Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling

Abstract

Emergency material allocation and scheduling is a combination optimization problem, which is essentially a Non‐deterministic Polynomial (NP) problem. Aiming at the problems such as slow convergence, easy prematurely falling into local optimum, and parameter constraints to solve high‐dimensional and multi‐modal combination optimization problems, this article proposes an adaptive weighted dynamic differential evolution (AWDDE) algorithm. The algorithm uses a chaotic mapping strategy to initialize the population. By weighting the standard differential evolution (DE) mutation strategy, a new weighted mutation operator is proposed. The scaling factor and cross probability can be adaptively adjusted. A disturbance operator is introduced to randomly generate the perturbation mutation and to accelerate the premature individuals to jump out of the local optimum. The algorithm is applied to the problem of emergency material allocation and scheduling, and a two‐stage emergency material allocation and scheduling model is established. Compared with the standard DE algorithm and the chaos adaptive particle swarm algorithm, the results show that the AWDDE algorithm has the characteristics of stronger global optimization ability and faster convergence speed compared with other optimization algorithms, which provide assistance for smart cities research, including smart city services, applications, case studies, and policymaking considerations for emergency management.

Government regulation of emergency supplies under the epidemic crisis

“…Therefore, the change in market demand and cost are incorporated into our model to better reflect reality. In addition, there is much literature on other aspects of emergency supplies, such as site selection, logistics, distribution, and dispatch (Caunhye et al, 2015;Liu et al, 2013Liu et al, , 2020Luscombe & Kozan, 2016;Pacheco & Batta, 2016;Wang et al, 2020). These studies provide support and further enrich the direction of this article.…”

An Enhanced Binary Multiobjective Hybrid Filter-Wrapper Chimp Optimization Based Feature Selection Method for COVID-19 Patient Health Prediction

“…2) Including weighted components. In the revised approaches, weighted factors are introduced to the operators [10]. 3) Algorithms that can be combined with one another [11].…”

Fish Migration Optimization with Dynamic Grouping Strategy for Solving Job-Shop Scheduling Problem

“…The goal can be to maximize the resource utilization of the system, or minimize the maximum completion time, etc. As the basic model of scheduling problems, the job-shop scheduling problem (JSP) has a wide range of application backgrounds, such as transportation [1][2], manufacturing, network communication [3], medical and health care, wireless sensor networks, cloud computing [4] and so on. Therefore, the research on the JSP problem has important theoretical value and practical significance [5][6].…”

“…Many meta-heuristics have existed so far. The representative algorithms are: simulated annealing (SA) [13], genetic algorithm (GA) [14][15], differential evolution (DE) [2,16], tabu search (TS) [17][18], particle swarm optimization (PSO) [19][20][21], ant colony optimization (ACO) [22], artificial neural network (ANN) [23], grey wolf optimizer (GWO) [24], cuckoo search (CS) algorithm [25][26], phasmatodea population evolution (PPE) algorithm [27], sine cosine algorithm (SCA) [28], quasi-affine transformation evolution (QUATRE) [29], etc.…”