George Q. Huang

Supply Hub in Industrial Park (SHIP): The value of freight consolidation

Industrial parks, characterized as a cluster of enterprises situated in one location to share common resources, have played an indispensible role in boosting regional economic and industrial development. However, further development has been impeded by the shortage of land space, especially for the construction of warehouses. This paper proposes the concept of Supply Hub in Industrial Park (SHIP) as a promising approach addressing this challenge. SHIP is defined as a public provider of warehousing and logistics services for manufacturing enterprises located within an industrial park. The research reported in this paper focuses on evaluating the value of freight consolidation, one of the typical benefits of applying the SHIP approach. Two mathematical models of the supply chain in a typical industrial park are formulated: with and without SHIP. Genetic Algorithm (GA) is applied for solving the two models, and sensitivity experiments are conducted for comparative analysis between the two scenarios. The computational results show that through consolidating shipments, SHIP brings benefits to the whole industrial park. Total cost savings resulted from the application of SHIP would increase with the size of the supply chain, the vehicle capacity, and the rates of fixed transportation costs and holding costs of finished products at manufacturers.

Active scheduling for hybrid flowshop with family setup time and inconsistent family formation

This research is motivated by a real-life hybrid flowshop scheduling problem where jobs are organized in families according to their machine settings and tools. This type of problem is common in the production process of standard metal components. The problem is complicated by the requirement of family setup time when a machine changes from processing one job family to another and the formation of job families varies in different stages. This problem has been previously solved with a non-delay scheduling heuristic in which no machine is kept idle. This research illustrates that inserting intentional idle time into a non-delay schedule can further reduce the total setup time as well as makespan. With the inserted idle time, the non-delay schedules are extended to active schedules. This paper presents a mechanism to determine the locations and lengths of intentional idle times in the efficient active schedules. Four active scheduling approaches are developed by integrating two types of waiting factor operators into two non-delay approaches. Computational experiments have been conducted to compare the proposed active scheduling approaches in terms of effectiveness and efficiency. The results have shown that the proposed active scheduling approaches are superior to non-delay scheduling. The analysis of variance has been applied on the factors related to scheduling environment, problem size and scheduling approach. The analysis has identified factors that are most influential on the scheduling result.

A Bilevel Analytical Model for Dynamic Storage Pricing in a Supply Hub in Industrial Park (SHIP)

A Supply Hub in Industrial Park (SHIP) is a thirdparty business entity that leases storage space and logistics services among manufacturers located in the same industrial park. Manufacturers may hire warehouses outside the park if SHIPs storage price is exorbitant. This paper discusses how SHIP and manufacturers interact to optimize their decisions on storage pricing, replenishment, and delivery. A dynamic storage pricing strategy depending on storage length is adopted. This problem is modeled as a bilevel program where the SHIP is the leader and manufacturers are followers. Based on further assumptions, the proposed bilevel model is solved in closed-form. A series of sensitivity analyses are conducted to examine the influences of major cost parameters. The results show that SHIPs delivery charge has little impact on the total cost of large manufacturers, and its increase will not always bring profit improvement to SHIP. Contrary to intuition, the SHIP could attract more space demands from large manufacturers when charging higher in delivery or when the public warehouses delivery charge is lower, and more space demands from small manufacturers when the market storage price is lower. Note to Practitioners-It becomes increasingly common for an industrial park to provide a supply hub for its member enterprises to share warehousing and transportation services. The SHIP gains its main revenue from leasing storage space. Hence, it is natural that SHIP must establish a suitable storage pricing strategy to attain the maximum profit. Being motivated by this challenge, this paper investigates a dynamic storage pricing of SHIP in a typical supply chain with one SHIP and multiple manufacturers. We develop a bilevel model to study this problem and derive the optimal solutions in closed-form for special cases. We provide valuable managerial guidance for the SHIP operator to charge dynamic storage price, and for member enterprises to schedule their replenishment and delivery under different scenarios. Several counterintuitive findings are obtained regarding the influence of delivery costs inside and outside the industrial park, which will attract attention from managers of SHIP and member firms. This paper is limited to certain assumptions in deriving the optimal decisions. In future research, we will relax the assumption and design other methods to solve the proposed bilevel model. We will also investigate other forms of storage pricing strategies, which will shed further light on SHIPs decision automation.

