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Marketing management plays a crucial role in successful companies. However, marketing has been either misunderstood or entirely neglected in numerous construction companies because it is difficult to apply conventional marketing strategies due to specific characteristics of the industry. This study systematically investigates the characteristics of the construction industry and their effects on marketing management. A systematic literature review covering scientific papers from 1995 to 2015 (556 scientific papers) identifies 16 characteristics, which are organized into two main categories-the construction industry and construction product-related industry. 'Fragmentation' was the most highlighted characteristic.A total of 10 identified characteristics, according to the experts' opinions gathered through the questionnaire, may affect the construction marketing, of which "competitive bidding mechanism" and "project-based nature of the industry" have the greatest effect. Through a combination of literature review and focus group study, the challenges resulting from each of these 10 particularities regarding various dimensions of marketing are examined and practical solutions are suggested for overcoming these challenges. The main suggestion is the modification of the traditional marketing mix (4Ps) by eliminating "place" and adding four elements-profile, pooling, phase, and presenter. Therefore, a tailored marketing mix for construction companies is developed, with seven elements (7Ps). Moreover, marketing research and marketing intelligence systems are vital marketing tools for construction companies and should allocate adequate resources and responsible staffing levels for these activities.

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This paper studies a supply chain design problem with the risk of disruptions at facilities. At any point of time, the facilities are subject to various types of disruptions caused by natural disasters, man-made defections, and equipment breakdowns. We formulate the problem as a mixed-integer nonlinear program which maximizes the total profit for the whole system. The model simultaneously determines the number and location of facilities, the subset of customers to serve, the assignment of customers to facilities, and the cycle-order quantities at facilities. In order to obtain near-optimal solutions with reasonable computational requirements for large problem instances, two solution methods based on Lagrangian relaxation and genetic algorithm are developed. The effectiveness of the proposed solution approaches is shown using numerical experiments. The computational results, in addition, demonstrate that the benefits of considering disruptions in the supply chain design model can be significant.