Interactive Optimization Approach Research Articles

Reverse logistics platform decisions integrating crowdsourced contracting: A three-level interactive optimization approach for product design considering remanufacturing

Currently most original equipment manufacturers are reluctant to remanufacture their end-of-life products due to lack of adequate infrastructure for remanufacturing profitable products. To solve the problem, we propose a three-level interactive decision-making framework for product design considering remanufacturing by establishing reverse logistics platform integrating crowdsourced contracting. The reverse logistics platform can incorporate a large population of manufacturer and consumer crowds in an extended manufacturing network. The crowdsourced manufacturing paradigm can maximize the utilization of external resources, thereby enhancing overall remanufacturing efficiency. We propose a three-level mixed 0–1 programming model based on above framework with a Stackelberg game to handle the complex decision-making process. A combination of analytical solutions and a nested genetic algorithm is developed to implement the model. A laptop case study is presented. To verify the effectiveness of our algorithm, we compare the results of the three algorithms in solving the model proposed in this paper. Furthermore, applying our proposed model to three additional cases of varying levels of complexity further validates the applicability of the model for our problem over traditional integrated optimization models and two-stage optimization models. Sensitivity analyses are conducted on three key parameters of the model, providing valuable insights for both industry and academia.

Green product family design with low-carbon postponement fulfilment: A bilevel interactive optimization approach

In the globalization process, postponement is still an effective strategy for enterprises to reduce supply chain risks and uncertainties. The enterprises must additionally consider low-carbon issues in product development process with the increasing environmental concerns. The inherent coupling of product design and postponement fulfillment that considers environmental concerns necessitates joint optimization of green product family (GPF) design and low-carbon postponement (LCP) fulfillment. However, existing literature tends to aggregate the postponement and environmental concerns into a single-level and all-in-one optimization problem based on the assumption that a (fixed) GPF architecture is pre-determined at the outset. Different from existing modes, this article proposes a bilevel interactive optimization (BIO) approach to reveal the hierarchical interactions and complex decisions involved in GPF design and LCP fulfillment. Based on bilevel programming theory, a non-linear BIO model consisting of an upper-level GPF design problem and two lower-level LCP fulfillment problems is established. A nested genetic algorithm is developed to solve the proposed BIO model. A case study of LCP manufacturing of a green electric vehicle product family is adopted to verify the feasibility and superiority of the BIO approach for the GPF design and the LCP fulfillment. Some important managerial insights are derived from the comparison experiments and the sensitivity analysis on the proposed demand postponement parameter.

Bilevel interactive optimisation for rebatching scheduling problem with selectivity banks in high variety flow line production

Mass customization enables the integration of traditional flow line production with product platforms to accommodate abundant product-process varieties. These platform-based flow lines explore common process routes while highlighting rebatching scheduling with selectivity banks (RBS) to handle large process varieties across production stages at minimum setup cost. Given the inherent coupling between decision making in job diverging and retrieval quality, an interactive optimization approach is necessary for the RBS problem. This study proposes a bilevel interactive optimization (BIO) model for RBS to accommodate high variety flow line production. The model addresses the conflicting goals of lane occupancy cost, process setup cost, and job divergence and retrieval efficiency. Regarding job divergence at the leader-level, a vehicle routeing problem with precedence constraints is formulated and solved by a constructed genetic algorithm (GA). Concerning job retrieval at the follower-level and the ongoing characteristic of selectivity banks, a dispatching problem with various batch size preference and dynamic time window is established and dealt with a restricted dynamic programming (RDP) algorithm after balancing search efficiency and accuracy. Thus, to solve the BIO, a hybrid GA-RDP is developed and implemented. A practical application to an automotive painting shop illustrates the operational benefits of the BIO model for the RBS problem.

An integrated AHP-DEA multi-objective optimization model for sustainable transportation in mining industry

Due to increased attention to coal mining, the industrial transport in most countries including India necessitated search for sustainable transportation leading to environmental protection, maximum speed of delivery, minimum cost of transportation, and enhanced traffic safety. In this paper, we formulate an integrated multi-objective optimization model for an extended capacitated sustainable transportation problem in a coal mining industry using the analytic hierarchy process (AHP) and data envelopment analysis (DEA) techniques. The AHP technique is used to estimate the weights of different types of vehicles available for transportation on the basis of all three parameters of sustainability, namely economic, environmental, and corporate social responsibility. The DEA technique is used for calculating efficiency scores of vehicles on various routes of the given transportation network using inputs and outputs considered critical in the industrial sector particularly the mining industry. Furthermore, we reduce dependency on carbon based fuels for transportation leading to reduction in greenhouse gas emissions. A fuzzy interactive optimization approach is presented to get preferred compromise transportation solutions including the optimal number of vehicles employed for sustainable transportation. A real-world case of a mining industry in India is discussed to demonstrate applicability of the proposed optimization model and solution method. Moreover, some comparisons are done with existing transportation models in order to present advantages of our approach.

Interactive multi-criteria planning for radiofrequency ablation

Image-guided radiofrequency ablation (RFA) is a broadly used minimally invasive method for the thermal destruction of focal liver malignancies using needle-shaped instruments. The established planning workflow is based on examination of 2D slices and manual definition of the access path. During that process, multiple criteria for all possible trajectories have to be taken into account. Hence, it demands considerable experience and constitutes a significant mental task. An access path determination method based on image processing and numerical optimization is proposed. Fast GPU-based simulation approximation is utilized to incorporate the heat distribution including realistic cooling effects from nearby blood vessels. A user interface for intuitive exploration of the optimization results is introduced. The proposed methods are integrated into a clinical software assistant. To evaluate the suitability of the interactive optimization approach for the identification of meaningful therapy strategies, a retrospective study has been carried out. The system is able to propose clinically relevant trajectories to the target by incorporating multiple criteria. A novel method for planning of image-guided radiofrequency ablation by means of interactive access path determination based on optimization is presented. A first retrospective study indicates that the method is suited to improve the classical planning of RFA.

An interactive optimization approach to a real-world oceanographic campaign planning problem

In this paper we deal with a real-world routing problem that consists of finding the best route for an oceanographic campaign. The planning task involves setting up the initial route and managing the route variations along the way. Bearing in mind the idea of building an experimental tool to support the decision making process of the expert when planning the route, two different approaches to solve the problem were developed. First of all, we assumed that the problem was well characterized by the initial description of the expert, using as fitness function the time employed in the route and applying classical local search optimization heuristics. The difficulty of a precise constraint definition and the infeasibility of mathematically describing all the evaluation criteria of the expert led us towards interactive optimization as a way to introduce the knowledge of the expert. According to the results, this last approach was able to provide satisfactory solutions.

Interactive optimization methodology for fleet scheduling

This article addresses the problem of scheduling the United States Navy's Atlantic Fleet to satisfy overseas strategic requirements. An integer programming formulation is developed but results in a model with prohibitive size. This fact and the qualitative nature of additional secondary objectives and constraints suggest an interactive optimization approach. A system that solves a natural relaxation of the integer program within an interactive environment is discussed.