Influencing on tactical and operational decisions, LOGISTICS NETWORK design problem, in particular green supply chain management, has attracted more attention in recent years. This paper concentrates on designing a non-linear, multi-commodity and multi-stage closed-loop LOGISTICS NETWORK problem with considering hybrid facilities to determine the location of facilities and their optimum capacity levels as well as appropriate flows between each two consecutive echelons of the NETWORK maximizing the total expected profit.

Purpose: In this paper, a humanitarian LOGISTICS NETWORK is designed considering the purchase contract in conditions of uncertainty. Due to the importance of such NETWORKs in the event of unforeseen events, the designed model seeks to determine the central and local warehouses as well as shelters to transport the injured. Also, determining the optimal amount of inventory and the correct way of transferring items and injured are other NETWORK decisions. In this article, the contract for purchasing items before and after the accident is concluded with suppliers in order to supply raw materials to the severity of the accident. Methodology: Due to the uncertainty in the model, the robust optimization method is used to control the uncertainty, and due to the NP-Hardness of the model, the new Gray Wolf-Genetics Algorithm (GGWA) is used to solve the model. Findings: The results show that contract operation has reduced the costs of the entire humanitarian LOGISTICS NETWORK. The comparison of the means of the objective function and the computational time shows the high speed of the GGWA algorithm in finding near-optimal solutions compared to the PSO and GA algorithms. Originality/Value: In this paper, a new model of humanitarian supply chain NETWORK has been designed, which has obtained very favorable results from the problem using the GGWA algorithm in the shortest time.

Nowadays, reverse LOGISTICS is one of the major activities of supply chain management that include, all of the physical activities related to returned products (e.g., collection, recovery and recycling). So that designing and implementing of appropriate reverse LOGISTICS, not only leads to the increase of customer loyalty, but also reduces the inventory and transportation costs.This paper presents a mixed-integer linear programming (MILP) model for the NETWORK design of forward and reverse LOGISTICS, which minimizes the costs consisting of the fixed cost, material cost and construction cost of a potential center. To validate this model, a number of test problems are generated, and then its parameters are determined. The presented model is solved by the Lingo software, and the associated computational results along with the sensitivity analysis are illustrated. Additionally, the returned products with different demands for one and two products are illustrated. Finally, a remarkable conclusion is presented.

An increase in environmental issues has encouraged the consideration of various factors that influence the environment. In this regard, the green supply chain has attracted the attention of researchers because of its considerable impacts on the environment. This study, therefore, was an attempt to design a forward/revers LOGISTICS NETWORK by putting emphasis on some environmental issues like the quantity of CO2 emission in its model. In this LOGISTICS NETWORK, three objective functions including minimizing the total cost and quantity of CO2 emission as well as maximizing the satisfaction of customers are considered simultaneously. This persuaded the researchers to adopt multi-objective optimization methods. Thus, Non-dominated sorting genetic algorithms (NSGA-ӀӀ) and Multi-objective particle swarm optimization (MOPSO) are proposed to cope with the problem. Finally, the results of the experiments on several test problems are verified by GAMS software. They confirm the superiority of NSGA-ӀӀ over MOPSO in terms of all comparison metrics.

