Ali Husseinzadeh Kashan’s research while affiliated with Tarbiat Modares University and other places

This paper introduces the three-dimensional bin packing problem with item fragmentation (3D-BPPIF) and explores its application in the storage location assignment problem (SLAP) to efficiently allocate warehouse spaces to product groups. Based on real-world constraints, the aim is to find an effective 3D-packing of the product groups into warehouse storage spaces to minimize the total distance. Given the internal limitations present in many warehouses, the storage spaces are not homogeneous, making the allocation to product groups a challenging task that can reduce space utilization efficiency. Accordingly, to effectively utilize warehouse storage spaces, we developed a MILP formulation incorporating the concepts of shape changeability and item fragmentation, significantly enhancing the flexibility of the arrangements. Due to the NP-hard nature of the problem, we proposed a simulated annealing-based meta-heuristic to solve large-scale real-world problems. Numerous computational experiments prove the validity of the proposed model and illustrate that the proposed algorithm can provide appropriate 3D assignments.

In today’s competitive world, organizations are required to respond to customers and their stakeholders whose goals are profit, cost reduction, quality improvement, and customer satisfaction. Therefore, it is vital to realize the ways for continuous quality improvement, customer satisfaction and organizational performance management. A performance measurement system should be designed to consider the chain specifications of the network and its interrelations. Data envelopment analysis (DEA) is broadly used to evaluate the relative performance of a set of manufacturing processes because their models do not require considering the precise production functions. In this paper, a new approach based on network DEA is developed to evaluate the performance of an organization’s supply chain. The proposed model considers different constraints simultaneously. The purpose of this research is to provide a new approach by effective network DEA in order to evaluate the supply chains with different assumptions. This model is input–output-oriented and has a target setting, considering dual-role factors and desirable/undesirable outputs as well as flexible intermediate measures to evaluate the efficiency in a more realistic situation. Finally, a case study in drink industry is reanalyzed relying on the proposed model, and the results are presented and discussed. The results show that the approach presented in this research by considering the targets for inputs and outputs, in addition to improving efficiency, can also calculate and report the distance between DMUs and their targets. Also, the proposed model by considering the dual-role factors, desirable and undesirable outputs, has provided a comprehensive method that with all of these assumptions can evaluate the network DMUs under more realistic conditions. So, through this model with the considered assumptions, not only the efficiency scores of the units can be calculated but also an analysis of how to improve the units is also obtained according to the decision makers’ opinion.

Purchase subsidy is one of the most applied policies for electric vehicle adoption, which reduces the initial purchasing cost while imposing high expenses on governments. Although the purchasing price of the electric vehicle is higher than that of the fuel one, the lower operational costs of the former may compensate for the difference between vehicles' prices during the ownership period. In this way, governments can further persuade customers to purchase electric vehicles by adopting policies that make more reductions in operational costs, in addition to the purchase subsidy. This study optimizes the government policy-making in an electric-and-fuel automotive (i.e., passenger cars) market through designing a Stackelberg game model under the government budget constraint. Three subsidy scenarios are formulated: I) the purchase subsidy, II) the electricity subsidy, and III) the simultaneous purchase and electricity subsidies. Automotive manufacturers optimize their vehicles' selling prices following optimal government policy-making. A customer choice model is designed based on the total cost of ownership for vehicles with a random mileage and a new customer acceptance level parameter. Results lead to optimal subsidy plans for the government under various budget levels. When the available budget is very low, the electricity subsidy is the dominant policy, except for high customer acceptance levels and low fuel prices. The purchase subsidy is the dominant policy for low customer acceptance levels when low budgets are available. The government should subsidize both purchase and electricity costs in >50 % of possible situations concerning various customer acceptance levels and fuel prices. When the government adopts both types of subsidies, >70 % of the budget should be assigned to the purchase subsidy in >50 % of possible situations.

