A capacitated location-allocation problem with stochastic demands using sub-sources: An empirical study

Fuzzy Demand Vehicle Routing Problem with Soft Time Windows

Considering the vehicle routing problem with fuzzy demand and fuzzy time windows, a vehicle routing optimization method is proposed considering both soft time windows and uncertain customer demand. First, a fuzzy chance-constrained programming model is established based on credibility theory, minimizing the total logistics cost. At the same time, a random simulation algorithm is designed to calculate the penalty cost of delivery failures caused by demand that cannot be satisfied. In order to overcome the shortcomings of GA, which easily falls into the local optimum in the process of searching, and the slow convergence speed of SA when the population is too large, a hybrid simulated annealing–genetic algorithm is adopted to improve the solution quality and efficiency. Finally, the Solomon standard example is used to verify the effectiveness of the algorithm, and the influence of decision-makers’ subjective cost preference is analyzed.

Development of a coded suite of models to explore relevant problems in logistics

Logistics is the aspect of the supply chain which is responsible of the efficient flow and delivery of goods or services from suppliers to customers. Because a logistic system involves specialized operations such as inventory control, facility location and distribution planning, the logistic professional requires mathematical, technological and managerial skills and tools to design, adapt and improve these operations. The main research is focused on modeling and solving logistic problems through specialized tools such as integer programing and meta-heuristics methods. In practice, the use of these tools for large and complex problems requires mathematical and computational proficiency. In this context, the present work contributes with a coded suite of models to explore relevant problems by the logistic professional, undergraduate/postgraduate student and/or academic researcher. The functions of the coded suite address the following: (1) generation of test instances for routing and facility location problems with real geographical coordinates; (2) computation of Euclidean, Manhattan and geographical arc length distance metrics for routing and facility location problems; (3) simulation of non-deterministic inventory control models; (4) importing/exporting and plotting of input data and solutions for analysis and visualization by third-party platforms; and (5) designing of a nearest-neighbor meta-heuristic to provide very suitable solutions for large vehicle routing and facility location problems. This work is completed by a discussion of a case study which integrates the functions of the coded suite.