Optimizing an integrated inventory-routing system for multi-item joint replenishment and coordinated outbound delivery using differential evolution algorithm

Qu, Hui; Ai, Xue-Yi; Wang, Lin

doi:10.1016/j.asoc.2019.105863

Cited by 23 publications

(7 citation statements)

References 34 publications

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“…This paper proposes a new algorithm for optimum load shedding using differential evolution(DE). DE has been found to offer a suitable solution method, due to its ability and effectiveness when solving joint replenishment and delivery (JRD) problems in the studies by [12,13], and as reported by [14]. This is further supported here since the parameters of this intelligent solution's algorithms play an important role in its performance.…”

Section: Introductionsupporting

confidence: 54%

Identification of Weak Buses for Optimal Load Shedding Using Differential Evolution

2022

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For the sustainability of power supply and operation systems, planners aim to deliver power at an optimum value to consumers, while maintaining stability in the system. The load-shedding approach has proven to be an effective means of achieving the desired stability. This paper presents a nodal analysis to establish critical bus identification in the power grid. A power simulation for load shedding was created using the power system analysis toolbox (PSAT) for identifying and isolating weak buses on the power system. A computational algorithm was developed using differential evolution (DE) for minimizing service interruptions and blackouts, and was tested against the conventional genetic algorithm (GA). The proposed algorithm was implemented on an IEEE 30-bus test system. The simulation results were analyzed before and after the application of DE. It was observed that after the application of DE, load shedding gives an efficient result of 10.6%, 8.7%, and 13.4% improvement at buses 26, 29, and 30, respectively, after being tested using a genetic algorithm (GA), with a result of 10.2%, 7.6% and 13.1% on the same respective buses. This work will further expand the reliability and availability of power systems toward a sustainable, steady power supply that is void of nodal or bus cutoffs.

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Section: Introductionsupporting

confidence: 54%

Identification of Weak Buses for Optimal Load Shedding Using Differential Evolution

2022

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“…In this test, there are two optimization variables with three optimization constraints, stress, deflection, and buckling. It is formulated as shown in (10). is example is reported in [44] as a highly constrained search space.…”

Section: Ree-bar Truss Design Problemmentioning

confidence: 99%

“…On the other hand, in the second group, among the population-based metaheuristics, evolutionary algorithms are metaheuristics inspired by the process of natural selection. Genetic Algorithms (GA) [8], Genetic Programming [9], Differential Evolution (DE) [10], and Evolution Strategy (ES) [11] are considered as state-ofthe-art population-based evolutionary algorithms. Moreover, Ant Colony Optimization (ACO) [12], Cuckoo Search Algorithm (CSA) [13], and Particle Swarm Algorithm (PSO) [14] are some representative population-based metaheuristics categorized as swarm based [15].…”

Section: Introductionmentioning

confidence: 99%

A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies

Peraza-Vázquez

Peña-Delgado²,

Echavarría-Castillo

et al. 2021

Mathematical Problems in Engineering

114

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A novel bio-inspired algorithm, namely, Dingo Optimization Algorithm (DOA), is proposed for solving optimization problems. The DOA mimics the social behavior of the Australian dingo dog. The algorithm is inspired by the hunting strategies of dingoes which are attacking by persecution, grouping tactics, and scavenging behavior. In order to increment the overall efficiency and performance of this method, three search strategies associated with four rules were formulated in the DOA. These strategies and rules provide a fine balance between intensification (exploitation) and diversification (exploration) over the search space. The proposed method is verified using several benchmark problems commonly used in the optimization field, classical design engineering problems, and optimal tuning of a Proportional-Integral-Derivative (PID) controller are also presented. Furthermore, the DOA’s performance is tested against five popular evolutionary algorithms. The results have shown that the DOA is highly competitive with other metaheuristics, beating them at the majority of the test functions.

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“…For production/replenishment and transportation planning, the determination of the procurement and delivery policy of the different products is a critical issue. As shown in Table 1, the CRDP is heavily studied because of the importance of delivery policy (Cui et al, 2020;Qu et al, 2020). Cha et al (2008) designed a hybrid genetic algorithm to solve a typical integrated replenishment and delivery problem with single-warehouse, m-supplier structure and static demand.…”

Section: Literature Review and Contributionsmentioning

confidence: 99%

Heuristic for the new coordinated dynamic demand lot-size and delivery planning problem

Wang

Peng

et al. 2020

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Purpose The purpose of this study is to propose a new coordinated dynamic demand lot-size and delivery planning problem (CDLSDP), in which the delivery policy is integrated into the coordinated dynamic demand lot-size problem (CDLSP). Design/methodology/approach As a non-deterministic polynomial complete (NP-complete) problem, this CDLSDP seems difficult to be solved by a polynomial-time method. To handle this problem effectively and efficiently, a four-phase heuristic that balances the setup and inventory costs in the coordinating and delivery stages is designed to find near-optimal solutions. Findings Numerous computational experiments show that the proposed four-phase heuristic is effective and efficient. For 1,800 experiments with different scales, and different joint setup costs, solutions by the proposed heuristic have an average gap no more than 1.34% from the optimal solution. Research limitations/implications To decrease total system cost, the CDLSDP optimizes the time-phased replenishment and delivery schedule, which includes joint setup cost, item setup, delivery and inventory cost, for each period. An effective and efficient four-phase heuristic is designed to solve the CDLSDP. Originality/value Compared with the traditional CDLSP, the delivery policy is considered by the new CDLSDP. Moreover, the proposed four-phase heuristic is a good candidate for solving the CDLSDP.

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Optimizing an integrated inventory-routing system for multi-item joint replenishment and coordinated outbound delivery using differential evolution algorithm

Cited by 23 publications

References 34 publications

Identification of Weak Buses for Optimal Load Shedding Using Differential Evolution

Identification of Weak Buses for Optimal Load Shedding Using Differential Evolution

A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies

Heuristic for the new coordinated dynamic demand lot-size and delivery planning problem

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