Increasing in attentions to the environment, city legislative and social problems make companies change their prospects towards supply chain management and design sustainable transportation networks. In this paper, two-stage problem have been investigated in which the transportation stage is considered before Location-Routing Problem, so we call it Transportation-Location-Routing Problem (TLRP). It is an extension of the two-echelon Location-Routing Problem. In the first stage, there is a transportation problem with truck capacity limitation. Furthermore, customers’ time windows should be met in the second stage to make the mode more realistic. Minimization of distribution cost, fuel consumption, and carbon dioxide emission along with balancing the workloads for city drivers are considered as the objective functions of the mathematical model to design a sustainable distribution network. To tackle these conflicting objectives, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are applied to solve the problem. A new customized chromosome based on a priority based technique is presented for the problem. Due to the three comparison metrics for multi-objective problems, with tolerating a little more computational time, MOPSO has the better performance in this problem than NSGA-II.

The purpose of this study is to solve a complex multi-product four-layer capacitated location-routing problem (LRP) in which two specific constraints are taken into account: 1) plants have limited production capacity, and 2) central depots have limited capacity for storing and transshipping products. The LRP represents a multi-product four-layer distribution network that consists of plants, central depots, regional depots, and customers. A heuristic algorithm is developed to solve the four-layer LRP. The heuristic uses GRASP (Greedy Randomized Adaptive Search Procedure) and two probabilistic tabu search strategies of intensification and diversification to tackle the problem. Results show that the heuristic solves the problem effectively.

Distribution network design is an important issue in supply chain management and plays an important role in making new market development. Because of JIT philosophy, most of managers now have focused on designing appropriate distribution networks. Thus, categorizing distributors and selecting the best ones are crucial for companies. This paper provides a new method to categorize and select distributors. The fuzzy Adaptive Resonance Theory (ART) algorithm is utilized to categorize distributors according to their similarity. To improve the performance of the algorithm, we train the algorithm using the past data. Finally, a numerical example is illustrated to examine the validity of the proposed algorithm.

Distribution refers to the steps taken to move and store a product from the suppliers to a customers in the supply chain and is a key driver of the overall profitability of a firm and overall supply chain. In this paper, a problem regarding managing of the move and store of goods are articulated and a mathematical model is presented to solve the model. The objective function is the total costs of distribution network, including transportation, storage rental, general warehousing, goods damages due to the transportation and storage, procurement, packing, and finally loading and unloading costs. The cost components described are defined based on the assumptions for a real distribution network of a chain stores firm. The aim of developing such a model is to find the optimum pattern to move and store goods based on the minimum cost of the distribution network.