JOURNAL OF OPTIMIZATION IN INDUSTRIAL ENGINEERING (JOURNAL OF INDUSTRIAL ENGINEERING)
Year:2015 | Volume:8 | Issue:19|Papers Count:10

The problem of designing a set of routes with minimum cost to serve a collection of customers with a fleet of vehicles is a fundamental challenge when the number of customers to be dropped or picked up is not known during the planning horizon. The purpose of this paper is to develop a vehicle routing Problem (VRP) model that addresses stochastic simultaneous pickup and delivery in the urban public transport systems of Addis Ababa city Bus Enterprise, in Ethiopia. To this effect, a mathematical model is developed and fitted with real data collected from Anbessa City Bus Service Enterprise (ACBSE) and solved using Clark-Wright saving algorithm. The form-to-distance is computed from the data collected from Google Earth and the passenger data from the ACBSE. The findings of the study show that the model is feasible and showed an improvement as compared to the current performances of the enterprise. It showed an improvement on the current number of routes (number of buses used) and the total kilometer covered. The average performances of the model show that on average 6.48 routes are required to serve passenger demands of 271 and on average the simulation run was performed with 0.40 seconds of CPU time. During this instance, the average distance traveled by the vehicles in a single trip is 552.92kms.

Acceptance Sampling models have been widely applied in companies for the inspection and testing of the raw materials as well as the final products. A number of lots of the items are produced in a day in the industries so it may be impossible to inspect/test each item in a lot. The acceptance sampling models only provide the guarantee for the producer and consumer confirming that the items in the lots are according to the required specifications so that they can make appropriate decision based on the results obtained by testing the samples. Acceptance sampling plans are practical tools for quality control applications which consider quality contracting on product orders between the vendor and the buyer. Acceptance decision is based on sample information. In this research, dynamic programming and Bayesian inference is applied to decide among decisions of accepting, rejecting, tumbling the lot or continuing to the next decision making stage and more sampling. We employed cost objective functions to determine the optimal policy. First, we used the Bayesian modelling concept to determine the probability distribution of the nonconforming proportion of the lot and then dynamic programming was utilized to determine the optimal decision. Two dynamic programming models have been developed. The first one is for the perfect inspection system and the second one is for imperfect inspection. At the end, a case study is analysed to demonstrate the application the proposed methodology and sensitivity analyses are performed.

Manufacturers around the globe are competing for the identification of innovative value propositions to survive in the competitive and complex market. This paper addresses implementation of Value Engineering (VE) technique into the product design concept for necessary changes in the design of the humidifier system in order to lower unnecessary costs and to increase quality of the product. Humidifiers are used for ventilation and cooling the air in most of the houses in cities located in hot and dry areas. Value Engineering as a systematic attempt is used to increase efficiency of the products and to optimize the life cycle cost. This leads to a shift from traditional design towards new efficient designs. For this study, an 8- stage job plan is used for VE job plan. In this article, different components of a humidifier were analyzed thoroughly and then numerous suggestions were made at the brainstorming sessions. Function Analysis System Technique (FAST) was also utilized at the first stage. The main findings lead to a conclusion that there were more suggestions at the brain storming session. Further, it is concluded that the best suggestion for improved design of the humidifier is changing the material of fan cover from galvanized iron to hard plastic or fiber plastic.

This study deals with a two-level supply chain consisting of one manufacturer and one retailer. We consider an integrated production inventory system where the manufacturer processes raw materials in order to deliver the finished product with imperfect quality to the retailer, where the number of defective product has a uniform distribution. The retailer receives product and conducts a 100% inspection.We assume that unit price charged by the retailer influences the demand of the product. Shortages are allowed and assumed to be completely backordered. The proposed model is based on the joint total profit of both the manufacturer and the retailer, and it finds the optimal ordering, shipment and pricing policies. The numerical study shows that the coordination between supply chain members is more beneficial in industries by less defective percentage at manufacturer.

