A Hyperheuristic Approach for Location-Routing Problem of Cold Chain Logistics considering Fuel Consumption

Abstract

In response to violent market competition and demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emission for better development. In this paper, a biobjective mathematical model is established for cold chain logistics network in consideration of economic, social, and environmental benefits; in other words, the total cost and distribution period of cold chain logistics are optimized, while the total cost consists of cargo damage cost, refrigeration cost of refrigeration equipment, transportation cost, fuel consumption cost, penalty cost of time window, and operation cost of distribution centres. One multiobjective hyperheuristic optimization framework is proposed to address this multiobjective problem. In the framework, four selection strategies and four acceptance criteria for solution set are proposed to improve the performance of the multiobjective hyperheuristic framework. As known from a comparative study, the proposed algorithm had better overall performance than NSGA-II. Furthermore, instances of cold chain logistics are modelled and solved, and the resulting Pareto solution set offers diverse options for a decision maker to select an appropriate cold chain logistics distribution network in the interest of the logistics company.

Pythagorean fuzzy combined compromise solution method integrating the cumulative prospect theory and combined weights for cold chain logistics distribution center selection

“…The evaluation values can be normalized based on compromise normalization equations: (11) The weighted sum measure (WSM) and weighted product measure (WPM) were used to get the weighted sum comparability sequences S i i m ( = 1, 2, …, ) according to the gray relational generation method and the power weight comparability sequences P i i m ( = 1, 2, …, ) 29 :…”

“…9,10 Scholars proposed different methods to solve this problem considering cost, time windows, goods quality, and customer service level constraints. For example, Wang et al 11 developed a model that improved the multiobjective heuristic framework; Rosa et al 12 proposed a stable method for capacity planning; Singh et al 13 introduced an approximation algorithm to solve the location-allocation problem in cold chain configuration. However, these methods only considered quantitative data but did not consider the influence of subjective and qualitative factors on the rationality of decision results.…”

Uma nova abordagem para o problema de roteamento de veículos green bi-objetivo: otimização na distribuição de jornais

“…(2) and ( 3) define the space of the decision variables in R n of the feasible region X and any point of x ∊ X as a feasible solution. In (4) there is the decision variable domain and, finally, in (5), the objective function domain (Wang et al, 2020).…”

Optimization Model of Carbon Footprint of Fresh Products in Cold Chain from the Energy Conservation and Emission Reduction Perspective

“…In order to solve the multiobjective problem, Wang et al established a double objective mathematical model of cold chain logistics network based on economic, social, and environmental benefits and proposed a multiobjective hyperheuristic optimization framework including four selection strategies and four acceptance criteria [10]. Beretta et al focused on the conflict between fresh food consumption and energy consumption.…”