Advances in Industrial Engineering

During the recent years, the companies in a wide range of industries have to design their activities in such a way to reduce the costs. A most popular way to reduce the costs in logistics is cross-docking. It is a strategy which is used to serve different purposes including the fast consolidation of received volume of commodities from suppliers, improving the responsiveness by shortening delivery lead time, reducing the inventory holding costs, eliminating spoilage costs of commodities, reducing transportation costs by employing full truck loading policy etc. The objective of this paper is to develop a mixed integer linear programming (MILP) model considering supplier selection and order allocation, perishability of commodities, group purchasing strategy and multi-agent scheduling into the well-known vehicle routing problem with cross-docking. Some small-sized test instances are applied to validate the new proposed model. A weighted-sum method is applied to solve small-sized instances. Then sensitivity analysis of the new proposed model is performed on the key parameters of the objective functions so that the supply decisions are evaluated while the parameters of the distribution costs are changed. Due to NP-hardness of the new proposed problem, two meta-heuristic algorithms including NSGA-II and MOPSO are applied to solve a wide range of instances. The obtained results by applying statistical hypothesis tests are compared through six different criteria. Also, an ordering technique that is called Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is applied to rank the meta-heuristic approaches.

This study seeks to bridge the existing gaps in previous researches by introducing acomprehensive data-driven network design model. The process begins with an in-depth analysisof customer demand, utilizing unsupervised learning algorithms to gain valuable insights intoconsumer behavior. This analysis will help identify demand levels across various geographicalregions while uncovering patterns that fluctuate over time. These insights will serve as essentialinputs for the network design model. To facilitate effective data classification and analysis, theDensity-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm will beemployed, enabling accurate estimation of customer demand based on innovative parameters.Building upon these findings, a new mathematical model will be created that incorporates fleetoptimization constraints. Importantly, during this modeling process, emphasis will be placed notonly on optimizing the number, location, and capacity of facilities but also on refining fleet typesand their compositions to enhance overall efficiency. Due to the complexity of the model, it willbe solved using various numerical case problems. Due to the complexity of the model, it will besolved using various numerical case problems. The results demonstrate that the proposed data-driven model achieves an average profit improvement of 10-15% compared to traditional non-clustered approaches. Furthermore, the model yields noticeable cost savings of approximately 8-12% in transportation and fleet-related expenses. Furthermore, the integrated nature of modelallows for an examination of key parameters to extract valuable managerial insights,demonstrating the synergy between data-driven clustering and mathematical optimization fordistribution network design.

Rapid urban growth, driven by urbanization and agglomeration of goods and services in metropolitan cities, jeopardizes land conversion for agriculture and challenges traditional models of urban development. The traditional urban growth models emit carbon, destroy the environment, and create food deserts, thereby compromising food security and health for urban residents. Sustainable urban agriculture can be an alternative solution since it provides food security and access to fresh and affordable food. The present research employs a two-stage model: regional ranking with an integrated ANP-TOPSIS method, and best allocation of resources, such as location selection, cultivation technology, and gender-balanced human resource deployment, with the mixed integer programming methodology. Implemented in Iran, Kermanshah, the proposed model recommends vertical hydroponic production of cauliflower and tomatoes to reduce water and land use. It also promotes gender balance in working opportunities, thus lessening the disparity among men and women in job activities. Economic evaluation using the net present value method reaffirms the economic viability of urban farming, with a mention of the effect of land price and return on investment. Analysis of the ANP-TOPSIS model establishes that increasing the level of sustainability raises the farm's sustainability, albeit non-linearly in all the dimensions and sub-criteria. This approach supports observations regarding effective urban agriculture practices towards sustainable urban development.

Despite growing interest in digital transformation and modular services, many logistics firms—especially in Iran—lack a cohesive framework that integrates modular architecture with operational and technological needs amid rising complexity and customer demands. This study aims to develop a conceptual framework for a modular logistics service platform that enhances flexibility, innovation, and collaboration across supply chain actors. The research identifies core service modules and examines how modularity can support the design of efficient, adaptive service offerings. Using a qualitative case study approach, the study investigates one of Iran’s leading courier companies. Data were collected through semi-structured interviews with ten senior managers, direct observations, and analysis of internal documents. Thematic content analysis revealed key service modules, processes, and a three-layer modular architecture consisting of service, process, and activity layers. These are structured around the First Mile, Mid Mile, and Last Mile segments, incorporating nodes, links, and carriers as core elements. The platform supports modular processes, such as routing and packaging, and enables outsourcing at multiple levels. It integrates Artificial Intelligence (AI) and Internet of Things (IoT) technologies to optimize performance. The framework addresses significant gaps in existing literature, including role definition, modular governance, innovative technology integration, and service scalability. This research presents a novel, multi-level, modular logistics framework validated in a real-world context, offering a practical blueprint for logistics firms seeking to transition to flexible, modular platforms that enhance efficiency and collaboration.

The optimal use of natural resources and energy has become an important issue and has been given attention worldwide due to the increase in population and environmental pollution. In recent years, Eco-industrial parks (EIPs) have gained popularity as a way to make better use of natural resources. In these parks, companies try to cooperate with each other by exchanging materials and energy and pay more attention to environmental issues. Unlike previous models proposed in this field, which were based on existing EIPs and were presented for improvement, we present a new model for creating these parks. In this study, we propose a mixed integer programming (MIP) model, considering real and feasible exchanges, which, taking into account sustainability conditions (economic, social, and environmental), selects industries to establish the park so that the value of exchanges is maximized and the costs of infrastructure construction are minimized. The results show that the selected set of industries leads to economic benefits, where the total value of exchanges exceeds the costs of infrastructure, thus supporting profitability and sustainability.