Development and analysis of hybrid genetic algorithms for flow shop scheduling with sequence dependent setup time

Generally, each member of a supply chain (SC) optimizes his own individual objective and accordingly, plans his activities (e.g. production operations, inventories) without considering a global perspective. The goal of this work is the development of a multiobjective optimization model for cooperative planning between different manufacturing plants belonging to the same SC. The model aims at minimizing simultaneously the total production cost and the average of inventory level for several items and over a multi-period horizon. To solve this problem, a non-dominated sorting elitist genetic algorithm (NSGA-II) is developed to derive the Pareto front solutions. Several tests are developed to show the performance of the solution method and the behavior of the cooperative planning model with respect to different demand patterns. The proposed model shows high performance in the tested cases with comparison to the literature.

Section: The Resolution Methodsmentioning

confidence: 99%

A multi-objective optimisation model for cooperative supply chain planning

Yahia

Cheikhrouhou

Ayadi

et al. 2017

“…Vanchipura and Sridharan (2013) investigated the impact of varying levels of setup time on the performance of algorithms for scheduling a flow shop with sequence dependent setup time. Vanchipura and Sridharan (2014) deals with the development and analysis of hybrid genetic algorithms for flow shop scheduling problems with sequence dependent setup time. Xie et al (2014) proposed teaching-learning-based optimisation algorithm for PFSP to determine the job sequence with minimisation of makespan criterion and minimisation of maximum lateness criterion.…”

Section: Literature Surveymentioning

confidence: 99%

“…Literature on FMS scheduling problems addresses exact solution procedures and heuristic algorithms (Lee and Korbaa, 2006;Caumond et al, 2009;Felix et al, 2011;Wang and Wang, 2012;Ramezanian et al, 2013;Viagas and Framinan, 2014;Abedinnia et al, 2016). Several meta-heuristic algorithms have been developed to solve the flowshop permutation scheduling problems (Hajinejad et al, 2011;Li and Yin, 2013;Vanchipura and Sridharan, 2014;Gao et al, 2013;Tosun and Marichelvam, 2016). Little research has been conducted on multi-cell manufacturing scheduling problem.…”

Section: Literature Surveymentioning

confidence: 99%

Particle swarm optimisation algorithm and multi-start simulated annealing algorithm for scheduling batches of parts in multi-cell flexible manufacturing system

Balaji¹,

Porselvi²,

Jawahar

2019

This paper considers the problem of scheduling batches of parts in a multi-cell flexible manufacturing system (MCFMS) with sequence dependent batch setup time. The goal is to find the best sequence of batches and hence to minimise the makespan. Two mathematical models are developed namely: batch availability model and job availability model. As the problem is known to be NP-hard, particle swarm optimisation (PSO) algorithm and multi-start simulated annealing (MSA) algorithm are proposed to solve the problem. The proposed algorithms are validated by testing the benchmark problems available in the literature. In addition to that, 80 problems with various sizes have been generated at random and then the performance of the proposed MSA and PSO algorithms are compared with CPLEX solver. The experimental results show that MSA provides better solution compared with PSO, the same solution as CPLEX and very close to the lower bound value provided by CPLEX.

“…GA is a widely used meta-heuristic technique which finds a near optimal solution in a reasonable time (Ertay et al, 2006). Vanchipura and Sridharan (2014) utilised the hybrid genetic algorithm for the flow shop scheduling problem, this approach resulted gave better bounds as compared to other methods. A combination of AHP and genetic algorithm provided a good decision-making tool to the manager for vendor selection in a supply chain (Rao, 2007).…”

Section: Introductionmentioning

confidence: 99%

Product assignment using quadratic assignment model in retail

Sah

Titiyal

Bhandari³

2019

This paper studies the problem of product assignment in retail. It solves the problem of assigning various products to various in-store locations so that there is increased sale as well as a better customer convenience. Retail managers often work out of intuition to place various products at various locations inside the retail store. While doing so, it is often seen that retail managers ignore the phenomenon of cross-selling. This study quantifies this problem and formulates it as a quadratic assignment problem which is solved by implementing a genetic algorithm. The resulting solution maximises cross-selling among products and at the same time improves customer convenience making it a win-win situation for everybody.