H.Y. Lam

Survey of Green Vehicle Routing Problem: Past and future trends

Green Logistics has emerged as the new agenda item in supply chain management. The traditional objective of distribution management has been upgraded to minimizing system-wide costs related to economic and environmental issues. Reflecting the environmental sensitivity of vehicle routing problems (VRP), an extensive literature review of Green Vehicle Routing Problems (GVRP) is presented. We provide a classification of GVRP that categorizes GVRP into Green-VRP, Pollution Routing Problem, VRP in Reverse Logistics, and suggest research gaps between its state and richer models describing the complexity in real-world cases. The purpose is to review the most up-to-date state-of-the-art of GVRP, discuss how the traditional VRP variants can interact with GVRP and offer an insight into the next wave of research into GVRP. It is hoped that OR/MS researchers together with logistics practitioners can be inspired and cooperate to contribute to a sustainable industry.

A decision support system for optimizing dynamic courier routing operations

In this paper, we propose a prototype of a decision support system (DSS) that integrates a hybrid neighborhood search algorithm to solve the offline and online routing problems arising in courier service. In the dynamic operational environment of courier service, new customer orders and order cancellations continually arrive over time and thus disrupt the optimal routing schedule that was originally designed. This calls for the real-time re-optimization of routes. As service level is sensitive to whether allowable service time intervals are wide or narrow, it is valuable to study how adjustable and flexible time windows influence the courier service efficiency in a dynamic environment. To capture these dynamic features, a dynamic vehicle routing problem (DVRP) that simultaneously considers new customer orders and order cancellations is investigated in this study. Meanwhile, fuzzy time windows are formulated in the DVRP model to quantify the service level and explore the service efficiency. To tackle the new problem, we propose a competitive hybrid neighborhood search heuristic for (re)optimizing the offline and online routes. Numerical computational experiments and the comparison with results from Lingo show that our algorithm is capable of re-optimizing dynamic problems effectively and accurately in a very short time. The proposed model and algorithms are able to enhance courier service level without further expense of a longer traveling distance or a larger number of couriers.

A real-time risk control and monitoring system for incident handling in wine storage

Due to the fact that wine is highly sensitive to storage conditions such as temperature and humidity, it is a challenging task for a regional distribution hub to provide reliable wine storage facilities for maintaining wine quality during storage. This is especially true when an incident occurs unexpectedly that violates the criteria of suitable storage conditions. Improper incident handling and storage conditions may cause damage to the taste of wine, resulting in depreciation of the wines value. Therefore, controlling and monitoring risk in real-time during wine storage is critical to providing a quick response to prevent the wine quality from deterioration. In this paper, a RFID-based risk control and monitoring system (RCMS), which integrates radio frequency identification (RFID) technology and case-based reasoning (CBR), is proposed for monitoring real-time physical storage conditions and for formulating an immediate action plan for handling incidents. In the retrieval process of the CBR engine, genetic algorithms (GA) are applied to search for case clusters by considering the best combination of multi-dimensional parameters. With the help of RCMS, a shortlist of critical control actions, possible causes of incidents and corresponding actions can be generated to reduce the risk of deteriorating wine quality and possible compensation costs being incurred, while customer satisfaction can be maintained.

Customer relationship mining system for effective strategies formulation

Purpose – The purpose of this paper is to propose a customer relationship mining system (CRMS) to analyze the data collected from franchisees and formulates a marketing strategy based on customer demand and behavior. Design/methodology/approach – The system makes use of cloud technology to collect and manage data among the franchisees. An integrated approach of association rule mining and the neural network technique is adopted to investigate customer behavioral patterns and to forecast sales demand, respectively. Findings – The significance and contribution of this paper are demonstrated by adopting the CRMS in the education industry in Hong Kong. The findings led to the identification of student learning intentions such as course preferences, and the forecasting of enrolment demand in terms of demand forecast. It is believed that better resources allocation can be achieved and an increase in customer satisfaction is foreseeable. Research limitations/implications – The proposed CRMS could be applied to v...

A B2C e-commerce intelligent system for re-engineering the e-order fulfilment process

The e-commerce internal order processing flow is streamlined and re-designed.A GA-rule-based system for efficient e-commerce order fulfilment is proposed.An optimal order processing plan is generated by genetic algorithm technique.A system implementation shows a significant order processing time reduction. In todays world of digitization, the rise of the e-commerce business around the globe has brought a tremendous change not only in our purchasing habits, but also to the entire retail and logistics industry. Given the irregular e-commerce order arrival patterns, limited time for order processing in e-fulfilment centres, and the guaranteed delivery schedules offered by e-retailers, such as same-day or next-day delivery upon placing an order, logistics service providers (LSPs) must be extremely efficient in handling outsourced e-commerce logistics orders. Without re-engineering the order fulfilment processes, the LSPs are found to have difficulties in executing the order fulfilment process due to the tight handling requirements. This, in turn, delays the subsequent processes in the supply chain, such as last-mile delivery operations, consequently affecting customer satisfaction towards both the retailer and the LSP. In view of the need to improve the efficiency in handling e-commerce orders, this study aims at re-engineering the fulfilment process of e-commerce orders in distribution centres. The concept of warehouse postponement is embedded into a new cloud-based e-order fulfilment pre-processing system (CEPS), by incorporating the genetic algorithm (GA) approach for e-commerce order grouping decision support and a rule-based inference engine for generating operating guidelines and suggesting the use of appropriate handling equipment. Through a case study conducted in a logistics company, the CEPS provides order handling solutions for processing e-commerce logistics orders very efficiently, with a significant reduction in order processing time and traveling distance. In turn, improved operating efficiency in e-commerce order handling allows LSPs to better align strategically with online retailers, who provide customers with aggressive, guaranteed delivery dates.

