Successful flow-shop scheduling outlines a more rapid and efficient process of order fulfilment in warehouse activities. Indeed the way and the speed of order processing and, in particular, the operations concerning materials handling between the upper stocking area and a lower forward picking one must be optimized. The two activities, drops and pickings, have considerable impact on important performance parameters for Supply Chain wholesaler companies. In this paper, a new flow shop scheduling algorithm is formulated in order to process a greater number of orders by replacing the FIFO logic for the drops activities of a wholesaler company on a daily basis. The System Dynamics modelling and simulation have been used to simulate the actual scenario and the output solutions. Finally, a t-Student test validates the modelled algorithm, granting that it can be used for all wholesalers based on drop and picking activities.
In most of the published articles dealing with optimal order quantity model under permissible delay in payments, it is assumed that the supplier only put forwards fully permissible delay in payments if retailer ordered a bulky sufficient quantity otherwise permissible delay in payments would not be permitted. Practically, in competitive market environments and recession phases of business, every supplier wants to attract more retailers by the help of providing good facilities for trading. Necessity of order quantity may put a negative pressure on supplier’s demand. So, within the economic order quantity (EOQ) framework the main purpose of this paper is to broaden this extreme case by introducing a new credit policy, Flexible Trade Credit Policy (FTCP), for supplier which can help him provide more free space of trading to retailers. This policy, after adopting by suppliers, not only provides attractive trading environments for retailers but also enhances the demand of supplier due to the large number of new retailers. Here in, under this policy, an inventory system is investigated as a cost minimization problem to establish the retailer’s optimal inventory cycle time and optimal order quantity. Three theorems are established to describe and to lighten optimal replenishment policies for the retailer. Finally, numerical examples are considered to illustrate all these theorems and managerial insights are given based on considered numerical examples.
Cell load variation is considered a significant shortcoming in scheduling of cellular manufacturing systems. In this article, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to control cell load variation during the process of determining the best trading off values between in-house manufacturing and outsourcing. A genetic algorithm (GA) is developed because of the high potential of trapping in the local optima, and results are compared with the results of LINGO® 12.0 software. The Taguchi method (an L_9 orthogonal optimization) is used to estimate parameters of GA in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors, and control charts are used on machine-load variation. Our findings indicate that the dynamic condition of product demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. An increase in product uncertainty level causes the loading level of each cell to vary, which in turn results in the development of “complex dummy sub-cells”. The effect of the complex sub-cells is measured using another mathematical index. The results showed that the proposed GA can provide solutions with limited cell-load variations.
Nickel based super alloys are excellent for several applications and mainly in structural components submitted to high temperatures owing to their high strength to weight ratio, good corrosion resistance and metallurgical stability such as in cases of jet engine and gas turbine components. The current work presents the experimental investigations of the cutting parameters effects (cutting speed, depth of cut and feed rate) on the surface roughness, cutting force components, productivity and power consumption during dry conditions in straight turning using coated carbide tool. The mathematical models for output parameters have been developed using Box-Behnken design with 15 runs and Box-Cox transformation was used for improving normality. The results of the analysis have shown that the surface finish was statistically sensitive to the feed rate and cutting speed with the contribution of 43.58% and 23.85% respectively, while depth of cut had the greatest effect on the evolution of cutting force components with the contribution of 79.87% for feed force, 66.92% for radial force and 66.26% for tangential force. Multi-objective optimization procedure allowed minimizing roughness Ra, cutting forces and power consumption and maximizing material removal rate using desirability approach.
This paper presents an experimental study on rough cut, trim cut using distilled water as a dielectric fluid and Al & Si metal powders in dielectric fluid for WEDM of Nimonic-90. First, the influence of discharge energy (DE) in rough cut is evaluated for machining rate (MR) and surface roughness (SR) and compared with trim cut without any metal powder additives in dielectric fluid. The effect of Al and Si metal powders (varying concentration of 1g/L, 2g/L and 3g/L) in dielectric fluid is studied separately and comparison is also made for MR, SR, recast layer and micro hardness of machined Nimonic-90. From the results it is observed that using trim cut, a fine and uniform surface texture is obtained irrespective of the high discharge energy of rough cut. Al and Si powders additives show a significant reduction in MR for trim cutting operation whereas a remarkable modification is obtained in surface textures after trim cut using metals powder mixed dielectric. SR improves with a concentration of 1g/L and shows a little increase with high concentration of both metals powder. Using metals powder in dielectric fluid, the recast layer becomes smooth and denser and thus, micro hardness increases.
