In this study, a multi-objective mathematical model is developed to design a construction supply chain (CSC) including a main supplier and several projects as customers who need their materials to be supplied in different periods according to the technical requirements of each product based on its life cycle. The waste of products as a result of late transfer impacts management decisions, but what is critical is the existence of uncertainty. This issue has been dealt with through the robust programming approach introduced by Bertsimas and Sim. Before the approach is practiced, a sensitivity analysis is done to identify the sensitive and effective parameters of the proposed model. Next, the model is accomplished in a real case study. In order to solve the mathematical model, the modified weighted Chebyshev method is used in small-size problems. Then, the best-worst multi-criteria decision-making technique serves to assess the ability of the suggested mathematical model to solve various problems. Finally, MOGA-II, MORDA, and MOSA meta-heuristic algorithms are applied to resolve large-scale problems. According to the computational results, the MORDA algorithm executes better than the other algorithms. In addition to the results taken from the sensitivity analysis of the problem in terms of different parameters, some insight is gained about the great effects of uncertainty on the various levels of a chain. The results of this study can be of help to optimize CSC design problems for the management of construction projects.
The combination of traditional retail channel with direct channel adds a new dimension of competition to manufacturers’ distribution system. In this paper, we consider a make-to-order manufacturer with two channels of sale, sale through retailers and online direct sale. The customers are classified into different classes, based on their sensitivity to price and due date. The orders of traditional retail channel customers are fulfilled in the same period of ordering. However, price and due date are quoted to the online customers based on the available capacity as well as the other orders in the pipeline. We develop two different structures of the supply chain: centralized and decentralized dual-channel supply chain which are formulated as bilevel binary nonlinear models. The Particle Swarm Optimization algorithm is also developed to obtain a satisfactory near-optimal solution and compared to a genetic algorithm. Through various numerical analyses, we investigate the effects of the customers’ preference of a direct channel on the model’s variables.
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