Minimum-energy broadcast and multicast in wireless networks: An integer programming approach and improved heuristic algorithms

In multihop wireless networks, many applications require end-to-end reliable multicast communication with 100% packet delivery ratio. Recently, network coding has been applied to the reliable link-level multicast in wireless networks, where multiple lost packets with distinct intended receivers are XOR-ed together as one packet and forwarded via single retransmission, resulting in a significant reduction of bandwidth consumption in the scenarios of many receivers. However, the simple XOR operation cannot fully exploit the potential coding opportunities. Moreover, for the reliable multicast in multihop wireless networks, the number of receivers of link-level multicast is usually not large and thus this coding-based link-level multicast cannot provide a promising gain. In this work, we propose a new network coding-based reliable multicast scheme which exploits both network coding and the physical-layer broadcast nature. Specifically, first, at each link-level multicast the source node performs the more general coding operations instead of XOR and conducts the random linear packet coding such that more coding gain can be achieved. Second, each node snoops on all communications over the wireless medium and stores the overheard packets for packet decoding, such that an additional coding gain can be obtained. Extensive simulations demonstrate that the proposed scheme can greatly improve the transmission efficiency, especially in the case of large-group multicast.

Traditional fixed pricing mode limits the accepted number of users and traffic flows in networks. This results in low profits for operators. By applying appropriate pricing strategies, wireless radio resources can be utilized more adequately, and promising improvement of operation profits can be achieved. In this paper, a dynamic pricing strategy for delay tolerant service aggregation multicast in wireless networks is proposed. With this strategy, client behaviors are well adjusted by differentiation of prices, and the congestions in peak-traffic periods can be well relieved, leading to increments of operators' profits. Simulation results show that our proposed dynamic pricing scheme outperforms existing pricing schemes, with regard to service rejection rate and operation profits.

We address the multicast problem in a wireless multihop network in the presence of errors. Finding the multicast schedule that minimizes the expected number of transmissions, while accounting for both the wireless broadcast advantage (WBA) and the wireless unreliable transmission (WUT) characteristics, has not been solved optimally before. We formally define the problem and propose an initial algorithm of non-polynomial complexity to obtain the optimal solution. The algorithm starts by transforming the wireless network topology into an auxiliary expanded graph that captures the WBA property, and assigning appropriate weights to the links so as to capture the WUT property. The proposed transformation is interesting on its own merit, since it permits us to consider only point-to-point transmissions (as in wireline networks) in the expanded graph, instead of the point-to-multipoint transmissions present in the original wireless network, and can also be useful in other optimization problems some of which we briefly describe. By solving the minimum Steiner tree problem on the expanded graph we obtain the optimal multicast solution for the initial graph. Since the optimal algorithm is of non-polynomial complexity, we also propose a number of heuristic algorithms. In particular, we first present a truncated graph transformation and then describe two heuristic algorithms for obtaining the multicast schedule. Simulation results show that the proposed heuristics have performance close to that of the optimal algorithm, at least for the instances for which we were able to track optimal solutions, while outperforming other heuristic multicast algorithms.

Optimal sequencing of tasks in an aluminium smelter casthouse

A delay-constrained and priority-aware channel assignment algorithm for efficient multicast in wireless mesh networks

In this paper, new error control techniques are introduced for wireless networks with broadcasting or multicasting the information from one source to many sinks. They are compared with the standard selective repeat ARQ strategy. Reed-Solomon code is exploited in these techniques. Simulation results are presented, which show that the quantity of data to be retransmitted can be significantly decreased using these new methods.

