With growing emphasis on sustainable manufacturing, we address the energy-aware Simple Assembly Line Balancing Problem with Power Peak Minimization (SALB3PM). We propose a metaheuristic to solve the SALB3PM. Our metaheuristic combines (1) a multi-start framework with three neighborhood operators (insertion, swap, delay incrementing) and (2) a dynamic penalty mechanism for handling cycle-time infeasibility. We designed and compared four algorithm variants to evaluate the impact of components. We conducted computational experiments on benchmark instances. The results show that our best variant achieves an average optimality gap of 2.38% when considering known optima, and optimal solutions were achieved in all runs for over 50% of instances. The approach contributes to sustainable manufacturing through energy-efficient production line design.

In this work, we focus on a non-preemptive unrelated parallel machines scheduling problem with a common server and job-sequence dependent setup times. This problem arises when planning the production of some mechanical parts of automobile, hydraulic and electrical sectors. It's well known to be NP-Hard. We first propose new mixed integer linear programming formulations for the problem. We then compare them with the only state-of-the-art formulation for the same problem. Based on these results, we devise a Branch-and-Cut algorithm along with computational results are presented to evaluate the performance of our approach. Moreover, we provide a warm starting algorithm for the problem, and further show its influence on boosting the Branch-and-Cut algorithm.

A recent evolution in urban logistics involves the usage of drones. In this article, we address a heuristic solution of the parallel drone scheduling traveling salesman problem, recently introduced by Murray and Chu. In this problem, deliveries are split between a vehicle and drones. The vehicle performs a classical delivery tour, while the drones are constrained to perform back and forth trips. The objective is to minimize completion time. We propose an iterative two‐step heuristic, composed of: a coding step that transforms a solution into a customer sequence, and a decoding step that decomposes the customer sequence into a tour for the vehicle and trips for the drones. Decoding is expressed as a bicriteria shortest path problem and is carried out by dynamic programming. Experiments conducted on benchmark instances confirm the efficiency of the approach and give some insights on this drone delivery system.

This paper addresses the job and device scheduling problems in flexible manufacturing systems (FMS) using an automated guided vehicle system (AGV) by simultaneously dealing with material processing and transportation functions. The problem is solved using a two stage iterative approach which includes optimization and computer simulation. An iterative procedure is developed. At the first stage, a meta-heuristic determines the AGV schedule, i.e., the order in which the job transfers by AGVs are made. At the second stage, a discrete event simulation model is used to evaluate the makespan depending on the AGVs schedule. This evaluation is used by the meta-heuristic to improve the initial AGV schedule. Finally, the iterative procedure determines jobs and AGV schedules which minimize the makespan (the schedule length). The investigated meta-heuristics are based on the iterated local search method and simulated annealing. An efficient neighboring system used inside meta-heuristic schemes is proposed. Our approach is numerically tested under different experimental conditions.