This article describes the problem in which the edges of a network represent customers, and a quantity of material is delivered to them so that each one achieves a desired inventory level while finding the lowest-cost route of delivery. Routing and inventory decisions are made at the same time. An example of an application of this problem is dust suppression in open-pit mines. A fleet of trucks spray water along the roads of a mine. Humidity increases the effectiveness of dust-particle retention. Because the level of humidity decreases, replenishment is done periodically. Other examples of applications include dust suppression in forest roads and plants watering in street medians and sidewalks. We develop a mathematical model that combines two objectives: An inventory objective that minimizes the penalty for the lack of humidity and a routing objective that minimizes watering and traversing costs. Due to the complexity of the mathematical model, we developed an adaptive large neighborhood search algorithm that combines several destroy and repair operators dynamically.
The purpose of this paper is to study the periodic arc-routing problem when the arcs of a network behave as customers, and sufficient material is delivered so that each achieves its desired inventory level. Therefore, routing and inventory decisions are made simultaneously. Applications include dust suppression in open-pit mines or forest roads and plant watering along sidewalks or street medians. A truck periodically sprays water along the edges of a network. The humidity reaches a desired level and is then consumed over time until water is delivered again. The quantity of water delivered can be fixed or variable; we consider both scenarios and propose a mathematical model for each. Results are reported to validate the model. The contribution of this paper is the first mathematical model that combines inventory and routing decisions in the arc-routing domain.
Meta-analysis, a systematic statistical examination that combines the results of several independent studies, has the potential of obtaining problem- and implementation-independent knowledge and understanding of metaheuristic algorithms, but has not yet been applied in the domain of operations research. To illustrate the procedure, we carried out a meta-analysis of the adaptive layer in adaptive large neighborhood search (ALNS). Although ALNS has been widely used to solve a broad range of problems, it has not yet been established whether or not adaptiveness actually contributes to the performance of an ALNS algorithm. A total of 134 studies were identified through Google Scholar or personal e-mail correspondence with researchers in the domain, 63 of which fit a set of predefined eligibility criteria. The results for 25 different implementations of ALNS solving a variety of problems were collected and analyzed using a random effects model. This dataset contains a detailed comparison of ALNS with the non-adaptive variant per study and per instance, together with the meta-analysis summary results. The data enable to replicate the analysis, to evaluate the algorithms using other metrics, to revisit the importance of ALNS adaptive layer if results from more studies become available, or to simply consult the ready-to-use formulas in the summary file to carry out a meta-analysis of any research question. The individual studies, the meta-analysis and its results are described and interpreted in detail in Renata Turkeš, Kenneth Sörensen, Lars Magnus Hvattum, Meta-analysis of Metaheuristics: Quantifying the Effect of Adaptiveness in Adaptive Large Neighborhood Search, in the European Journal of Operational Research.
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