DaeSoo Kim

Optimal joint pricing and lot sizing with fixed and variable capacity

This paper examines previously unexplored fixed and variable capacity problems of jointly determining an items price and lot size for a profit-maximizing firm facing constant but price-dependent demands over a planning horizon. We apply geometric programming to these constrained nonlinear maximization problems with nonconcave objective functions and obtain global optimal solutions. Using Kuhn-Tucker condition, marginal and sensitivity analyses, we investigate model interactions, provide managerial implications on the optimal capacity decisions, and explore the postoptimal behavior of the price, lot size, and capacity expansion and reduction size. Some findings cast interesting insights, different from previous just-in-time management studies without pricing consideration.

Optimal two-stage lot sizing and inventory batching policies

Abstract This paper extends previous studies of two-stage lot sizing problems with finite production rates. We develop various lot sizing and inventory batching (i.e., operation–unit batching (OUB) and unit–unit batching (UUB)) models under different system characteristics and lot sizing and inventory policies. The analysis of the optimality of the lot size ratio between the two stages reveals (1) that both non-increasing and non-decreasing lot sizing policies can be optimal in both OUB and UUB, (2) that a non-integer lot size ratio can be optimal in OUB, and (3) that an integer lot size ratio is always optimal in UUB. We present a simple, easy-to-implement, optimal solution approach to the two-stage lot sizing and inventory batching problem, along with examples.

Optimal Demand Rate, Lot Sizing, and Process Reliability Improvement Decisions

Because product quality is not always perfect in practice, process reliability improvement is an important managerial concern. This study considers this reliability/quality issue in the context of linking production (lot sizing and inventory) and marketing (pricing) decisions. We address some modeling issues of a related previous study. We develop a profit-maximization model, which we investigate under two different decision-making approaches to linking production and marketing (sequential and joint decisions). Calculus and geometric programming are used to solve the model, to compare the decisions from the two approaches analytically, and to develop a practical heuristic approach. The comparative results show that decision patterns can be the opposite of the optimal decisions for the perfect-quality case reported in the literature.

Cycle scheduling for discrete shipping and dynamic demands

Abstract This study examines previously unexplored issues regarding the performance of integrative cycle scheduling ((ICS), i.e., lot sizing and sequencing ) in a multi-item, capacitated, repetitive manufacturing environment with discrete shipping and dynamic demands . Through an extensive experimental study, we provide a more complete understanding and managerial insights on cycle scheduling for discrete shipping and dynamic demands. Experiment 1 identifies the dominance relationship between lot sizing and sequencing in the cycle scheduling performance in the repetitive manufacturing environment with different daily demand variations, by comparing a large set of ICS and other heuristics in terms of total inventory costs and CPU time. Experiment 2 investigates the performance sensitivity of the ICS heuristics under different capacity utilization factor settings determined by capacity tightness, setup time, and processing time. And experiment 3 examines the robustness of the ICS in initial inventory buildup for feasibility and rolling horizon scheduling situations. Important managerial insights into cycle scheduling from the findings, besides the overall favorable performance of the ICS heuristics, include: (1) the situation-dependent dominance of different lot sizing rules, i.e., the best cost performance of the modified Pinto–Mabert based heuristics for the low demand variation and the common cycle based heuristics for the high variations, and the good cost performance of selecting the best feasible sequence from random sampling, (2) the crucial role of capacity factors in procedure performance, and (3) the robust initial inventory buildup performance of the ICS in a changing demand environment.