Sunkanghong Wang

🎓 Graduation moments

Wed, 13 May 2026 00:00:00 +0000

Graduation photos and a small note of gratitude for everyone who walked with me through these master's years.

☕ Launching my personal academic website

Sun, 10 May 2026 00:00:00 +0000

Started in a café during my graduation trip, shaped in small passes over weeks, and finally live—just the way I hoped it would be.

A goal-driven iterated local search approach based on the maximal-space for the circle bin-packing problem with rectangular items

Mon, 13 Oct 2025 00:00:00 +0000

Abstract

This study explores a specific variant of the classic two-dimensional bin-packing problem, known as the Circle Bin-Packing Problem with Rectangular Items (CBPP-RI). This problem involves the orthogonal packing of rectangular items into the fewest possible circular bins and has significant practical implications. We propose a novel and efficient Goal-Driven Iterated Local Search (GDILS) approach for solving CBPP-RI, which integrates a customized method that effectively addresses cold starts and prevents entrapment in local optima. To avoid unnecessary searches, we use lower bounds, which are improved by accounting for the inevitable waste produced by rectangular items at the edges of circular bins. To achieve good performance in single-bin packing, we propose a maximal-space-based heuristic, which introduces the widely used concept of maximal-space from other rectangle packing problems. The experimental results demonstrate that GDILS performs well and show that our method is not only applicable to CBPP-RI but also effective for other related packing problems. To establish a valid benchmark for future research, we also generate a new dataset for CBPP-RI and conduct extensive experiments.

An Efficient Algorithm for Exact SRAM Verification via Novel Pattern Matching Techniques

Tue, 26 Aug 2025 00:00:00 +0000

Abstract

As semiconductor manufacturing advances into ultra-scaled technology nodes, Static Random Access Memory (SRAM) verification faces critical challenges stemming from increasingly stringent geometries, heightened process variability, and the sheer density of contemporary designs. Existing methodologies, notably Design Rule Checking and conventional pattern matching, often fall short in capturing the intricate multilayer interactions and subtle geometric deviations characteristic of advanced SRAM layouts. These limitations can lead to missed layout anomalies and potential functional failures, resulting in diminished yield. This paper introduces a specialized patternmatching-based algorithm designed to address these challenges in SRAM verification. The algorithm integrates novel localization techniques, efficient spatial indexing, and an overlap detection procedure that together achieve exact verification while delivering substantial improvements in runtime performance. Experimental results confirm that this approach not only maintains 100% detection accuracy on complex SRAM benchmarks but also achieves speedups ranging from threefold to over twentyfold compared to state-of-the-art methods, and from twofold to several thousandfold on classic single-layer pattern matching datasets.

A combinatorial Benders decomposition approach for the constrained two-dimensional guillotine cutting problem with defects

Tue, 03 Jun 2025 00:00:00 +0000

Abstract

In this paper, we study the constrained two-dimensional guillotine cutting problem with defects, which involves cutting a set of items from a large sheet containing defects while satisfying guillotine constraints. The goal is to maximise the value of the cut items. We propose an exact combinatorial Benders decomposition approach to solve this problem, decomposing it into a master problem and a subproblem. The master problem involves deciding which items to cut and determining the y-coordinates of the cut items. The subproblem is a feasibility problem that identifies a two-dimensional packing, ensuring no overlap and satisfying the guillotine constraints. Valid inequalities and enhanced Benders cuts are applied to strengthen the linear programming relaxation of the master problem. Numerical results on benchmark instances show that our algorithm proves 5,384 optimal solutions out of 5,400 instances. We also compare it with state-of-the-art algorithms in the literature to demonstrate the good performance of the proposed method.

EATKG: An Open-Source Efficient Exact Algorithm for the Two-Dimensional Knapsack Problem with Guillotine Constraints

Tue, 03 Jun 2025 00:00:00 +0000

Abstract

This paper addresses the Two-Dimensional Knapsack Problem with Guillotine Constraints, which is a famous NP-hard problem and is commonly encountered in industries where rectangular raw materials are cut into smaller pieces using guillotine cuts. We propose an efficient exact algorithm (EATKG) to solve this problem, which incorporates advanced techniques and novel elements, including an adapted preprocessing procedure, two enhanced upper bounds, an improved bidirectional tree search approach, and an iterative combination enumeration process. These components effectively balance the computation of upper and lower bounds and handle the issue of memory overflow. We extensively evaluate EATKG on eight classic benchmark sets, comprising 1,277 instances. Our algorithm solves 87% of the instances with an average computing time of 7 seconds, and 93% with an average computing time of 49 seconds. Moreover, EATKG efficiently solves nearly all small- and medium-sized instances, providing better solutions for 46 instances and tighter upper bounds for 109 instances. These results demonstrate the superior performance of our algorithm compared to leading algorithms. To support future research, we have made the source code for the proposed algorithm, along with the corresponding instance data, aggregated results, and detailed solutions, publicly available. This will facilitate further investigations and comparisons of solution methods.

Arc-flow formulation and branch-and-price-and-cut algorithm for the bin-packing problem with fragile objects

Wed, 01 Jan 2025 00:00:00 +0000

Abstract

This study introduces an arc-flow formulation and the first branch-and-price-and-cut (BPC) algorithm designed to solve the bin-packing problem with fragile objects (BPPFO). This variant of the bin-packing problem originates in the field of telecommunications, particularly in the allocation of cellular calls to frequency channels. The arc-flow formulation is inspired by previous studies and modifies the graph construction method to accommodate fragility constraints. We proved the correctness of this formulation and demonstrated its superiority in instances with small maximum fragility through extensive experiments. The proposed BPC algorithm leverages advanced cutting and packing techniques and incorporates innovative elements such as problem reduction, additional cutting planes, and a label-setting-based exact pricing algorithm. The experimental results demonstrate that the proposed BPC algorithm is highly competitive with the state-of-the-art algorithm for solving the BPPFO and can successfully solve several previously unsolved instances.

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