neda manavizadeh

Governments use online platforms to keep track of transactions in the supply chain (SC) of subsidized foods to prevent fraud. Although regular checks of warehouses and documents were conducted, current platforms failed to resolve the issue. Blockchain technology (BT) provides governments with the ability to access transparent and real-time data to address these challenges. In this paper, we examine the key challenges influencing the implementation of a BT platform for managing subsidized food products in Iran. The barriers appear to be interconnected. We present a model that integrates the Best-Worst method (BWM) for obtaining independent weights and the Weighted Influence Non-Linear Gauge System (WINGS) using a rescaling scheme for considering the interrelatedness between the criteria. Expert opinions and literature reviews are used to identify critical factors. According to the findings, the costs of implementing and maintaining the system, as well as the regular restructuring of government rules regarding the data to be collected, are the two main challenges of implementing this new technology. Moreover, there are concerns about the cooperation with downstream entities of SC, cultural differences among partners, and their knowledge level, which may affect the complexity of downstream implementation. The results of sensitivity analysis show that WINGS gives greater weight to factors that have more impact on others. Conversely, the weight of factors that are interwoven with other factors and factors that aren't influenced by other factors is reduced as compared to the independent relative importance obtained from BWM.

Contrary to the past that inventory decisions and pricing are taken into consideration separately, due to the influence of these decisions on each other and thus profit, researchers have investigated these two issues simultaneously. Sometimes, wholesalers offer incentive financial policies to their customers in order to increase their sale. In this paper, a different combined model of inventory control and the way of its pricing for a deteriorating item with different incentive schemes including totally advance payment and partially advance, partially delayed payment are developed. We adopt a demand function jointly time and price-dependent and a backordering rate waiting time-dependent. Also shortage of allowable inventory considered. In each case, optimum price, replenishment cycle, the time with no shortage are obtained. Sensitivity analysis is performed and represented in several figures and tables. The results show that with increasing deterioration and backordering rates, the total annual profit is reduced.

In real life conditions, the buyers sometimes pay all or a percentage of the product price before receiving it, and the wholesaler sometimes allows them to prepay it at equal intervals. The present study develops a new mathematical model for products with non-instantaneous deteriorating rates by considering consecutive advance payments. In the proposed inventory model, the shortage is consisting of lost sales along with backorders simultaneously. In addition, the model considers the backlogging as totally dependent on the waiting time for the further cycle. In addition, the appropriate conditions to achieve the optimal solutions have been developed, and numerical instances have been provided to verify and evaluate the results and solution method. The useful methods to effectively reduce the annual total cost are provided according to the results of the sensitivity analysis.

In this paper, a type II robotic mixed-model two-sided assembly line balancing problem is considered. This paper presents a new mixed-integer programming model for type II robotic mixed-model two-sided assembly line balancing to minimize the cycle time, energy consumption, and purchased cost of robots for a given number of workstations. We provided an effective framework for optimizing the multi-objective robotic mixed-model two-sided assembly line balancing problem considering energy consumption and smoothing workload in the make to order environment to help the decision makers make the right decisions under stochastic demand. An augmented epsilon constraint and Lp-metric methods applied to solve the problem, and then, with the help of defining two vertical and horizontal criteria, we attempt to help the decision maker to choose a more efficient solution to make the production line more smooth workload. We demonstrate the efficiency of the proposed method by designing the numerical experiments. ZZ ZZ ZZ ZZ ZZ

Cooperative advertising is a cost-sharing mechanism in which a part of retailers' advertising investments are financed by the manufacturers. In recent years, investment among advertising options has become a difficult marketing issue. In this paper, the cooperative advertising problem with advertising options is investigated in a two-period horizon in which the market share in the second period depends on the decisions made in the first period. The problem is solved for two cases of the absence and presence of cooperative advertising contract, and the results are compared. The solution to the problem is presented using the concepts of the Nash equilibrium, Stackelberg game, stochastic games, and dynamic programming. The computational results using numerical examples show that if the cooperative advertising contract is offered in the win-win condition, the players’ profit will increase significantly.

This paper proposes a bi-objective model for the waste collection problem and considers the location, routing and inventory of the system simultaneously. Considering the reverse flow of the system is another feature of the current study. In the proposed model, the total costs of the system are minimized. In addition, the related risks of opening new centers and transportaion are included as the second objective function of the problem. Considering the delivery time and cpacity of vehicels constraints, are the other features of the model. Due to the NP-hardness of the model, two metaheuristic algorithms namely a non dominated sort ordering genetic algorithm (NSGA-II) and a multi objective particle swarm optimization algorithm (MOPSO) are applied to solve the problem. According to the results, NSGA-II is able to reach better answers in all the propsed metrics. According to sesitivity analysis, foreign transportation fleets make a great impact on the costs of the system.

