A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Abstract

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms.

Modeling the gameplay actions of elite volleyball players and teams based on statistical match reports

“…Endriani et al [44] proposes a model based on Umbrella Learning, while Leng et al [45], Li et al [46], and Wang et al [47] use random matrix models for analyzing learning and gameplay actions. Salimifard et al [48], in turn, is developing an algorithm for scheduling in the volleyball premier league. All these works demonstrate innovative and diverse approaches to modeling in contemporary volleyball.…”

“…Meanwhile, a series of works [45,46,47] leverage random matrix models to gauge the efficacy of collegiate volleyball training and offer a quantitative portrayal of offensive player maneuvers. Rounding off the discussion, a study [48] unveils an algorithm tailored for green planning in the context of the volleyball premier league. Collectively, these studies spotlight the diverse and avant-garde methodologies in volleyball modeling today.…”

Dynamic Allocation of Manufacturing Resources in IoT Job Shop Considering Machine State Transfer and Carbon Emission

“…Zhang et al [18] and Li et al [19] studied the resource allocation of the hybrid flow shop and distributed displacement flow shop with the target completion time and energy consumption, respectively; Rifai et al [20] also considered the cost target to study the resource allocation of distributed reentrant permutation job shop; Feng et al [21] studied the resource allocation of flexible job shop with quality as one of the goals; Nouiri et al [22] and Sun et al [23] proposed a rescheduling method (GRM) that considered energy saving; Wang et al [24] established a two-stage optimization model for flexible job shop scheduling evaluating energy and operational dynamic characteristics; Liu et al [25] combined the green scheduling optimization of flexible job shop with crane transportation, paying attention to the workshop's total energy consumption and expanding the research scope of the energy consumption. In terms of solving the resource allocation problem, Salimifard et al [26] proposed a dual-objective integer linear programming model with task segmentation and resource constraints to study the resource allocation problem in the injection moulding process. Wu et al [27] applied the improved NSGA-II algorithm to solve the problem of job shop resource allocation, aiming at energy consumption.…”

Scheduling Large-Size Identical Parallel Machines with Single Server Using a Novel Heuristic-Guided Genetic Algorithm (DAS/GA) Approach

“…In modern manufacturing, the Parallel Machine Scheduling (PMS) problem amounts to scheduling several jobs using various identical machines while fulfilling specific practical requirements, such as minimum total tardiness while executing the jobs [1][2][3][4][5]. Thus, the PMS problem can be formulated as an NP-hard optimization problem that requires sophisticated optimization techniques for scheduling the jobs using the available machines while satisfying some practical constraints [6][7][8][9][10].…”