Policy-makers should focus on solar energy due to the increasing energy demand and adverse consequences such as global warming. Conflicting criteria influence choosing the most desirable place to construct a Solar Power Plant (SPP). Researchers have popularized multicriteria decision-making (MCDM) methods because of the potential. Although the simultaneous use of several methods increases the robustness and accuracy of the results, existing methods to integrate MCDM methods mainly consider the same weight for all methods and utilize the alternatives ranking for the final comparison. This paper presents a hybrid decision-making framework to determine the best location for SPPs in Iran using a set of criteria extracted from the literature and expert opinions. An initial list of decision-making alternatives is prepared and evaluated using GIS software in terms of criteria. Decision-makers prioritized the identified alternatives using the MCDM methods, including SWARA and different ranking methods (TOPSIS, TODIM, WASPAS, COPRAS, ARAS, and MULTIMOORA). Finally, the CCSD method aggregates the results and identifies the best location. Results highly correlate with the results of previous methods and demonstrate the robustness of the proposed approach and its capability to overcome the limitations of previous methods.

The Supply Chain Network Design (SCND) with perishability is an active research topic. The Agri-fresh Food Supply Chain (AFSC) is a relevant topic to SCND and this study aims to model a new AFSC for a real-world case study. Regarding the traditional AFSC, the geographically dispersed small farmers transport their products individually to the market for selling. This leads to a higher transportation cost, which is the major cause of farmers' low profitability. This paper formulates a traditional product movement model to represent the existing AFSC. The concept of sharing economic approach is employed by the aggregate and collaborative transportation of products to minimize transportation inefficiency. This paper proposes an aggregate product movement with the vehicle routing model to redesign an AFSC for a case study in Iran based on the data of ETKA Company-the largest domestic agri-fresh food supply chain. A four-echelon, multi-period, Mixed Integer Non-Linear Programming (MINLP) approach for the proposed location-inventory-routing model is formulated for perishable products via considering the clustering of farmers to minimize the total distribution cost. According to the comparison analysis, the location-inventory-routing model is effective and leads to a reduction of 33% in total distribution cost compared to the present supply chain.

Multi-criterion decision-making models are widely used in supplier selection problems. This study contributes to a green supplier selection problem considering the green manufacturing, green transportation, and green procurement. This study contributes to reverse logistics, eco-design, reusing, recycling, and remanufacturing with their high impact on the industries. In addition to the logistics costs and transportation costs, the carbon emissions are considered. With regard to the game theory, this paper uses a cooperative green supplier selection model. If transportation requirements of two or more companies are combined, it will help manufacturers to have less CO2${CO}_{2}$ emissions with lower cost. After creating the optimization model to consider the uncertainty, this cooperative game theory model is established in a fuzzy environment. In this regard, a fuzzy rule-based (FRB) system is deployed and the set of fuzzy IF–THEN rules is considered. The proposed FRB model is contributed for the first time in the area of green supplier selection problem. Finally, some sensitivity analyses are conducted in a numerical example to evaluate the proposed model. With regard to the findings, although the cost of CO2 emission of horizontal cooperation is increased, the cost saving of companies is increased. It means our total cost is optimal in a logistic network using the cooperative game theory. The results also indicate that horizontal cooperation in logistic network causes less cost and benefits for each company.

This study proposes a sustainable closed-loop supply chain under uncertainty to create a response to the COVID-19 pandemic. In this paper, a novel stochastic optimization model integrating strategic and tactical decision-making is presented for the sustainable closed-loop supply chain network design problem. This paper for the first time implements the concept of sustainable closed-loop supply chain for the application of ventilators using a stochastic optimization model. To make the problem more realistic, most of the parameters are considered to be uncertain along with the normal probability distribution. Since the proposed model is more complex than majority of previous studies, a hybrid whale optimization algorithm as an enhanced metaheuristic is proposed to solve the proposed model. The efficiency of the proposed model is tested in an Iranian medical ventilator production and distribution network in the case of the COVID-19 pandemic. The results confirm the performance of the proposed algorithm in comparison with two other similar algorithms based on different multi-objective criteria. To show the impact of sustainability dimensions and COVID-19 pandemic for our proposed model, some sensitivity analyses are done. Generally, the findings confirm the performance of the proposed sustainable closed-loop supply chain for the pandemic cases like COVID-19.

A Correction to this paper has been published: https://doi.org/10.1007/s11356-021-14245-2