نشریه پژوهشهای مهندسی صنایع در سیستمهای تولید
پیاپی 18 (بهار و تابستان 1400)

In warehousing, products are collected from suppliers by incoming trucks and then transported to customers by outgoing trucks by unloading the load simultaneously. In case of accident, the timing of trucks plays an important role in the simultaneous loading unloading system. In this study, a case of simultaneous unloading in random conditions has been investigated in which the incoming truck can also be used as an output truck. This has the advantage of reducing unloading, loading and rental costs. In order to investigate this state of the problem, a mathematical model has been developed in order to obtain the appropriate answer to solve the mathematical model and to determine the validity and accuracy of modeling from gams optimization software in low and medium and high sizes that optimization software can finding the optimal answer matlab software and ant meta-algorithms of ant lion, memetic and ant colony have been used to solve the mathematical model. The results indicate the usability of the proposed proposal and the good efficiency of the anthill algorithm in solving this problem.

This article has developed a three-level (single supplier-single distributor-single customer) inventory model with defective products in which the supplier delivers the distributor’s ordering quantity and the distributor inspects the finished products. In this inventory model in which order includes some imperfect products, it is assume that (a) Inspectors make mistakes during the inspection process. (b) Two-level credit payment is considered to the customers, in which customers are first divided into new and old categories. Accordingly, trade credit is assigned fully to old customers and partially to new customers. (c) New customers are also divided into well-accounted and bad accounted customers in which bad accounted customers are those who refuse to pay the price of purchased finished products after ending the credit payment period. (d) Demand is dependent on number of advertisements. In this research, five sub-case problems have been investigated based on the trade credit relation of supplier and distributor and for each case, the optimal number of promotional efforts and optimal length of the order cycle have been determined. In addition, the proposed model has numerically compared with the basic model and then solved with actual case study data. The results of sensitivity analysis of this real data set showed that the effect of the percentage of imperfect products and type one inspection error have a significant impact on the total profit function. Eventually, based on these results, managerial insights and some implications for future contributions are outlined.

Supply chain is known as a network of linked and organized elements to control, manage and improve the flow of material, finance and information within the chain (from supplier to final consumer). Evolving competitive objectives among enterprises leading them to increase competitiveness via improving flexibility and response to customers. This research is to optimize time in a forward, multi-layer, multi- product, and multi-period supply chain. Production time is composed of processes times of all manufacturing cells being investigated in cell manufacturing system. Therefore, a nonlinear integer mathematical program is formulated to minimize the total cycle time and the maximum delay time of the supply chain. Then, numerical instances are solved in GAMS software package by Branch & Bound method. . In the two sizes of the example provided, the behavior of the model was reasonable and in the larger problem size the solution time increased, which can be justified by increasing the branching by increasing the number of time periods, product and item in each layer. In addition, sensitivity analysis was performed on the important parameters of the problem and the behavior change of the objective function based on the parameter changes was investigated. The obtained results can be used in decision-making planning of supply chain operations as a separate layer and also in an integrated manner with flexibility, in the presence of crisis.

Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. Multi-agent scheduling problem is a subset of multi-objective scheduling problems in which each agent has a set of jobs and its aim is to optimize its own objective function. This study addresses a three-agent flow shop deteriorating scheduling problem. In the investigated problem, the actual processing time of jobs is a linear function of their normal processing time and starting time. To make the proposed problem more realistic, two practical assumptions such as sequence-dependent setup times and release date of jobs are considered.A mixed integer programming model has also been developed for the problem, which is solved using the Augmented ε-constraint exact method. Also due to the complexity of the model and its inability to solve large-scale problems, Multi Objective Particle Swarm Optimization (MOPSO) algorithm is developed. Since the parameters of meta-heuristic algorithms affect their overall performance and output, the Taguchi method has been used to adjust the parameters of the MOPSO algorithm. Finally, in order to evaluate the performance of the proposed algorithm, numerical sample problems of different structures are solved using this algorithm as well as the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the exact augmented ε-constraint method. The computational results confirm the acceptable convergence and very good dispersion of the solutions of the MOPSO algorithm as well as the better performance of this algorithm compared to the augmented ε-constraint method and the NSGA-II algorithm.

