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Open shortest path first (OSPF) is the most widely used intra-domain Internet routing protocol. The OSPF protocol directs the Internet traffic along the shortest paths that are defined by the links weight. Traffic engineering is responsible for improving the network performance, for instance, the objective function that minimizes the maximal link utilization which is usually adopted to avoid the network congestion. This paper formulates the model of OSPF routing, and further evaluates the performance of different approaches. We also propose a heuristic algorithm to solve the routing problems by optimizing OSPF weights. Computational results indicated that the proposed algorithm could lead to good load balancing, and make efficient utilization of network resources.

In this study, we present the problems of shipping a single product from a single supply origin, through a serial system of storage depots, to a single demand destination by trucks or ships, and aim to minimize the sum of ordering, inventory and transportation costs over an infinite time horizon. We formulate the problem consisting of general-integer (GI), stationary and nested replenishment policies for the multistage inventory/distribution system. This paper presents the near-optimal cost-curve structure analysis for this serially distributed storage depot problem (SDSDP) and show that the ordering quantities for each storage depot always comply with the GI replenishment policy. Moreover, we develop a heuristic approach, which is based on an efficient search procedure to obtain satisfactory results. A comparison of the results obtained from the proposed heuristic method has been made with that from the LINGO® software on randomly generated problems. The proposed heuristic is the first solution approach in the literature to solve the single link problems based on the cost-curve structure analysis. Furthermore, we provide an important foundation for carrying out investigations on lot sizing and distribution problems with other supply chain configurations.

This paper considers quality of service in terms of end-to-end delay in planning backup routes for multicast communications. The problem of preplanning backup routes considering both cost minimization and end-to-end delay guarantee for multicast communications in the case a single link failure is investigated. Two delay labels, the limited label and the tolerable label, are defined to evaluate the end-to-end delay requirement. Four heuristic algorithms, including two tree-based algorithms, one subtree-based algorithm, and one link-based algorithm, are proposed to determine the delay-constrained backup routes having the minimum costs. Two procedures to determine a node selection sequence, Random and Minimum-Cost, are used in the tree-based algorithm. Experimental results show that the tree-based algorithm by the Minimum-Cost sequence yields the best performance in cost minimization with guarantee of end-to-end delay.

More and more multimedia applications rely on the ability of networks to provide multicast communications. Network survivability becomes a critical issue in network planning, design, and operation to reduce the impacts of failures in high-speed networks. A rapid and cost-effective restorable network must be planned to maintain network survivability and use network bandwidth effectively. This paper considers the problem of preplanning delay-constrained backup path for multicast networks to minimize the total cost of all the backup paths. Based on the path restoration scheme, a tabu search (TS) algorithm is proposed to find a near optimal solution for the problem under investigation. Diversification and intensification strategies are suggested. The computational experiments are conducted to demonstrate the performance of the proposed TS algorithm. The results show that it offers high-quality solutions within a short amount of time.

This study aims at utilizing the dynamic behavior of artificial neural networks (ANNs) to solve multiobjective programming (MOP) and multilevel programming (MLP) problems. The traditional and non-traditional approaches to the MLP are first classified into five categories. Then, based on the approach proposed by Hopfield and Tank [1], the optimization problem is converted into a system of nonlinear differential equations through the use of an energy function and Lagrange multipliers. Finally, the procedure is extended to MOP and MLP problems. To solve the resulting differential equations, a steepest descent search technique is used. This proposed nontraditional algorithm is efficient for solving complex problems, and is especially useful for implementation on a large-scale VLSI, in which the MOP and MLP problems can be solved on a real time basis. To illustrate the approach, several numerical examples are solved and compared.

