Sharif Digital Repository

We analyse optimal pricing for a retailer that sells two products using a mixed price bundling strategy. We consider substitutable products, where there is a disutility associated with jointly consuming both products. Mixed price bundling implies that customers can freely choose to buy one of the two products, both of them, or neither of them. The retailer determines the price of each product when it is sold separately, the bundle discount if these two products are sold in a bundle, and the inventory levels for each product. We first derive the optimal price and bundle discount when the store traffic is deterministic. Our results show that the mixed bundling strategy is the most beneficial... 

The goal of this paper is to develop a model to integrate "pricing" and "advertisement" for short life cycle products, such as branded fashion clothing products. To achieve this goal, we apply the concept of "Dynamic Pricing". There are two classes of advertisements, for the brand (regardless of product) and for a particular product. Advertising the brand affects the demand and price of all the products. Thus, the model considers all these products in relation with each other. We develop two different methods to integrate both types of advertisement and pricing. The first model is developed within the framework of dynamic programming. However, due to the complexity of the model, this method... 

We study the problem of optimizing the symbol error probability (SEP) performance of cluster-based cooperative wireless sensor networks (WSNs). We propose that to achieve the minimum SEP at the destination, a joint optimization of power allocation and relay selection should be accomplished. To this aim, we reformulate the SEP performance of a simple cluster-based cooperative WSN in the general form and solve the joint optimization problem efficiently. Simulation results demonstrate that the proposed joint optimization can efficiently improve the SEP performance of the network in comparison with the previous disjoint optimal relay selection schemes  

We study the tradeoff between the sum rate and the error probability in downlink of wireless networks. Using the recent results on the achievable rates of finite-length codewords, the problem is cast as a joint optimization of the network sum rate and the per-user error probability. Moreover, we develop an efficient algorithm based on the divide-and-conquer technique to simultaneously maximize the network sum rate and minimize the maximum users’ error probability and to evaluate the effect of the codeword length on the system performance. The results show that, in delay-constrained scenarios, optimizing the per-user error probability plays a key role in achieving high throughput. IEEE  

The aim of this research is to optimize the geometry of the overlap in mixed adhesive single-and double-lap joints using a modified version of Bees and Genetic Algorithms (BA and GA). Accounting for adherends Poisson's ratio in the deduced equilibrium equations, the proposed shear lag model gives a more accurate approximation of joint failure load in comparison with Volkersen's solution. The objective functions used in this work are used separately to maximize the load bearing capacity f and the specific strength (f/w) of the joint. This procedure is applied to optimize aeronautical adhesively bonded assemblies, while taking manufacturing constraints into account. The employed constraints... 

In this paper, we study a pricing-inventory model with multiple interdependent products and stochastic demand. In the relevant literature, there are many researches that try to optimize the inventory or price independently and also optimizing joint inventory and pricing problems for single product. But joint optimization of inventory and pricing for multiple interdependent products is relatively new and still there are many potential research gaps in this area. We show that the multiplicative demand form problem, that we develop, is a convex nonlinear programming (NLP) model or is convertible to it. We also provide upper and lower bounds for the additive demand form model  

We study the problem of optimizing the symbol error probability (SEP) performance of cluster-based cooperative wireless sensor networks (WSNs). It is shown in the literature that an efficient relay selection protocol based on simple geographical information of the nodes to execute the cooperative diversity transmission, can significantly improve the SEP performance at the destination of such networks. Also, a similar analysis on the optimal power allocation for the source and the relay exists in the literature. However, we propose that to achieve the minimum SEP at the destination, a joint optimization of power allocation and relay selection should be accomplished. To this aim, we... 

This letter is concerned with the optimal power allocation in femtocell networks using a new technique based on multiobjective optimization. In this letter, we consider a cochannel framework for the coexistence of orthogonal frequency division multiple access (OFDMA)-based macrocell and femtocell networks. We jointly maximize the total rate of OFDMA femtocell network and minimize the total transmit power of femtocell base stations while satisfying a target constraint on transmit power when either the cross femtocell interference is included or excluded. Additionally, the results show that the proposed method for power transmission contributes toward improving the energy efficiency of... 

Considering a multihop cellular system with one relay per sector, an effective modeling for the joint base-station/relay assignment, rate allocation, and routing scheme is proposed and formulated under a single problem for the downlink. This problem is then formulated as a multidimensional multichoice knapsack problem (MMKP) to maximize the total achieved throughput in the network. The well-known MMKP algorithm based on Lagrange multipliers is modified, which results in a near-optimal solution with a linear complexity. The notion of the infeasibility factor is also introduced to adjust the transmit power of base stations and relays adaptively. To reduce the complexity, and in order to... 

To address sustainability challenges appeared in today's power grids, it is essential for emerging demand control paradigm to be adapted more to the lifestyle of the customers. In this paper, due to the ever-growing interconnectivity of the grids, a distributed Commodity Market (CM) framework is proposed in which intelligent agents embedded inside of customers want to maximize their preferred welfare through real-time demand of power from an energy market. Since there is not a comprehensive model for the grids, utilizing Reinforcement Learning (RL) proves that the global optimal performance is achieved in the Nash Equilibrium (NE) of the proposed framework. This solution not only maximizes... 

In this letter, we consider a cache-enabled cloud radio access network with single-antenna base stations and single-antenna mobile users. Each base station can access the central processor of the network via a separate backhaul link with limited capacity. We utilize the interference alignment scheme as the transmission strategy between base stations and users. By assuming a block transmission scheme, we define a related data transmission delay. We derive the transmission delay and network power consumption. At last, we solve the problem of joint optimization of data transmission delay and network power consumption using DC programming algorithm. In our numerical results, we investigate the... 

In this study, a new mathematical programming approach for solving the joint timetabling and train stop planning problem in a railway line with double-track segments is proposed. This research aims to design an optimized train timetable subject to the station- capacity and time-dependent dwell time constraints. The objective function is to maximize the railway infrastructure capacity by minimizing the schedule makespan. The problem is formulated as a particular case of blocking permutation flexible flow shop scheduling model with dynamic time window constraints. Due to the combinatorial nature of the problem, heuristic algorithm and bound tightening methods are proposed that generate... 

We consider joint optimization of data routing and resource allocation in multicast multihop wireless networks where interference between links is taken into account. The use of network coding in such scenarios leads to a nonconvex optimization problem. By applying the probability collectives (PCs) technique the original problem is turned into a new problem which is convex over probability distributions. The resulting problem is then further decomposed into a data routing subproblem at network layer and a power allocation subproblem at physical layer in order to achieve a cross-layer distributed solution for the whole range of SINR values. The proposed approach is also extended to minimum... 

In this paper joint optimization of layers in the layered video coding is investigated. Through theoretical analysis and simulations, it is shown that, due to higher interactions between the layers in a SNR scalable codec, this type of layering technique benefits most from joint optimization of the layers. A method for joint optimization is then proposed, and its compression efficiency is contrasted against the separate optimization and an optimized single layer coder. It is shown that, in joint optimization of SNR scalable coders when the quantization step size of the enhancement layer is larger than half the step size of the base layer, an additional improvement is gained by not sending...