Nowadays, traffic safety is one of the primary principles of traffic engineering and transportation planning. Road accidents have been major problems that cause human losses, social and economic challenges. The first step to improve road traffic safety is to identify black spots, which preserve high potential of road accidents. Therefore, identifying, analyzing, prioritizing and refining these places play important role in improving transportation safety. This paper applies fuzzy Technique for Order Preference by Similarity to Ideal (TOPSIS) for ordering different black spots for a case study of highway between two cities of Qazvin and Saveh, Iran. The study performs the ranking based on two categories of tangent distance and horizontal curve. The ranking is performed according to Roadway width, Shoulder width, Traffic volume, Percentage of vehicle, Load and utility, Network access, The number of horizontal curves and Equivalent Property Damage Only (EPDO). The results are compared with frequencies of incidents and analyzed.

In this study, a multi-product, multi-period and non-linear programming model is developed for production planning problem where demand is under uncertainty. The proposed study is designed for a real-world case study of chemicals production factory with 1 production line and 2 manual and automatic technologies. In manual technology, workers are working with 3 amateur, typical and professional skills in 2 typical and overtime working. Automatic technology in this system has n machines in which the repairing and maintenance of the machineries are also included. This system has n products and the products are life-limited and with diversity. The primary goal is to propose a model for improvement of the production planning and minimization of the production system costs. The products in high volume and various types are produced and they are stored in bottles as the final products. For different production periods, the human forces capacities are considered and the level of employment or forces dismissal are considered. The production process is forwarding and backward process is not acceptable; that is, it is not allowable to rework in this system. Delivering final product from stockpiles to the retailers is conducted using vehicles with limited capacity. To solve the model in larger space and because of the complexity of the model, meta-heuristic algorithm is used. Finally, it is concluded that due to covering most of the assumptions in perishable products production, the proposed model is closer to the real-world circumstances and reduces costs in production systems.

This paper presents a method to determine factors influencing alternator failure causes. Failure Mode and Effects Analysis (FMEA) is one of the first systematic techniques for failure analysis based on three factors including Probability (P), Severity (S) and Detection (D). Traditional FMEA method considers equal weights for all three factors, however, in read-world cases; one may wish to consider various weights. The proposed study develops a mathematical model to determine optimal weights based on analytical hierarchy process technique. The implementation of the proposed study has been demonstrated for a read-world case study of alternator failure causes.

Quality of services in banking industry plays essential role in measuring the performance of banks. As customer awareness increases on the services offered by banks, expectations from service quality increases too. Presently, managers of banks use different financial factors such as deposits, credits, etc. to rank their banks. This paper uses SERVQUAL technique to measure customer satisfaction for 14 branches of a bank in city of Kermanshah, Iran. The study first statistically shows that customer satisfaction was not the same for all these banks and then using analytical hierarchy process and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranks these branches using five components of SERVQUAL method; namely tangibles, reliability, assurance, responsiveness and empathy.

One of the primary concerns in product development is to meet customers’ wishes and this could be accomplished through detecting the most popular characteristics of products. In other words, the fulfillment of customers’ preferences in a profitable way needs that companies recognize which specifications of their product and service are most valued by the customer. Conjoint analysis is believed to be one of the most popular techniques for achieving this purpose. Conjoint analysis includes generating and conducting specific experiments among customers for modeling their purchasing decision. This paper presents an empirical investigation on detecting appropriate customer preferences in an auto-industry. The results of the survey indicate that price, braking system and having airbag are the most important characteristics for selling cars in Iranian market.

Cross docking play an important role in management of supply chains where items delivered to a warehouse by inbound trucks are directly sorted out, reorganized based on customer demands, routed and loaded into outbound trucks for delivery to customers without virtually keeping them at the warehouse. If any item is held in storage, it is usually for a short time, which is normally less than 24 hours. The proposed model of this paper considers a special case of cross docking where there is temporary storage and uses GRASP technique to solve the resulted problem for some realistic test problems. In our method, we first use some heuristics as initial solutions and then improve the final solution using GRASP method. The preliminary test results indicate that the GRASP method performs better than alternative solution strategies.

The increase competition and decline economy has increased the relevant importance of having reliable supply chain. The primary objective of many supply chain problems is to reduce the cost of services and, at the same time, to increase the quality of services. In this paper, we present a multi-level supply chain network by considering multi products, single resource and deterministic cost and demand. The proposed model of this paper is formulated as a mixed integer programming and we present two metaheuristics namely MOPSO and NSGA-II to solve the resulted problems. The performance of the proposed models of this paper has been examined using some randomly generated numbers and the results are discussed. The preliminary results indicate that while MOPSO is able to generate more Pareto solutions in relatively less amount of time, NSGA-II is capable of providing better quality results.

In this paper, we study a supply chain problem where a whole seller/producer distributes goods among different retailers. The proposed model of this paper is formulated as a more general and realistic form of traditional vehicle routing problem (VRP). The main advantages of the new proposed model are twofold. First, the time window does not consider any lower bound and second, it treats setup time as separate cost components. The resulted problem is solved using a hybrid of particle swarm optimization and simulated annealing (PSO-SA). The results are compared with other hybrid method, which is a combination of Ant colony and Tabu search. We use some well-known benchmark problems to compare the results of our proposed model with other method. The preliminary results indicate that the proposed model of this paper performs reasonably well.

Cross docking is one of the most important issues in management of supply chains. In cross docking, different items delivered to a warehouse by inbound trucks are directly arranged and reorganized based on customer demands, routed and loaded into outbound trucks for delivery purposes to customers without virtually keeping them at the warehouse. If any item is kept in storage, it is normally for a short amount of time, say less than 24 hours. In this paper, we consider a special case of cross docking where there is temporary storage and implements genetic algorithm to solve the resulted problem for some realistic test problems. In our method, we first use some heuristics as initial solutions and then improve the final solution using genetic algorithm. The performance of the proposed model is compared with alternative solution strategy, the GRASP method.

Supplier selection, inventory management and optimal lot sizing has been one of the most important issues in many industries especially in production planning issues associated with texture industry. The proposed model of this paper first introduces an algorithm to choose the best supplier and it determines the optimal lot size using discount strategy. The proposed model of this paper considers different influencing factors such as location, quality of materials, cost, and mutual trust for supplier selection, determines their relative importance weights and then a discounting method is used to determine the ordering lot-size. The preliminary results indicate that the proposed model of this paper can be implemented in texture industry, very efficiently since the ordering discount policy is not sensitive to changes on inventory holding and setup expenditures.