This paper formulates a mathematical model for the Green Vehicle Routing Problem (GVRP), incorporating bi-fuel (natural gas and gasoline) pickup trucks in a mixed vehicle fleet. The objective is to minimize overall costs relating to service (earliness and tardiness), transportation (fixed, variable and fuel), and carbon emissions. To reflect a real-world situation, the study considers: (1) a comprehensive fuel consumption function with a soft time window, and (2) an en-route fuel refueling option to eliminate the constraint of driving range. A linear set of valid inequalities for computing fuel consumption were introduced. In order to validate the presented model, first, the model is solved for an illustrative example. Then each component of cost objective function is considered separately so as to investigate the effects of each part on the obtained solutions and the importance of vehicles speed on transportation strategies. Computational analysis shows that, despite the limitation of an appropriate service infrastructure, the proposed model demonstrated an average reduction of 44%, 6% and 5% in carbon emission costs, total distribution costs, and transportation costs respectively. Moreover, the study found paradoxical effects of average speed, suggesting the need to manage trade-offs: while higher speeds reduced service costs, they increased carbon emission costs. In the next stage, some experiments modified from the literature are solved. According to these experiments, in all instances greater objective function values for Gasoline vehicles are gained. The difference in the carbon emission objective is also significant, with an average of 44.23% increase. Finally, managerial and institutional implications are discussed.

This study proposes a novel option-revenue sharing coordination contract framework. In the proposed model, the retailer determines the number of order sales effort. The manufacturer sets the price of products for the wholesale strategy. The investigated supply chain problem analyzes the results of different strategies. In the proposed coordination contract problem, two types of games including retailer-based game and manufacturer-based Stackelberg game are considered. In both cases the retailer adopts the value of order and the sales effort and the manufacturer determines the wholesale price. To assess the performance of the proposed contract, a wholesale and a basic selection contract are considered in the model. To obtain the Nash equilibrium in the retailer-based state of the proposed option-revenue sharing coordination contract problem, a hybrid algorithm consisting of a heuristic and a genetic algorithm is proposed by considering the computational complexities of the proposed model. A numerical comparison between the proposed contract and other cases demonstrates that the option-revenue sharing contract significantly dominates the basic option and the wholesale price contract. Finally, we implemented some numerical experiments on the critical parameters of the contract. Based on the results, increasing the price-dependency of demand results in less number of products ordered by the retailer.

This paper presents a novel multi-objective location arc-routing model in order to locate disposal facilities and to design optimal routes of residential waste taking into consideration many complicated real constraints such as a heterogeneous fleet of vehicles, time windows for customers, disposal facilities and the depot, capacities for vehicles and facilities. The first objective is the minimization of transportation costs, including service costs and fuel costs of vehicles. The second one minimizes total number of utilized vehicles. And finally, the third objective function is considered for minimizing total number of established disposal centers. Moreover, to come closer to reality the service time, amount of demands, capacities and cost parameters are considered as fuzzy ones. To solve the proposed model, a credibility-based fuzzy mathematical model and its interactive solution method with three recent approaches, are used and the results are compared with each other.

This study introduces a green location, routing and inventory problem with customer satisfaction, backup distribution centers and risk of routes in the form of a non-linear mixed integer programming model. In this regard, time window is considered to increase the customer satisfaction of the model and transportation risks is taken into account for the reliability of the system. In addition, different factors are detected as the major factors affecting the risk of routs and a fuzzy TOPSIS method is applied to rank the related risk factors. Next, due to the complexity of the investigated model, two algorithms including multi-objective gray wolf optimization algorithms (MOGWO) and Non-Dominated Sorting Genetic algorithm (NSGA-II) are applied to solve the large-sized instances. The results prove the superiority of MOGWO in dealing with large-sized instances. In the next step, some sensitivity analysis is implemented on the model based on a case study andthe related results of case study are reported as well.

Capacity coordination, as the tactical level of hierarchical production planning in hybrid MTS/MTO systems, includes numerous important decisions. In this paper, two of these decisions i.e. finding the best strategy for the acceptance/rejection of incoming orders and determining orders’ due dates – are investigated. Also a simulation model is proposed to evaluate the efficiency of the presented mixed integer model. Finally, an industrial case study is considered in a food processing plant to evaluate the proposed framework and conduct suitable sensitivity analysis.

