A Benders decomposition approach to the weighted number of tardy jobs scheduling problem on unrelated parallel machines with production costs

This paper addresses the problem of scheduling on-time jobs on unrelated parallel machines with machine production costs. The objective is to maximise the net profit which is the sum of the weights of on-time jobs and the cost of using the machines. This scheduling problem is very important and frequent in industrial settings. It is herein solved using an exact approach that applies Benders decomposition to obtain tight upper and lower bounds and uses the bounds within a branch and bound. The computational investigation shows the efficacy of the approach in solving large instances. Most importantly, the proposed approach provides a new venue for solving large-scale scheduling problems.     

Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines

This paper investigates an energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines (HFSP-UPM) with the energy-saving strategy of turning off and on. We first analyse the energy consumption of HFSP-UPM and formulate five mixed integer linear programming (MILP) models based on two different modelling ideas namely idle time and idle energy. All the models are compared both in size and computational complexities. The results show that MILP models based on different modelling ideas vary dramatically in both size and computational complexities. HFSP-UPM is NP-Hard, thus, an improved genetic algorithm (IGA) is proposed. Specifically, a new energy-conscious decoding method is designed in IGA. To evaluate the proposed IGA, comparative experiments of different-sized instances are conducted. The results demonstrate that the IGA is more effective than the genetic algorithm (GA), simulating annealing algorithm (SA) and migrating birds optimisation algorithm (MBO). Compared with the best MILP model, the IGA can get the solution that is close to an optimal solution with the gap of no more than 2.17% for small-scale instances. For large-scale instances, the IGA can get a better solution than the best MILP model within no more than 10% of the running time of the best MILP model.     

Status of engineering change management in the engineer-to-order production environment: insights from a multiple case study

Engineering Changes (ECs) are a fact of life for companies in the Engineer-To-Order (ETO) production environment. Various Engineering Change Management (ECM) strategies, practices and tools exist, but no explicit distinction has been made regarding ECM in different production environments. Using a multiple case study method, this article investigates how ETO companies manage ECs and how ETO characteristics influence ECM. A generic ECM framework was developed and used to map ECM in the cases. The study showed that ETO companies use similar practices for handling ECs, while ECM tools are either not used by the companies or used to a very limited extent. It was found that the use of some ECM practices and tools is complicated by specific ETO company characteristics. However, no reasons were found for the lack of computer-based tools, change propagation and impact assessment tools, change reduction and front-loading tools, and design tools. This suggests, firstly, that there is vast room for improvement in ETO companies when it comes to ECM; and secondly, that the applicability of such tools should be further tested in the ETO environment. Based on the findings, some suggestions as to how ECM can be improved in ETO companies are given to practitioners.