Kwei-Long Huang

Solving a multi-objective master planning problem with substitution and a recycling process for a capacitated multi-commodity supply chain network

This study focuses on solving the multi-objective master planning problem for supply chains by considering product structures with multiple final products using substitutions, common components, and recycled components. This study considers five objectives in the planning process: (1) minimizing the delay cost, (2) minimizing the substitution priority, (3) minimizing the recycling penalty, (4) minimizing the substitution cost, and (5) minimizing the cost of production, processing, inventory holding and transportation. This study proposes a heuristic algorithm, called the GA-based Master Planning Algorithm (GAMPA), to solve the supply-chain master planning problem efficiently and effectively. GAMPA first transforms the closed-loop supply chain into an open-loop supply chain that plans and searches the sub-networks for each final product. GAMPA then uses a genetic algorithm to sort and sequence the demands. GAMPA selects the chromosome that generates the best planning result according to the priority of the objectives. GAMPA plans each demand sequentially according to the selected chromosome and a randomly-selected production tree. GAMPA tries different production trees for each demand and selects the best planning result at the end. To show the effectiveness and efficiency of GAMPA, a prototype was constructed and tested using complexity analysis and computational analysis to demonstrate the power of GAMPA.

Dynamic pricing of limited inventories for multi-generation products

In this research, we consider a retailer selling products from two different generations, both with limited inventory over a predetermined selling horizon. Due to the spatial constraints or the popularity of a given product, the retailer may only display goods from one specific generation. If the transaction of the displayed item cannot be completed, the retailer may provide an alternative from another generation. We analyze two models – posted-pricing-first model and negotiation-first model. The former considers negotiation as being allowed on the price of the second product only and in the latter, only the price of the first product is negotiable. Our results show that the retailer can adopt both models effectively depending on the relative inventory levels of the products. In addition, the retailer is better off compared to the take-it-or-leave-it pricing when the inventory level of the negotiable product is high.

Optimizing rolling stock assignment and maintenance plan for passenger railway operations

Assignment between rolling stock and daily utilization schedule is optimized.Practical constraints in operations and multi-level maintenance are considered.A heuristic process is proposed to significantly improve solution efficiency.Our optimization process increases utilization of rolling stock by around 5%.The empirical study shows that monthly maintenance cost reduces by 5.41%. The efficient use of railway rolling stock is an important objective pursued in a railway agency or company because of intensive capital investment in rolling stock. Daily rolling stock assignment assigns appropriate equipment to cover a given set of utilization paths in the utilization schedules while considering practical requirements, such as maintenance, depot capacity, and circulation rules. Experienced railway practitioners can generally produce a feasible assignment plan; however, this manual process is time consuming, and an optimal solution is not guaranteed. This research develops an exact optimization model to improve the efficiency in rolling stock usage with consideration of all necessary regulations and practical constraints. Compared to the manual process, a hybrid heuristic process is also developed to improve solution quality and efficiency. Empirical results demonstrate that the heuristic process can successfully increase the efficiency of rolling stock use by about 5% and significantly reduce the solution time from 3h to 11.2 s. Using this decision support tool can help railways with similar characteristics to improve the efficiency in rolling stock usage and productivity in rolling stock assignment process.

Wholesale price rebate vs. capacity expansion: The optimal strategy for seasonal products in a supply chain

We consider a supply chain in which one manufacturer sells a seasonal product to the end market through a retailer. Faced with uncertain market demand and limited capacity, the manufacturer can maximize its profits by adopting one of two strategies, namely, wholesale price rebate or capacity expansion. In the former, the manufacturer provides the retailer with a discount for accepting early delivery in an earlier period. In the latter, the production capacity of the manufacturer in the second period can be raised so that production is delayed until in the period close to the selling season to avoid holding costs. Our research shows that the best strategy for the manufacturer is determined by three driving forces: the unit cost of holding inventory for the manufacturer, the unit cost of holding inventory for the retailer, and the unit cost of capacity expansion. When the single period capacity is low, adopting the capacity expansion strategy dominates as both parties can improve their profits compared to the wholesale price rebate strategy. When the single period capacity is high, on the other hand, the equilibrium outcome is the wholesale price rebate strategy.

Hybrid genetic algorithms for flowshop scheduling with synchronous material movement

This paper considers a flowshop scheduling problem with synchronous material movement in an automated machining center. This automated machining center consists of a loading/unloading (L/U) station, m processing machines, and a rotary table. The L/U station and the processing machines surround the rotary table which transports jobs between machines. The table rotates to simultaneously move jobs when all machines finish with their jobs, including the loading and unloading operations at the L/U station. The cycle time of each rotation is determined by the longest operation on machines. Finding an optimal sequence which minimizes its makespan in this type of machining centers has been shown to be strongly NP-hard. A genetic algorithm combined with a local search is proposed to solve the problem in a large scale. For a given sequence, the local search is applied to the cycle with the largest deviation of completion times. A numerical result shows that the proposed algorithm finds a better solution against other algorithms such as Tabu search, a particle swarm optimization algorithm, and genetic algorithms.