The decentralized decision-making structure in the design of crisis emergency warehouse network challenges the use of classical optimization models. The aim of this paper is to develop a new multi-follower bi-level optimization model for the emergency warehouse location-allocation problem in terms of national and regional levels. This type of modeling is suitable for countries whose crisis warehouse network design is decentralized. The parameters of the models are based on real data in Iran. Due to the high complexity of the solution, a co-evolutionary approach based on innovative allocation methods and genetic algorithms has been developed to solve the problems with different sizes. The solution structure is designed to be flexible and can be adjusted based on the number of followers and their authority. Finally, an analysis has been done about the change in the number of decision makers and their power to absorb facilities on the objective functions of the bi-level model.

This study examines the pricing and product greenness level determining policies in two competing supply chains. Each supply chain consists of one manufacturer and one retailer, in which the manufacturer produces a green product, and the retailer delivers it to the end consumer. The manufactured products in supply chains are substitutable. This problem is considered in two scenarios: either the manufacturer or the retailer leads the market. In both cases, the government, as a powerful party, can affect the market by establishing a subsidy/punishment system. This system sets a threshold for the product greenness level and punishes or supports the manufacturer accordingly. In this research, we present the manufacturer and retailer profit functions, taking advantage of wholesale and retail prices, the amount of subsidy and punishment, and demand functions. Next, we use a game-theoretic approach to provide the equilibrium wholesale and retail prices and the equilibrium greenness levels. Numerical results show that the retailer-Stackelberg structure provides better conditions for the supply chain members and the environment, and it is even more highlighted with the competition intensity between the members.

After-sale service is one of the most important competitive factors in the automotive industry. Subsequently, having a time and cost-effective spare part supply strategy is vital to sustaining the competitiveness and profitability of every car manufacturer. The proper supply of spare parts for relief cars is a significant key success factor in the after-sale service companies. In this paper, we suggested a new supply strategy for directly distributing spare parts to relief cars, Instead of delivering spare parts at the warehouse. By applying the VRP concept, we showed that this strategy significantly decreases the logistical costs of supplying spare parts.

In recent years, with the competitive environment, the importance of on time delivery and customer satisfaction is crucial. The objective functions, such as minimizing the number of tardy jobs, have attracted an increasing attention. This study aims to schedule the operations by minimizing the number of weighted tardy jobs in two modes of delay with a financial penalty and delay with lost sales. In this paper, the start time of operation is a decision variable. Furthermore, the operations can be only pursued one time, but the time of their availability is considered uncertain. In addition, the completion time of each operation is variable in two intervals; At the beginning of the interval, the delivery time of the task is considered without any penalty while at the end of the interval, the delivery time is determined with a penalty. As another innovation, this research examines the priority of variables so that by approaching the due dates and deadlines as well as the degree of customer urgency. It is updated based on some factors including the weight of the product order and the remaining processing time. In this paper, first the mathematical problem formulation is established and then a heuristic algorithm is used to solve the proposed model and find near-optimal solutions. To accomplish that, 2700 sample problems are generated and each is solved by GAMS software and the mentioned heuristic algorithm. The obtained results show the satisfactory performance of the heuristic algorithm in obtaining the near-optimal solution in a reasonable time.

