Haiqing Song

A successive convex approximation method for multistage workforce capacity planning problem with turnover

Workforce capacity planning in human resource management is a critical and essential component of the services supply chain management. In this paper, we consider the planning problem of transferring, hiring, or firing employees among different departments or branches of an organization under an environment of uncertain workforce demands and turnover, with the objective of minimizing the expected cost over a finite planning horizon. We model the problem as a multistage stochastic program and propose a successive convex approximation method which solves the problem in stages and iteratively. An advantage of the method is that it can handle problems of large size where normally solving the problems by equivalent deterministic linear programs is considered to be computationally infeasible. Numerical experiments indicate that solutions obtained by the proposed method have expected costs near optimal.

SimMan-A simulation model for workforce capacity planning

Motivated by the difficulty in predicting the actual performance of workforce capacity planning solutions obtained from mathematical models, we develop a simulator, SimMan, to serve as a test bed for evaluating the effectiveness and robustness of different planning options and assignment rules. To facilitate the ease of use, the simulator is developed in C++ language with a modular structure so that different users or planners can customize the modules according to their specifications. We further develop a mathematical model for the workforce capacity planning problem, where the uncertainty of the demand is handled by the safety stock concepts. We also suggest a number of practical planning alternatives and assignment rules based on information from IBM. These rules are implemented as modules in SimMan and tested numerically to provide managerial insights.

Distribution Coordination Between Suppliers and Customers with a Consolidation Center

We study a problem faced by a third-party logistics provider (3PL) who needs to coordinate shipments between suppliers and customers through a consolidation center in a distribution network. Products from a supplier have one release time and are consolidated into a single shipment to the consolidation center. At the center, products to the same destination are also consolidated into a single shipment, and the consolidation time can be as early as possible or as late as possible, depending on the customer requirement and cost structure. The 3PL needs to determine the pickup times from the suppliers, delivery times to the customers, and the transportation options while considering product release times, latest arrival times, different consolidation policies, and the transportation and storage costs involved. In this paper, we formulate this problem as a nonlinear optimization problem, show it is an NP-hard problem, and develop a dual-based solution method for the general problem. Utilizing the problem’s special structure, we show that the Lagrangian dual of the general problem can be solved optimally as a linear program, thus allowing us to accelerate the computation of a lower bound to the optimal objective function value. The experimental results show that the dual-based algorithm provides solutions with objective function values, which are on average within 3.24% of optimality. We also consider a version of the problem where each customer orders products from all suppliers, for which we develop a polynomial-time algorithm. Subject classifications: shipment consolidation; distribution network optimization; logistics coordination. Area of review: Transportation. History: Received July 2006; revisions received March 2007, May 2007; accepted June 2007. Published online in Articles in Advance May 15, 2008.

Minimum cost delivery problem in intermodal transportation networks

Intermodal movements are those in which two or more different transportation modes are linked end-to-end in order to move freight and/or people from point of origin to point of destination. In the intermodal transportation network, the departure times of the transportation modes are pre-scheduled and there is a list of departure times associated with each transportation mode. This paper considers the problem of finding the minimum cost delivery route for an origin- destination pair where the total cost of a delivery consists of the transportation cost, the transition cost and the holding cost of possible transshipping. We provide a method which expends the intermodal transportation network on time-space into a general network in which each arc only associates with one attribute, namely, the arc cost. We show that given a release time at the origin and a due date at the destination, the minimum cost delivery problem is equivalent with a shortest path problem in the time-space network. Hence, the problem can be solved efficiently.

