Zhengwen He

Multi-mode project payment scheduling problems with bonus-penalty structure

This paper involves the multi-mode project payment scheduling problem with bonus-penalty structure where activities can be performed with several modes and a bonus-penalty structure exists at the deadline of the project. In the problem the decisions on when to schedule events and payments, the magnitude of each payment, and the performing mode of each activity need to be optimized. A two-module simulated annealing heuristic is proposed to solve the mixed integer non-linear programming models for the contractor and the client, and a satisfactory solution, which consists of payment event set, event schedule, and payment amount set, may be found through iterations between the heuristics two modules. The profits of the two parties of the contract are changed significantly by the bonus-penalty structure and the structure may be considered as a coordination mechanism essentially, which may enhance the flexibility of payment scheduling and be helpful for the two parties to get more profits from the project. Through solving and analyzing an instance the insight that the bonus-penalty structure may advance the project completion effectively and improve the profits of the two parties in the meantime can be obtained.

Metaheuristics for multi-mode capital-constrained project payment scheduling

This paper involves the multi-mode capital-constrained project payment scheduling problem, where the objective is to assign activity modes and payments so as to maximize the net present value (NPV) of the contractor under the constraint of capital availability. In the light of different payment patterns adopted, four optimization models are constructed using the event-based method. For the NP-hardness of the problem, metaheuristics, including tabu search and simulated annealing, are developed and compared with multi-start iterative improvement and random sampling based on a computational experiment performed on a data set generated randomly. The results indicate that the loop nested tabu search is the most promising procedure for the problem studied. Moreover, the effects of several key parameters on the contractor’s NPV are investigated and the following conclusions are drawn: The contractor’s NPV rises with the increase of the contractor’s initial capital availability, the payment number, the payment proportion, or the project deadline; the contractor has a decreasing marginal return as the contractor’s initial capital availability goes up; the contractor’s NPVs under the milestone event based payment pattern are not less than those under the other three payment patterns.

Analysis of the bullwhip effect in two parallel supply chains with interacting price-sensitive demands

This paper offers insights into how the bullwhip effect in two parallel supply chains with interacting price-sensitive demands is affected in contrast to the situation of a single product in a serial supply chain. In particular, this research studies two parallel supply chains, each consisting of a manufacturer and a retailer, and the external demand for a single product depends on its price and the others price in a situation in which each price follows a first-order autoregressive process. In this paper, we propose an analytical framework that incorporates two parallel supply chains, and we explore their interactions to determine the bullwhip effect. We identify the conditions under which the bullwhip effect is amplified or lessened with interacting price-sensitive demands relative to the situation without interaction.

The impact of consumer price forecasting behaviour on the bullwhip effect

As prices fluctuate over time, a strategic consumer may buy more in advance to reduce his or her future needs in anticipation of higher prices in the future, or may choose to postpone a purchase in anticipation of lower prices in the future. We investigate the bullwhip effect from a consumer price forecasting behavioural perspective in the context of a simple two-level supply chain composed of a supplier and a retailer. We consider two different forms for the demand function – linear and iso-elastic demand functions, both depending on the prices in multiple periods. Assuming that the retailer employs an order-up-to inventory policy with exponential smoothing forecasting technology, we derive analytical expressions for the bullwhip effect under the two demand functions, and extend the results to the multiple-retailer case. We find that consumer forecasting behaviour can reduce the bullwhip effect, most significantly when the consumer sensitivity to price changes is medium (approximately 0.5) for both the demand forms. In addition, for iso-elastic demand, the mitigation of the bullwhip effect induced by consumer price forecasting behaviour becomes more significant as the product price sensitivity coefficient and standard deviation of the price decrease. These findings are applicable to the development of managerial strategies by supply chain members that are conducive to bullwhip effect reduction through customer behaviour.

Simulated annealing for financing cost distribution based project payment scheduling from a joint perspective

Based on the distribution of the project financing cost over the contractor and the client, this paper involves the project payment scheduling problem from a joint perspective of the two parties. In the problem, the project financing cost is defined as the expense for raising money from the outside or the opportunity cost of the capital devoted into the project and the objective is to find the project payment schedule that can not only maximize the joint revenue of the two parties but also be accepted by them. Based on the characteristics of the problem, an optimization model consisting of two submodels is constructed using the activity-based method. For the strong NP-hardness of the problem, two simulated annealing algorithms with different searching structures are developed and compared with the multi-start iterative improvement method on the basis of a computational experiment performed on a data set generated randomly. The results show that the simulated annealing algorithm with the nested loop module seems to be the most promising algorithm for solving the defined problem especially when the scale of the problem becomes larger. In addition, the influences of some key parameters on the computational results are investigated through the full factorial experiment and a few useful conclusions are drawn.

