This paper deals with an integrated multi-stage supply chain inventory model with the objective of cost minimization by synchronizing the replenishment decisions for procurement, production and delivery activities. The supply chain structure examined here consists of a single manufacturer with multi-buyer where manufacturer orders a fixed quantity of raw material from outside suppliers, processes the materials and delivers the finished products in unequal shipments to each customer. In this paper, we consider an imperfect production system, which produces defective items randomly and assumes that all defective items could be reworked. A simple algorithm is developed to obtain an optimal production policy, which minimizes the expected average total cost of the integrated production-inventory system.
The overall objective of this paper is to present a comprehensive comparison between the EOQ model and JIT system to see either of them under which circumstances is more cost effective. There have been a few researchers dealing with the EOQ/JIT comparison model to guide companies whether or not switch to JIT or EOQ, however, their proposed models could be more realistic by taking some effective factors, such as hidden costs of a JIT system, interest rate, inflation rate, etc., into account. This research, by considering some less seen costs of both EOQ model and JIT system, develops the previous proposals of the EOQ/JIT model. This paper analyzes the impact of increasing or decreasing some determinant factors such as the interest rate, from cost perspective, to help the decision on whether or not to switch the inventory system, however, to make such a decision, companies may also take some other factors into account. A sensible link is created between the EOQ/JIT model and financial management to assure the decision makers that their financial concerns are observed in this model.
This paper demonstrates an experimental scrutiny into turning process of hot work tool steel AISI H21 under dry machining plight. In this paper, face centered central composite design concealed by response surface methodology is practiced and analysis of variance is implemented to analyze the eloquent benefaction of machining parameters on responses. To access accommodate between the surface roughness and the MRR, an approach for concurrent optimization of multi-objective characteristics based on comprehensive desirability function is employed. The multi objective optimization concludes a spindle speed of 1599.568 rpm, feed rate of 0.262 mm/rev and depth of cut of 2 mm.
There is an increase in awareness about sustainable manufacturing process. Manufacturing industries are backbone of a country’s economy. Although it is important but there is a great concern about consumption of resources and waste creation. The primary aim of this study was to explore sustainability concern in turning process in an Indian machining industry. The effect of cutting parameters, Speed/Feed/Depth of Cut, the machining environment, Dry/MQL/Wet, and the type of cutting tool on sustainability factors under study were observed. Analysis of Variance (ANOVA) was used to analyse the data obtained from experimentation in a small scale machining industry. The process is modelled mathematically using response surface methodology (RSM).The economic and environmental aspect like surface roughness, material removal rate and energy consumption were considered as sustainability factors. The model helps to understand the effect of the cutting parameters and conditions on surface finish, energy consumption, and material removal rate. The process was optimized for minimum power consumption considering environmental concern as prime importance. Studies suggest that the cutting environment and tool type influenced on the power consumption during turning process. Extended form of the proposed model could be useful to predict the environmental impact due to machining process, which would bring environmental concern into conventional machining.
This paper presents a method of calendar (weekly) scheduling for production teams, when the average orders utility function is used as the quality criterion. The method is based on the concept of “production intensity”, which is a dynamic parameter of production process. Applied software package allows scheduling for medium quantity of jobs. The result of software application is the team load on the planning horizon. The computed schedule may be corrected and recalculated in interactive mode. Current load of every team is taken into account at each recalculation. The method may be used for any combination of complex and specialized teams.
Trim cutting operation in wire electrical discharge machining (WEDM) is considered as a probable solution to improve surface characteristics and geometrical accuracy by removing very small amount of work materials from the surface obtained after a rough cutting operation. In this study, an attempt has been made to model the surface roughness and dimensional shift in trim cutting operations in WEDM process through response surface methodology (RSM). Four process parameters; namely pulse-on time (Ton), servo voltage (SV), wire depth (Wd) and Dielectric flow rate (FR) have been considered as input parameters in trim cutting operations for modelling. Desirability function has been employed to optimize multi performance characteristics. Increasing the value of Ton, Wd and FR increases the surface roughness and dimensional shift but increasing SV decreases both surface roughness and dimensional shift. Quadratic models have been proposed for both the performance characteristics. In present experimentation, thickness of recast layer was observed in the range of 6?m to 12?m for low to high value of discharge parameters.
This research discusses an integer batch scheduling problems for a single-machine with position-dependent batch processing time due to the simultaneous effect of learning and forgetting. The decision variables are the number of batches, batch sizes, and the sequence of the resulting batches. The objective is to minimize total actual flow time, defined as total interval time between the arrival times of parts in all respective batches and their common due date. There are two proposed algorithms to solve the problems. The first is developed by using the Integer Composition method, and it produces an optimal solution. Since the problems can be solved by the first algorithm in a worst-case time complexity O(n2n-1), this research proposes the second algorithm. It is a heuristic algorithm based on the Lagrange Relaxation method. Numerical experiments show that the heuristic algorithm gives outstanding results.
The optimum selection of process parameters has played an important role for improving the surface finish, minimizing tool wear, increasing material removal rate and reducing machining time of any machining process. In this paper, optimum parameters while machining AISI D2 hardened steel using solid carbide TiAlN coated end mill has been investigated. For optimization of process parameters along with multiple quality characteristics, principal components analysis method has been adopted in this work. The confirmation experiments have revealed that to improve performance of cutting; principal components analysis method would be a useful tool.
This paper proposes a method to quote the due date and the price of incoming orders to multiple customers simultaneously when the contingent orders exist. The proposed method utilizes probabilistic information on contingent orders and incorporates some negotiation theories to generate quotations. Rather than improving the acceptance probability of quotation for single customer, the method improves the overall acceptance probability of quotations being submitted to the multiple customers. This helps increase the total expected contribution of company and acceptance probability of entire new orders rather than increasing these measures only for a single customer. Numerical analysis is conducted to demonstrate the working mechanism of proposed method and its effectiveness in contrast to sequential method of quotation.
In the existing literature, there are a huge number of studies focused on p-hub median problems and inventing heuristic or metaheuristic algorithms for solving them. But such analogous body of literature does not exist for its counterpart problem; p-hub center problem. In fact, since p-hub center has been lately introduced and has a particular objective function, minimizing the maximum cost between origin-destination nodes, there are few studies investigating the problem and the challenges for solving it. In this study, after presenting a complete definition of the uncapacitated multiple allocation p-hub center problem (UMApHCP) two well-known metaheuristic algorithms are proposed to solve the problem for small scale and large scale standard data sets. These two algorithms are one single solution-based algorithm, Simulated Annealing (SA), and one population-based metaheuristic, Genetic Algorithm (GA). Because of the particular nature of the problem, Dijkstra’s algorithm has been incorporated in the fitness function calculation part of the proposed methods. The numerical results of running the GA and SA for standard test problems show that for smaller scale test problems, single solution-based SA shows greater performance versus GA but for larger scales of data sets the GA generally yield more desirable solutions.