
Weiwei ChenRutgers, The State University of New Jersey | Rutgers · Business School - Newark and New Brunswick
Weiwei Chen
Doctor of Philosophy
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59
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Publications (59)
The service sector has become increasingly important in today's economy. To meet the rising expectation of high‐quality services, efficiently allocating resources is vital for service systems to balance service qualities with costs. In particular, this paper focuses on a class of resource allocation problems where the service‐level objective and co...
In this paper, we study a capacitated production-distribution problem where facility location, production, and distribution decisions are tightly coupled and simultaneously considered in the optimal decision making. Such an integrated production-distribution problem is complicated, and the current commercial mixed-integer linear programming (MILP)...
In online-to-offline (O2O) on-demand services, customers place orders online and the O2O platform delivers products from stores to customers within a prescribed time. The platform usually hires crowd-sourced drivers as a cost-effective option owing to their flexibility. However, the delivery speed and delivery capacity of the crowd-sourced drivers...
Integrated supply chain perspective of energy-efficiency and energy-resilience is a new and unexplored research domain. This special issue welcomes modeling and empirical papers from operations management, industrial engineering and operations research communities.
This special issue is targeted towards, but not restricted to the 10 th IFAC Conference MIM 2022 that will take place on June 22-24, 2022 in Nantes (France). The MIM 2022 conference papers submitted to this special issue must make an additional contribution: they must cite the relevant conference paper and explicitly state what the additional contr...
This Special Issue is motivated by novel decision-making settings entailed in supply chain resilience in the wake of the COVID-19 pandemic and characterized by crisis-like environment, epistemic and deep uncertainty, and adaptability as a “new normal” instead of stability and long-term planning.
This Special Issue aims to collate the recent and original research dealing with designing and managing the reconfigurable supply chain networks in the context of digital technology and platforms, or epidemics/pandemics, or climate change, or their synergetic effects (e.g., digital platforms to manage supply chains under pandemic conditions). We st...
Electron beam inspection (EBI) with high resolution is a promising technique to improve the defect inspection on the surface of patterned wafer. However, high resolution usually means long inspection time, which results in the low throughput and limitation of EBI applied in practice. This study aims to optimize the inspection time of EBI by reducin...
The container relocation problem is one of important issues in seaport terminals which could bring a significant saving on the operating cost even with a slight improvement due to the huge number of containers processed across the world each year. Given a specific layout and container retrieval priorities, the container relocation problem aims to f...
O2O (Online to Offline) services enable customers to place orders online and receive products/services offline. In addition to traditional in-house drivers, the emergence of crowd-sourced drivers provides an opportunity to re-organise offline delivery services. In practice, three types of workforce, namely, in-house, full-time, and part-time crowd-...
In this paper, we study the multi-period newsvendor problem with quantity discounts and stationary demand, where order quantities need to be decided sequentially over a finite or infinite horizon without making statistical assumption on demands. The Weak Aggregating Algorithm (WAA), which is an online learning method of prediction with expert advic...
We study the problem of scheduling in manufacturing environments which are dynamically configurable for supporting highly flexible individual operation compositions of the jobs. We show that such production environments yield the simultaneous process design and operation sequencing with dynamically changing hybrid structural-logical constraints. We...
In this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection (R&S) problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection...
O2O (Online to Offline) service enables customers to place orders online and receive products/services offline. In addition to traditional in-house drivers, the emergence of crowd-sourced drivers provides an opportunity to re-organize the offline delivery. In practice, three types of workforce, namely, in-house drivers, full-time and part-time crow...
This paper explores the relationships that exist between business models and ecosystems evolving in the 3D printing industry using qualitative evidence drawn from three countries: China, UK and USA. In particular, this research proposes that it is important to understand the process of business model dynamics and formulation and how the associated...
In this paper, we study the problem of coordinating supplier selection and project scheduling, motivated by a real-life operational challenge encountered in the construction industry. In particular, we consider a project network consisting of multiple concurrent projects, with the objective of minimising the total tardiness of all projects. These p...
After a disaster happens, emergency response operations are critical to save humans’ lives and properties. The limited resources and time requirements call for coordinated supply chain operations. This paper studies supply chain operations for rescue kits in disaster reliefs, motivated by a real-world application. The objective is to minimise the t...
This paper models a cash conversion system in a subsidiary of a parent company where there is an active internal capital market, but otherwise the subsidiary has no access to additional external funds. The cash conversion system consists of a treasury, a single-product make-to-stock inventory, and a receivables pool. It implements a perpetual flow...
In this paper, we propose a heuristic for solving finite-horizon Markov decision processes. The heuristic uses the nested partitions (NP) framework to guide an iterative search for the optimal policy. NP focuses the search on certain promising subregions, flexibly determined by sampling weights for each action branch. Within each subregion, an effe...
In this paper, we consider the ranking and selection (R&S) problem with input uncertainty. It seeks to maximize the probability of correct selection (PCS) for the best design under a fixed simulation budget, where each design is measured by their worst-case performance. To simplify the complexity of PCS, we develop an approximated probability measu...
Electric utilities have historically treated power demand as an uncontrollable input, requiring generation and transmission resources to maintain the supply-demand balance. In recent years, demand response (DR) has emerged as a means to manage customer loads to balance the grid. This paper presents analytic solutions to enable utilities to optimize...
In this paper, we present a new budget allocation framework for the problem of selecting the best simulated design from a finite set of alternatives. The new framework is developed on the basis of general underlying distributions and a finite simulation budget. It adopts the expected opportunity cost (EOC) quality measure, which, compared to the tr...
Temporal shaping of time series is the activity of deriving a time series model with a prescribed marginal distribution and some sample path characteristics. Starting with an empirical sample path, one often computes from it an empirical histogram (a step-function density) and empirical autocorrelation function. The corresponding cumulative distrib...
Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities. A key to success for a bike sharing systems is the effectiveness of rebalancing operations, that is, the efforts of restoring the number of bikes in each station to its target value by routing vehicles through pick-up...
Over the past 30 years managers have increasingly focused on improving inventory management both within their own firms and across the supply chain. To this end, inventory turns metrics have been adopted as a popular tool for measuring flow velocity through the inventory and the efficiency of inventory-related asset utilization. There are multiple...
In this paper, the problem of selecting an optimal subset from a finite set of simulated designs is considered. Given the total simulation budget constraint, the selection problem aims to maximize the probability of correct selection (PCS) of the top m designs. To simplify the complexity of PCS, an approximated probability measure is developed and...
A lot of problems in control engineering aim at solving discrete-event systems in presence of performance measure constraints. In many cases, these problems are most suitable to be modeled as constrained simulation optimization, and a key question for solving these problems is to efficiently and accurately select all the feasible designs from a fin...
Feasibility determination has emerged as a widely applied problem in simulation optimization. It seeks to provide all the feasible designs from a finite set of design alternatives based on which the final decision can be chosen by the decision maker. In this paper, we consider the feasibility determination problem in presence of multiple performanc...
We consider the global optimization problem over finite solution space with deterministic objective function and stochastic constraints, where noise-corrupted observations of the constraint measures are evaluated via simulation. This problem is challenging in that the solution space often lacks rich structure that can be utilized in identifying the...
A lot of problems in automatic control aim at seeking top designs for discrete-event systems. In many cases, these problems are most suitable to be modeled as simulation optimization problems, and a key question for solving these problems is how to efficiently and accurately select the top designs given a limited simulation budget. This paper consi...
Demand response (DR) programs provide incentives for load reductions during event periods. Measuring these reductions requires estimating load if there was no event, known as baselines. Differences in metered load below the baseline are assumed to be load reductions. Baselines are typically calculated from historical data, sometimes with adjustment...
Systems and methods for performing data anomaly detection and/or removal are usable to accurately assess baseline power consumption. According to one embodiment of the invention, a system can be provided. The system can be operable to receive energy consumption data of a location; select, based at least in part on a collection period of the energy...
Partition-based random search (PRS) provides a class of effective algorithms for global optimization. In each iteration of a PRS algorithm, the solution space is partitioned into subsets which are randomly sampled and evaluated. One subset is then determined to be the promising subset for further partitioning. In this paper, we propose the problem...
Methods and systems suitable for negotiating air traffic trajectory modification requests received from multiple aircraft that each has trajectory parameters. The methods include transmitting from at least a first aircraft a first trajectory modification request to alter the altitude, speed and/or lateral route thereof. A first conflict assessment...
Electric utilities have been investigating methods to reduce peak power demand. Demand response (DR) is one such method which intends to reduce peak electricity demand. DR programs typically have limits on the number and timing of events that may be triggered for a selected group of customers. This paper presents a methodology for optimizing the sc...
Nested Partitions (NP) is a partition and sampling based framework for solving large-scale optimization problems. It has been successfully applied to solve industrial problems, such as product design, supply chain, logistics and healthcare. This chapter first reviews the research background and its connection with ordinal optimization. Then the gen...
The Local Pickup and Delivery Problem (LPDP) has drawn much attention, and optimization models and algorithms have been developed to address this problem. However, for real world applications, the large-scale and dynamic nature of the problem causes difficulties in getting good solutions within acceptable time through standard optimization approach...
A dynamic programming formulation was proposed in the paper entitled ‘A new dynamic programming formulation of (n × m) flowshop sequencing problems with due dates’ [Sonmez, A.I. and Baykasoglu, A., 1998, International Journal of Production Research, 36 (8), 2269–2283] to deal with a flow shop problem considering sequence-dependent setup times to mi...
The local pickup and delivery problem (LPDP) is an essential operational problem in intermodal industry. While the problem with deterministic settings is already difficult to solve, in reality, there exist a set of loads, called uncertain loads, which are unknown at the beginning of the day. But customers may call in during the day to materialize t...
Demand Response (DR) programs are designed to reduce energy consumption for relatively short time periods (e.g., a few hours per event). It has been widely recognized that DR can help to meet both reliability and market needs. In order for DR programs to achieve their full benefits, however, it is critical for utilities to accurately predict the re...
Metaheuristics are an important branch of optimiza- tion algorithms that attract lots of research and application ef- forts. In this paper, the research of predicting solution value for Nested Partitions (NP) is proposed, which is a newly developed metaheuristic algorithm for solving large-scale optimization prob- lems. The lower bound embedded pre...
A counterexample is given to illustrate that a key model transformation in the paper entitled "Deriving decision rules to locate export containers in container yards" [Kim, K.H., Park, Y.M., Ryu, K.-R., 2000. Deriving decision rules to locate export containers in container yards. European Journal of Operational Research 124 (1), 89-101] is not corr...
The nested partitions (NP) method has been proven to be a useful framework for effectively solving large-scale discrete optimization
problems. In this chapter, we provide a brief review of the NP method and its applications. We then present a hybrid algorithm
that integrates mathematical programming with the NP framework. The efficiency of the hybr...
The examination timetabling problem is a typical combinatorial optimization problem. In literature, heuristic methods are usually chosen to solve these problems, and mathematical programming approaches have not been well developed. This paper raises a variant of exam timetabling, with multiple exam paper versions, which comes from real-world applic...
The Local Pickup and Delivery Problem (LPDP) has drawn much attention during recent years. In the literature, optimization models and algorithms have been developed to address this problem. However, for some real world applications, the large-scale and dynamic nature of the problem causes some difficulties in getting good solutions within an accept...