Iranian Journal Of Operations Research
Volume:10 Issue: 1, Winter and Spring 2019

This paper analyzes the performance of a robotic system with two machines in which machines are configured in a circular layout and produce non-identical parts repetitively. The non-destructive testing (NDT) is performed by a stationary robotic arm located in the center of the circle, or a cluster tool. The robotic arm integrates multiple tasks, mainly the NDT of the part and its transition between a pair of machines. The robotic arm cannot complete the transition if it identifies a fault in the part. The main feature of the NDT technology is that its required time is changed by altering the testing cost. This generates a trade-off between cost and cycle time. Initially, the problem of robotic arm scheduling and part sequencing is jointly solved to supports the decision making for reliability improvement of small-scale robotic systems with NDT technologies. We show how the case of non-identical parts can be converted into a travelling salesman problem (TSP). Then, we provide a generalization of the framework based on three characteristics: pickup criterion, layout and travel time metric. The results are extended for the interval and no-wait pickup criteria, and then some notes are provided for travel time saving of different layout and travel time metric. It is shown whether circular systems are equivalent to linear systems, or they dominate linear cases in general terms.

This paper presents a structure that empower designing supervisory groups to survey the estimation of real options in projects of enormous scale, incompletely standardized frameworks actualized a couple of times over the medium term. Specific options writing is done using a methodology of planning the design and making prior decisions regarding the arrangements of specific options, with a recreation-based value measure designed to be near-current construction rehearsals and to resolve financial problems in particular cases. To study the case and demonstrate the actual application of this method, drug chain modeling at the tactical level was investigated. The physical and financial flow and their disturbance are simultaneously modulated. In order to complete the financial flow, financial ratios are also entered into the model. Problem uncertainty has been modeled using one of the most recent robust optimization approaches called Robust Possibilistic Programming (RPP) in combination with real options theory. The model was applied to a case study and its results were analyzed and validated by GAMS software. The results show that without violating the limitations of the problem, it shows appropriate decisions to deal with the problem.

Nowadays, airline industries should overcome different barriers regarding the fierce competition and changing consumer behavior. Thus, they attempt to focus on joint decision making which enables them to set pricing and capacity allocation to maximize their profits. In this research, we develop a model to optimize pricing and capacity allocation in a duopoly of single-flight leg for two competitive airlines. The problem considers actual assumptions about flexible partitions in flight’s cabins and additionally demand uncertainty. There is a flexible partitioning of business and economy cabins and demand is assumed price-dependent with additive uncertainty. The capacity and pricing decisions are simultaneously determined through indirect channels. Moreover, a numerical study is developed to investigate how market components and competition conditions change pricing, capacity, and profit levels. The results show that increasing market volume like decreasing price sensitivity provides higher levels of price and profits. Moreover, intensified competition never leads to higher prices. Thus, a competitive network of airlines provides better impact on market mechanism to achieve competitive prices for both economy and business classes.

One important step to achieve a sustainable transportation system is to control the impact and evaluate the effect of various influencing factors toward three dimensions of sustainability. Within this context, diverse analytical approaches have been developed to assess the sustainability level of various transportation systems, however, sustainability evaluation based on fuzzy multiple criteria decision-making approaches are still limited. In current research activity, an integrated quantitative evaluation technique is proposed to narrow the identified gap. The developed decision-making approach is consisted of two main phases. Firstly, fuzzy analytic hierarchy process is utilized to weigh the sustainability dimensions resulting in the incorporation of the experts’ knowledge along with the evaluation process. Then, a proposed fuzzy inference mechanism is proposed to provide an indication on the performance of an evaluated road transportation system. The developed approach is applied on a real-world case study. Finally, future works are presented together with some concluding remarks.

Nowadays, manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of supply chain planning. On the other hand, increasing worldwide environmental, lack of food resources and social concerns are motivating manufacturers and consumers to implement recycling strategies such as product recovery, waste management, or usage of recycled materials. In this study, the closed-loop supply chain network has been proposed which consists of four echelons (suppliers, plants, distribution centers, and customers) in the forward chain and three echelons (collection centers, inspection centers, and disposal centers) in the backward chain. We present a multi-product and multi-period mixed-integer linear programming problem in this paper. The objective of this study is to maximize the profit in the closed-loop supply chain network. The proposed model is applied to an illustrative example based on inspiration from the dairy industry in Iran. The solution of the proposed model is achieved by using Gams software. The results give important insight for fostering the decision making process.

Nowadays, due to population increase and expansion of the transportation networks, the monitoring of this network, control, and prevention of accidents and crimes are very important. The main tasks of traffic patrols, like other emergency-security facilities, are the monitoring and prevention of crime as well as handling and fining in the case of committing crimes. Traffic patrols should be present in the scene and also in high traffic congestion, to reduce accidents and crimes. This paper presents a bi-objective and stochastic optimization model to design an emergency-security system. This network includes traffic patrol vehicles and manpower in patrol vehicles. The objective of the proposed model is to maximize the number of vehicles passing through patrols and minimize the costs according to different scenarios. To solve the model, the epsilon-constraint method is used which simultaneously determines the location of the patrols, allocation of demand points to patrols, and determining the number of existing manpower in patrols. To evaluate and analyze the proposed model, a numerical example is used.

Capacitated vehicle routing problem (CVRP) is one of the most well-known and applicable issues in the field of transportation. It has been proved to be an NP-Complete problem. To this end, it is needed to develop a high-performance algorithm to solve the problem, particularly in large scales. This paper develops a novel mathematical model for the CVRP considering the satisfaction level of demand nodes. Then, the proposed model is validated using a numerical example and sensitivity analyses that are implemented by CPLEX solver/GAMS software. To solve the problem efficiently, a Genetic Algorithm (GA) is designed and implemented. The obtained results demonstrate that the proposed GA can yield high-quality solutions compared to exact solutions.