Cyber-Physical System (CPS) is envisioned to tightly integrate the cyber-world of computation, communication, and control with the physical world. CPS is typically designed as a networked system of interacting sensors, actuators, and embedded computing devices to monitor and control the physical world. Thus, one of the essential building blocks of such a system is a highly efficient networking infrastructure. In this paper, we aims to develop an efficient wireless networking technology which can be utilized in CPS. More specifically, we develop a cross-layer optimization model based on the Network Utility Maximization (NUM) framework and its distributed solution for wireless multihop multicast networks exploiting multi-user diversity. It is known that the capacity of a wireless network can be increased by exploiting different channel conditions at different users, i.e., multi-user diversity; however, it is yet to be determined how much performance gain can be achieved by exploiting multi-user diversity in wireless multihop multicast networks. To address this problem, we extend the NUM framework and derive a new optimization problem including the benefits of multi-user diversity for multicasting scenarios in wireless multihop networks under a probabilistic media access control (MAC). In our problem, multi-user diversity is achieved via opportunistic scheduling. Then, we propose a distributed approximation algorithm for the problem. Our numerical results confirm that the benefit of multi-user diversity is prominent in a wireless multihop network with multicast flows.

Network coding (NC) is an efficient way to decline the retransmission and provides a reliable connection in the wireless multicast network. However, in the NC schemes based on XOR, if one coded packet is lost, the transmitter must retransmit the same one until all the receivers correctly receive it. This strategy falls into the routine of ARQ (Automatic Repeat Request), which has already been proved with low bandwith efficiency. Accordingly, in this paper we proposed a dynamic linear combining retransmission(DLCR) strategy and fixed linear combining retransmission (FLCR) strategy based on the random network coding to improve the bandwidth efficiency in wireless multicast network. The key idea of the proposed schemes is to form the coded packet dynamically according to the real-time network status during the retransmission. Besides, we also analyzed the proposed schemes and get the corresponding theory analysis results. The simulations show that our schemes can well improve the efficiency of transmission.

It may be very difficult to achieve the optimal shift schedule in call centers which have highly uncertain and peaked demand during short time periods. Overlapping shift systems are usually designed for such cases. This paper studies shift scheduling and rostering problems for inbound call centers where overlapping shift systems are used. An integer programming model that determines which shifts to be opened and how many operators to be assigned to these shifts is proposed for the shift scheduling problem. For the rostering problem both integer programming and constraint programming models are developed to determine assignments of operators to all shifts, weekly days-off, and meal and relief break times of the operators. The proposed models are tested on real data supplied by an outsource call center and optimal results are found in an acceptable computation time. An improvement of 15% in the objective function compared to the current situation is observed with the proposed model for the shift scheduling problem. The computational performances of the proposed integer and constraint programming models for the rostering problem are compared using real data observed at a call center and simulated test instances. In addition, benchmark instances are used to compare our Constraint Programming (CP) approach with the existing models. The results of the comprehensive computational study indicate that the constraint programming model runs more efficiently than the integer programming model for the rostering problem. The originality of this research can be attributed to two contributions: (a) a model for shift scheduling problem and two models for rostering problem are presented in detail and compared using real data and (b) the rostering problem is considered as a task-resource allocation and considerably shorter computation times are obtained by modeling this new problem via CP.

Space Information Flow (SIF) is a new promising research area that studies network coding in geometric space, such as Euclidean space. The design of algorithms that compute the optimal SIF solutions remains one of the key open problems in SIF. This work proposes the first exact SIF algorithm and a heuristic SIF algorithm that compute min-cost multicast network coding for N (N ≥ 3) given terminal nodes in 2-D Euclidean space. Furthermore, we find that the Butterfly network in Euclidean space is the second example besides the Pentagram network where SIF is strictly better than Euclidean Steiner minimal tree. The exact algorithm design is based on two key techniques: Delaunay triangulation and linear programming. Delaunay triangulation technique helps to find practically good candidate relay nodes, after which a min-cost multicast linear programming model is solved over the terminal nodes and the candidate relay nodes, to compute the optimal multicast network topology, including the optimal relay nodes selected by linear programming from all the candidate relay nodes and the flow rates on the connection links. The heuristic algorithm design is also based on Delaunay triangulation and linear programming techniques. The exact algorithm can achieve the optimal SIF solution with an exponential computational complexity, while the heuristic algorithm can achieve the sub-optimal SIF solution with a polynomial computational complexity. We prove the correctness of the exact SIF algorithm. The simulation results show the effectiveness of the heuristic SIF algorithm.