Modified base-stock policies for semiconductor production system with dependent yield rates

We consider a two-stage production system faced by semiconductor manufacturing which produces a hierarchy of multiple grades of outputs. In the first stage, a single type of input (wafer) is used to produce multiple types of semi-finished parts with dependent yield rates, and in the second stage, each type of semi-finished parts can be transformed into a corresponding type of final products, or downgraded to a type of lower grade final products. Random customer demands are faced on the final products, and demands of different types of final products are not allowed to be substituted. The advantage of this production system is that it can prevent unhealthy ordering from customers who intentionally send out false demand signals for high grade products and revise the orders to lower grade products when the delivery time is close, which was observed in semiconductor manufacturing. The objective of the study is to plan the quantity of the input at the first stage and the respective downgrade quantities at the second stage so as to meet the required service level at the minimum cost. With some common assumptions, we propose a modified base-stock policy for this two-stage production system and show that the occurrence of nil excess inventory above the base-stock level follows a renewal process. We further extend the modified base-stock policy to a better policy that invokes risk pooling over multiple grade products. The performance of these two polices are evaluated via simulation to provide managerial insights.

Stochastic Relay Routing in Peer-to-Peer Networks

Network Address Translation (NAT) commonly prevents nodes without globally valid IP addresses from establishing direct Internet paths. In peer-to-peer networks, peers may utilize intermediate nodes as relays for this NAT traversal. We develop a stochastic relay routing algorithm for selecting appropriate relay nodes. The proposed relay routing algorithm is constructed in a stochastic programming framework by leveraging the actual delay of local links and the statistical delay distributions of non-local overlay links. Single or multiple paths are established via relays between two peers for achieving packet delivery with low delay and small loss. The simulation results showed that the proposed stochastic single/multi-path routing algorithm achieved a much lower packet delay than deterministic shortest path algorithms, which utilize average link delays. We demonstrated the effectiveness of the path diversity provided by our algorithm in reducing packet loss significantly via simulations. Our algorithm is fully distributed and requires only accurate local information. The employment of our algorithm is beneficial for provisioning real-time streaming applications in peer-to-peer networks.

Dynamic stochastic programming for asset allocation problem

Asset allocation is an important decision problem in financial planning. In this paper, we study the multistage dynamic asset allocation problem which an investor is allowed to reallocate its wealth among a set of assets over finite discrete decision points, in which the stochastic return rates of the assets follow a Markov chain with nonstationary transition probabilities. The objective is to maximize the utility of the wealth at the end of the planning horizon where the utility of the wealth follows a general piecewise linear and concave function. Transaction costs are considered. We formulate the problem with a dynamic stochastic programming model and develop a method that decomposes the problem into stage-based subproblems to solve it. The main advantage of this method is that it provides a computationally tractable tool to deal with the dynamic asset allocation problem of long planning horizon.

An arc-exchange decomposition method for multistage dynamic networks with random arc capacities

Multistage dynamic networks with random arc capacities (MDNRAC) have been successfully used for modeling various resource allocation problems in the transportation area. However, solving these problems is generally computationally intensive, and there is still a need to develop more efficient solution approaches. In this paper, we propose a new heuristic approach that solves the MDNRAC problem by decomposing the network at each stage into a series of subproblems with tree structures. Each subproblem can be solved efficiently. The main advantage is that this approach provides an efficient computational device to handle the large-scale problem instances with fairly good solution quality. We show that the objective value obtained from this decomposition approach is an upper bound for that of the MDNRAC problem. Numerical results demonstrate that our proposed approach works very well.

An optimization model and its complexity on liner scheduling problem with customer choice

In this paper, we study the liner scheduling problem with consideration of the customer choice which depends on the transportation time of a route. We provide a mixed integer model for the problem and show that it is NP-complete. We then provide two ideas on solving this model.

A Dynamic Stochastic Network Model for Asset Allocation Problem

Asset allocation is an important decision problem in ¿nancial planning. In this paper, we study the multistage dynamic asset allocation problem in which an investor is allowed to reallocate its wealth among a set of assets over ¿nite discrete decision points and the stochastic return rates of the assets follow a Markov chain with nonstationary transition probabilities. The objective is to maximize the utility of the wealth at the end of the planning horizon where the utility of the wealth follows a general piecewise linear and concave function. Transaction costs are considered. We formulate the problem with a dynamic stochastic net-work model which has potential to introduce a computationally tractable tool to deal with the dynamic asset allocation problem of large number of assets and long planning horizon.