The awareness of the need for environmental protection is increasing throughout the world. The focuses of green supplier selection are on considering environmental criteria and strengthening the competitiveness of the entire supply chain. The purpose of this study is to develop a green supplier selection procedure for the thin film transistor liquid crystal display (TFT-LCD) industry using polarizer suppliers as an example. First, a decision framework for green supplier selection is developed based on literatures and the supplier audit forms provided by an anonymous flat panel display manufacturer in Taiwan. Then, a hybrid multiple criteria group decision-making (MCGDM) method is proposed based on analytic hierarchy process (AHP), entropy, elimination and choice expressing the reality III (ELECTRE III), and the linear assignment method to assist the manufacturer in choosing among four polarizer suppliers. The final ranking results for green supplier selection and different opinions from each department are provided. An improvement report is suggested to enhance suppliers’ performance. For the evaluation procedure, most managers emphasize the importance of current capability and the capability of research and development. Furthermore, we found that the subsidiary supplier should improve quality control competence immediately to be considered as the potential candidate of primary supplier.

In an asynchronous transfer mode (ATM) network, given the network topology and traffic demands, the establishment of the system of virtual paths (VPs), and the assignment of connections to them so that the network performance is optimized, entails a number of computationally hard subproblems. The optimization problem discussed here focuses on finding a system of VP routes for a given set of VP terminators and VP capacity demands. Although it has been proven that the existing random path algorithm yields the worst case time bound, the solution performance still depends highly on the number of iterations. In this paper, an exact solution procedure and a heuristic method based on a simple tabu search have been developed for optimizing the system of VPs. Computational results show that the proposed tabu search algorithm is effective in obtaining high quality solutions, and the performance of the proposed algorithm is increasingly attractive as the problem size becomes larger.

This paper employs tabu search to solve the network spare capacity planning problem. Our study involves the determination of the necessary spare capacity on each link of a network required to achieve a certain desired level of restoration at minimum cost during network failure. We have developed here a procedure based on tabu search. The computational results show that the proposed tabu search algorithm offers an effective and efficient way for handling this network restoration problem. Scope and purpose Telecommunication network plays a crucial role in modern communication. An ever increasing number of end users are completely relying on telecommunication in their business activities today. The use and integration of computers with telecommunication have created a new information era. In order to provide the much desired broadband services, high capacity transport systems are widely adopted in the transmission network. While these systems offer great economic advantages through multiplexing and sharing of transmission facilities, they also increase the vulnerability of the telecommunication services dependent on them. If a failure occurs on a high speed facility of such a network, all the related services get affected. Network survivability has, therefore, become a major concern in broadband network planning. One of the methods to improve network survivability is to keep redundant or spare capacity for Digital Cross-Connect System (DCS) to restore failed services. However, the problem of spare capacity deployment on the links of a restorable network is NP-hard, implying exponential growth in execution time with network size for an exact solution. In this paper, an algorithm based on tabu search is proposed to find an approximate optimal solution for this problem.

An automatic storage system comprised of two storage carousel conveyors and a retrieval apparatus integrated by computers has been considered in this paper. The order picking problem for such a two‐carousel‐single‐server system is presented here. The retrieval time is analyzed by a two‐stage solution procedure for the scheduling sequence of retrieving items from the system in order to satisfy an order requirement. The analytical results can be directly applied for the scheduling of the carousel storage system.

In this paper, the problem of scheduling robot operations for a robot‐centered parallel‐processor cell is considered. N parts and m identical processors are assumed in the cell, a robot is responsible for both loading and unloading the parts, and the schedule of robot activities is to minimize the makespan. A mixed integer program is formulated to describe the characteristics of the problem. However, in the case of a large number of parts, it is not easy to obtain an optimal solution because of inordinate computational time. Therefore, three heuristic algorithms are proposed to get an approximately optimal schedule. The number of processors and the interval of the ratio of loading time to processing time are shown to have significant effects on the performance of heuristic algorithms. Experimental results and analyses are presented.

Multi-level programming is characterized as mathematical programming to solve decentralized planning problems. The decision variables are partitioned among ordered levels. A decision-maker at one level of the hierarchy may have his own objective function and decision space, but may be influenced by other levels.

The general multilevel programming problem is a set of nested optimization problems over a single feasible region. Control over the decision variables is partitioned among ordered levels within a hierarchical structure. The two‐level resource control problem is a special case where the level‐two planner controls the effective resource space for the level‐one planner. This produces a solution structure with the feasible region viewed by level‐two planner as a nonconvex subset of the overall feasible region. In this paper, we will develop an algorithm to solve this problem. Computational results are presented.