This paper aims to investigate the integrated production/distribution and inventory planning for perishable products with fixed life time in the constant condition of storage throughout a two-echelon supply chain by integrating producers and distributors. This problem arises from real environment in which multi-plant with multi-function lines produce multi-perishable products with fixed life time into a lot sizing to be shipped with multi-vehicle to multi-distribution-center to minimize multi-objective such as setup costs between products, holding costs, shortage costs, spoilage costs, transportation costs and production costs. There are many investigations on production/distribution planning area with different assumptions. However, this research aims to extend this planning by integrating an inventory system in which for each distribution center, net inventory, shortage, FIFO system and spoilage of items are calculated. A mixed integer non-linear programming model (MINLP) is developed for the considered problem. Furthermore, a genetic algorithm (GA) and a simulated annealing (SA) algorithm are proposed to solve the model for real size applications. Also, Taguchi method is applied to optimize parameters of the algorithms. Computational characteristics of the proposed algorithms are examined and tested using t-tests at the 95% confidence level to identify the most effective meta-heuristic algorithm in terms of relative percentage deviation (RPD). Finally, Computational results show that the GA outperforms SA although the computation time of SA is smaller than the GA.

This paper proposes a mathematical model as the bi-objective capacitated multi-vehicle allocation of customers to distribution centers. An evolutionary algorithm named non-dominated sorting ant colony optimization (NSACO) is used as the optimization tool for solving this problem. The proposed methodology is based on a new variant of ant colony optimization (ACO) specialized in multi-objective optimization problem. To help the decision maker to choose the best compromise solution from the Pareto front, the fuzzy-based mechanism is employed. For ensuring the robustness of the proposed method and giving a practical sense of this study, the computational results are compared with those obtained by NSGA-II. Results show that both NSACO and NSGA-II algorithms can yield an acceptable number of non-dominated solutions. In addition, the results show that while the distribution of solutions in the trade-off surface of both NSACO and NSGA-II algorithms do not differ significantly, NSACO algorithm is more efficient than NSGA-II with regard to optimality, convergence and the CPU time. Also, the results in some small cases are compared with those obtained by LP-metric method. The error percentages of objective functions in comparison to the LP-metric method are less than 2%. Furthermore, it can be seen that with increasing size of the problems, while the time of problem solving increases exponentially by using the LP-metric method, the running time of NSACO and NSGA-II are more stable.

We compare two approaches for a Markovian model in flexible manufacturing systems (FMSs) using Monte Carlo simulation. The model, which is a development of Fazlollahtabar and Saidi-Mehrabad (2013), considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (AGV), namely, the reliability of machines and the reliability of AGVs in a multiple AGV jobshop manufacturing system. The current methods for modeling reliability of a system involve determination of system state probabilities and transition states. Since the failure of the machines and AGVs could be considered in different states, a Markovian model is proposed for reliability assessment. The traditional Markovian computation is compared with a neural network methodology. Monte Carlo simulation has verified the neural network method having better performance for Markovian computations.

In modern production systems, finding a way to improve the product and system reliability in design is very important. The reliability of the products and systems may improve using different methods. One of these methods is redundancy allocation problem. In this problem, by adding redundant components to sub-systems under some constraints, the reliability would improve. In this paper, we worked on a three-objective redundancy allocation problem. The objectives are maximizing system reliability and minimizing the system cost and weight. The structure of sub-systems are k-out-of-n and the components have constant failure rate. Because this problem belongs to “Np. Hard problems”, we used NSGA II multi-objective Meta-heuristic algorithm to solve the presented problem.

This paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. The output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (LLNF) model, is useful for assessing a project status at different time horizons. Being trained by a locally linear model tree (LOLIMOT) learning algorithm, the model is intended for use by members of the project team in performing the time control of projects in the construction industry. The present paper addresses the effects of different factors on the project time and schedule by using both fuzzy sets theory (FST) and artificial neural networks (ANNs) in a construction project in Iran. The construction project is investigated to demonstrate the use and capabilities of the proposed model to see how it allows users and experts to actively interact and, consequently, make use of their own experience and knowledge in the estimation process. The proposed model is also compared to the well-known intelligent model (i.e., BPNN) to illustrate its performance in the construction industry.

Time, cost and quality are considered as the main components in managing each project. Previous studies have mainly focused on the timecost trade-off problems. Recently quality is considered as the most important factor in project’s success, which is influenced by time acceleration. That is the less time is spent, the more success is gained. In time-cost-quality trade-off problems, each activity can be done in various execution modes and determination of these execution modes is seen as to minimize the project time and cost and maximize its quality. In this paper, three integer programming models are provided and one of the main objectives is optimized in each model by assigning the proper bound to other objectives. Following the non-dominated solutions obtained by solving models, and by means of hybrid approach of Fuzzy AHP strategy and VIKOR method regarded as multi-criteria decision making methods, the best possible alternative (from among non-dominated solutions) has been suggested.