Industrial hazardous materials (hazmat) are byproduct of industrial production and include hazardous goods, such as flammable, toxic and corrosive materials that pose a risk to the environment.Hazardous waste management includes collection, transportation, treatment, recycling and disposal of industrial hazardous material in an organized manner. With the increasing industrialization of countries, the issue of waste management is more important than before. Therefore, the main purpose of this research is to optimize locations of recycling centers and routing hazardous. The methods used to solve the mathematical model include the ε-constraint method and the NSGA II algorithm.First, we examine the validation of proposed model. Then, the optimal values of the parameters of multi-objective meta-heuristic algorithm are determined by Taguchi approach and the proposed algorithms are used to solve the given problem for 19 examples with different sizes. Finally, two algorithms are compared based on the fiveidentified criteria. In addition, the run time for both methods was calculated and large-scale results were presented based on the multi-objective genetic algorithm. The results show the efficiencyofmulti-objective genetic algorithm in solving given problem, and in particular for problems with larger sizes.

In this paper a dynamic cell formation problem is presented considering some new and special characteristics. The concept of machine requirement by lucky parts, the parts which are allowed to be produced in a specific period, is combined with the depreciable property of machines. Therefore, purchasing and selling of machines according to their book-value and generating income have been taken into account. This leads to a new vantage characteristic in cell formation where in each period we are only dealing with the types and number of machines required. The new mathematical model is presented and solved by exact and ant colony optimization methods for three problem sizes.

Mixed-model assembly line (MMAL) is a type of production line where a variety of products’ models similar to products’ characteristics is assembled in the same line. Many manufacturers tend to use mixed-model assembly line in their production lines, since this policy make possible to assemble various products with the Make to order (MTO) environment. In this research, the sequence of U-type mixed model assembly line is achieved through considering downstream help and kits’ storage as effective help policies for reducing total line stoppages and tardiness of products’ delivering time to customers. Since this problem, is NP-hard, hybrid GA-Beam search algorithm is developed to solve the problem. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. To the best of our knowledge, this is the first study that considers getting help from downstream worker or using kits’ storage, which has ready to assemble parts in the conditions that workers cannot complete the remained task in the work horizon.

Due to strict competition in the global market for Tourism services and Hotels in the tourism industry and also the importance of satisfying tourists, awareness about the efficiency of hotel for hotel owners and hotel managers is very important. The purpose of this paper is measuring performance of hotels in Crete by using Robust Data Envelopment analysis (RDEA) technique considering uncertain data. The proposed method of this paper develops a RDEA method with the consideration of uncertainty on output parameters. In order to use robust optimization methods in this article, after the introduction of input and output, we calculate the efficiency of 50 luxury hotel in Crete by means of GAMS software. The method is based on the adaption of robust optimization approaches proposed in the literature. Finally, we compare the performance achieved with previous research on these hotels and our results. It is found that the efficiency decreases, but the level of confidence increases.

In this paper, supply chain network design problem is modeled as a fuzzy multi objective mixed integer programming which seeks to locate the plants, DCs, and warehouses by considering disruption, supply and demand risk. Maximizing net present value of supply chain cash flow, minimizing delivery tardiness and maximizing reliability of suppliers are considered as objective functions in the proposed mathematic model. In order to have a more reliable model in case of disruption, the robustness measure is used in the model. Moreover, because of the lack of information, the economic factors such as tax rate, interest rate, and inflation are considered as uncertain factors in the model. An interactive possibilistic programming approach is applied for solving the multi-objective model. To solve larger size instances, genetic algorithm is proposed. Finally numerical examples are presented to show how the model works in practice.

In this article, multiple-product PVRP with pickup and delivery that is used widely in goods distribution or other service companies, especially by railways, was introduced. A mathematical formulation was provided for this problem. Each product had a set of vehicles which could carry the product and pickup and delivery could simultaneously occur. To solve the problem, two meta-heuristic methods, both based on particle swarm optimization, were provided and ran for small and large class problems and their efficiency were demonstrated. Also, efficiency of binary PSO to general PSO was tested and BPSO was shown to outperform the general method. This approach can be used in railway transportation.

In this paper، we consider a multi-site production planning problem subject to uncertainty in demand and workforce expenses. In our new mathematical model، we presented a production planning system considering failure in rework and breakdown. We also survey human workforce allocation and its expenses which are considered uncertain due to some tradeoff between company’s benefits and workforce union’s advantages. We presented a new robust particle swarm optimization to propose a model with the ability of handling uncertainties. Firstly، we apply the presented robust optimization to handle demand uncertainty separately، and then we extended our model to regard both uncertainties simultaneously. To show the practicability of the proposed algorithm، we solved a real problem in an industrial case study. We also analyzed the most important parameters in the presented robust model to find out which level of uncertainty has less constraint violation and determine the maximum budget of uncertainties that could be considered in the proposed model to expect acceptable optimal objective. The results showed that the proposed model can prepare a promising approach to fulfill an efficient production planning in a multi-site production planning.