The Bullwhip Effect in an Online Retail Supply Chain: A Perspective of Price-Sensitive Demand Based on the Price Discount in E-Commerce

This paper investigates the difference in bullwhip effects in online and offline retail supply chains, offering insights into how frequent price discounts in e-commerce influence the bullwhip effect in the online retail supply chain. We consider a two-level online retail supply chain with a manufacturer and an online retailer in which the demand faced by the retailer is price sensitive and based on the price discount. Assuming that the online retailer employs an optimal order-up-to inventory policy with an optimal minimum mean-squared error forecasting technique, we derive the expression of the bullwhip effect in the online retail supply chain and make analysis and comparison. Finally, we develop a dual-channel supply chain model to directly observe the impact of price discounts in e-commerce on the bullwhip effect. The results suggest that price discounts in the online retail market generally amplify the bullwhip effect in the online retail supply chain, but in certain conditions, the bullwhip in the online supply chain may be smaller than that in the offline supply chain. We also find that the relationship between the lead time and the bullwhip effect in the online supply chain presents a distinctive feature contrary to the conclusions of previous studies. Based on the analysis, we develop important managerial insights regarding online retail supply chains.

Multiobjective Optimization for Multiperiod Reverse Logistics Network Design

In recent years, the ever-rising return streams for repair service have forced the electronics manufacturers to expand their reverse logistics capacities. However, most existing papers on the reverse logistics network design neglected the time sensitivity of the return flows. Moreover, most of these investigations were primarily concerned with the single objective problems of either minimizing the total cost or maximizing the profit. In this paper, we propose a biobjective mixed-integer linear programming model for the multiperiod design problem of a reverse logistics network for repair service. A multiperiod setting is taken into account to make the reverse logistics network flexible to accommodate the gradual changes in the capacity of the facilities and the network configuration. To solve the NP-hard problem with biobjective, we develop a hybrid evolutionary algorithm that combines nondominated sorting genetic algorithm II (NSGA-II) with a local search method. We compare the hybrid evolutionary algorithm with NSGA-II and ε-constraint method using numerical examples. The comparison results indicate that the hybrid evolutionary algorithm outperforms the NSGA-II in most cases. The ε-constraint method performs best for the small instances, but it cannot solve large instances within reasonable time. Finally, an extensive parametric analysis is conducted and several managerial insights are derived.

Design of a Multiobjective Reverse Logistics Network Considering the Cost and Service Level

Reverse logistics, which is induced by various forms of used products and materials, has received growing attention throughout this decade. In a highly competitive environment, the service level is an important criterion for reverse logistics network design. However, most previous studies about product returns only focused on the total cost of the reverse logistics and neglected the service level. To help a manufacturer of electronic products provide quality postsale repair service for their consumer, this paper proposes a multiobjective reverse logistics network optimisation model that considers the objectives of the cost, the total tardiness of the cycle time, and the coverage of customer zones. The Nondominated Sorting Genetic Algorithm II (NSGA-II) is employed for solving this multiobjective optimisation model. To evaluate the performance of NSGA-II, a genetic algorithm based on weighted sum approach and Multiobjective Simulated Annealing (MOSA) are also applied. The performance of these three heuristic algorithms is compared using numerical examples. The computational results show that NSGA-II outperforms MOSA and the genetic algorithm based on weighted sum approach. Furthermore, the key parameters of the model are tested, and some conclusions are drawn.

Proactive and reactive resource-constrained max-NPV project scheduling with random activity duration

This paper addresses the resource-constrained project problem in which activity durations are stochastic variables and the objective is to maximize the net present value of cash flow in the project. First, using the two classical time buffering methods, proactive scheduling optimization models are constructed to generate the robust schedules. Then, two reactive scheduling models with different objectives are proposed to adjust the baseline schedules when disruptions occur during their execution. For the NP-hardness of the studied problem, three heuristic algorithms, including tabu search (TS), variable neighbourhood search (VNS), and a mixed version of VNS and TS, are developed and compared with the multi-start iteration improvement algorithm through a computational experiment conducted on a randomly generated data set. In addition, based on the computational results obtained, the effects of several key parameters on the proactive and reactive scheduling results are analysed, and some managerial insights are obtained. The research in this paper has practical implications for contractors to improve the project profit in an uncertain environment.

A Location-Inventory-Routing Problem in Forward and Reverse Logistics Network Design

We study a new problem of location-inventory-routing in forward and reverse logistic (LIRP-FRL) network design, which simultaneously integrates the location decisions of distribution centers (DCs), the inventory policies of opened DCs, and the vehicle routing decision in serving customers, in which new goods are produced and damaged goods are repaired by a manufacturer and then returned to the market to satisfy customers’ demands as new ones. Our objective is to minimize the total costs of manufacturing and remanufacturing goods, building DCs, shipping goods (new or recovered) between the manufacturer and opened DCs, and distributing new or recovered goods to customers and ordering and storage costs of goods. A nonlinear integer programming model is proposed to formulate the LIRP-FRL. A new tabu search (NTS) algorithm is developed to achieve near optimal solution of the problem. Numerical experiments on the benchmark instances of a simplified version of the LIRP-FRL, the capacitated location routing problem, and the randomly generated LIRP-FRL instances demonstrate the effectiveness and efficiency of the proposed NTS algorithm in problem resolution.