The present work addresses the issue of Order Acceptance Scheduling (OAS) problem on a single machine, incorporating periodic energy constraints and time-of-use (TOU) electricity pricing. The objective is to maximize net revenue by strategically selecting and scheduling orders while ensuring compliance with energy constraints and TOU pricing structures. This optimization process involves making informed decisions about order acceptance based on profitability, processing time, and energy requirements, while considering the cost fluctuations imposed by TOU pricing. To effectively address this problem, a time-indexed formulation is proposed that provides a structured approach to optimizing scheduling decisions under these constraints. In order to further enhance the performance of the proposed MILP model, two families of valid constraints have been developed. These developments have led to significant improvements in computational efficiency and solution quality.

This paper introduces a novel pre-positioned inventory model aimed at mitigating supply chain disruptions by enhancing the resilience of supply networks characterized by multiple facilities subject to disruptions. Based on a Time-To-Recover model, we explore a single-tier supply chain framework, incorporating real-world disruption scenarios to assess the efficacy of pre-positioned inventories in disruption mitigation. A two-stage stochastic programming approach is used to formulate the problem, incorporating a special case scenario that allows for the development of a closed-form equation. This enables a detailed analysis of the impact of pre-positioned inventory on supply chain resilience, examining various scenarios to ascertain the optimal inventory levels required to mitigate disruption risks effectively. Some numerical examples are presented to illustrate the practical application of the model, offering valuable insights into the strategic positioning of inventories and the implications for supply chain design.

The vehicle routing problem (VRP) is one of the most interesting NP-Hard problems due to the multitude of applications in the real world. This work tracks a VRP with zone-based prices inwhich each customer belongs to a particular zone, and the goal is to maximize the profit. The particularity of this VRP variant is that the provider needs to determine the prices for each zone and routes for all vehicles. However, depending on the selected zone prices, only a subset of customers will have to be visited. In this work, we propose a novel route generation scheme (RGS) that considers both decisions simultaneously. The RGS is guided by a priority function (PF), which determines the next customer to visit. Since designing efficient PFs manually is a difficult and time-consuming task, hyper-heuristic methods, specifically genetic programming (GP), have been used in this study to generate them automatically. Furthermore, to test the performance of the generated PFs, a genetic algorithm is also used to exploit the RGS to construct the solution. The experimental analysis shows that the evolved heuristics provide reasonable quality solutions quickly, in contrast with the current state-of-the-art. Furthermore, GP produces better results than GA for some problem instances.