Cell formation deals with grouping of machines and parts in manufacturing systems according to their compatibility. Manufacturing processes are surrounded with an abundance of complex constraints which should be considered carefully and represented clearly for obtaining high efficiency and productivity. Constraint programming is a new approach to combinatorial optimization and provides a rich language to represent complex constraints easily. However, the cell formation problems are well suited to be solved by constraint programming approach since the problem has many constraints such as part-machine requirements, availabilities in the system in terms of capacity, machine and worker abilities. In this study, the cell formation problem is modeled using machine, part processing and worker flexibilities via resource element–based representation. Resource elements define the processing requirements of parts and processing capabilities of machines and workers, which are resource-independent capability units. A total of 12 case problems are generated, and different search phases of constraint programming are defined for the solution procedure. The cell formation problem is modeled in both constraint programming and integer programming, and a comparative analysis of constraint programming and integer programming model solutions is done. The results indicate that both the models are effective and efficient in the solution of the cell formation problem.

This paper considers the problem of constructing minimum-delay multicast trees for message dissemination in overlay networks in which nodal delays for processing messages are taken into account in addition to communication delays. The objective is to find a multicast tree to deliver a message from a source to multiple destinations in minimum delay time. This paper introduces a new delay measure called reception-and-processing delay that takes nodal processing delays into account for constructing multicast trees. In the process of constructing a multicast tree, the branches of the multicast tree may intersect or overlap with each other. An efficient method is devised to avoid this undesirable phenomenon. Several heuristic algorithms are developed for constructing multicast trees. The performances of the heuristic algorithms are studied via simulation. Our simulation results show that constructing the multicast tree by iteratively attaching the destination node with the smallest reception-and-processing delay to the current multicast tree yields the lowest average multicast delay among the heuristic algorithms studied in this paper.

ABSTRACTThe solution to the assembly line balancing with the hierarchical worker assignment problem (ALBHWP) provides the optimal allocation of workers and tasks to the stations that minimise the total worker cost. In the ALBHWP, tasks differ in terms of the qualification requirements of workers, and the qualification levels of workers are hierarchical. In the hierarchical workforce structure, a lower qualified worker can be replaced by higher qualified ones with higher costs, while the vice versa is not applicable. This problem has only been studied for straight assembly lines so far. In this paper, we introduce the ALBHWP for U-shaped assembly lines. We developed integer and constraint programming models for solving the ALBHWP and compared their effectiveness using an extensive set of benchmark instances. We solved the ALBHWP for straight and U-shaped assembly lines comparatively. Constraint programming models have been statistically proven to provide better quality solutions faster than integer programming models. Besides, the CP model outperforms the only available metaheuristic in the literature for the S-ALBHWP in almost all problem sizes. Another observation is that a U-shaped line design is more cost-effective than a straight line design, but solving the ALBHWP for U-shaped lines is more difficult regarding computational complexity.

Forming minimum-cost machine cells with exceptional parts using zero-one integer programming

High blood pressure or hypertension is a global public health issue. High blood pressure can lead to cardiovascular disease, stroke, diabetes and others. The diet problem of high blood pressure patients has always been concerned by the field of operational research. Some previous and recent research have proposed that diet plays an important role in affecting the condition of high blood pressure. A balanced diet will provide essential nutrients for high blood pressure patients that help to control and lower blood pressure. The purpose of this study is to construct a diet menu that meet the nutritional requirements of high blood pressure patient as well as minimize the food cost by developing a mathematical model using linear and integer programming approaches. The model of this study involved 10 type of food groups with 413 types of food variables based on the Malaysian recipes. The findings show that integer programming is a good alternative for linear programming. Integer programming leads to an optimal and effective solutions for the diet problem of high blood pressure patients.