The ceramic and tile industry is one of the traditional Iranian jobs used in every historical period of different forms. Iran is one of the five largest producers of tiles and ceramics, which have been able to become the biggest exporter of these products in Western Asia by having a huge capacity in the industry. In recent years, ceramics and tiles exports have become one of the country's most important foreign trade. Therefore, to achieve a larger part of the export share, the competitive environment has dominated the industry. On the other hand, supply chain management is considered more and more attention due to the increasing globalization of industries and international competition. Many companies prefer to supply and manage their procurement, production and distribution sectors separately in several countries. Therefore, in dealing with global competitiveness, how to design a supply chain network with minimum cost and with emphasis on financial considerations, insurance and customs, has become an important issue for multinational companies. In this paper, using mixed integer programming method, a four level global positioning model is proposed considering the limitation of water consumption in the production line using GAMS software. The proposed model was used in one of the ceramic tile manufacturing industries in Yazd province to validate the model and investigate the effect of important parameters on strategic location decisions, which led to the location of two domestic warehouses and three foreign distribution centers in neighboring countries.

Today, due to the intelligent nature of each agent, agent-based simulation has become an effective tool for predicting many complex systems between independent agents. These complex systems exhibit behaviors that cannot be inferred from the behavior of the components alone, and each experience of the system may lead to different results.In this study, the Agricultural Supply Chain (ASC) is examined as one of these systems in which agents try to make the best decisions to maximize their benefits through learning from the environment. Agents of this study include farmers, wholesalers, and sellers who independently seek to achieve their individual goals in competing with other agents. The price of crops is determined in a competitive bidding price mechanism. Each farmer can allocate his resources to cultivate a particular crop based on his own and other neighboring farmers experience. They can also become a contract farmer with the nearest wholesaler. Wholesalers decide on a similar mechanism for their contract operation. Eventually, sellers try to meet their demand at the lowest cost. The statistical analysis results showed that as the attractiveness of conventional agriculture in the supply chain decreases, they gradually lose their financial resources and go bankrupt in price fluctuations due to the impact of uncertainties in the market and the environment. These results also showed that the creation of supportive prices and the effects of behavioral and social patterns of agents play an important role in price stability and control of fluctuations in ASC.

In today's world, organ donation has been identified as a life-giving process worldwide and has been welcomed by many people in different societies. This process can increase the people's quality of life in various countries and also increase the level of social security. In this study, to investigate the issue of organ donation, a supply chain network is presented that includes three sections: donation hospitals, transplant centers, and recipient zones. Besides, to make the appropriate decision to select the best receiver, a new multi-criteria decision-making (MCDM) method is used under intuitionistic fuzzy conditions. Then the proposed mathematical model is presented. In this model, the requirements of climate change and its effects on the transportation system, the quality of organs affected by cold ischemic time, and queuing in transplant centers are examined. A new decision-making method to select the best type of organ recipient and benefit from mathematical modeling provides to apply the issues of organ quality, climate change risk, and the concept of queuing that are the strengths points and innovations of this paper. Then, after presenting the model, using a compromise solution approach, the proposed multi-objective model with the objectives of cost, time, and quality of the organs becomes an equivalent one-objective model. Then, a golden eagle meta-heuristic algorithm is used to solve the problem. Finally, a practical example validates the decision-making method and the proposed mathematical model.

Incentive by the recent global trend in greenhouse gases (GHG) emission reduction strategies and considering the evolution of business models from traditional to multi-channel, in this research, we analyze a dual-channel green supply chain in which a manufacturer delivers a product through one direct channel and one retail channel whose demand are uncertain and prices in channels can effect on customer demand. Modeling this chain is carried out by accounting for the optimal price of distribution channels, total profit optimization, and reduction of transportation cost, we well as minimizing CO2 emission that the CO2 emission is investigated through transportation mode choice. The proposed model has developed first a mixed-integer nonlinear programming and a theoretical technique is applied to linearizing the problem. To solve the proposed multi-objective Possibilistic programming model, the Jimenez and TH approach has been used. The model is verified by numerical experiments and then several sensitivity analyses have performed. Computational results proved the efficiency of the model in finding a trade-off between economic and environmental objectives so that if transportation costs are equal to both markets, in order to balance the goals, the model uses vehicles that have a less environmental impact. In addition, the proposed solution method enables the decision maker to determine the importance of each objective function. The decision-maker is also able to influence the optimal price of distribution channels by changing the parameters in the demand-price equation and maximize the total profit of the supply chain.