A bi-level model and memetic algorithm for arc interdiction location-routing problem

Nadizadeh, Ali; Zadeh, Ali Sabzevari

doi:10.1007/s40314-021-01453-2

Cited by 9 publications

(4 citation statements)

References 79 publications

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“…MA expedites the process of finding the global optima and avoids premature convergence by balancing the exploration of the search space using GA while enabling the exploitation of the current neighbourhood using local search. MA has been successfully applied in solving various combinatorial problems such as scheduling [70], vehicle routing [47], assignment problems [46], supply chain network [69] and location routing [1,48]. MA has also been used in solving healthcare planning problems such as ambulance routing [28,75] and home healthcare planning [32].…”

Section: Solution Methodologymentioning

confidence: 99%

Ambulance location under temporal variation in demand using a mixed coded memetic algorithm

2022

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Emergency medical services (EMS) are among the most important services in any society due to their role in saving people's lives and reducing morbidities. The location of ambulance stations and the allocation of ambulances to the stations is an important planning problem for any EMS system to ensure adequate coverage while minimising the response time. This study considers a mixed-integer programming model that determines the ambulance locations by considering the time of day variations in demand. The presented model also considers heterogeneous performance measures based on survival function and coverage for different patient types with varying levels of urgency. A memetic algorithm based-approach that applies a mixed chromosome representation for solutions is proposed to solve the problem. Our computational results indicate that neglecting time-dependent variation of demand can underestimate the number of ambulances required by up to 15% during peak demand. We also demonstrate the effectiveness of the proposed solution approach in providing good quality solutions within a reasonable time.

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Section: Solution Methodologymentioning

confidence: 99%

Ambulance location under temporal variation in demand using a mixed coded memetic algorithm

2022

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“…The Stackelberg Game problem can be described by the bilevel programming model [33]; so this paper establishes a real-time charging price model based on the bilevel programming theory and solves the model through the multi-objective particle swarm algorithm [34]. The lower layer is the follower strategy space S2, the upper layer is the leader strategy space S1, and the optimal strategy space combination calculated in each iteration is the initial strategy set of the next iteration.…”

Section: Model Solving Processmentioning

confidence: 99%

Bounded rational real‐time charging pricing strategy under the traffic‐grid coupling network

Wang

Cai

et al. 2022

IET Electrical Syst in Trans

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This study proposes a bounded rational charging guidance strategy based on mental account theory, which guides users to charge in an orderly manner by formulating real‐time charging prices. Firstly, an orderly guidance framework for fast‐charging EVs under the traffic‐grid coupling network is constructed, and the influencing factors of various dimensions when users make charging decisions are analysed. Secondly, considering the bounded rational behaviour of users when making charging decisions, a multifactor bounded rational charging model for EV users based on mental account theory is proposed so as to obtain different charging costs for users when selecting charging stations. On this basis, a real‐time charging price strategy based on the Stackelberg game model is constructed, with the goal of maximising the economic benefits of charging station operators while reducing the charging cost of EV users as much as possible. Finally, the particle swarm optimisation algorithm is used to solve the game model so as to solve the real‐time charging price under various constraints. The simulation of an example verifies the rationality of the proposed real‐time charging price formulation method and the superiority of the bounded rational charging guidance strategy.

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“…Xu et al [42] proposed a bi-level model for a 72 h post-earthquake emergency logistics LRP under a random fuzzy environment. Nadizadeh et al [43] used a bi-level programming model to study the arc interdiction LRP and proposed an efficient memetic algorithm based on a dynamic local search. Xu et al [44] studied the multi-depot LRP considering a no-load backhaul in a collaborative logistics network based on a bi-level programming model.…”

Section: Application Of Bi-level Programming In the Lrpmentioning

confidence: 99%

“…The bi-level programming model formulated in this paper is very difficult to solve, which belongs to a NP-hard problem, and there is no accurate algorithm [42][43][44]. ACO is used to solve discrete and continuous optimization problems.…”

Section: Hybrid Algorithm Designmentioning

confidence: 99%

A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

2021

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To identify the impact of low-carbon policies on the location-routing problem (LRP) with cargo splitting (LRPCS), this paper first constructs the bi-level programming model of LRPCS. On this basis, the bi-level programming models of LRPCS under four low-carbon policies are constructed, respectively. The upper-level model takes the engineering construction department as the decision-maker to decide on the distribution center’s location. The lower-level model takes the logistics and distribution department as the decision-maker to make decisions on the vehicle distribution route’s scheme. Secondly, the hybrid algorithm of Ant Colony Optimization and Tabu Search (ACO-TS) is designed, and an example is introduced to verify the model’s and algorithm’s effectiveness. Finally, multiple sets of experiments are designed to explore the impact of various low-carbon policies on the decision-making of the LRPCS. The experimental results show that the influence of the carbon tax policy is the greatest, the carbon trading and carbon offset policy have a certain impact on the decision-making of the LRPCS, and the influence of the emission cap policy is the least. Based on this, we provide the relevant low-carbon policies advice and management implications.

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A bi-level model and memetic algorithm for arc interdiction location-routing problem

Cited by 9 publications

References 79 publications

Ambulance location under temporal variation in demand using a mixed coded memetic algorithm

Ambulance location under temporal variation in demand using a mixed coded memetic algorithm

Bounded rational real‐time charging pricing strategy under the traffic‐grid coupling network

A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

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