A carrier’s optimal bid generation problem in combinatorial auctions for transportation procurement

We consider the carrier’s optimal bid generation problem in combinatorial auctions for transportation procurement. Bidders (carriers) employ vehicle routing models to identify sets of lanes (origin-destination pairs) based on the actual routes that a fleet of trucks will follow in order to maximize profit. Routes are constructed by optimally trading off repositioning costs of vehicles and the rewards associated with servicing lanes. The carrier optimization represents simultaneous generation and selection of routes and can incorporate any existing commitment. We employ both column generation and Lagrangian based techniques for solving the carrier optimization model and present numerical results.     

Multi-level inventory management decisions with transportation cost consideration

In this article we address specific inventory management decisions with transportation cost consideration in a multi-level environment consisting of a supplier–warehouse–retailers. We develop two models – namely, decentralized ordering model and centralized ordering model to investigate the effect of collective ordering by retailers on the total inventory cost of the system. A numerical study shows that the proposed model is robust and generates reasonable cost savings. The models have potential in several multi-level applications such as fresh or frozen food delivery to stores of different supermarkets or the supply of medicine to a number of hospitals from a wholesaler.     

A Linear Programming approach for robust network revenue management in the airline industry

The classical revenue management problem consists of allocating a fixed network capacity to different customer classes, so as to maximize revenue. This area has been widely applied in service industries that are characterized by a fixed perishable capacity, such as airlines, cruises, hotels, etc.It is traditionally assumed that demand is uncertain, but can be characterized as a stochastic process (See Talluri and van Ryzin (2005) for a review of the revenue management models). In practice, however, airlines have limited demand information and are unable to fully characterize demand stochastic processes. Robust optimization methods have been proposed to overcome this modeling challenge. Under robust optimization framework, demand is only assumed to lie within a polyhedral uncertainty set (Lan et al. (2008); Perakis and Roels (2010)).In this paper, we consider the multi-fare, network revenue management problem for the case demand information is limited (i.e. the only information available is lower/upper bounds on demand). Under this interval uncertainty, we characterize the robust optimal booking limit policy by use of minimax regret criterion. We present an LP (Linear Programming) solvable mathematical program for the maximum regret so our model is able to solve large-scale problems for practical use. A genetic algorithm is proposed to find the booking limit control to minimize the maximum regret. We provide computational experiments and compare our methods to existing ones. The results demonstrate the effectiveness of our robust approach.     

Principles of micro-behavior commodity transport modeling

This paper presents an agent-based approach to commodity transport modeling. It assesses the effects of behavior-oriented transport policy measures while taking complex logistics reaction patterns into account. It is structured by modules describing company generation, supplier choice, shipment-size choice, carrier choice and tour construction. The behavior of individual actors is simulated using normative logistics models and accumulated market knowledge. Using a bottom-up approach, shippers and carriers interact through simulated auctions of transport contracts resulting in the generation of tours. Simulations using the model prototype INTERLOG calibrated with German data demonstrate the capabilities and limitations of this approach.     

Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium

This paper presents a bi-level robust optimization model, where a food company maximizes its profit and minimizes post-harvest loss by optimally deploying grain processing/storage facilities and determining grain purchase price, while a group of spatially distributed non-cooperative farmers determine harvest time, shipment, storage, and market decisions under yield uncertainty and market equilibrium. The non-cooperative behavior of the food company and the farmers is represented by a bi-level Stackelberg leader follower’s game model with mixed-integer decision variables. The proposed model and solution approach are applied to case studies for Illinois and Brazil.     

A robust optimization approach for the road network daily maintenance routing problem with uncertain service time

This paper studies the robust optimization approach for the routing problem encountered in daily maintenance operations of a road network. The uncertainty of service time is considered. The robust optimization approach yields routes that minimize total cost while being less sensitive to substantial deviations of service times. A robust optimization model is developed and solved by the branch-and-cut method. In computational experiments, the behavior of the robust solutions and their performance are analyzed using Monte Carlo simulation. The robust optimization model is also compared with a classic chance-constrained programming model. The experimental analysis provides managerial insights for decision makers to determine an appropriate routing strategy.     

