نشریه پژوهشهای مهندسی صنایع در سیستمهای تولید
پیاپی 4 (پاییز و زمستان 1393)

Hubs are special facilities acting as mediating stations in distribution systems to organize flow transmission between origins and destinations through the best possible paths. Penalty parameters such as χ and δ can seriously affect hubs number and location in a network، however in most of the existing hub location models، these parameters are set as greater than one coefficient in the objective function with no consideration for the amount of exchanged flow between hubs and spokes. Also in classic models of hub location problem، unlimited entrance flows to a hub is assumed. Since this assumption may lead to a structure in which one hub is forced to handle a large percent of flow distribution، in cases like bad weather conditions، heavy traffics or terrorist attacks، the hub become unavailable. To address the above issues، in this study، instead of penalty parameters between hubs and spokes، capacitated transportation vehicles are implemented. Hub entrance flow is also considered limited. To do so، transportation costs from hubs (spokes) to spokes (hubs) are calculated according to the number of vehicles. Due to the complexity of the proposed model، a hybrid metaheuristic approach based on genetic algorithm and variable neighborhood search descent algorithm is developed. Results show higher performance of the proposed hybrid metaheuristic.

Bullwhip effect، which is an undeniable phenomenon in a supply chain; imposes considerable negative impact on the performance and the effectiveness of the chain. There are varieties of causes known for the presence of this phenomenon، among which lack of accurate and in-time demand information as well as long lead times are of the most important ones. To manage the bullwhip effect، this factor needs to be measured accurately. In this paper considering a 3-stage supply chain consisting of supplier، warehouse and retail stages we propose a new method to quantify the bullwhip effect at the warehouse level. The method uses Moving Average (MA) technique to forecast the demand. This paper، using numerical examples، illustrates the performance of the proposed method to measure the bullwhip effect as well as to quantify order rate variance ratio (OVR) and inventory variance ratio (IV). It is also argued that the proposed method، due to its not using some simplifying assumptions، represents better accuracy as compared to some other existing bullwhip effect quantifying methods. The paper also represents the capability of the proposed method for the bullwhip effect sensitivity analysis with regard to the service level parameter.

In this research، job shop scheduling problem by considering a parallel assembly stage is studied to minimize completion time for all products. At first، a linear model is introduced to describe the problem. To confirm the accuracy of model، it is solved by LINGO software. Since this problem is classified as NP-Hard، a particle swarm optimization algorithm is suggested to solve the problem in large and medium dimensions. To analyze the efficiency of the suggested algorithm، result of the proposed algorithm are compared with the results gained from GA algorithm. This results show that suggested algorithm can reach to near-optimal solutions in various dimensions of problems.

Warehouses، as one of the key elements of supply chain systems، play critical role in establishing the logical relationship between suppliers and customers. There are many different methods for warehouse management in companies، that the well-known method، is the simultaneous use of private and public warehouses. The purpose of this article is to develop a queuing theory-based model for determining the optimal capacity of private warehouse in order to minimize the total costs. At first، the warehouse and its associated supply chain are modeled using the queuing theory. Then، a cost function model related to the construction of private warehouse in different sizes is introduced considering the time value of money. As the values of some mentioned cost are unknown and a definite estimation of their values cannot be reached، a triangular fuzzy number for each cost parameter is assigned، based on experts opinion to overcome the related uncertainty. As the values of cost function for different capacities of warehouses are fuzzy numbers، a fuzzy prioritization method is proposed for the prioritizing different capacities and choosing the best capacity for constructing a private warehouse. The results of numerical examples confirm that unlike the previous approaches which are applicable for certain conditions، the proposed model could be easily and efficiently appropriate for various environments and conditions.

Problems in real world include various constraints. So the reliable models should be designed that satisfy the reasonable number of these constraints. These models should be applicable. In this paper، regarding the mentioned circumstances، we design a model for multi depots capacitated location-routing problem with simultaneous pickup and delivery and split loads. The paper emphasis is on the split property that happens when demand of a customer exceeds vehicle capacity. We propose a mixed integer-programming model. Also we develop two metaheuristics for solving large scale instance problems. The first approach is based on discrete genetic algorithm and continuous genetic algorithm and the second one is based on discrete genetic algorithm and particle swarm optimization. Results of solving instance problems by CPLEX solver and proposed DCGA and DGAPSO algorithms show that both algorithms are effective and DGAPSO outperforms DCGA in solution quality and computation times.

In production systems، line balancing is carried out once at beginning of line construction. In this situation، any change in market needs a new design of assembly line to meet market requirements. However this new design needs a lot of cost. Hence، a new type of assembly line balancing is proposed in this research in which a current assembly line is working. In this situation، a new cycle time is required due to some changes in market settings. Therefore، a multi-objective model titled assembly line re-balancing problem is proposed whose aim is to schedule tasks in such a way that new cycle time is realized via minimum changes in current assembly line. Moreover، work stations’ non-smoothness is considered as a relatively new measure. After modeling the problem، an efficient differential evolution algorithm was suggested to solve the problem.

In recent years، the important concept of pricing policy including the complementary and replacement of goods in the supply chain has been considered by many authors. In this paper، the effectiveness of complementary and substitute products on seller benefits are investigated. Moreover، Complementary and substitute the product sales simultaneously was modeled with a seller for the first time. Since the product prices have an effect on each other، therefore، The sales in the three states، retail، package sales with no rebate and discount were examined. According to the above-mentioned، the seller''s profit in the three scenarios، choice between retail and package selling، selecting between retail and package selling with discount and package selling with discount were evaluated. Pricing and package sales with no rebate and discount policy are tools that seller use to maximize profits. Propositions and algorithms for solving the model were proposed. Offering global optimal solutions to the problem of different modes are the strengths of this study. Finally، a numerical example was used to show the models effect. Numerical example shows that package sales with discount have more profits rather than other states.