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This paper proposes a new Vehicle Routing Problem (VRP) with fair Carrier Collaboration (CC) which split multi-pickup and delivery services are considered for serving customers. This study has focused on VRP and serving customers with several commodity requirements from different geographically scattered suppliers subject to constraints on the vehicle capacity. A Mixed Integer Programming (MIP) model to maximize the total profit and the fair sharing of profit among the carriers by considering the travel time minimization is developed. Each carrier with its limited capacity can have reserved requests which must be served by itself and selective requests which can be served by itself or other vehicles or not served at all. There are various applications of the proposed model in the environment which can help reducing number of vehicles serving to the customers and eliminating empty back hauls. A Genetic Algorithm (GA) is proposed to solve this problem due to its Non-deterministic Polynomial-time hard (NP-hard) nature. In addition, Variable Neighbourhood Search (VNS) method is developed for improving the quality of initial solutions. Some instances are generated at different scales to evaluate the algorithm's performance by comparing the results of an exact optimal solution with that of the proposed algorithm. The obtained results demonstrate the efficiency of the proposed algorithm in providing reasonable solutions within an acceptable computational time. The algorithm is also tested for an online shopping website in Tehran, Iran. The test outcome shows that the proposed model returns a better benefit compared to the manual methods. The results of sensitivity analysis suggest that increasing the fairness coefficient among carries can led to a decrease in the total obtained profit. • Develop a novel carrier collaboration routing problem model with multi-commodity service and sharing fair profit. • Extend the carrier collaboration to general problems with in many-to-one problem pickup and delivery category. • Solve the problem with genetic algorithm with a new specific chromosome representation. • Result of implementation of the model in the real-world and sample instances. • Sensitivity analysis of the model based on fair distribution between participants is conducted. [ABSTRACT FROM AUTHOR]

Highlights • Developing a new framework for coordination of redistricting, service sharing and intra-district service transfer in a CLSC. • Proposing a bi-objective robust mathematical programming model for reducing demand overload and its disparity in a CLSC. • A real case study in gasoline industry is conducted. Abstract This paper presents a bi-objective model to design a closed loop supply chain with simultaneous consideration of districting regions, facility location–allocation, service sharing, and intra-district service transfer. The first objective function is to minimize the total costs, and the second one is to minimize the maximum volume of surplus demand from service facilities. In this model, costs, returns, and maximum service supply are uncertain; a robust optimization approach is used to take into account the uncertainty. Additionally, the validity of the proposed mathematical model on a real case study in the oil and gas industry sector has been investigated. [ABSTRACT FROM AUTHOR]

Cross-docking is one of the effective strategies in logistic systems that lead to the reduction of inventory costs and timely delivery of cargos. Overall, activities related to cross-docks include three general classes of loading, sorting, and unloading of cargo. By sorting, it means integration and consolidation. However, most research works merely take loading and unloading related consideration into account. Observing the considerations associated with integration and consolidation processes in cross-docking can demonstrate the effectiveness of this strategy among other logistical systems alone. Increasing social awareness on sustainable development has also led to the emergence of concepts related to green and sustainable supply chain management. Thus, current research provides an integrated model for coordination between decisions related to pickup the cargo from the suppliers, vehicle routing from suppliers to the cross-dock, allocation of inbound vehicles to cross-dock, integration, and consolidation of cargo in cross-dock, allocation of sorted cargo to outbound vehicle, and vehicle routing from cross-dock to customers. The mathematical programming model provided in this research has two purposes. Firstly, it seeks for minimizing total system cost, and secondly, it aims at minimizing total fuel consumption by vehicles, which leads to the reduction of greenhouse gases. Additionally, in order to solve the proposed model, three multi-objective meta-heuristic algorithms, non-dominated ranking genetic algorithm (NRGA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective particle swarm optimization (MOPSO) are developed. The results show that taking into account the processes of integration and consolidation in the cross-docking, processes of collecting products from suppliers and delivering them to customers makes it possible to obtain the more accurate estimates of the final date of delivering the products to the customers. Furthermore, the above considerations reduce the system costs and, as a result, make economic savings. Therefore, it is reasonable to justify the use of cross-docking. As a result, a sustainable and integrated structure is achieved in cross-docking planning. [ABSTRACT FROM AUTHOR]