arXiv subjects

Ek Peng Chew

Publications and source records attributed to Ek Peng Chew.

3 recordsLinked to original sources

arXiv2026

From Annual Throughput to Vessel Schedules: A Stochastic Generator for Transshipment Hub Simulation

Simulating container transshipment hubs requires vessel arrivals reflecting cyclical liner schedules and origin-destination (OD) cargo pairing. Existing models relying on Poisson arrivals and aggregate transshipment volumes severely distort waiting-time and yard-occupancy predictions. We propose a three-phase schedule generator, calibrated entirely from public port statistics, eliminating the need for proprietary data. The framework introduces a two-level Gamma mathematical model that balances structured weekly services with operational perturbations. For cargo routing, we develop GreedyDwellFit (GDF), a fast allocation heuristic to pair transshipment batches to specific connecting services using a gravity model. Validated against Busan and Singapore mega-hubs, the model reproduces throughput and call frequencies with under 1\% error. Our results show that replacing Poisson models with this Gamma-GDF framework eliminates significant distortions in terminal performance projections, offering a robust, generalisable foundation for port simulation.

stat.AP↗

arXiv2025

A Budget-Adaptive Allocation Rule for Optimal Computing Budget Allocation

Simulation-based ranking and selection (R&S) is a popular technique for optimizing discrete-event systems (DESs). It evaluates the mean performance of system designs by simulation outputs and aims to identify the best system design from a set of alternatives by intelligently allocating a limited simulation budget. In R&S, the optimal computing budget allocation (OCBA) is an efficient budget allocation rule that asymptotically maximizes the probability of correct selection (PCS). In this paper, we first show the asymptotic OCBA rule can be recovered by considering a large-scale problem with a specific large budget. Considering a sufficiently large budget can greatly simplify computations, but it also causes the asymptotic OCBA rule ignoring the impact of budget. To address this, we then derive a budget-adaptive rule under the setting where budget is not large enough to simplify computations. The proposed budget-adaptive rule determines the ratio of total budget allocated to designs based on the budget size, and its budget-adaptive property highlights the significant impact of budget on allocation strategy. Based on the proposed budget-adaptive rule, two heuristic algorithms are developed. In the numerical experiments, the superior efficiency of our proposed allocation rule is shown.

math.OC↗

arXiv2021

Profit-Maximizing Parcel Locker Location Problem under Threshold Luce Model

The growth of e-commerce has created increasing complexity in logistics services. To remain competitive, logistics and e-commerce companies are exploring new modes as supplements to traditional home delivery, one of which is the self-service parcel locker. This paper studies a parcel locker location problem where a company plans to introduce the locker service by locating locker facilities to attract customers. The objective is to maximize the profit, accounting for the revenue and the cost of facilities. To estimate the revenue, we use the threshold Luce model (TLM) to predict customers' likelihood of using the locker service. We then propose a combinatorial optimization model and develop exact solution methodologies that are practically implementable according to our extensive computational experiments. In effect, our modeling framework generalizes the traditional facility location problems based on the binomial logit model (BNL) and the multinomial logit model (MNL), both of which impose strong and strict assumptions on the customer's choice sets. That is, they assume that the choice sets will either contain only one facility or all facilities. In our numerical experiment, we demonstrate that using the BNL and the MNL in the locker location problem could lead to, respectively, pessimistic and optimistic revenue estimation. Consequently, the suggested location decisions will be either conservative or aggressive. Our proposed model, by contrast, can effectively relax these assumptions. Our results also reveal that the aggressive decision due to the use of the MNL will incur an unnecessarily high facility cost that cannot be compensated by the additional revenue, leading to profit loss that can be significant in various scenarios. Finally, we conduct sensitivity analysis on the input parameters and draw additional implications.

math.OC↗