• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.401-412
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• 2015

In this paper, we present a modeling approach to production planning for an actual production line and a heuristic method. We also illustrate the successful implementation of the proposed method on the production line. A heuristic algorithm called the push-back algorithm was designed for a single machine earliness/tardiness production planning with distinct due date. It was developed by combining a minimum slack time rule and shortest processing time rule with a push-back procedure. The results of a numerical experiment on the heuristic's performance are presented in comparison with the results of IBM ILOG CPLEX. The proposed algorithm was applied to an actual case of production planning at Woongjin Chemical, a leading manufacturer of filter products in South Korea. The seven-month execution of our algorithm led to a 24.5% decrease in the company's inventory level, thus demonstrating its practicality and effectiveness.

• IE interfaces
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• v.16 no.3
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• pp.280-290
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• 2003

Many firms are trying to optimize their production and distribution functions separately, but possible savings by this approach may be limited. Nowadays, it is more important to analyze these two functions simultaneously by trading off the costs associated with the whole. In this paper, I treat a production and distribution planning problem for single-period inventory products comprised of a single production facility and multiple customers, with the aim of optimally coordinating important and interrelated decisions of production sequencing and vehicle routing. Then, I propose a hybrid genetic algorithm incorporating several local optimization techniques, HGAP, for integrated production-distribution planning. Computational results on test problems show that HGAP is effective and generates substantial cost savings over Hurter and Buer's decoupled planning approach in which vehicle routing is first developed and a production sequence is consequently derived. Especially, HGAP performs better on the problems where customers are dispersed with multi-item demand than on the problems where customers are divided into several zones based on single-item demand.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.14 no.24
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• pp.43-52
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• 1991

The short-term production planning of an automated manufacturing system is to determine the production rate of each part type or family of part types. The purpose of this paper is to develop an optimal algorithm for solving the short-term production planning problem while machine failures, repairs. and changes in demand requirements are anticipated. The problem is formulated by LP and it shows that the production surplus is approached or stays at the hedging point. In addition, the long-term average frequencies of set-us with relation to a multilevel hierarchical production planing scheme are considered An example to show the effectiveness of the algorithm is presented.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.65-74
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• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.2
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• pp.1-15
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• 2003

This paper presents a new evolutionary algorithm to solve complex multi-level integration problems, which is called multi-level symbiotic evolutionary algorithm (MEA). The MEA uses an efficient feedback mechanism to flow evolution information between and within levels, to enhance parallel search capability, and to improve convergence speed and population diversity. To show the MEA's applicability, It is applied to the integrated planning of production and distribution in supply chain management. The encoding and decoding methods are devised for the integrated problem. A set of experiments has been carried out, and the results are reported. The superiority of the algorithm's performance is demonstrated through experiments.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.1
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• pp.1-18
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• 1993

The family disaggregation model of hierarchical production planning (HPP) is the problem of (0 -1) mixed integer programming that minimizes the total sum of setup costs and inventory holding costs over the planning horizon. This problem is hard in a practical sense since optimal solution algorithms have failed to solve it within reasonable computation times. Thus effective familoy disaggregation algorithm should be developed for HPP. The family disaggregation algorithm developed in this paper consists of the first stage of finding initial solutions and the second stage of improving initial solutions. Some experimental results are given to verify the effectiveness of developed disaggregation algorithm.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.793-796
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• 1996

This study proposes a new algorithm which performs a production simulation under various constraints and maintains computational efficiency. In order to consider the environmental and operational constraints, the proposed algorithm is based on optimization techniques formulated in LP form In the algorithm, "system characteristic constraints" reflect the system characteristics such as LDC shape, unit loading order and forced outage rate. By using the concept of Energy Invariance Property and two operational rules i.e. Compliance Rule for Emission Constraint, Compliance Rule for Limited Energy of Individual Unit, the number of system characteristic constraints is appreciably reduced. As a solution method of the optimization problem, the author uses Karmarkar's method which performs effectively in solving large scale LP problem. The efficiency of production simulation is meaningful when it is effectively used in power system planning. With the proposed production simulation algorithm, an optimal expansion planning model which can cope with operational constraints, environmental restriction, and various uncertainties is developed. This expansion planning model is applied to the long range planning schemes by WASP, and determines an optimal expansion scheme which considers the effect of supply interruption, load forecasting errors, multistates of unit operation, plural limited energy plants etc.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.29-39
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• 2009

In an environment of global competition, the success of a manufacturing corporation is directly related to how it plans and executes production in particular as well as to the optimization level of its process in general. This paper proposes a production planning algorithm for the Multi-Level, multi-item Capacitated Lot Sizing Problem (MLCLSP) in supply chain network considering back-order. MLCLSP corresponds to a mixed integer programming (MIP) problem and is NP-hard. Therefore, this paper proposes an effective algorithm, GRHS (GRASP-based Rolling Horizon Search) that solves this problem within reasonable computational time and evaluates its performance under a variety of problem scenarios.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.3
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• pp.71-79
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• 1993

Dynamic production planning problems are to determine the optimal production times and production quantities of product for discrete finite periods. In previous many researches, the solutions for these problems have been developed through the algorithms using dynamic programming. The purpose of this research is to suggest the new algorithm using linear programming. This research is to determine optimal production quantities of product in each period to satisfy dynamic for discrete finite periods, minimizing the total of production cost and inventory holding cost. Cost functions are concave, and no backlogging for product is allowed. The new algorithm for capacity constrained problem is developed.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.413-419
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• 2015

This study solved the problem of unstable production chains by considering allocation rate conformance. We proposed two phased algorithm suitable for solving production planning that considers allocation rate conformance; the first phase was heuristic initial solution generation, and the second phase was tabu-search based solution improvement. By using three data sets which have different sizes of data and three different criteria, the results of proposed algorithm were compared with MIP results. The proposed algorithm showed the best production plan in terms of allocation rate conformance, and it was appropriate for other criteria; it solved the problem of unstable production chains by solving concentrated and unfair allocation.