Wikrom Jaruphongsa

A Dynamic Model for Inventory Lot Sizing and Outbound Shipment Scheduling at a Third-Party Warehouse

This paper presents a model for computing the parameters of an integrated inventory replenishment and outbound dispatch scheduling policy under dynamic demand considerations. The optimal policy parameters specify (i) how often and in what quantities to replenish the stock at an upstream supply chain member (e.g., a warehouse), and (ii) how often to release an outbound shipment to a downstream supply-chain member (e.g., a distribution center). The problem is represented using a two-echelon dynamic lot-sizing model with pre-shipping and late-shipping considerations, where outbound cargo capacity constraints are considered via a stepwise cargo cost function. Although the paper is motivated by a third-party warehousing application, the underlying model is applicable in the general context of coordinating inventory and outbound transportation decisions. The problem is challenging due to the stepwise cargo cost structure modeled. The paper presents several structural properties of the problem and develops a polynomial time algorithm for computing the optimal solution.

Supply chain performance measurement system: a Monte Carlo DEA-based approach

A supply chain operates in a dynamic platform and its performance efficiency measurement requires intensive data collection. The task of collecting data in a supply chain is not trivial and it often faces with uncertainties. This paper develops a simple tool to measure supply chain performance in the real environment, which is stochastic. Firstly, it introduced the Data Envelopment Analysis (DEA) supply chain model to measure the supply chain performance. Next, it enhanced the model with Monte Carlo (random sampling) methodology to cater for efficiency measurement in stochastic environment. Monte Carlo approximations to stochastic DEA have not been practically used in empirical analysis, despite being an important tool to make statistical inferences on the efficiency point estimator. This method proves to be a cost saving and efficient way to handle uncertainties and could be used in other relevant field other than supply chain, to measure efficiency.

A dynamic lot-sizing model with multi-mode replenishments: polynomial algorithms for special cases with dual and multiple modes

Abstract This paper generalizes the classical dynamic lot-sizing model to consider the case where replenishment orders may be delivered by multiple shipment modes. Each mode may have a different lead time and is characterized by a different cost function. The model represents those applications in which products can be purchased through various suppliers or delivered from a single source using various transportation modes with different lead times and costs. The problem is challenging due to the consideration of cargo capacity constraints, i.e., the multiple set-ups cost structure, associated with a replenishment mode. The paper presents several structural optimality properties of the problem and develops efficient algorithms, based on the dynamic programming approach, to find the optimal solution. The special, yet practical, cases of the two-mode replenishment problem analyzed in this paper are analytically tractable, and hence, the respective problems can be solved in polynomial time.

A Two-Echelon Inventory Optimization Model with Demand Time Window Considerations

This paper studies a two-echelon dynamic lot-sizing model with demand time windows and early and late delivery penalties. The problem is motivated by third-party logistics and vendor managed inventory applications in the computer industry where delivery time windows are typically specified under a time definite delivery contract. Studying the optimality properties of the problem, the paper provides polynomial time algorithms that require O(T3) computational complexity if backlogging is not allowed and O(T5) computational complexity if backlogging is allowed.

Dynamic lot-sizing model with demand time windows and speculative cost structure

We consider a deterministic lot-sizing problem with demand time windows, where speculative motive is allowed. Utilizing an untraditional decomposition principle, we provide an optimal algorithm that runs in O(nT^3) time, where n is the number of demands and T is the length of the planning horizon.

Dynamic lot-sizing problem with demand time windows and container-based transportation cost

In this paper, we study the dynamic lot-sizing problem with demand time windows and container-based transportation cost. For each particular demand, there are corresponding earliest and latest times, and the duration between such earliest and latest times is the demand time window. If a demand is satisfied by a delivery within demand time window, then there is no holding or backlogging cost incurred. Our purpose is to satisfy demand at a minimum total cost, including setup cost, procurement cost, container cost, and inventory holding cost.

Outbound shipment mode considerations for integrated inventory and delivery lot-sizing decisions

We present a two-echelon dynamic lot-sizing model with two outbound delivery modes where one mode has a fixed set-up cost structure while the other has a container-based cost structure. Studying the optimality properties of the problem, we provide a polynomial solution algorithm based on a dynamic programming approach.

Dynamic lot-sizing model for major and minor demands

Abstract This paper deals with a lot-sizing model for major and minor demands in which major demands are specified by time windows while minor demands are given by periods. For major demands, the agreeable time window structure is assumed where each time window is not strictly nested in any other time windows. To incorporate the economy of scale of large production quantity, especially from major demands, concave cost structure in production must be considered. Investigating the optimality properties, we propose optimal solution procedures based on dynamic program. For a simple case when only major demands exist, we propose an optimal procedure with running time of O ( n 2 T ) where n is the number of demands and T is the length of the planning horizon. Extending the algorithm to the model with major and minor demands, we propose an algorithm with complexity O ( n 2 T 2 ) .

A preliminary study on using Data Envelopment Analysis (DEA) in measuring supply chain efficiency

This paper illustrates the use of Data Envelopment Analysis (DEA) in measuring supply chain efficiency. Two models were developed – Technical Efficiency (TE) model and Cost Efficiency (CE) model. The information obtained enable managers to identify inefficient operations and take appropriate actions for improvement. The opportunity cost serves as a good reference on resource allocations. The models are further enhanced with scenario analysis to derive more meaningful insights for resources planning. The study proves the usefulness of DEA as a decision-making tool in supply chain. Future research could look into the possibility of modelling DEA in a stochastic supply chain environment.