Mehmet Bayram Yildirim

Operational methods for minimization of energy consumption of manufacturing equipment

This paper develops operational methods for the minimization of the energy consumption of manufacturing equipment. It is observed that there can be a significant amount of energy savings when non-bottleneck (i.e. underutilized) machines/equipment are turned off when they will be idle for a certain amount of time. Using this fact, several dispatching rules are proposed. A detailed performance analysis indicates that the proposed dispatching rules are effective in decreasing the energy consumption of especially underutilized manufacturing equipment. In addition, a multi-objective mathematical programming model is proposed to minimize the energy consumption and total completion time. Using this approach, a production manager will have a set of non-dominated solutions (i.e. the set of efficient solutions) which he/she can use to determine the most efficient production sequence which will minimize the total energy consumption while optimizing the total completion time.

A framework to minimise total energy consumption and total tardiness on a single machine

A great amount of energy is wasted in industry by machines that remain idle due to underutilisation. A way to avoid wasting energy and thus reducing the carbon print of an industrial plant is to consider minimisation of energy consumption objective while making scheduling decisions. To minimise energy consumption, the decision maker has to decide the timing and length of turn off/turn on operation (a setup) and also provide a sequence of jobs that minimises the scheduling objective, assuming that all jobs are not available at the same time. In this paper, a framework to solve a multiobjective optimisation problem that minimises total energy consumption and total tardiness is proposed. Since total tardiness problem with release dates is an NP‐hard problem, a new greedy randomised multiobjective adaptive search metaheuristic is utilised to obtain an approximate pareto front (i.e. an approximate set of non‐dominated solutions). Analytical Hierarchy Process is utilised to determine the ‘best’ alternative among the solutions on the pareto front. The proposed framework is illustrated in a case study. It is shown that a wide variety of dispersed solutions can be obtained via the proposed framework, and as total tardiness decreases, total energy consumption increases.

A Model for the Multiperiod Multiobjective Power Generation Expansion Problem

A long-term multiobjective model for the power generation expansion planning of electric systems is described and evaluated in this paper. The model optimizes simultaneously multiple objectives (i.e., minimizes costs, environmental impact, imported fuel and fuel price risks) and decides the location of the planned generation units in a multiperiod planning horizon. Among the attributes considered in the model are the investment and operation cost of the units, the environmental impact, the amount of imported fuel, and the portfolio investment risk. The approach to solve this problem is based on multiobjective linear programming and the analytical hierarchy process. A case study from the Mexican Electric Power System is used to illustrate the proposed framework

A Toll Pricing Framework for Traffic Assignment Problems with Elastic Demand

This paper extends the notion of toll pricing and the toll pricing framework previously developed for fixed demand traffic assignment (Bergendorff, Hearn and Ramana, 1997; Hearn and Ramana, 1998) to the problem with elastic demand. The system problem maximizes net benefit to the network users (Gartner, 1980; Yang and Huang, 1998) and the user problem is the usual one of finding equilibrium with elastic demand. We define and characterize T, the set of all tolls for the user problem that achieve the system optimal solution. When solutions to the two problems are unique, T is a polyhedron defined by the optimal solution of the system problem, similar to the case in (Bergendorff, Hearn and Ramana, 1997; Hearn and Ramana, 1998). The Toll Pricing Framework in (Hearn and Ramana, 1998) is also extended to allow optimization of secondary criteria over T. Examples include minimizing the number of toll booths and minimizing the maximum toll on any link. A numerical example illustrates the results.

An ant colony optimization algorithm for load balancing in parallel machines with sequence-dependent setup times

This study introduces the problem of minimizing average relative percentage of imbalance (ARPI) with sequence-dependent setup times in a parallel-machine environment. A mathematical model that minimizes ARPI is proposed. Some heuristics, and two metaheuristics, an ant colony optimization algorithm and a genetic algorithm are developed and tested on various random data. The proposed ant colony optimization method outperforms heuristics and genetic algorithm. On the other hand, heuristics using the cumulative processing time obtain better results than heuristics using setup avoidance and a hybrid rule in assignment.

A Multiobjective Evolutionary Programming Algorithm and Its Applications to Power Generation Expansion Planning

The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios.

Machine number, priority rule, and due date determination in flexible manufacturing systems using artificial neural networks

When there is a production system with excess capacity, i.e. more capacity than the demand for the foreseeable future, upper management might consider utilizing only a portion of the available capacity by decreasing the number of workers or halting production on some of the machines/production lines, etc. while preserving the flexibility of the production system to satisfy demand spikes. To achieve this flexibility, upper management might be willing to attain some pre-determined/desired performance values in a production system having identical parallel machines in each work center. In this study, we propose a framework that utilizes parallel neural networks to make decisions on the availability of resources, due date assignments for incoming orders, and dispatching rules for scheduling. This framework is applied to a flexible manufacturing system with work centers having parallel identical machines. The artificial neural networks were able to satisfactorily capture the underlying relationship between the design and control parameters of a manufacturing system and the resulting performance targets.

A sustainability approach for selecting maintenance strategy

Appropriate maintenance can prolong the life of an asset and prevent costly breakdowns that may result in lost production, failed shipping schedules and a decline in customer satisfaction. The goal of this study was to present a comprehensive framework that utilises sustainability metrics based on social, environmental and economic criteria to select an appropriate maintenance strategy among a variety of maintenance strategy alternatives, such as preventive, failure-based, reliability-centred, condition-based and total productive maintenance strategies, for a manufacturing company. The sustainability-based approach may significantly influence the personnel, energy, material and the overall costs in a company. In the first step of this paper, a sustainability-based decision-making structure is proposed for the maintenance strategy selection problem applying the concept of factor analysis to determine the leading factors in each of the sustainability pillars. In the next step, a fuzzy VIKOR framework is used to select the most appropriate maintenance strategy. The provided approach is illustrated using a manufacturing industry case study.

Acoustical Properties of Electrospun Fibers for Aircraft Interior Noise Reduction

Electrospun micro and nanofibers produced via electrospinning method were used for the sound absorption purposes. Polymers were initially dissolved in dimethyleformamide (DMF) or ethanol with a ratio of 80:20 and electrospun at 20 kV, 20 cm separation distance and 3 ml/hrs pump speed. The two/microphone transfer function method of the B&K impedance tube was used to determine the acoustical properties of the manufactured nanofibers at various frequencies. The test results showed that the absorption coefficients of nanofibers (~500 nm) drastically increased. The reason behind this phenomenon may be attributed to the higher surface area of nanofibers and their interactions with more sound waves/air molecules. This result may open up new possibilities for the sound absorption problems in many fields, such as aircrafts, other transportation vehicles and infrastructures.

A Neuro-Genetic Approach to Design and Planning of a Manufacturing Cell

In this paper, we propose a neuro-genetic decision support system coupled with simulation to design a job shop manufacturing system by achieving predetermined values of targeted performance measures such as flow time, number of tardy jobs, total tardiness and machine utilization at each work center. When a manufacturing system is designed, the management has to make decisions on the availability of resources or capacity, in our setting, the number of identical machines in each work station and the dispatching rule to be utilized in the shop floor to achieve performance values desired. Four different priority rules are used as Earliest due date (EDD), Shortest Processing Time (SPT), Critical ratio (CR) and First Come First Serve (FCFS). In reaching the final decision, design alternatives obtained from the proposed system are evaluated in terms of performance measures. An illustrative example is provided to explain the procedure.