting sectors. Based on the introduction of the train connection graph which describes the possible connection relationship between trains, the integer programming model of EMU circulation planning is constructed. In order to analyzing the resolution of the model, a heuristic which shares the characteristics with the existing methods is introduced first. This method consists of two stages: one is a greedy strategy to construct a feasible circulation plan fragment, and another is to apply a stochastic disturbance to it to generate a whole feasible solution or get a new feasible solution. Then, an exact branch and bound method which is based on graph designing is proposed. Due to the complexity, the lower bound is computed through a polynomial approximation algorithm which is a modification from the one solving the degree constraint minimum 1-tree problem. Then, a branching strategy is designed to cope with the maintenance constraints. Finally, we report extensive computational results on a railway corridor in which the sectors possess the basic feature of railway networks. [ABSTRACT FROM AUTHOR]

The service-oriented architecture is considered as a new emerging trend for the future of wireless sensor networks in which different types of sensors can be deployed in the same area to support various service requirements. The accuracy of the sensed data is one of the key criterions because it is generally a noisy version of the physical phenomenon. In this paper, we study the node selection problem with data accuracy guarantee in service-oriented wireless sensor networks. We exploit the spatial correlation between the service data and aim at selecting minimum number of nodes to provide services with data accuracy guaranteed. Firstly, we have formulated this problem into an integer nonlinear programming problem to illustrate its NP-hard property. Secondarily, we have proposed two heuristic algorithms, namely, Separate Selection Algorithm (SSA) and Combined Selection Algorithm (CSA). The SSA is designed to select nodes for each service in a separate way, and the CSA is designed to select nodes according to their contribution increment. Finally, we compare the performance of the proposed algorithms with extended simulations. The results show that CSA has better performance compared with SSA. [ABSTRACT FROM AUTHOR]

We present a hybridization of two different approaches applied to the well-known Capacitated Facility Location Problem (CFLP). The Artificial Bee algorithm (BA) is used to select a promising subset of locations (warehouses) which are solely included in the Mixed Integer Programming (MIP) model. Next, the algorithm solves the subproblem by considering the entire set of customers. The hybrid implementation allows us to bypass certain inherited weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. In this paper we demonstrate that BA can be significantly improved by use of the MIP algorithm. At the same time, our hybrid implementation allows the MIP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the BA with a mathematical programming approach appears to be an interesting research area in combinatorial optimization. [ABSTRACT FROM AUTHOR]

This paper presents a filled function method for finding a global optimizer of integer programming problem. The method contains two phases: the local minimization phase and the filling phase. The goal of the former phase is to identify a local minimizer of the objective function, while the filling phase aims to search for a better initial point for the first phase with the aid of the filled function. A two-parameter filled function is proposed, and its properties are investigated. A corresponding filled function algorithm is established. Numerical experiments on several test problems are performed, and preliminary computational results are reported. [ABSTRACT FROM AUTHOR]

The growing market of GNSS capable mobile devices is driving the interest of GNSS software solutions, as they can share many system resources (processor, memory), reducing both the size and the cost of their integration. Indeed, with the increasing performance of modern processors, it becomes now feasible to implement in software a multichannel GNSS receiver operating in real time. However, a major issue with this approach is the large computing resources required for the base-band processing, in particular for the correlation operations. Therefore, new algorithms need to be developed in order to reduce the overall complexity of the receiver architecture. Towards that aim, this paper first introduces the challenges of the software implementation of a GPS receiver, with a main focus given to the base-band processing and correlation operations. It then describes the already existing solutions and, from this, introduces a new algorithm based on distributed arithmetic. [ABSTRACT FROM AUTHOR]

This paper deals with the development of a strategic approach for optimizing the operation of public transport system that considers both user's objective and operator's objective. Passengers of public transport are assumed to seek a minimum wait time to conduct the trips, while on the other hand, operators are concerned with the efficient operation such as minimum fleet size. The average minimum wait time is to be achieved by creating an optimal despatching policy for each vehicle from the terminal. As for efficient operation the utilisation of vehicle should be maximised by having a minimum number of vehicles in operation. User's and operator's objectives are optimized within certain operational constraints such as vehicle capacity to maintain acceptable level of service. The i-model is contructed in a bi-level programming form in which the user's objective is minimized by dynamic programming and the operator's objective is minimized by various routing strategies. Furthermore, an algorithm and a contrived example are developed to solve and see the performance of the approach. [ABSTRACT FROM AUTHOR]