Danping Lin

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.

Integrating analytical hierarchy process to genetic algorithm for re-entrant flow shop scheduling problem

This research considers a hybrid flow shop scheduling problem with dynamic re-entrant characteristics substantiated by the complexity of the problem in a repairing company. Multiple types of jobs are involved in the problem with individual buffer times that are strongly related to the previous processing job. These jobs need to go through tandem workstations, while some jobs may re-enter the processing line more than once. In order to reduce complexity, jobs are considered as basic units for scheduling. A novel combination of the analytical hierarchy process (AHP) and genetic algorithm (GA) is proposed to deal with the dynamic re-entrant scheduling problem which takes many criteria into consideration. GA is applied to obtain near-optimal schedules, while AHP works with a twofold effect. One is to fulfil the multiple criteria, while the other is adopted in the selection process of GA to fasten GAs convergence speed. The proposed model and solution algorithm are applied to solve the problem in a repairing company under a set of actual constraints. Comprehensive studies are conducted with real-life data. The results are consistent with the company operational scenario and are better than those of the manual schedules.

Research on effect factors evaluation of internet of things (IOT) adoption in Chinese agricultural supply chain

This paper investigates the effect factors in the adoption of Internet of Things (IoT) technology in the agricultural supply chain in China by constructing a Technology-Organization-Environment (TOE) framework. The data was analyzed using Structural Equation Modelling. Through statistics analysis, the effect factors were recognized and the TOE model was modified appropriately. The results indicated that resistance from employees and uncertainties are not important factors that influence the IoT adoption. Referring to those supported factors, technical factors (complexity, compatibility, perceived benefit, and cost) have a complicated influence on the technology adoption of IoT in agriculture. In addition, organizational factors (scale of enterprise, executive support, trust among the businesses in the supply chain, and technical knowledge) and environmental factors (external pressure and government support) all have positive relationships with IoT adoption.

Strategic procurement from forward contract and spot market

Purpose – The purpose of this paper is to formulate procurement strategies and determine the optimal procurement quantity in order to maximize profit through forward contracting and the spot market. Design/methodology/approach – The procurement process is modeled at various stages along a time horizon from the perspective of the buyer, with consideration of uncertain yields, stochastic demand and dynamic spot market prices. Monte Carlo simulation based experiments were conducted to figure out the best procurement quantity for five different scenarios. The framework was developed to understand the impact of different uncertain variables on a firms profit. A case study was carried out in a steel making company in India, with real data. Findings – The results indicate that the proposed approach enables buyers to achieve higher profits under volatile demand conditions. In the case study, it was found that the profit is higher for the spot market than for contract pricing if there is significant demand and sp...

Strategic response to Industry 4.0: an empirical investigation on The Chinese automotive industry

Purpose The purpose of this paper is to examine the strategic response to Industry 4.0 for Chinese automotive industry and to identify the critical factors for its successful implementation. Design/methodology/approach A technological, organizational, and environmental framework is used to build the structural models, and statistical tools are used to validate the model. The data analysis helps to determine which factors have impact on the strategic response and whether their relationships are positive or negative. Interpretive structural modeling method is applied to further analyze these derived factors for depicting the relationship. Findings The result shows that company size and nature do not increase the use of advanced production technologies, while other factors have positive impacts on improving the technology adoption among the companies surveyed. Practical implications A strategic response to Industry 4.0 not only helps in improving organizational competitiveness, but it also has social and economic implications. For this purpose, empirical data are collected to measure the understanding of Industry 4.0 in the Chinese automotive industry. Originality/value Despite the fact that the Chinese Government has proposed the “Made in China 2025” approach as a way to promote smart manufacturing, little empirical evidence exists in the literature validating company’s perspective toward Industry 4.0. This paper is to fill the research gap.

Heuristics for integrated job assignment and scheduling in the multi-plant remanufacturing system

We consider a multi-plant remanufacturing system where decisions have to be made on the choice of plant to perform the remanufacturing and the remanufacturing options. Each plant is in different geographical locations and differs in technological capability, labour cost, distance from customers, taxes and duties. There are three options of remanufacture: replacement, repair and recondition. Furthermore, the probability that each remanufacture job needs to be reworked depends on the remanufacturing option selected. We show the interdependencies among the plant selection, remanufacturing option and job scheduling when subject to resource constraints, which motivate the integrated solution proposed in this paper. The solution method is composed of the linear physical programming and the multi-level encoding genetic algorithm (GA). By performing a case study, we illustrate the use of the model and we present the resulting managerial insights. The results show that the proposed integrated approach performs better compared with the regular GA in terms of makespan.

Integrated GA and AHP for re-entrant flow shop scheduling problem

This paper proposes a novel way to incorporate the analytical hierarchy analysis into the genetic algorithm to solve the flow shop scheduling problem with reentrant jobs. The proposed approach allows the manufacturers take many criteria into consideration genetic algorithm gets the near-optimal sequence while the analytical hierarchy analysis assists to fulfill the multiple criteria as well as fasten the convergence that nested in the selection procedure. Initial population given by the genetic algorithm is filtered by the AHP so that the preferred chromosomes are kept as parents to generate the offspring. To demonstrate how the proposed approach works for the re-entrant flow shop scheduling, a case study of a repairing company whose jobs with dynamic re-entrant characteristic have been conducted. The experiments simulate the case scenario and the results indicate the superiority of proposed method over the practical approach. This finding is able to provide a solid foundation on which the scheduler can enhance the efficiency and accuracy of the re-entrant scheduling.

Ground crew rostering for the airport check-in counter

The construction of ground crew rosters for the airport check-in counters that satisfies all rostering constraints is a complex problem. The rostering problem involves the decision of check-in counters to be opened and shift allocation of crew member over a roster interval. The rostering problem in this paper is described based on the summary of the demand characteristics and a linear programming model is built over a specific time window. The proposed model was verified by a case study where the results illustrated better performance in terms of less open counters and decreased shift labor hours.

Determining the Throughput of a CONWIP System Using Adjusted ARM

Purpose – The purpose of this paper is to propose an adjusted approximate regenerative model (ARM) for a constant‐work‐in‐process (CONWIP)‐based production system that solves the problem of deciding the number of intermediate bulk container in a pharmaceutical company to hold the work in process.Design/methodology/approach – The problem was modeled as a CONWIP system and the ARM was adjusted to estimate the throughput.Findings – By comparing the results from the original ARM and adjusted ARM, a clear superiority of the proposed method is shown for the real case. In addition, the robustness of the adjusted ARM is demonstrated in terms of the violation of rigid assumption and the impacts brought by the limitation of buffer space before the bottleneck station.Originality/value – The novelty of the proposed model is to take the long transportation time between machines into consideration.

Hybrid genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

This paper presents a simulated genetic algorithm model of scheduling the flow shop problems with re-entrant jobs. The objectives of this research are to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines with 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 re-enter 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 industrial practices.