An optimization model for a battery swapping station in Hong Kong

In this paper, a battery swapping station (BSS) model is proposed as an economic and convenient way to provide energy for the batteries of the electric vehicles (EVs). This method would overcome some drawbacks to the use of electric vehicles like long charging time and insufficient running distance. On the economic concern of a battery swapping station, the station would optimize the availability of the batteries in stock, and at the same time determine the best strategy for recharging the batteries on hand. By optimizing the charging method of the batteries, an optimization model of BSS with the maximum number of batteries in stock has been developed for the bus terminal at the Hong Kong International Airport. The secondary objective would be to minimize a cost on the batteries due to the use of different charging schemes. The genetic algorithm (GA) has been used to implement the optimization model, and simulation results are shown.

An intelligent battery information management system to support information sharing and vehicle routing planning for battery distribution in Hong Kong

Recently, the deployment of electric vehicles (EV) has been recognised as a key element for establishing sustainable transport systems within countries. Research studies have been conducted to tackle challenges regarding the commercial introduction of EV. However, the current plug-in EV infrastructure poses operational limitations, causing users refusing to change from fossil-fuelled vehicles to EV. To eliminate the limitations of long charging time and huge infrastructure costs of the current EV models, an intelligent battery information management system (IBIS) is designed for supporting battery switching logistics operations. The system shows the potential value of the battery switching management and the minimisation of the supply chain cost. A case study in launching the proposed model in Hong Kong is conducted. The result provides a practical solution to balance the level of battery charging efficiency in the battery management hub, service rate of battery switching stations, and the inventory cost of holding batteries.

Assess the effects of different operations policies on warehousing reliability

In this study, an assessment model for analysing the reliability of a warehouse system, focusing on resource capability, under given combinations of storage, routing, batching and zoning process policies, is proposed. A reliability assessment model is developed in order to evaluate the effect of different combinations of operations policies on warehouse reliability. To better reflect the real order pick up operations at the warehouse, a simulation model based on the operation scenarios of a Hong Kong logistics service company is developed. The simulation results are used for supporting the evaluation of warehouse reliability through using the proposed reliability assessment model. The most important finding indicates that order batching together with zoning policies yields greater warehouse reliability particularly when the daily order volume is large. In addition, another important research finding shows that warehouse performance is not constant and can drop when certain combinations of operations policies are adopted. The degrading rate of the resource (forklift) when used according to a given combination of polices is also defined by the proposed assessment method. Hence, the result helps logistics service providers enhance resource durability through modifying operations policies and by implementing proper preventive maintenance policies within a dynamic operations environment.

An IoT-based cargo monitoring system for enhancing operational effectiveness under a cold chain environment:

Differing from managing a general supply chain, handling environmentally sensitive products (ESPs) requires the use of specific refrigeration systems to control the designated range of storage conditions, such as temperature, humidity, and lighting level in a cold chain environment. In general, third-party logistics (3PL) companies are authorized to handle ESPs, who therefore need to have a good cargo monitoring system in the cold chain environment, without which the functional quality is difficult to control and manage. This may result in product deterioration and even inventory obsolescence of the ESPs due to the lack of such systems, so there is a need to develop an effective cargo monitoring system to prevent such situations. This article proposes an Internet of Things-based cargo monitoring system (IoT-CMS) to monitor any environmental changes of ESPs in order to ensure their functional quality throughout the entire cold chain operational environment. Operational efficiency, maintenance strategy, environmental change, and electricity consumption are considered in real-life cold chain operations. Through applying (i) a wireless sensor network to collect real-time product information, together with (ii) fuzzy logic and case-based reasoning techniques to suggest appropriate storage conditions for various ESPs, effective storage guidance can be established. Through conducting the case study in a 3PL company in Hong Kong, the performance in customer satisfaction, obsolescence rate, and inventory visibility after adoption of IoT-CMS is evaluated. It is found that the functional quality of ESPs can be effectively assured, and the overall customer satisfaction is increased.

Design of a case-based multi-agent wave picking decision support system for handling e-commerce shipments

The emerging trend of e-commerce business poses serious challenges in the field of logistics. To handle e-commerce shipments, warehouses must be able to efficiently handle a large number of stock-keeping units (SKUs), pick and pack small volume orders, and deliver them on time in small parcel shipments to consumers. In this sense, traditional order fulfillment, which encompasses receiving, put-away, picking, and transport through the warehouse, might not be able to fully fulfill the requirements of e-commerce. Considering the fact that order picking in warehouses is one of the most costly activities amongst the logistics operating categories, there is a crucial need to adopt a wave picking strategy to handle e-commerce shipments, an order picking approach that groups the orders for picking at the same time to minimize repeated visits to nearby storage locations. To apply the wave picking strategy properly, decision support for establishing the timing of each wave and the quantity of items to be picked is essential. Therefore, in this paper, a case-based multi-agent wave picking decision support system is proposed to help decision-makers in generating wave picking sequences in order to handle e-commerce shipments, through the integration of case-based reasoning and multi-agent technique. After a pilot study of the proposed system in a third-party logistics service provider, the order-processing efficiency was greatly enhanced.