Skill management is a key factor in improving effectiveness of industrial companies, notably their maintenance services. The problem considered in this paper concerns scheduling of maintenance tasks under resource (maintenance teams) constraints. This problem is generally known as unrelated parallel machine scheduling. We consider the problem with a both objectives of minimizing total weighted tardiness (TWT) and number of tardiness tasks. Our interest is focused particularly on solving this problem under skill constraints, which each resource has a skill level. So, we propose a new efficient heuristic to obtain an approximate solution for this NP-hard problem and demonstrate his effectiveness through computational experiments. This heuristic is designed for implementation in a static maintenance scheduling problem (with unequal release dates, processing times and resource skills), while minimizing objective functions aforementioned.
Global competition pressures have forced manufactures to adapt their productive capabilities. In order to satisfy the ever-changing market demands many organizations adopted flexible resources capable of executing several products with different performance criteria. The unrelated parallel-machines makespan minimization problem (Rm||Cmax) is known to be NP-hard or too complex to be solved exactly. In the heuristics used for this problem, the MCT (Minimum Completion Time), which is the base for several others, allocates tasks in a random like order to the minimum completion time machine. This paper proposes an ordered approach to the MCT heuristic. MOMCT (Modified Ordered Minimum Completion Time) will order tasks in accordance to the MS index, which represents the mean difference of the completion time on each machine and the one on the minimum completion time machine. The computational study demonstrates the improved performance of MOMCT over the MCT heuristic.
In recent years, supply chain management is known as the key factor for achieving competitive advantage. Better customer service, revenue improvement and cost reduction are the results of this philosophy. Organizations can manage the performance of their firms by appropriate goal setting, identifying criteria and continuous performance measurement, which creates a good view for the business circumstances. Developing and defining appropriate indicators at different levels of chain is necessary for implementing a performance measurement system. In this study, we propose a new method to determine the measurement indicators and strategies of the company in term of balanced scorecard. The study is a combination of balanced scorecard, path analysis, evolutionary game theory and cooperative game theory for strategic planning. The study offers an appropriate program for future activities of organizations and determines the present status of the firm. The implementation of the proposed method is introduced for a food producer and the results are analyzed.
Scheduling ‘n’ jobs on ‘m’ machines in a flow shop is NP- hard problem and places itself at prominent place in the area of production scheduling. The essence of any scheduling algorithm is to minimize the makespan in a flowshop environment. In this paper an attempt has been made to develop a heuristic algorithm, based on the reduced weightage of machines at each stage to generate different combination of ‘m-1’ sequences. The proposed heuristic has been tested on several benchmark problems of Taillard (1993) [Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64, 278-285.]. The performance of the proposed heuristic is compared with three well-known heuristics, namely Palmer’s heuristic, Campbell’s CDS heuristic, and Dannenbring’s rapid access heuristic. Results are evaluated with the best-known upper-bound solutions and found better than the above three.
In this paper, we consider the inventory model for perishable items with quadratic trapezoidal type demand rate, that is, the demand rate is a piecewise quadratic function under constant deterioration rate. The model consider allows for shortages and the demand is partially backlogged. The model is solved analytically by minimizing the total inventory cost. The result is illustrated with numerical example. Finally, we discuss sensitivity analysis for the model.
The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.
This paper considers the capacity determination in a closed-loop supply chain network when a queueing system is established in the reverse flow. Since the queueing system imposes costs on the model, the decision maker faces the challenge of determining the capacity of facilities in such a way that a compromise between the queueing costs and the fixed costs of opening new facilities could be obtained. We develop a De Novo programming approach to determine the capacity of recovery facilities in the reverse flow. To this aim, a mixed integer nonlinear programming (MINLP) model is integrated with the De Novo programming and the robust counterpart of this model is proposed to cope with the uncertainty of the parameters. To solve the model, an interactive fuzzy programming approach is combined with the hard worst case robust programming. Numerical results show the performance of the developed model in determining the capacity of facilities.
The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.
Electrical Discharge Machining (EDM) is one of the most basic non-conventional machining processes for production of complex geometries and process of hard materials, which are difficult to machine by conventional process. It is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. The present study is focusing on the die sinking electric discharge machining (EDM) of AISI H 13, W.-Nr. 1.2344 Grade: Ovar Supreme for finding out the effect of machining parameters such as discharge current (GI), pulse on time (POT), pulse off time (POF) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) & Overcut (OC) using Square-shaped Cu tool with Lateral flushing. A well-designed experimental scheme is used to reduce the total number of experiments. Parts of the experiment are conducted with the L9 orthogonal array based on the Taguchi methodology and significant process parameters are identified using Analysis of Variance (ANOVA). It is found that MRR is affected by gap current & Ra is affected by pulse on time. Moreover, the signal-to-noise ratios associated with the observed values in the experiments are determined by which factor is most affected by the responses of MRR, Ra and OC. These experimental data are further investigated using Grey Relational Analysis to optimize multiple performances in which different levels combination of the factors are ranked based on grey relational grade. The analysis reveals that substantial improvement in machining performance takes place following this technique.
Vertex and p-center problems are two well-known types of the center problem. In this paper, a p-center problem with uncertain demand-weighted distance will be introduced in which the demands are considered as fuzzy random variables (FRVs) and the objective of the problem is to minimize the maximum distance between a node and its nearest facility. Then, by introducing new methods, the proposed problem is converted to deterministic integer programming (IP) problems where these methods will be obtained through the implementation of the possibility theory and fuzzy random chance-constrained programming (FRCCP). Finally, the proposed methods are applied for locating bicycle stations in the city of Tabriz in Iran as a real case study. The computational results of our study show that these methods can be implemented for the center problem with uncertain frameworks.