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.     

Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand

In this paper, we propose a simultaneous approach to incorporate inventory control decisions––such as economic order quantity and safety stock decisions––into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk pooling phenomenon. We present a non-linear-mixed-integer model and a heuristic solution approach, based on Lagrangian relaxation and the sub-gradient method. In a numerical application, we found that the potential cost reduction, compared to the traditional approach, increases when the holding costs and/or the variability of demand are higher.     

基于车流接续代价最小的编组站配流计划优化研究

编组站是铁路运输系统的重要一环,其作业效率直接影响铁路运输效率和经济效益。为了高效、经济地完成列车解编,提高铁路运输的市场竞争力,需要优化编组站配流。通过引入"惩罚系数"衡量到、发列车接续的代价,将配流问题转化为运筹学的运输问题,考虑编组站实际作业的不确定性和复杂性,以车流接续总代价最小为目标函数,建立编组站配流优化模型,并设计算法进行求解,得到优化的配流计划,验证编组站配流优化模型和算法的有效性,可以提高和优化编组站作业效率。     

An exact algorithm for the multi-trip vehicle routing and scheduling problem of pickup and delivery of customers to the airport

Door-to-Door service of Pickup and Delivery of Customers to the Airport (D2PDCA) is a new service provided by certain Airline Ticket Sales Agencies (ATSAs) in China. This new service provides an attractive alternative way by picking up customer at this/her specified position and at any time he/she preferred and delivering to the airport more conveniently than airport shuttle and thus earn high customer service quality. Compared with the single-trip mode, the multi-trip mode of D2PDCA (MTM-D2PDCA) service can reduce travel distances, the number of vehicles required and the operating cost. To obtain the exact solution of the MTM-D2PDCA problem, we propose a novel, exact algorithm based on the trip-chain-oriented set-partitioning (TCO-SP) model, where a trip-chain represents multiple trips made by a specific vehicle. In the exact algorithm, we propose an improved label-correcting method to remove infeasible trip-chains quickly and thus speed the search process. Based on the feasible trip-chains, the MTM-D2PDCA problem is formulated as the novel TCO-SP model, which can be solved exactly by the optimization software CPLEX. In addition, we present several mathematical insights into the relationship between the number of trip-chains and the number of local optimal trips that are applicable in both theory and practice. Extensive experiments are conducted to illustrate the application of the model and demonstrate the cost savings of the MTM-D2PDCA mode over the single-trip mode and provide managerial insights into successfully operating a MTM-D2PDCA service.     

Optimal vendor selection in a multiproduct supply chain with truckload discounts

When products are sold by multiple vendors in various locations, the purchaser must decide what to order from each vendor and where to send it. To solve this decision problem, a novel optimization model is developed and applied to a situation involving the nationwide wholesale distribution of grocery products. Comparing the model’s solution with the actual record of shipments reveals instances in which the model selected higher-priced vendors in order to capitalize on truckload cost savings, which are seen to be an important factor in vendor selection. Additional models are developed to reduce computation time and assign shipments to vehicles.     

供应链的战略管理模型研究

强调成本的有效性供应链和强调时间反应速度的反应性供应链是两种完全不同的供应链战略。企业在强调成本时不能完全忽略供应链的时间效用,同样在强调反应速度时也无法完全忽视成本的影响,因此通过对供应链战略的分析,对成本和时间赋予一定的权重,建立综合成本和反应速度的供应链战略模型,并通过实例对模型进行了验证。     

Efficient aircraft spare parts inventory management under demand uncertainty

In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.     

An integrative approach to determine store delivery patterns in grocery retailing

This article focuses on the tactical problem of selecting delivery patterns according to which grocery stores are repetitively supplied with products from different order segments by retail-owned distribution centers. The research environment considered consists of logistics processes in DCs, transportation and instore logistics. We identify dependencies on the delivery patterns selected and specify the relevant costs. These costs are reflected in the objective function of a binary selection model. Implementing and applying the model to the real case of a major European retail company yields substantial cost savings potential of 5.3%, amounting to tens of millions of euros per annum.     

A novel mathematical model for a multi-period,multi-product optimal ordering problem considering expiry dates in a FEFO system

One of the main challenges of retail units is to determine the order quantities of different types of products, each with a specific expiry date, so that the system cost including shortage cost is minimized. We study a new multi-product multi-period replenishment problem for a First Expired-First Out (FEFO) based warehouse management system. The proposed nonlinear model is first converted to a linear one and then solved by applying two evolutionary algorithms: the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), in which design parameters are set using Taguchi method. Computational results demonstrate the applicability of the proposed model for perishable items and comparing the results shows the efficiency of the proposed metaheuristics as well.     

Throughput time distribution analysis for a one-block warehouse

We develop discrete time models for the throughput time distribution of orders arriving to a one-block warehouse. The models accommodate single- or multi-line orders, and we show how to use them to determine the optimal batch size, given a desired probability of on-time order fulfillment. Experiments suggest that the optimal batch size is slightly higher than one would choose if minimizing average throughput time.     

Incorporating location,routing and inventory decisions in supply chain network design

This paper for the first time presents a novel model to simultaneously optimize location, allocation, capacity, inventory, and routing decisions in a stochastic supply chain system. Each customer’s demand is uncertain and follows a normal distribution, and each distribution center maintains a certain amount of safety stock. To solve the model, first we present an exact solution method by casting the problem as a mixed integer convex program, and then we establish a heuristic method based on a hybridization of Tabu Search and Simulated Annealing. The results show that the proposed heuristic is considerably efficient and effective for a broad range of problem sizes.     

The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO.     

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.     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

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.     

鲍摩-瓦尔夫模型在物流配送中心选址中的应用

物流配送中心是物流系统的重要组成部分,选址更是配送中心规划中至关重要的环节,合理的配送中心选址可以有效降低配送中心的运营成本和建造成本.鲍摩-瓦尔夫模型不仅考虑了运输成本,而且还考虑了可变成本和固定成本,因此可按其求解方法进行求解.通过实例计算,得到较好效果.最后指出该模型存在的缺点.     

配送中心订单分批拣货问题的数学模型及算法

以最小化行走距离为目标,构造了拣货作业中订单分批问题的数学模型,在一些模型假设的基础上,给出了订单分批拣货问题的节约启发式算法。并通过算例分析,表明此算法的订单分批结果优于传统的先到先服务分批结果,为实现拣货作业中的订单优化分批提供了一个新方法。     

Reliable design of an integrated supply chain with expedited shipments under disruption risks

This paper studied the design of a two-echelon supply chain where a set of suppliers serve a set of terminals that receive uncertain customer demands. In particular, we considered probabilistic transportation disruptions that may halt product supply from certain suppliers. We formulated this problem into an integer nonlinear program to determine the optimal system design that minimizes the expected total cost. A customized solution algorithm based on Lagrangian relaxation was developed to efficiently solve this model. Several numerical examples were conducted to test the proposed model and draw managerial insights into how the key parameters affect the optimal system design.     

Booking and flying with low‐cost airlines

The first part of the paper develops a theoretical framework for the analysis of strategic behaviour of European low‐cost airlines that emphasises the role of product differentiation. The second part uses original survey data to assess the effectiveness of low‐cost airlines' distribution strategies. Finally, an econometric model is developed to assess the joint impact of the factors affecting the level of the fares charged by low‐cost airlines. The evidence suggests that, among other things, the highest prices normally are paid for tickets bought between 30 and 8 days before departure, and thus indicates an original pricing strategy that differentiates low‐cost airlines from traditional carriers. Copyright © 2002 John Wiley & Sons, Ltd.     

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.