Guided Local Search

Alsheddy, Abdullah; Voudouris, Christos; Tsang, Edward; Alhindi, Ahmad

doi:10.1007/978-3-319-07124-4_2

Cited by 6 publications

(5 citation statements)

References 58 publications

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“…The specific metaheuristic we use in Feasibility Jump is known as guided local search (see [3] for a detailed survey). Guided local search works by modifying the objective function of the local search whenever the search is stuck in a local minimum.…”

Section: How To Guide the Searchmentioning

confidence: 99%

Feasibility Jump: an LP-free Lagrangian MIP heuristic

Luteberget

Sartor

2023

Math. Prog. Comp.

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We present Feasibility Jump (FJ), a primal heuristic for mixed-integer linear programs (MIP) using stochastic guided local search over a Lagrangian relaxation. The method is incomplete: it does not necessarily produce solutions to all feasible problems, the solutions it produces are not in general optimal, and it cannot detect infeasibility. It does, however, very quickly produce feasible solutions to many hard MIP problem instances. Starting from any variable assignment, Feasibility Jump repeatedly selects a variable and sets its value to minimize a weighted sum of constraint violations. These weights (which correspond to the Lagrangian multipliers) are adjusted for constraints that remain violated in local minima. Contrary to many other primal heuristics, Feasibility Jump does not require a solution of the continuous relaxation, which can be time-consuming for some problems. We compare FJ against FICO Xpress Solver 8.14 and we show that this heuristic is effective on a range of problems from the MIPLIB 2017 benchmark set, significantly improving the average time to find a first feasible solution. We also show that providing these quick solutions to Xpress produces a modest reduction in the average time to optimality in the same benchmark set. Our entry based on FJ to the MIP 2022 Computational Competition (which challenged participants to write LP-free MIP heuristics) won 1st place. Moreover, an implementation of Feasibility Jump now runs by default on FICO Xpress Solver 9.0, where similar results to the ones presented here could be observed.

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Section: How To Guide the Searchmentioning

confidence: 99%

Feasibility Jump: an LP-free Lagrangian MIP heuristic

Luteberget

Sartor

2023

Math. Prog. Comp.

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“…The second benchmark is from [125] and consists of 9 different scenarios: three different numbers of aisles (5,15,60), three different numbers of cross-aisles (3,6,11) and three different numbers of products in the order (15,60,240). We solve these instances on the multi-block warehouse.…”

Section: Multi-block: Comparison With the Instances Presented In [125]mentioning

confidence: 99%

“…Following the definitions from Alsheddy et al [5], let F be set of solution features by which the quality of a solution can be assessed. For each feature i ∈ F , let Ii be a binary variable indicating whether the feature is present in the current solution or not (also denoted as the indicator function).…”

Section: Step 2: Define the Augmented Objective Functionmentioning

confidence: 99%

“…We perform computational experiments by generating a set of random instances, based on the instance generation procedure described in Theys et al [159]. 1 The benchmark used in our study is the one from [125] and [159] consisting of 27 different scenarios: three different number of aisles (5,15,60), three different number of cross aisles (3,6,11) and three different number of products in the order (15,60,240). As previously mentioned, there are no papers in the literature considering the GTSP in multi parallel aisle warehouses and the order picking problem.…”

Section: The Instancesmentioning

confidence: 99%

“…In this section, we examine the performance of our exact and heuristic algorithms on 4 different cluster sizes (2,5,10,20) and solve our instances based on these cluster sizes. In tables 3.6-3.9, the numerical results for the cluster sizes 2,5,10 and 20 are reported respectively.…”

Section: Performance Analysis For Increasing Cluster Sizementioning

confidence: 99%

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Improving supply chain performance

Rajabighamchi

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I am very thankful for my Iranian colleagues, Ashkan and Arman. Spending time together with our Iranian colleagues in the Netherlands has been a source of comfort and camaraderie, especially as we navigate the challenges of being away from our homeland. The shared language and culture create a unique bond that goes beyond professional collaboration. Whether it's sharing stories over a cup of Persian tea, celebrating cultural festivities, or simply supporting each other during moments of homesickness, the sense of community we've cultivated is invaluable. In these shared moments, we find solace and connection, making our workplace feel like a home away from home. Contents

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Multi-agent Approach to the DVRP with GLS Improvement Procedure

Barbucha

2020

Intelligent Decision Technologies

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Guided Local Search

Cited by 6 publications

References 58 publications

Feasibility Jump: an LP-free Lagrangian MIP heuristic

Feasibility Jump: an LP-free Lagrangian MIP heuristic

Improving supply chain performance

Multi-agent Approach to the DVRP with GLS Improvement Procedure

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