Journal of Optimization in Industrial Engineering
Volume:3 Issue: 5, Winter and Spring 2010

In this paper, a new online robot motion planner is developed for systematically exploring unknown environ¬ments by intelligent mobile robots in real-time applications. The algorithm takes advantage of sensory data to find an obstacle-free start-to-goal path. It does so by online calculation of the Generalized Voronoi Graph (GVG) of the free space, and utilizing a combination of depth-first and breadth-first searches on the GVG. The planner is equipped with components such as step generation and correction, backtracking, and loop handling. It is fast, simple, complete, and extendable to higher spaces.

In this paper, we considered solving approaches to flexible job shop problems. Makespan is not a good evaluation criterion with overlapping in operations assumption. Accordingly, in addition to makespan, we used total machine work loading time and critical machine work loading time as evaluation criteria. As overlapping in operations is a practical assumption in chemical, petrochemical, and glass industries, we used simulated annealing algorithm for multi-objective flexible job shop scheduling problem with overlapping in operations to find a suitable solution. To evaluate performance of the algorithm, we developed a mixed integer linear programming model, and solved it with the classical method (branch and bound). The results showed that in small size problems, the solutions of the proposed algorithm and the mathematical model were so close, and in medium size problems, they only had lower and upper bounds of solution and our proposed algorithm had a suitable solution. We used an experimental design for improving the proposed algorithm.

Planning robot trajectory is a complex task that plays a significant role in design and application of robots in task space. The problem is formulated as a trajectory optimization problem which is fundamentally a constrained nonlinear optimization problem. Open-loop optimal control method is proposed as an approach for trajectory optimization of cable parallel manipulator for a given two-end-point task in point-to-point motion. Dynamic equations are organized in a closed form and are formulated in the state space form. A computational technique is developed for obtaining optimal trajectory to maximize dynamic load carrying capacity. By solving the corresponding nonlinear TPBVP, the problem of optimal path and maximum carrying for a 6 DOF spatial cable robot is studied. Finally, dynamic modelling in ADAMS is presented and to validate the optimal control method, optimal trajectory concerned with dynamic motion is compared with the software results.

Nowadays, outsourcing is viewed as a trade strategy and organizations tend to adopt new strategies to achieve competitive advantages in the current world of business. focusing on main copmpetencies, and transferring most of activities to outside resources of organization(outsourcing) is one such strategy is. In this paper, we aim to decide on decision maker agent of transportation system, by applying intelligent agent technology and using learning model which is modeled as a reinforcement learning problem. A Q-learning algorithm is proposed to solve the RL model. Results show that the proposed model given its ability to communicate with environment, adaptability with environment and correcting itself based on learnt data, the prposed model can be applied as a better and quicker learning model in comparison with other ways of solving of decision making problems.

Cellular manufacturing system (CMS) is highly important in modern manufacturing methods. Given the ever increasing market competition in terms of time and cost of manufacturing, we need models to decrease the cost and time of manufacturing. In this study, CMS is considered in condition of dynamic demand in each period. The model is developed for facing dynamic demand that increases the cost of material flow. This model generates the cells and location facilities at the same time and it can move the machine(s) from one cell to another cell and can generate the new cells for each period. Cell formation is NP-Complete and when this problem is considered in dynamic condition, surly, it is strongly NP- Complete. In this study, genetic algorithm (GA) is used as a meta-heuristic algorithm for solving problems and evaluating the proposed algorithm, Branch and Bound (B & B) is used as a deterministic method for solving problems. Ultimately, the time and final solution of both algorithms are compared.

Calculating total cast of bank resources procurement methods which include current -free loan deposit, saving interest-free loan deposit, regular and net short-term investment deposit, long-term investment deposit and surety bond cash deposit and presenting their optimal integration require precise scientific studies. Hence, this study is an attempt to know which methods are the best optimal integration banking resources. Linear and ideal planning techniques are used to find an optimal solution according to existing mathematical models. We use three algorithms to construct mathematical models. In the suggested mathematical models, linear planning has 6 variables and 2 constraints in getting no information algorithm and 6 variables and 7 constraints in getting information algorithm and the problem is solved by WINQSB software. But, 6 variables and 8 constraints are solved by LINGO8 software. The results of the study show that presenting an optimal integration of resources procurement methods by using mathematical models is possible and is applicable with regard to determining rational and suitable constraints and ideals for all resource procurement methods. In addition, with regard to the calculation and investigation of procurement cost in financial procurement methods, it is found out that total cost (i.e. real operational cost plus nonoperational cost) is the basis of judgment for studying resource procurement cost. Further, as with the total cost of resource procurement methods, current interest-free loan deposit and long-term investment deposit are the most expensive methods while surety bond cash deposit is the cheapest resource procurement method and other methods fall in between.

Data envelopment analysis (DEA) is a non-parametric approach for performance analysis of decision making units (DMUs) which uses a set of inputs to produce a set of outputs without the need to consider internal operations of each unit. In recent years, there have been various studies dealt with two-stage production systems, i.e. systems which consume some inputs in their first stage to produce some intermediate outputs which are used as the inputs of the second stage in producing final outputs. One of these researches done by Kao and Hwang (2008) gives a decomposition of system efficiency score based on the efficiency of its sub-processes in the case of constant returns to scale (CRS) technology. This paper presents an extension of this approach for the technologies with variable returns to scale (VRS) and explains the results.

Some servers are to be located at nodes of a network. Demand for services of these servers is located at each node and a subset of the nodes is to be chosen to locate one server in each. Each customer selects a server with a probability dependent on distance and a certain amount of benefit is achieved after giving service to the customer. Customers may waive receiving service with a known probability. The objective is to maximize the total benefit. In this paper, the problem is formulated, three solution algorithms are developed and applied to some numerical examples to analyze the results.