Simultaneous optimization of schedule coordination and cargo allocation for liner container shipping networks

A liner container shipping carrier usually collects immediately-delivered goods that are produced by manufacturers in world factories, and transports the products to worldwide market destination by offering weekly shipping service. In practice, the carrier has to consider extra demurrage cost of containerized cargos incurred from waiting for weekly shipping service at ports. In this paper, we develop a mathematic programming model to maximize the carrier’s profitability by simultaneously optimizing the ship route scheduling and interrelated cargo allocation scheme. The nonlinear optimization model is transformed into an equivalent mixed-integer linear program, and its applicability is demonstrated by a case study.     

A comprehensive evacuation planning model and genetic solution algorithm

We consider the problem of evacuating an urban area. Several planning aspects need to be considered in such a scenario, which are usually considered separately. We propose a macroscopic multi-criteria optimization model that includes several such questions simultaneously, and develop a genetic algorithm to solve the problem heuristically. Its applicability is extended by also considering how to aggregate instance data, and how to generate solutions for the original instance starting from a reduced solution. In computational experiments using real-world data, we demonstrate the effectiveness of our approach and compare different levels of data aggregation.     

Min–Max exact and heuristic policies for a two-echelon supply chain with inventory and transportation procurement decisions

We study the problem in which one supplier delivers a product to a set of retailers over time by using an outsourced fleet of vehicles. Since the probability distribution of the demand is not known, we provide a Min–Max approach to find robust policies. We show that the optimal Min-Expected Value policy can be very poor in the worst case. We provide a Min–Max Dynamic Programming formulation that allows us to exactly solve the problem in small instances. Finally, we implement a Min–Max Matheuristic to solve benchmark instances and show that it is very effective.     

Diversification strategy with random yield suppliers for a mean–variance risk-sensitive manufacturer

We consider the diversification strategy for a mean–variance risk-sensitive manufacturer with unreliable suppliers. We first analyze the linear model and find that the suppliers are selected according to the descending order of their contributed marginal expected profit, and increasing the manufacturer’s risk-averseness leads to a more even allocation of demand across the suppliers. Then, we study the general newsvendor model. By approximating the leftover inventory with a normal distribution, we establish the general properties of the active supplier set and show that the supplier selection rule is similar to that under the risk-neutral setting when the demand uncertainty is large. Moreover, we conjecture that the selection rule also applies when the demand uncertainty is low, which we verify with an extensive numerical study. Our paper makes two contributions: First, we establish the properties of the optimal diversification strategy and develop corresponding insights into the trade off between cost and reliability under the mean–variance framework. Second, we perform comparative statics on the optimal solution, with a particular emphasis on investigating how changes in the supplier’s cost or reliability affect the risk-averse manufacturer’s ordering decisions and customer service level.     

Coordination with a backup supplier through buy-back contract under supply disruption

This paper studies a buy-back contract between a buyer and a backup supplier when the buyer’s main supplier experiences disruptions. The expected profit functions and the optimal decisions of the contract players are derived through a sequential optimization process. The common properties of the contract as well as the differences under the demand uncertainty and the main supplier’s recurrent supply uncertainty are explored through comparative studies and numerical examples. The study contributes to the literature by providing a better understanding of the impacts of demand and supply uncertainties and by shedding insights on the value of a backup supply.     

供应链中基于VMI库存与运输协调问题的研究

通过对1个供应商、N个零售商组成的供应链形式的协调问题研究,提出了在VMI系统中协调库存与运输的配送策略。这种配送策略假设需求是Poisson随机过程,以更新理论为基础,基于供应商有权持有小额定单,直到定单数量到达一定程度时进行一起派送,即通过规模经济运输,达到降低成本的目的。     

On the use of RFID in the management of reusable containers in closed-loop supply chains under stochastic container return quantities

This paper studies a closed-loop supply chain that uses containers for transporting products from a supplier to a retailer. At the retailer, used containers are collected and returned to the supplier. The supplier inspects returned containers, and either repairs and reuses or disposes them. This paper studies the case where the fraction of containers that are returned to the supplier is stochastic, and where an RFID system can be used to support the tracking of container positions in the supply chain. The use of RFID leads to improved information on the return of containers and better return predictability as well as higher return rates, which we model as an increase in the mean return rate of containers and a reduction in return variance. The paper first develops a mathematical planning model for this scenario, and it then studies how the use of RFID impacts the performance of the system. In addition, it analyzes under which conditions the use of RFID is economical, and in which situations traditional container systems should be preferred.     

