A modified particle swarm optimization for disaster relief logistics under uncertain environment

Relief logistics is one of the most important elements of a relief operation. This paper investigates a relief chain design problem where not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands can arise and the possibility that a number of the facility could be partially destroyed by the disaster. The proposed model for this study is formulated as a mixed-integer nonlinear programming to minimize the sum of the expected total cost (which includes costs of location, procurement, transportation, holding, and shortage) and the variance of the total cost. The model simultaneously determines the location of relief distribution centers and the allocation of affected area to relief distribution centers. Furthermore, an efficient solution approach based on particle swarm optimization is developed in order to solve the proposed mathematical model. At last, computational results for several instances of the problem are presented to demonstrate the feasibility and effectiveness of the proposed model and algorithm.     

A new approach to solve time–cost trade-off problem with fuzzy decision variables

Time–cost trade-off problem is one of the main aspects of project scheduling. Due to variations in the real world, usually, risks in estimation of project parameters are considerably high. Therefore, use of uncertain models, which is capable of formulating vagueness in the real world, to solve time–cost trade-off problems, gives a scheduling with more stability against environmental variations. On the other hand, crisp decision making in uncertain environment causes loss of some parts of information. This paper presents a new optimal model for time–cost trade-off problem in a fuzzy environment. In order to solve this problem, a new solution method for possibility goal programming problems is developed. The significant feature of this model is the determination of optimal duration for each activity in the form of triangular fuzzy numbers. To validate the algorithm developed here, a case study will be presented.     

A heuristic-based multi-choice goal programming for the stochastic sustainable-resilient routing-allocation problem in relief logistics

Neural Computing and Applications - Natural or man-made disasters impose destructive effects like human injuries and urban infrastructure damages, which lead to disruptions that affect the entire...     

A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty

Humanitarian relief logistics is one of the most important elements of a relief operation in disaster management. The present work develops a multi-objective robust stochastic programming approach for disaster relief logistics under uncertainty. In our approach, not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands might arise and the possibility that some of the pre-positioned supplies in the relief distribution center or supplier might be partially destroyed by the disaster. Our multi-objective model attempts to minimize the sum of the expected value and the variance of the total cost of the relief chain while penalizing the solution’s infeasibility due to parameter uncertainty; at the same time the model aims to maximize the affected areas’ satisfaction levels through minimizing the sum of the maximum shortages in the affected areas. Considering the global evaluation of two objectives, a compromise programming model is formulated and solved to obtain a non-dominating compromise solution. We present a case study of our robust stochastic optimization approach for disaster planning for earthquake scenarios in a region of Iran. Our findings show that the proposed model can help in making decisions on both facility location and resource allocation in cases of disaster relief efforts.