Storage pricing, replenishment, and delivery schedules in a supply hub in industrial park: A bilevel programming approach

A supply hub in industrial park (SHIP) is a third-party business entity that leases storage space and logistics services among manufacturers located in the same industrial park. Manufacturers may hire warehouses outside the park if the SHIP’s storage price is exorbitant. This paper discusses how SHIPs and manufacturers interact to optimise their decisions on storage pricing, replenishment, and delivery. A dynamic storage pricing strategy depending on the duration of storage usage is adopted. A bilevel model is proposed to study this problem between the SHIP and manufacturers. After deriving the optimal conditions of manufacturers’ rational reactions, an enumerative algorithm is developed to cope with the bilevel model. A series of numerical experiments and sensitivity analyses are conducted to compare the dynamic and constant storage pricing strategies, to examine the influences of major cost parameters, and to evaluate the effects of the SHIP on manufacturers’ performance. The results show that the SHIP could achieve profit improvement through adopting dynamic storage pricing, especially when the public warehouse’s delivery charge is high. The SHIP’s profit increases significantly with the rising delivery charge of public warehouse and the decrease of the holding cost rate at the SHIP. The SHIP plays a role in mitigating demand risks, and brings further benefits to all manufacturers especially when their demand patterns are seasonally complementary.

Headquarter-centered Common Order Management: a simulation approach

This paper considers the customer order management in a group corporation which consists of one headquarter (HQ) and several geographically dispersed and operationally semi-autonomous production subsidiaries. Two order management models are formulated. One is the Headquarter-centered Common Order Management (HQ-COM) where customer orders are processed by the HQ and then allocated to its subsidiaries. The other is Subsidiary-Autonomous Order Management (SD-AOM) where subsidiaries take and process customer orders relatively independent of each other. The two models are applied to study two situations, respectively. One situation is that the total quantity of customer orders exceeds the production capacity of each individual subsidiary so that the order has to be split before allocating amongst multiple subsidiaries. The other situation is that the quantity of selected customer orders is within the production capacity of a single subsidiary so that orders should be merged into one batch before allocating to one subsidiary. Different heuristics are proposed and a series of simulation experiments are conducted. The results show that HQ-COM outperforms SD-AOM in terms of both its performance and its robustness against the demand variability. This achievement is largely due to the effects of pooling of different customer orders and sharing of production capacity amongst subsidiaries.

Agent-Based Service-Oriented Architecture for Heterogeneous Data Sources Management in Ubiquitous Enterprise

In a ubiquitous manufacturing environment, different devices such as radio frequency identification (RFID) technology are used to collect real-time data. Additionally, data is used by different enterprise information systems for supporting managerial decision making. Since data sources from applications and devices are characterized by multiple types of heterogeneities such as communication channels, blinding methods, and developing environments, the difficulty in managing heterogeneous data sources is greatly increased. This paper proposes an innovative Application Information Service (AIS) that serves as a middleware for information exchange in between different applications. The AIS possesses several key contributions. Firstly, AIS provides a centralized platform to manage distributed heterogeneous data sources so as to reduce the data duplications, increase consistency, and accuracy. Secondly, it combines software agent technologies with service-oriented architecture (SOA) so that services are capable of accomplishing tasks in an autonomous way without human intervention. Thirdly, agent-based service-oriented architecture paradigm is proposed to cultivate a collaborative environment to integrate different data sources as well as third party application providers.

Mining standard operation times for real-time advanced production planning and scheduling from RFID-enabled shopfloor data

Abstract Production planning and scheduling require standard operation times (SOTs) which have been obtained from time studies or based on past experiences. Wide variations exist and frequently cause serious discrepancies in executing plans and schedules. Radio frequency identification (RFID) technology has recently been applied to create a real-time ubiquitous manufacturing environment, where real-time shopfloor operational data about men, machines, materials, and orders could be captured and collected. Such data carry invaluable information and knowledge which might be used for supporting advanced production planning and scheduling (APS). APS usually needs precise SOTs for perfect decision-making within the RFID-enabled real-time ubiquitous manufacturing environment. This paper proposes a data mining model to estimate realistic SOTs and their standard deviations from RFID-enabled shopfloor data. Key impact factors on SOTs are examined, including working shifts, different machines, gender, and technology complexity. It is observed that working shifts and the learning curves of three types of operators (junior, intermediate, and senior) greatly influence the SOTs. The other factors have minor affection in this case. Considering the two significant impact factors, precise and reasonable SOTs could be worked out, aiming at improving the quality and stability of production plans and schedules.