A heuristic input control method for a single-product, high-volume wafer fabrication process to minimize the number of photomask changes*

This study proposed an input control policy, called the (k, W) rule, for a single-product, high-volume wafer fabrication process based on the special characteristics of steppers in the photolithography area, implying that the production environment possesses reentrant production flows. To add to the complexity, the setup time of each operation is time consuming. The (k, W) rule adopts the concepts of workload regulation and batch-sizing policies to release k lots of wafers for a stepper when the workload of this stepper is lower than a predefined threshold, W, for reducing setup time and raising stepper utilization. A search algorithm is proposed to determine k and W, to minimize the weighted sum of wafer waiting time, stepper idle time, and setup time. To compare the performace of the (k, W) rule with other input control policies, a simulation model is built with data collected from a DRAM wafer fabrication process in Taiwan. As observed from the simulation results, the (k, W) rule decreases setup time and increases throughput without increasing cycle time.

A heuristic master planning algorithm for recycling supply chain management

This study focuses on solving a multi-objective master planning (MP) problem for a recycling supply chain, including collectors, disassemblers, shredders, reconditioners and garbage handlers. An MP problem for a recycling supply chain is solved to determine the optimal transporting and processing operations, while considering multiple product structures, multiple discrete planning periods, and multiple demands, stocking and garbage handling quantities. To solve the MP problem, we propose a multiple-goal mixed integer programming model with two objectives: minimize the total delay cost and minimize the sum of processing cost, transportation cost, holding cost, setup cost and garbage handling cost. To improve the effectiveness and efficiency of the solution process, we propose a heuristic algorithm, RPMPA, which consists of three phases: preliminary works, demand grouping and sorting algorithm, and the Recycling Process Path Selection Algorithm. We built a prototype based on RPMPA, and constructed a scenario analysis to show the effectiveness and efficiency of RPMPA.

A production base-stock policy for recycling supply chain management in the presence of uncertainty

We consider master planning for a recycling supply chain with uncertainty.The objective is to maximize the total profit of the entire recycling supply chain.Stocking and operational policies for each member in the supply chain are studied.The proposed heuristic can effectively and efficiently find near optimal policies. This study focuses on solving master planning problems for a recycling supply chain with uncertain supply and demand. A recycling supply chain network includes collectors, disassemblers, remanufacturers, and redistributors working from the collection of returned goods to the distribution of recovered products to the market. The objective of this study is to maximize the total profit of the entire recycling supply chain. Considering the stochastic property of the recycling supply chain, this study institutes stocking and processing policies for each member of the recycling supply chain to better respond to unknown future demand. We propose a heuristic algorithm called stochastic recycling process planning algorithm (SRPPA) to address master planning problems in the recycling supply chain and its supply and demand uncertainties. The main SRPPA process consists of three phases. In the leader determination phase, SRPPA identifies the most important node as the leader of the recycling supply chain. In the candidate policy set generation phase, SRPPA defines the search range for the inventory policy and forms the candidate policy sets based on the characteristics of the leader. In the best policy set selection phase, SRPPA constructs the simulation process for each inventory policy candidate to identify the best policy set. A scenario analysis is then presented to show the effectiveness and efficiency of SRPPA.

Optimal contract design for cloud computing service with resource service guarantee

The optimal contract design for cloud computing service with resource guarantee under the consideration of resource redundancy and network externality is studied in this research. A model in which a service provider determines joint pricing and resource allocation decisions is constructed by proposing two types of contracts with different service-level agreements (SLAs). The SLA of each contract describes the price and associated penalty if the provider cannot provide the resource requested by the customers. Optimal pricing and resource allocation decisions as well as the equilibrium contracts of the service provider are analyzed based on the dynamics of the model characteristics. We found that optimal contract design is sensitive to both service levels and customers’ beliefs of compensation ratio when the requested resource is unfulfilled. Furthermore, service providers should evaluate the trade-off between benefit of price discrimination and effect of network externality when determining the optimal contract design.

Optimized Train-Set Rostering Plan for Taiwan High-Speed Rail

Railway rolling stock is one of the most expensive assets of railway operators. Efficient utilization of rolling stock is one of the most important objectives pursued in practice. This research focuses on the improvement in rolling stock rostering efficiency for high-speed rail system. According to the circulation schedule and long-term maintenance plan, the rostering planner decides the assignment between rolling stock and duties subject to a set of practical constraints. Owing to its complexity, this task remains a manual process at the Taiwan High Speed Rail Corporation. Experienced railway practitioners can generally create a good and feasible plan, but they cannot guarantee optimality of the solution considering only short-term process. In this research, we developed an exact optimization model and a heuristic method to automate this process and improve the efficiency of rolling stock utilization. Results from the case studies demonstrate that the efficiency of the rolling stock usage can be increased by