Jae-Dong Hong

Just-In-Time purchasing: Single or multiple sourcing?

Abstract We consider two Just-In-Time (JIT) purchasing models, one utilizing a few sources, and the other using the more conventional single source. We address the issue of splitting a large order quantity into multiple deliveries, taking account of the increase in the aggregate ordering, transportation, and inspection costs. For multiple sourcing, we formulate and solve a mathematical programming problem to obtain the optimal selection of suppliers and the size of the split orders. For single sourcing, we provide a procedure that yields the optimal number of deliveries.

Joint investment in quality improvement and setup reduction

Given a limited investment, we provide a general solution procedure that examines the trade-offs and that allocates that investment optimally for quality improvement and setup reduction. The paper is a generalization and an extension of Porteus [Operations Research 34, 141–143 (1986)]. We use general, continuous functions for quality improvement and for setup reduction. We find that in the face of a budget constraint, it is usually necessary to begin with either quality improvement or setup reduction, in order to bring one of these to some threshold, before joint investment should be undertaken. The contour lines of the total cost and the locus of the optimal solutions illustrate the behavior of the optimal joint investment. We also find that there could be some backtracking when the total relevant cost function for one type of option is strictly convex and is concave for the other type, i.e. a previous threshold investment in quality improvement or setup reduction may be reduced when the budget constraint is relaxed.

Process quality improvement and setup reduction in dynamic lot-sizing

Abstract Previous work on setup reduction to dynamic lot-sizing with process improvement is extended. In this dynamic lot-sizing model, a new calculation of lot size is made at every setup, but the decision to invest jointly in setup reduction and process improvement is made at the initial setup. In the analysis, it is assumed that setup reduction and process quality are functions of capital expenditure. Such setup cost reductions and process quality improvements can arise from investing in new technology. A procedure is developed to find the optimal lot size, total cost, and required investments, and the solution is illustrated using declining exponential functions and power functions to model both setup cost reduction and process quality improvement. The implications of the analysis and the tradeoffs involved are discussed and illustrated numerically and graphically.

JIT purchasing and setup reduction in an integrated inventory model

We study the effects of JIT order quantities on the purchase of raw materials and of the investments to reduce setup time. We examine the total relevant cost for an integrated inventory model, which unifies the finished product and the raw materials used for manufacturing it. A comparison of the total costs between our JIT model and the conventional ‘economic production quantity’ model suggests that JIT order splitting and investment in setup reduction could be cost-effective.

Optimal production cycles, procurement schedules, and joint investment in an imperfect production system

Abstract In this paper, we consider the simultaneous determination of production cycles for the end product, procurement schedules for its input materials, and joint investment in setup reduction and process quality improvement for a production system with imperfect production processes. In the analysis, we assume that setup reduction and process quality are functions of capital expenditure and that the input materials, which are purchased from outside suppliers, are gradually converted into the product during manufacture. We derive a solution procedure to find the optimal production cycle time, procurement schedules, joint investment, and the corresponding total relevant cost. We present numerical examples to illustrate the procedure and to delineate the relationships among production cycle times for the end product, the procurement schedules for its input materials, and setup reduction and quality improvement.

Dynamic lot sizing with setup reduction

Abstract This paper extends previous work on reducing setup cost in the dynamic lot-sizing problem with deterministic but time-varying demands. Using a marginal cost approach, a simple heuristic is developed to find the optimal production schedule for the reduced setup cost and the optimal investment in setup reduction. A numerical example is solved for the declining linear and exponential setup cost reduction functions, which respectively yield a concave and a convex total cost. Furthermore, an extensive experiment is performed to evaluate the efficiency of the heuristic, which is found to perform well in certain operational settings.

Emergency relief supply chain design and trade-off analysis

Purpose – Emergency relief supply chain (ERSC) design is an important strategic decision that significantly affects the overall performance of emergency management activities. The performance of an ERSC can be measured by several performance measures some of which may conflict with each other. The purpose of this paper is to propose an ERSC design framework by simultaneously taking total logistics cost (TLC), risk level, and amount of demands covered in an ERSC into consideration. Design/methodology/approach – The study considers TLC of an ERSC as the sum of logistics cost from distribution warehouses (DWHs) to Break of Bulbs (BOBs) and from BOBs to affected neighborhoods. The risk level of an ERSC is measured by estimating the expected number of disrupted relief items (EDI) distributed from DWHs through BOBs to neighborhoods. The covered demand (CDM) is defined as total populations that are supported in case of an emergency, the populations within the maximal coverage distance (MCD) from relief facilitie...

Dynamic setup reduction in production lot sizing with nonconstant deterministic demand

Abstract We examine three production policies under nonconstant, deterministic demand and dynamic setup cost reduction, where a decision to invest in setup reduction is made at the beginning of each period of a planning horizon. The three production policies are the reorder point, order quantity ( s , Q ) policy; the fixed production cycle, variable order quantity ( t , Q i ) policy; and the variable production cycle, variable order quantity ( t i , Q i ). We study the behavior of the total relevant cost and develop a lot sizing and an investment solution procedure. Numerical examples are provided and dynamic setup cost reduction is compared with static setup cost reduction, where the decision to invest in setup reduction is made only at the initial setup.

On the (t, Sj) policy in an integrated production/inventory model with time-proportional demand

Abstract In this paper, we consider a two-level continuous time lotsizing problem with setup costs, inventory holding costs and time-proportional demand for a single end product and the raw materials used for manufacturing it. We analyze a ( t , S j ) ordering policy for the production of the end product, where at every equal and fixed scheduling cycle, t , a variable production quantity, S j , is produced during the j -th cycle. With the objective of minimizing the integrated total relevant cost, we formulate a mathematical programming problem to determine simultaneously the economic batch sizes for the end product and the economic order sizes for the raw materials. A heuristic is developed using the Lagrangian multiplier, and its solution compares very well with the exact solution. We compare the numerical results with the equal batch sizing policy, i.e., the ( s , Q ) policy, which is expected to be outperformed by the ( t , S j ) policy, and find that is not always the case.

An optimal algorithm for integrating raw materials inventory in a single-product manufacturing system

Abstract In this paper, we develop an exact solution procedure for finding the optimal inventory policies and for grouping the raw materials into a few groups. This is under the provision of a common order cycle for a single-product manufacturing system where the raw materials used for manufacturing it are procured from outside sources. We also present a numerical example.