Scheduling rules to minimize total tardiness in a parallel machine problem with setup and calendar constraints

Abstract

International audienceQuality control lead times are one of most significant causes of loss of time in the pharmaceutical and cosmetics industries. This is partly due to the organization of laboratories that feature parallel multipurpose machines for chromatographic analyses. The testing process requires long setup times and operators are needed to launch the process. The various controls are non-preemptive and are characterized by a release date, a due date and available routings. These quality processes lead to significant delays, and we therefore evaluate the total tardiness criterion. Previous heuristics were defined for the total tardiness criterion, parallel machines, and setup such as ATC (Apparent Tardiness Cost) and ATCS (ATC with setups). We propose new rules and a simulated annealing procedure in order to minimize total tardiness

A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

“…(25), • Construct the personal Pareto archive for each particle to store the best individual found so far, • Construct the global Pareto archive which is empty at the beginning, • Repeat the following procedure for a predetermined number of iterations: -Perform fast-non-dominated sorting, -Update the global Pareto archive, -Determine the best global position matrix for each particle using the procedure explained in Section 3.5, -Determine the best personal position matrix for each particle using the procedure explained in Section 3.5, -Update the velocity and position of each particle using Eqs. (13) and (14), respectively. • Update the personal Pareto archive of each particle, Report solutions of global Pareto archive as final non-dominated solutions.…”

“…(13) and (14). The inertia weight is updated as w(t) = w(t − 1) × ˇ where ˇ is a decremental factor to control the effect of inertia weight on exploration and exploitation.…”

Bi-objective parallel machines scheduling with sequence-dependent setup times using hybrid metaheuristics and weighted min–max technique

“…Also recently, several application of SA is reported in parallel machine scheduling; see e.g. Li et al (2008), Yang (2009) and Lamothe et al (2010).…”

A Genetic Regulatory Network-Based Method for Dynamic Hybrid Flow Shop Scheduling with Uncertain Processing Times

“…The stages are composed of parallel machines to protect the job flow from being blocked by a single machine [1][2][3]. The operation processing time varies with different machines because capacities of parallel machines are normally unrelated at each stage [4,5]. This type of workshop exists in various industries, which include Printed Circuit Board (PCB) assembly, textile production and automobile assembly [6][7][8][9].…”