Journal of Optimization in Industrial Engineering
Volume:4 Issue: 8, Winter and Spring 2011

The strategic pricing decisions of assembly products in assembly products supply chain are studied in this paper. Firstly, a two-stage assembly products supply chain model is developed. By building Nash game model, the Nash equilibrium solution of pricing strategy of supplier and assemblers is obtained. Next, a union decision model is built to analyze the optimal combination pricing strategy of assembly products, and the relationship between the optimal strategies is established. The law of the changing in combination pricing strategy, assemblers’ profits and supplier’s profit along with the variety of some characteristics has been investigated by using numerical simulation. The results are consistent with economics principles.

This research presents a new application of the cloud theory-based simulated annealing algorithm to solve mixed model assembly line sequencing problems where line stoppage cost is expected to be optimized. This objective is highly significant in mixed model assembly line sequencing problems based on just-in-time production system. Moreover, this type of problem is NP-hard and solving this problem through some classical approaches such as total enumeration or exact mathematical procedures such as dynamic programming is computationally prohibitive. Therefore, we proposed the cloud theory-based simulated annealing algorithm (CSA) to address it. Previous researches indicates that evolutionary algorithms especially simulated annealing (SA) is an appropriate method to solve this problem; so we compared CSA with SA in this study to validate the proposed CSA algorithm. Experimentation shows that the CSA approach outperforms the SA approach in both CPU time and objective function especially in large size problems.

The dramatic increasing of sea-freight container transportations and the developing trend for using containers in the multimodal handling systems through the sea, rail, road and land in the present market cause some challenges to the general managers of container terminals such as increasing demand, competitive situation, new investments and expansion of new activities and the need to use new methods to fulfil effective operations both along quayside and within the yard. Among these issues, minimizing the turnaround time of vessels is considered to be the first aim of every container port system. Regarding the complex structure of container ports, this paper presents a simulation model that calculates the number of trucks needed in Shahid Rajaee Container Port for handling containers between the berth and the yard. In this research, some important criteria such as vessel turnaround time, gantry crane utilization and truck utilization have been considered. By analyzing the results of the model, it will be shown that increasing the number of trucks to 66 units has a significant effect on the performance indices of the port and can increase the capacity of loading and unloading up to 10.8%.

Data Envelopment Analysis (DEA) has been widely studied in the literature since its inception with Charnes, Cooper and Rhodes work in 1978. The methodology behind the classical DEA method is to determine how much improvements in the outputs (inputs) dimensions is necessary in order to render them efficient. One of the underlying assumptions of this methodology is that the units consume and produce real valued data. This paper deals with the extension of this methodology for the case of integer-valued data. Based on an additive DEA model, a mixed integer linear programming model is proposed for setting integer-valued targets. An empirical example illustrates the approach.

Due to the importance of supplier selection issue in supply chain management (SCM) and, also, the increasing tendency of organizations to their social responsibilities, In this paper, we survey the supplier selection issue as a multi objective problem while considering the factor of corporate social responsibility (CSR) as a mathematical parameter. The purpose of this paper is to design a model so that suppliers are selected and quota is allocated to them while raising their social responsibility to the maximum expected extent. Supplier selection objectives such as cost minimization, quality maximization and on-time delivery maximization have already been surveyed. In this paper, we add objectives such as CSR maximization, maximization of advantages of domestic supplier selection and minimization of sum total distance to suppliers, to the prior objective functions while considering the quality and on time delivery constraints. Observance of CSR is lineally related to quality and on-time delivery and will lead to their increase. The model is presented in linear and integer programming in two states, single product and multi product, then it is solved by Multi Objective Decision Making (MODM) methods (Utility Function, STEM and Goal Programming) and answers are obtained and compared.

In this paper, we present a genetic algorithm (GA) for optimization of a multi-mode resource constrained time cost trade off (MRCTCT) problem. The proposed GA, each activity has several operational modes and each mode identifies a possible executive time and cost of the activity. Beyond earlier studies on time-cost trade-off problem, in MRCTCT problem, resource requirements of each execution mode are also allocated and the highest quantities of these resources are limited. In the MRCTCT, the goal is to reduce the total project cost with respect to the resource restrictions. The gene value is encoded as the mode index which is selected from among modes of the activity randomly. For indicating construction mode of the activity, integer encoding is applied instead of binary encoding. Additionally, the selection of genes for mutation is based on chromosome value, as solution convergence rate is high. The crossover operator of GA is based on a two-point method. This paper also offers a multi-attribute fitness function for the problem. This function can vary by decision maker (DM) preferences (time or cost). In this paper, a two-phase algorithm is proposed in which both the effects of time-cost trade-off and resource-constrained allocation are taken into account. A GA-based time-cost trade-off analysis is improved for choosing the execution mode of every activity through the trade-off of time and cost, followed by proposing a resource constrained allocation algorithm to generate an optimum schedule without overriding the project constraints. Lastly, the model is verified by means of a case study and a real project.

This paper presents a new mathematical model for a redundancyallocation problem (RAP) withcold-standby redundancy strategy and multiple component choices.The applications of the proposed model arecommon in electrical power, transformation,telecommunication systems,etc.Manystudies have concentrated onone type of time-to-failure, butin thispaper, two components of time-to-failures which follow hypo-exponential and exponential distributionare investigated. The goal of the RAP is to select available components and redundancy level for each subsystem for maximizing system reliability under cost and weight constraints.Sincethe proposed model belongs to NP-hard class, we proposed two metaheuristic algorithms; namely, simulated annealing and genetic algorithm to solve it. In addition, a numerical example is presented to demonstrate the application of the proposed solution methodology.

The multiple traveling salesman problem (MTSP) involves scheduling m > 1 salesmen to visit a set of n > m nodes so that each node is visited exactly once. The objective is to minimize the total distance traveled by all the salesmen. The MTSP is an example of combinatorial optimization problems, and has a multiplicity of applications, mostly in the areas of routing and scheduling. In this paper, a modified hybrid metaheuristic algorithm called GA2OPT for solving the MTSP is proposed. In this algorithm, at the first stage, the MTSP is solved by the modified genetic Algorithm (GA) in each iteration, and, at the second stage, the 2-Opt local search algorithm is used for improving solutions for that iteration. The proposed algorithm was tested on a set of 6 benchmark instances from the TSPLIB and in all but four instances the best known solution was improved. For the rest instances, the quality of the produced solution deviates less than 0.01% from the best known solutions ever.