In this paper, a robust stochastic optimisation model for regret minimising is proposed for supply chain networks design. The model emphasises the ambiguity lying in both the risk preference and the probability distribution. A duality theory for the model is derived and the random utility functions are identified as the Lagrange multipliers. In addition, a tractable relaxation based on reformulation-linearisation technique is presented for the computational aspects of the model. The numerical experiments show that our regret minimising model can make a well balance between the conservativeness of a pure robust optimisation model and the optimism of risk-neutrality. We also present a case study to demonstrate the applicability of the proposed model.

Proactive ambulance management is constructive to improve the response efficiency for emergency medical service (EMS) systems under uncertainty. In this paper, we present a dynamic optimisation model concerning the ambulance dispatching and relocation. We develop a flexible operation policy driven by the interval rolling to match vehicles with calls in batch. We formulate the problem in Markov Decision Process and incorporate $M/G/c$ queues to minimise the average response and delay time. Considering the curse-of-dimensionality, we provide a simulation-based empirical dynamic programming with the state aggregation and post-decision state to solve the model. To further accelerate the computational efficiency, a greedy heuristic method is introduced to improve the quality of sampling operations. Then, a risk-averse model is developed based on the stochastic dominance strategy to improve operational reliability. We develop an equivalent linear programming to evaluate concave dominating functions. We test the performance by a numerical case and extract managerial insights for practitioners. Our results show that the proposed flexible and risk-averse solution outperforms the classic model on reducing the delay under uncertain calls. And the improvement is more active during peak hours, when real-time needs exceed available ambulances.

Changing customer requirements always poses a great challenge to designers in complex product design. Due to the complicated coupling relations between components, it is difficult to improve the efficiency of product reconfiguration. Therefore, this paper proposes a joint optimization model of complex product variant design responding to customer requirement changes. Based on synthesizing the advantages and deficiencies of “Scaled-based” and “Module-based”, a UB-BU hybrid approach is developed to improve the search efficiency and reduce conflicts. Then, a joint complex product variant design bi-level optimization to maximize customer satisfaction and minimum cost is established. As a solution, the genetic algorithm embedded double iteration comparison rules (GA-DICR) is developed based on canonical genetic algorithm. Finally, the method and model are proved to be effective in the Clutch variant design. The results can provide way of allowing the decision-makers to respond rapidly to customer rudiments changes request.