Intelligent Optimization

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled. They contain reusable resource that can be recycled and used to generate desired economic benefits. Therefore, performing their efficient disassembly is highly important in green manufacturing and sustainable economic development. Their typical examples are electronic appliances and electromechanical/mechanical products. This paper presents a survey on the state of the art of disassembly sequence planning. It can help new researchers or decision makers to search for the right solution for optimal disassembly planning. It reviews the disassembly theory and methods that are applied for the processing, repair, and maintenance of obsolete/discarded products. This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling. This survey should stimulate readers to be engaged in the research, development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud (DGC) systems for low response time and high cost-effectiveness in recent years. Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption. Many factors in DGCs, e.g., prices of power grid, and the amount of green energy express strong spatial variations. The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations. This work adopts a G/G/1 queuing system to analyze the performance of servers in DGCs. Based on it, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm (SBA) to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs, and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications. Realistic data-based experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

This work proposes Adaptive General Variable Neighborhood Search metaheuristic algorithms for the efficient solution of Pollution Location Inventory Routing Problems (PLIRPs). A comparative computational study, between the proposed methods and their corresponding classic General Variable Neighborhood Search versions, illustrates the effectiveness of the intelligent mechanism used for automating the reordering of the local search operators in the improvement step of each optimization method. Results on 20 PLIRP benchmark instances show the efficiency of the proposed metaheuristics.

It is generally accepted that cooperation-based strategies in parallel metaheuristics exhibit better performances in contrast with non-cooperative approaches. In this paper, we study how the cooperation between processes affects the performance and solution quality of parallel algorithms. The purpose of this study is to provide researchers with a practical starting point for designing better cooperation strategies in parallel metaheuristics. To achieve that, we propose two parallel models based on the general variable neighborhood search (GVNS) to solve the capacitated vehicle routing problem (CVRP). Both models scan the search space by using multiple search processes in parallel. The first model lacks communication, while on the other hand, the second model follows a strategy based on information exchange. The received solutions are utilized to guide the search. We conduct an experimental study using well-known benchmark instances of the CVRP, in which the usefulness of communication throughout the search process is assessed. The findings confirm that careful design of the cooperation strategy in parallel metaheuristics can yield better results.