This paper focuses on the appropriate selection of suppliers and assigning order quantities to them. This problem contains MULTIPLE OBJECTIVEs and their related constraints and utilizes fuzzy decision making in its solution approach. In the proposed model, goals, constraints, variables and coefficients are all fuzzy. It is shown that with the application of the fuzzy methodology, the multiy OBJECTIVE problem is converted to a single one that can be solved easily.

In this paper we present a method for optimal design of combined EWMA-EWMA control scheme. Shewhart control chart, introduced by Walter Shewhart, only takes into account present information of the process, so it is insensitive in detecting small shifts. EWMA and CUSUM control charts are good choices for detecting small shifts. A combined control scheme monitors the process for large and small shifts simultaneously. The purpose of this paper is to develop a computer program for optimal design of combined EWMA-EWMA control scheme based on MULTIPLE OBJECTIVE optimization problems for detecting large and small shifts simultaneously.

In this paper, we establish a proof for a necessary condition for MULTIPLE OBJECTIVE fractional pro-gramming. In order to derive the set of necessary conditions, we employ an equivalent parametric problem. Also, we present the related semi parametric model.

In this paper, a review of MULTIPLE OBJECTIVE Flowshop Scheduling Problems is introduced. Different version of the problem are classified according to different multi OBJECTIVE optimization approaches, flowshop assumptions, solving methods, and also OBJECTIVE functions terms. This paper can be considered as a complete and valuable reference for researchers in the field of scheduling, especially MULTIPLE OBJECTIVE ones.

Inventory classification is one of important techniques in inventory control context. Managers have to classify inventories because of their variety and high volume. So a stream of research has been to attempt to find methods that increase the management control by determining the number of inventory classes. In this paper the MULTIPLE OBJECTIVE particle swarm optimization algorithm has been used. This algorithm has been presented by Chi-Yang Tsai and Szu-Wei Yeh in 2008. MULTIPLE OBJECTIVE particle swarm optimization algorithm is an evolutionary algorithm that enables the management to optimize MULTIPLE OBJECTIVEs simultaneously. Minimizing costs of inventory holding and ordering and maximizing inventory turnover ratios are this model’s OBJECTIVEs. We write the software program of this model and then test it on a sample of 100 items. Results show that this algorithm can decrease costs of holding & ordering and also increase the inventory turnover ratios significantly.

One of the casualties in war zones is casualties due to the explosion of explosives near vehicles. The important issue in these vehicles isn’t to seriously injure the occupant during the explosion. It is necessary to optimize of the cabin structure of heavy vehicles to achieve high-performance vehicles. In this article, the 3-d model of the truck is created to study the effect of exploding near and under a truck and to optimize the truck cabin. The model is meshed by square and 10 mm^2 elements. Then, explosion tests are simulated in two situation: explosion under the vehicle and explosion near the truck according to AEP-55. In the explosion test under the car,using 4 kg TNT equivalent on the ground surface and In the test near the truck, using 8 kg TNT equivalent at the distance of 3 meter at the same level by under cabin surface and the amount of occupant injuries are checked. For meeting the requirements of the explosion and because this truck platform is a commercial vehicle, vehicle floor panels’ material and thickness are modified. The thickness of important parts include 1. Side floor panel 2. Middle floor panel 3. Front side panel 4. Front Panel 5 Door Panel are optimized in 16 modes and Only 7 modes meet the requirements of the explosion test under the vehicle. The optimal mode is 571.39 kg which has decreased 79.72 kg compared to the first mode meet AEP-55 requirements and has decreased 3.31kg compared to the initial mode.

MULTIPLE OBJECTIVE Programming (MOP) problems have become famous among many researchers due to more practical and realistic implementations. There have been a lot of methods proposed especially during the past four decades. In this paper, we develop a new algorithm based on a new approach to solve MOP problems by starting from a utopian point (which is usually infeasible) and moving towards the feasible region via stepwise movements and a plain continuous interaction with Decision Maker (DM). We consider the case where all OBJECTIVE functions and constraints are linear. The implementation of the proposed algorithm is demonstrated with two numerical examples.

A utility function is an important tool for representing a DM’s preference. We adjoin utility functions tomulti OBJECTIVE optimization problems. In current studies, usually one utility function is used for each OBJECTIVE function. Situations may arise for a goal to have MULTIPLE utility functions. Here, we consider a constrained multi-OBJECTIVE problem with each OBJECTIVE having MULTIPLE utility functions. We induce the probability of the utilities for each OBJECTIVE function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.