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The aim of this paper is to investigate the effect of information asymmetry on revenue sharing contracts and performance in a dual-channel supply chain. First, the authors model the optimum revenue sharing contract in a dual-channel supply chain under both the full information case and the asymmetric information case. Second, they contrast the optimal decisions of a dual-channel supply chain between the full information case and the asymmetric information case. Third, they explore the impact of asymmetric cost information on the performance of a dual-channel supply chain and investigate the information value.

The authors present two main issues associated with revenue sharing contracts to alleviate manufacturer–retailer conflicts in a dual-channel supply chain. In the first issue, a revenue sharing contract is designed in a dual-channel supply chain under asymmetric cost information conditions, based on the principal-agent model. In the second issue, an optimal revenue sharing contract under full information conditions, based on the Stackelberg game is discussed. They explore the impact of asymmetric cost information on the performance of a dual-channel supply chain and investigate the information value based on comparative static analysis.

First, the direct sale price is unchanged and independent of the retailer’s cost construct, but the wholesale price increases and the retail sale price does not decrease under asymmetric cost information. The information asymmetry leads to higher direct sale demand and lower retail sale demand. Second, information asymmetry is beneficial for the retailer, but imposes inefficiency on the manufacturer and the whole supply chain. Third, the performance of the dual-channel supply chain is improved if the retailer’s cost information is shared and the dual-channel supply chain reaches coordination. The retailer is willing to share its cost information if the lump sum side payment that the manufacturer offers can make up the retailer’s reduced profit due to sharing this information.

The authors proposed a contract menus design model in a dual-channel supply chain. They examine how information asymmetry affects optimal policies and performance. They compared the optimal policies under symmetric information and asymmetric information. Conditions under which the partners prefer sharing information are identified. They quantified the information value from the points of partners and the whole system.

The purpose of this paper is to investigate whether truthful information sharing can be achieved via informal cheap talk in a competitive setting, and how carbon emission constraint and information-sharing modes (no information sharing, partial information sharing and public information sharing) interact with each other under cap-and-trade regulation.

This paper establishes an emission-dependent supply chain consisting of a manufacturer, an incumbent retailer who has superior demand information and a new entrant retailer. The manufacturer abates carbon emissions under the pressures of government environmental regulation and consumers’ eco-friendly concern. The research formulates a multistage game to explore every party’s decision and the implications of information-sharing modes.

The results show that truthful information sharing can be achieved when the manufacturer decides both the wholesale price and carbon emission abatement. The results also show that the incumbent retailer’s information-sharing decision highly depends on the manufacturer’s capacity in abating carbon emissions and the demand uncertainty.

The research adds value to information management and sustainable production literature. This work emphasizes the interaction between the information flow and material flow. Not only it investigates the factors that affect information-sharing modes from a new point of view when considering carbon emission constraint, but also provides operational strategies for manufacturers to make more profit when facing asymmetric information and emission regulation.

The purpose of this paper is to develop a closed-loop supply chain (CLSC) network equilibrium model which consists of manufactures, retailers and consumer markets engaged in a Cournot pricing game with heterogeneous multi-product.

The authors model the optimal behavior of the various decision makers and CLSC network equilibrium, and derive the equilibrium conditions based on variational inequality approach. The authors present a new Newton method to solve the proposed model.

The authors find that the algorithm converges to the solution rapidly for most cases. Besides, the authors discuss the effect of some parameters on the equilibrium solution of the model, and give some insights for policy makers, such as improving the technology level of the manufacturer, reducing the cost of waste disposal and increase the minimum ration of used product to total quantity.

The authors derive the network equilibrium conditions by the variational inequality formulation in order to obtain the computation of the equilibrium flows and prices. The authors present a new Newton method to solve the proposed model. The authors discuss the effect of some parameters on the equilibrium solution of the model, and give some managerial insights

This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located.

The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms.

According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO.

In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered.

The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs.

This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.

In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new constraint method to improve the performance of the sampling-based planner.

In this work, a constraint method (TC method) based on the idea of cross-sampling is proposed. This method uses the tangent space in the workspace to approximate the constrained manifold pattern and projects the entire sampling process into the workspace for constraint correction. This method avoids the need for extensive computational work involving multiple iterations of the Jacobi inverse matrix in the configuration space and retains the sampling properties of the sampling-based algorithm.

Simulation results demonstrate that the performance of the planner when using the TC method under the end-effector constraint surpasses that of other methods. Physical experiments further confirm that the TC-Planner does not cause excessive constraint errors that might lead to task failure. Moreover, field tests conducted on robots underscore the effectiveness of the TC-Planner, and its excellent performance, thereby advancing the autonomy of robots in power-line connection tasks.

This paper proposes a new constraint method combined with the rapid-exploring random trees algorithm to generate collision-free trajectories that satisfy the constraints for a high-dimensional robotic system under end-effector constraints. In a series of simulation and experimental tests, the planner using the TC method under end-effector constraints efficiently performs. Tests on a power distribution live-line operation robot also show that the TC method can greatly aid the robot in completing operation tasks with end-effector constraints. This helps robots to perform tasks with complex end-effector constraints such as grinding and welding more efficiently and autonomously.

The purpose of this investigation is to examine how the adaption of digital supply chain management (DSCM) practices affects the efficiency of factories and sustainable production. The research consists of eight constructs which, respectively, inspired eight hypotheses.

The emergence of DSCM practices has significant importance for sustainable production and enhances overall firm performance.

The smart PLS-SEM approach allowed us to examine the data from 450 factories in Côte d'Ivoire. The results indicated that research hypotheses are highly significant and exhibit a strong correlation with DSCM for firm performance and competitiveness. The outcomes underscore the significance of DSCM strategies in achieving competitive advantage, enhancing firm performance and promoting sustainable production within the manufacturing sector.

This study is useful for policymakers, industrialists and the government of Côte d’Ivoire.

This paper intends to address the decision-making and coordination of green supply chain (GSC) considering risk-averse manufacturers under mixed carbon policy.

This paper focuses on a GSC consisting of a manufacturer and a retailer, in which the manufacturer is risk-averse (R-A). This paper employs Stackelberg game theory and mean variance analysis to assess the pricing decision-making process under various scenarios. Furthermore, cost-sharing contracts are introduced to coordinate the GSC.

The research results suggest that the green level of the product and the profit of the GSC under a centralized scenario are higher than those under a decentralized scenario, while the retail price is lower. Under the decentralized scenario, the green level of product, wholesale price and manufacturer’s profit in the R-A scenario are lower than the values in the risk-neutrality scenario, while retailer's profit is higher. In addition, when a cost-sharing contract is utilized for coordination in the GSC, it can lead to Pareto improvement, regardless of whether the manufacturer makes risk-neutrality or R-A decisions.

This research provides a deeper understanding of GSC decision-making and coordination strategy under mixed carbon policy with consideration of R-A from a theoretical perspective and provides decision support for enterprises to choose strategies in practice.