30 years of retail change:where (and how) do you shop?

Purpose – The purpose of this paper is to investigate the extent of retail change in the UK grocery sector over the last 30 years. Design/methodology/approach – In 1980, a press article by Richard Milner and Patience Wheatcroft attempted to anticipate retail change by 1984. Taking that as a template, the paper examines how retail did, in fact, change over a much longer timescale: with some unanticipated innovations in place even by 1984. Reference is made to academic research on grocery retailing in progress at the time and which has recently been revisited. Findings – Although Milner and Wheatcroft tackled the modest task of looking ahead just four years, the content of their article is intriguingly reflective of the retail structure and systems of the UK at the time. Whilst some innovations were not anticipated, the broad themes of superstore power and market regulation still command attention 30 years on. Originality/value – Through reconsidering 30 years of retail change, the paper highlights that with time how do you shop has come to pose at least as interesting a question as where do you shop.

Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

This paper presents a simulated genetic algorithm (GA) model of scheduling the flow shop problem with re-entrant jobs. The objective of this research is to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines in the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs reenter to the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the current industrial practices.

Multi-level genetic algorithm for the resource-constrained re-entrant scheduling problem in the flow shop

The re-entrant flow shop scheduling problem (RFSP) is regarded as a NP-hard problem and attracted the attention of both researchers and industry. Current approach attempts to minimize the makespan of RFSP without considering the interdependency between the resource constraints and the re-entrant probability. This paper proposed Multi-level genetic algorithm (GA) by including the co-related re-entrant possibility and production mode in multi-level chromosome encoding. Repair operator is incorporated in the Multi-level genetic algorithm so as to revise the infeasible solution by resolving the resource conflict. With the objective of minimizing the makespan, Multi-level genetic algorithm (GA) is proposed and ANOVA is used to fine tune the parameter setting of GA. The experiment shows that the proposed approach is more effective to find the near-optimal schedule than the simulated annealing algorithm for both small-size problem and large-size problem. © 2013 Published by Elsevier Ltd.