• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.98-103
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• 2006

In this research, the single machine capacitated lot-sizing and scheduling problem with sequence- dependent setup costs and setup times (CLSPSD) is considered. This problem is the extension of capacitated lot-sizing and scheduling problem (CLSP) with an additional assumption on sequence-dependent setup costs and setup times. The objective of the problem is minimizing the sum of production costs, inventory holding costs and setup costs satisfying customers' demands. It is known that the CLSPSD is NP-hard. In this paper, the MIP formulation is presented. To handle the problem more efficiently, a conceptual model is suggested, and one of the well-known meta-heuristics, the simulated annealing approach is applied. To illustrate the performance of this approach, various instances are tested and the results of this algorithm are compared with those of the CLPEX. Computational results show that this approach generates optimal or nearly optimal solutions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.2
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• pp.251-256
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• 2006

In this paper we consider multi-stage, multi-product production, inventory systems which have assembly-tree-structure. We propose a new mathematical model for pull type ordering systems based on JIT manufacturing systems. To apply the model to an actual automobile parts manufacturer, the objective of proposed model is to minimize the sum of inventory and setup costs. Finally, a numerical example and computational results are given to illustrate the proposed model.

• Journal of Korean Institute of Industrial Engineers
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• v.13 no.1
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• pp.1-10
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• 1987

This paper deals with a single-facility multiproduct lot-size model requiring consideration of setup costs. Each product is demanded at the constant rate per unit time in the next particular period. Due to the limitation of the production capacity, some productions of total demand requirement in that period must be pre-produced. The aim of this project is to determine when and how much of each product to make in order to minimize the total setup costs and inventory carrying-costs of all products. Also this paper contains the further realistic treatment of inventory carrying-costs.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.3
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• pp.44-53
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• 2007

Forming central warehouses for a number of stores can save costs in the continuous review inventory model due to economy of scale and information sharing. In this paper, transportation costs are included in this inventory model. Hence, the tradeoff between inventory-related costs and transportation costs is required. The main concern of this paper is to determine the number and location of central warehouses. Transportation costs are dependent on the distance from several central warehouses to each store. Hence, we develop an efficient simulated annealing algorithm using distance-based local search heuristic and merging heuristic to determine the location and the number of central warehouses. The objective of this paper is to minimize total costs such as holding, setup, penalty, and transportation costs. The performance of the proposed approach is tested by using some computational experiments.

• Journal of applied mathematics & informatics
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• v.42 no.2
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• pp.379-390
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• 2024

This study proposes a fuzzy inventory model for managing large-scale production, incorporating cost considerations. The model accounts for two types of expenditure scenarios-parametric and exponential. Uncertainty surrounds holding costs, setup costs, and demand rates. The approach considers a supply chain system with a complex manufacturing process, factoring in transportation costs based on the quantity of goods and distance between the supplier and retailer. The initial crisp model is then transformed into a fuzzy simulation, incorporating specific fuzzy variables affecting inventory costs. The proposed method significantly reduces overall inventory costs for the entire supply chain. Retailer demand is linked to inventory levels, and vendor/distributor storage deteriorates over time. The fuzzy condition assumes hexagonal variables for all associated factors. The study employs the signed distance method for defuzzification to determine the optimal order quantity with hexagonal fuzzy numbers. Mathematical examples are provided to illustrate the practicality of the proposed approach.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.2
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• pp.65-74
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• 1993

In this paper, a two-echelon production-inventory model is developed which integrates the production scheduling problem of the multi-products produced on a single facility and the inventory problem of the related raw materials. The setup costs of the final products are assumed to be dependent on the production sequence. The aim is to determine simultaneously the production cycle time and the production sequence of the final products, and the procurement cycle times of the raw materials. For the model developed, a solution algorithm is suggested and illustrated with a numerical example. And the result is compared with those obtained by two separate subproblems.

• Management Science and Financial Engineering
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• v.13 no.2
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• pp.117-139
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• 2007

This paper contributes by incorporating works addressing supply chain coordination and investing in setup reduction program. Consider a two-echelon, EOQ-like inventory system consisting of a supplier and a buyer. We assume that both the supplier and the buyer can invest in setup cost reduction programs in order to benefit from small order sizes. However, the costs of investing in setup cost reduction programs are different for the two parties, leading to mismatches in individually optimal setup costs and order cycle times. We propose a supply chain coordination contract that makes use of quantity discount as an incentive transfer scheme for supply chain coordination.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.3
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• pp.49-64
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• 2000

A vendor supplies a product to a sole/major buyer on a lot-for-lot basis under deterministic inventory control conditions. The basic premise is that the setup cost reduction technologies are available to both the buyer and the vendor, and that the vendor's inventory and setup reduction investment costs differ from the buyer's. Therefore, an individually designed ordering and setup cost reduction policy will likely cause mismatches between the vendor's and the buyer's optimal cycle times. For this situation, we show that a joint optimal setup cost reduction and ordering policy, together with an appropriate side payment(quantity discount or premium price) schedule, can be designed in a spirit in a spirit of coordination to eliminate mismatches in individual optimal cycle times.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.532-541
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• 1997

This article derives an analytic solution to determine the optimal size of multiple noncontinuous process and storage units. The total cost to be minimized consists of the setup cost of noncontinuous processing units and the inventory holding cost of feedstock/product storages. A novel approach, which is called PSW(Periodic Square Wave) model, is applied to represent the material flow among non-continuous units and storages. PSW model presumes that the material flow between unit and storage is periodic square wave shaped. The resulting optimal unit size has similar characteristics with the classical economic lot sizing model such as EOQ(Economic Order Quantity) or EPQ(Economic Production Quantity) model in a sense that the unit size is determined as the balance between setup and inventory holding cost. However, the influence of inventory holding cost of PSW model is different from that of EOQ/EPQ model. EOQ/EPQ model includes only the product inventory holding cost but PSW model includes all inventory holding costs around the non-continuous unit with proportional contribution. PSW model is suitable for analyzing interlinked process-storage system. The optimal lot size of PSW model is smaller than that of EOQ/EPQ model. This is quitea remarkable result considering that the EOQ/EPQ model has been is widely used since last half century.

• Management Science and Financial Engineering
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• v.13 no.1
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• pp.35-53
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• 2007

We consider a(worst-case) robust optimization version of the Economic Order Quantity(EOQ) model. Order setup costs and inventory carrying costs are assumed to have uncertainty in their values, and the uncertainty description of the two parameters is supposed to be given by an ellipsoidal representation. A genetic algorithm combined with Monte Carlo simulation is proposed to approximate the ellipsoidal representation. The objective function of the model under ellipsoidal uncertainty description is derived, and the resulting problem is solved by another genetic algorithm. Computational test results are presented to show the performance of the proposed method.