Introduction: Annually, large quantities of agricultural products are wasted and a lot of damage is done to the country's financial and food resources. The most important causes of this waste are related to the various sectors of transportation, packaging, storage, processing and distribution of agricultural products. One significant tool to increase in supply chain productivity and reduce LOGISTICS costs of the agricultural sector is to establish agricultural LOGISTICS centers. The current study seeks to identify a suitable area for the construction of an agricultural LOGISTICS center in Isfahan province. In this regard, criteria affecting the selection of locations for LOGISTICS centers (compensatory and non-compensatory) were first identified and then the process of locating was implemented in two separate phases. In the first phase, in order to identify potential site options for a LOGISTICS center within the province, geographical boundaries associated with non-compensatory criteria were excluded from the study. In the second phase, according to the mathematical model presented, the cost of establishing a LOGISTICS center was calculated in possible sites in terms of compensatory criteria and then relevant geographic data layers were developed and, at the end, all data layers were integrated. Materials and Methods: This paper, first, studies the experimental literature in the field of successful LOGISTICS centers across the world as to identify criteria affecting the location of agricultural LOGISTICS centers in terms of non-compensatory grammatical (geological and environmental factors) and compensatory (measurable factors with quantitative values) criteria. Next, the process of locating is implemented during two stage. In the first stage, in order to identify possible sites for LOGISTICS Park within the province, geographic data layers associated with non-compensatory criteria are gathered and the respective boundaries are excluded from the study. Note that in order to unify mapping units being studied with respect to 1: 2500000 scale map of analytical maps used, the province is divided into 4510 separate blocks of 25 Km2. For the second stage, based on the general framework of LOGISTICS activities, the spatial analysis is carried out for load flows (the production and consumption profiles) of agronomic, horticultural and livestock. According to the location theories in minimizing costs, a mathematical model is developed to compute costs of agricultural LOGISTICS center establishment per each 1440 blocks located in potential sites in terms of compensatory criteria and then under the Arc GIS software environment, all data layers related to these criteria are developed and integrated. The best option is to install a block LOGISTICS center having the minimum cost. Due to the large volume of data gathered as well as the variety of analysis and calculation units (blocks of 25 Km2), the C++ programmer is used to perform algebraic and matrix calculations. (Road and rail) distances applied in the model for different origins and destinations are calculated and extracted as the distance of the NETWORK through the Arc GIS software. Raw data of road bill of lading blocks have been obtained from the Road Maintenance and Transportation Organization of Isfahan for the period between 2013 and 2018 for the commodity groups including agronomic, horticultural and livestock Conclusion: The most optimal (least expensive) area for the establishments of an agricultural LOGISTICS center is located in the west of the urban complex of Isfahan and between the counties of Tiran and Karvan, Najafabad and Lenjan. The proposed area acts as an intermediary between the production centers of agronomic, horticultural and livestock products in the west of the province and the urban complex of Isfahan as a major consumer of agronomic, horticultural and livestock products. Proximity to the west-north freeway makes it possible to send the province's surplus products to the northern and eastern provinces of the country.

World’s growing population and the frequency of natural disasters as well as managing disasters and continuous improvements in methods and strategies adopted has become an essential global concern. The current paper introduces a two-stage mathematical model designed to minimize operational costs while improving service delivery through incorporating smart city infrastructure. In pre-disaster phase, multiple suppliers, warehouses, and regions are considered, along with such key objectives as allocating suppliers, locating warehouses, managing inventory in addition to identifying regions for smart city development. Post-disaster, the model focuses on routing drone flights for collecting data, distributing relief items, and establishing make-shift relief centers. The second stage comprises both ground and aerial vehicles for LOGISTICS and data collection. To handle uncertainty and the model's dual-level nature, a distributionally robust optimization (DRO) approach is exploited. Sensitivity analysis of a numerical example points to the fact that storage costs, demand correlation, and average demand significantly affect total costs. So much so that an increase in these parameters brings about an increase in total operation costs. First-stage decisions yielded 12 efficient solutions for the second-stage model. The obtained results indicate that for reducing shortages in the humanitarian LOGISTICS NETWORK, there should happen an increase in total costs. The increase should be directed towards using more drones for distributing aid items and gathering information. Given the NP-hard nature of the model, hybrid algorithms were employed, which outperformed exact methods in terms of efficiency. In a real-world case study in Isfahan Province, seven cities (Fereydunshahr, Kashan, Najafabad, Ardestan, Varzaneh, Isfahan, and Shahreza) were identified as smart city infrastructure sites. Five warehouses in Naeen, Kashan, Fereydan, Isfahan, and Shahreza were selected for disaster relief LOGISTICS operations. Findings highlight the trade-off between cost and service level, emphasizing the importance of drones in reducing shortages and enhancing disaster response efficiency. Managerial insights highlight the cost-effectiveness of IoT deployment in reducing demand uncertainty and enhancing response efficiency. The proposed framework offers a practical and scalable solution for disaster preparedness and post-disaster management in diverse urban contexts.