A robust optimization approach to the integrated berth allocation and quay crane assignment problem

This paper investigates the integrated berth allocation and quay crane assignment problem in container terminals. A deterministic model is formulated by considering the setup time of quay cranes. However, data uncertainties widely exist, and it may cause the deterministic solution to be infeasible. To handle the uncertainties, a robust optimization model is established. Furthermore, to control the level of conservativeness, another robust optimization model with the price constraints is proposed. A genetic algorithm and an insertion heuristic algorithm are suggested to obtain near optimal solutions. Computational experiments indicate that the presented models and algorithms are effective to solve the problems.     

A rolling planning horizon heuristic for scheduling agents with different qualifications

This paper presents an optimization model for the tour scheduling problem for agents with multiple skills and flexible contracts in check-in counters at airports. The objective is to minimize the total assignment costs subject to demand fulfillment and labor regulations. In order to solve this problem we develop a rolling planning horizon-based heuristic. Our heuristic is robust and provides near-optimal schedules within reasonable computation time for real-world cases, although the parameter selection is important to its performance. In addition, we discuss the impact of the skill distribution on the scheduling costs for several instances.     

Robust supply chain strategies for recovering from unanticipated disasters

Recovering from unanticipated disasters is critical in today’s global market. This paper examines the effectiveness of popular recovery strategies used to address unpredictable disasters that derail supply chains. We create a formal model to portray dynamic operational performance among supply chain firms facing disruptions caused by natural and man-made disasters. Our analysis shows that a supply chain recovers best if member firms adopt a radical, rapid, costly recovery strategy that immediately resolves the disruption. This observation is robust to various resource consumption requirements. We apply our methodology in the case of Taiwan’s 2011 food contamination scandal and provide managerial insights.     

供应服务商资金支付结算方式的优化管理

为降低资金成本和提高资金使用效率,上海铁路局优化供应服务商资金支付结算方式管理。通过支付结算和资金融资结合的手段,利用商业汇票和现金折扣两种方式,提高资金筹集能力,节约资金成本,实现路企银合作共赢。     

Robust hub network design problem

This paper presents a robust formulation for the uncapacitated single and multiple allocation hub location problem where demand is uncertain and its distribution is not fully specified. The proposed robust model is formulated as a mixed integer nonlinear program and then transformed into a mixed integer conic quadratic program. An efficient linear relaxation strategy is proposed which is found to deliver the optimal solutions for all the cases considered in this paper. Numerical experiments suggest location of more number of hubs when accounting for demand uncertainty using robust optimization compared to the deterministic setting.     

Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach

This paper investigates an order allocation problem of a manufacturer/buyer among multiple suppliers under the risks of supply disruption. A mixed integer non-linear programming (MINLP) model is developed for order allocation considering different capacity, failure probability and quantity discounts for each supplier. We have shown that the formulated problem is NP-hard in nature and genetic algorithm (GA) approach is used to solve it. The model is illustrated through a numerical study and the result portrays that the cost of supplier has more influence on order quantity allocation rather than supplier’s failure probability.     

Retail supply chain network design under operational and disruption risks

Designing robust and resilient retail networks under operational and disruption risks can create substantial competitive advantage. In this paper, a deterministic multiple set-covering model is first proposed. Then, it is extended to a possibilistic scenario-based robust model by scenario generation and disruption profiling to design a robust and resilient retail network. The developed models are validated through randomly generated examples and a real case study in retailing. Numerical results demonstrate that designing retail chains without considering operational and disruption risks is really misleading. Also, multiple covering of retail stores as the measure of redundancy increases the network’s resilience significantly.