Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm

Abstract

In intelligent manufacturing, it is important to schedule orders from customers efficiently. Make-to-order companies may have to reject or postpone orders when the production capacity does not meet the demand. Many such real-world scheduling problems are characterised by processing times being dependent on the start time (time dependency) or on the preceding orders (sequence dependency), and typically have an earliest and latest possible start time. We introduce and analyze four algorithmic ideas for this class of time/sequence-dependent over-subscribed scheduling problems with time windows: a novel hybridization of adaptive large neighbourhood search (ALNS) and tabu search (TS), a new randomization strategy for neighbourhood operators, a partial sequence dominance heuristic, and a fast insertion strategy. Through factor analysis, we demonstrate the performance of these new algorithmic features on problem domains with varying properties. Evaluation of the resulting general purpose algorithm on three domains-an order acceptance and scheduling problem, a real-world multi-orbit agile Earth observation satellite scheduling problem, and a time-dependent orienteering problem with time windows-shows that our hybrid algorithm robustly outperforms general algorithms including a mixed integer programming method, a constraint programming method, recent state-of-the-art problem-dependent meta-heuristic methods, and a two-stage hybridization of ALNS and TS.

Integrated Production and Distribution Problem of Perishable Products with a Minimum Total Order Weighted Delivery Time

“…A destroy method destructs part of the current solution, while a repair method rebuilds the destroyed solution. By alternating between an infeasible solution and a feasible solution through destroy and repair methods, the LNS algorithm has been widely used in various combinatorial optimization problems, and has been proved effectively, such as for vehicle routing problems [47][48][49], machine scheduling problems [50,51], supply chain network design [52], satellites scheduling [53], and exam scheduling problems [54]. By applying the LNS algorithm to solve our problem, the neighbors could be enlarged, so as to solve the solution space.…”

El problema de secuenciamiento parcialmente flexible, multiobjetivo, basado en un algoritmo genético de selección natural

“…La planificación de la fabricación es un elemento esencial de la gestión de la cadena de suministro; en la fabricación inteligente, la programación eficiente de los pedidos de los clientes juega un papel importante para maximizar la productividad y las ganancias [1]. El problema de secuenciamiento de la producción, es uno de los campos más importantes en la fabricación inteligente, donde cada trabajo consta de un conjunto específico de operaciones, que deben procesarse de acuerdo con un orden determinado [2].…”

A two-layer tabu search-based distributed satellite online collaboration method for batch arrival emergency tasks

“…While centralized scheduling methods designed for single satellite systems offer high solution quality and easy implementation, they are not well-suited for the BAETs in the on-board environment. These centralized scheduling methods often generalize most original problems into a common problem structure 1 , such as mixed-integer models 2 , primal constraint satisfaction problems 3 , or multi-objective frameworks 4 . Meta-heuristic methods and heuristic methods that combines random factors or reinforcement learning mechanisms are widely used to solve these types of problem model 5 .…”