An effective modified migrating birds optimization for hybrid flowshop scheduling problem with lot streaming

Abstract

In this paper, the problem of hybrid flowshop hybridizing with lot streaming (HLFS) with the objective of minimizing the total flow time is addressed. We propose a mathematical model and an effective modified migrating birds optimization (EMBO) to solve this problem within an acceptable computational time. A so-called shortest waiting time rule (SWT) is introduced to schedule the jobs concurrently arriving at stages more reasonably. A combined neighborhood search strategy is developed that unites two different neighborhood operators during evolution, not only taking full advantage of their specializations but also promoting their joint efforts. Two competitive mechanisms are respectively used to increase the probability of locating better solutions at the front of the flock and enhance the interaction between two lines. The scout phase on the basis of the Glover operator and a well-designed local search is applied to the individuals trapped into local optimums and helps the algorithm explore potential promising domains. The dynamic solution acceptance criteria is developed to strike a compromise between intensification and diversification mechanisms. The performance of our proposed algorithm is evaluated by comparisons with seven other efficient algorithms in the literature. And the extensive numerical illustrations demonstrate that the proposed algorithm performs much more effectively for the addressed problem.

Model and migrating birds optimization algorithm for two-sided assembly line worker assignment and balancing problem

“…MBO simulates birds' migration behavior of flying in V-shape and is based on local search (Duman et al 2012). MBO is selected to solve this problem due to better performance over other algorithms in solving problems of a similar type (Duman et al 2012;Gao and Pan 2016;Zhang et al 2017).…”

“…For solving complex problems in different applications with uncertainty and vagueness like the one considered here researchers have utilized fuzzy logic and metaheuristic approaches to solve them in an acceptable computation time and using these algorithms it helps in decision making which is very critical in such systems (Fahmi et al 2018a;Fahmi et al 2018c;Fahmi et al 2017b;Fahmi et al 2018e). MBO is a relatively new metaheuristic algorithm that has shown superior performance in solving similar types of optimization problems (Duman et al 2012;Gao and Pan 2016;Zhang et al 2017). The main properties of the MBO are a set of individuals searching in parallel for a good solution.…”

Mathematical models and migrating birds optimization for robotic U-shaped assembly line balancing problem

“…In addition, this research also improves and enhances the performance of MBO by employing several problem-specific improvements such as modified consecutive assignment procedure for robot selection, iterative mechanism for cycle time update, new population update mechanism and diversity controlling mechanism. MBO is selected due to its superiority over others in solving problems similar to the considered problems such as planning and scheduling area as reported in Duman et al (2012), Gao and Pan (2016) and Zhang et al (2017). A comprehensive comparative study demonstrates that these improvements enhance the performance of MBO to a large extent, and the proposed MBO outperforms eight other algorithms statistically.…”

Coordinating order acceptance and integrated lot streaming-batch delivery scheduling considering third party logistics

“…Bożek and Werner (2017) studied the flexible job shop LS problem to minimize the makespan. Zhang et al (2017) addressed the LS in a hybrid flow shop to minimize the flow time. Lalitha et al (2017) and Ming Cheng et al (2016) considered the LS in the same environment to minimize the makespan.…”

“…Table 1 summarizes the assumptions and features of some of recently closely related papers of OA&S and LS. Emami et al, 2016)     Lagrangian relaxation (Chaurasia & Singh, 2016)     GA (Ou & Zhong, 2016)     Heuristic (Nguyen, 2016)     GA (Lei & Guo, 2015)     PNS (C. Chen et al, 2014)     GA (Wang et al, 2013)      (Cesaret et al, 2012)     TS (Noroozi et al, 2017)      GA+PSO (Lalitha et al, 2017)    Heuristic (Zhang et al, 2017)   MBO (Han et al, 2016)   NSGA-II (Mukherjee et al, 2017)    Optimal properties (Nejati et al, 2016)   GA&SA (Ming Cheng et al, 2016)   Heuristic (Sang et al, 2015)   IWO As can be seen in the Table 1, the OA&S or LS studies only focused on determining schedules of the orders to minimize the production cost without taking account of the distribution costs and revenue of the orders. While to achieve business goals integrating the production and distribution scheduling is critical (Chen, 2010).…”