Job selection and scheduling are among the most important decisions for production planning in today’s manufacturing systems. However, the studies that take into account both problems together are scarce. Given that such problems are strongly NP-hard, this paper presents an approach based on two heuristic algorithms for simultaneous job selection and scheduling. The objective is to select a subset of jobs and schedule them in such a way that the total net profit is maximized. The cost components considered include jobs & apos; processing costs and weighted earliness/tardiness penalties. Two heuristic algorithms; namely scatter search (SS) and simulated annealing (SA), were employed to solve the problem for single machine environments. The algorithms were applied to several examples of different sizes with sequence-dependent setup times. Computational results were compared in terms of quality of solutions and convergence speed. Both algorithms were found to be efficient in solving the problem. While SS could provide solutions with slightly higher quality for large size problems, SA could achieve solutions in a more reasonable computational time.
This paper addresses the scheduling problem in a Permutation Flow Shop (PFS) environment, which is associated with many types of industries such as chemical, petrochemical, automobile manufacturing, metallurgical, textile, etc. Thus, this work intends to solve a PFS scheduling problem in order to minimize the total weighted tardiness, since it is an important sequencing criterion not only for on time delivery jobs but also for customer satisfaction. To solve the problem, GRASP (Greedy Randomized Adaptive Search Procedure) metaheuristic is proposed as a solution, which has shown competitive results compared with other combinatorial problems. In addition, two utility functions called Weighted Modified Due Date (WMDD) and Apparent Tardiness Cost (ATC) are proposed to develop GRASP. These are based on dynamic dispatching rules and also known for solving the problem of total weighted tardiness for single machine scheduling problem. Next, an experimental design was carried out for comparing the GRASP performance with both utility functions and against the WEDD dispatching rule results. The results indicate that GRASP-WMDD could improve the total weighted tardiness in 47.8% compared with WEDD results. Finally, the GRASP-WMDD performance for the PFS total tardiness problem was evaluated, obtaining a relative deviation index of 13.89% and ranking the method over 26 heuristics and metaheuristics.
Optimization of hole-making operations plays a crucial role in which tool travel and tool switch scheduling are the two major issues. Industrial applications such as moulds, dies, engine block etc. consist of large number of holes having different diameters, depths and surface finish. This results into to a large number of machining operations like drilling, reaming or tapping to achieve the final size of individual hole. Optimal sequence of operations and associated cutting speeds, which reduce the overall processing cost of these hole-making operations are essential to reach desirable products. In order to achieve this, an attempt is made by developing an effective methodology. An example of the injection mould is considered to demonstrate the proposed approach. The optimization of this example is carried out using recently developed particle swarm optimization (PSO) algorithm. The results obtained using PSO are compared with those obtained using tabu search method. It is observed that results obtained using PSO are slightly better than those obtained using tabu search method.
Turning experiments were carried out on AA 7075/SiC composite workpiece in dry and spray cooling environments based on L16 Taguchi design of experiments. Multiple performance optimization of process parameters was performed using grey relational analysis. The performance characteristics considered were average surface roughness, cutting tool temperature and material removal rate. Uncoated carbide inserts were used for machining the workpiece in a high speed precision lathe. A grey relational grade obtained from grey relational analysis was used to optimize the process parameters. Optimal combination of process parameters was then determined by the Taguchi method using the grey relational grade as the performance index. Experimental results indicated that the turning in spray cooling environment was beneficial compared to that in dry environment for the quality response characteristics under consideration. Analysis of variance showed that feed was the most significant parameter for the multiple performance characteristics during turning in both the environments.
The theory of constraints is an approach for production planning and control, which emphasizes on the constraints in the system to increase throughput. The theory of constraints is often referred to as Drum-Buffer-Rope developed originally by Goldratt. Drum-Buffer-Rope uses the drum or constraint to create a schedule based on the finite capacity of the first bottleneck. Because of complexity of the job shop environment, Drum-Buffer-Rope material flow management has very little attention to job shop environment. The objective of this paper is to apply the Drum-Buffer-Rope technique in the job shop environment using a Markov chain analysis to compare traditional method with Drum-Buffer-Rope. Four measurement parameters were considered and the result showed the advantage of Drum-Buffer-Rope approach compared with traditional one.