Journal of the Operational Research Society

Masri, K; Warburton, A

doi: 10.1057/palgrave.jors.2600616pmid: N/A

AbstractThis paper describes an optimisation based method that was used for analysing and improving the yield of aluminium extrusion operations at a local Alcan extrusion plant. Application of an early version of the system led to sustained annual raw material savings of approximately $1M CDN.

Nicholls, M G

doi: 10.1057/palgrave.jors.2600635pmid: N/A

AbstractIn this paper, a mathematical model is developed that facilitates daily production scheduling in a tobacco processing plant. The implied objectives are to meet specific horizon production targets (obtained from a master production schedule), to maintain safety stock requirements and to ensure that the demand for labour lies within given limits. The express objective is to minimise the number of machines used in the production process. Additionally, the model incorporates work-in-progress, aspects of the demand for product transportation within the plant and machine capacity (utilisation) reduction effects associated with production sequencing. These aspects are relevant when dealing with time intervals as small as a day but can be averaged out when dealing with monthly time intervals. The developments in this paper represents stage II of the modelling of the tobacco plant, where stage I (already completed) was centred on obtaining a monthly master production schedule for a year ahead and assisting in macro planning activities. This paper also sees the development of a simple user-friendly heuristic which facilitates production sequencing on a daily basis given the master production schedule obtained from Stage I.

Sriskandarajah, C; Jardine, A K S; Chan, C K

doi: 10.1057/palgrave.jors.2600627pmid: N/A

AbstractWe have developed a Genetic algorithm (GA) for the optimisation of maintenance overhaul scheduling of rolling stock (trains) at the Hong Kong Mass Transit Railway Corporation (MTRC). The problem is one of combinatorial optimisation. Genetic algorithms (GAs) belong to the class of heuristic optimisation techniques that utilise randomisation as well as directed smart search to seek the global optima. The workshop at MTRC does have difficulties in establishing good schedules for the overhaul maintenance of the rolling stock. Currently, an experienced scheduler at MTRC performs this task manually. In this paper, we study the problem in a scientific manner and propose ways in which the task can be automated with the help of an algorithm embedded in a computer program. The algorithm enables the scheduler to establish the annual maintenance schedule of the trains in an efficient manner; the objective being to satisfy the maintenance requirements of various units of the trains as closely as possible to their due dates since there is a cost associated with undertaking the maintenance tasks either `too early’ or ‘too late’. The genetic algorithm developed is found to be very effective for solving this intractable problem. Computational results indicate that the genetic algorithm consistently provides significantly better schedules than those established manually at MTRC. More over, we provide evidence that the algorithm delivers close to optimal solutions for randomly generated problems with known optimal solutions. We also propose a local search method to reconfigure the trains in order to improve the schedule and to balance the work load of the overhaul maintenance section of the workshop throughout the planning horizon. We demonstrate that the reconfiguration of trains improves the schedule and reduces cost significantly.

Wood, J; Whitaker, D

doi: 10.1057/palgrave.jors.2600628pmid: N/A

AbstractThis paper reports on the formulation of a secondary school timetabling problem as a non-linear goal program, where students freely choose their courses of study from a complete list of subjects rather than the usual restricted sets of subjects. The problem as formulated is far too large to solve by traditional optimisation methods, so it is broken down into several stages for solution by heuristics to give good timetabling schedules which are at least as good as those built by manual methods. Timetable construction using a desktop computer is reduced from weeks to hours, giving schools the opportunity to construct timetables closer to the time when student choices and teaching staff are more settled.

Demeulemeester, E; De Reyck, B; Foubert, B; Herroelen, W; Vanhoucke, M

doi: 10.1057/palgrave.jors.2600634pmid: N/A

AbstractWe describe a new exact procedure for the discrete time/cost trade-off problem in deterministic activity-on-the-arc networks of the CPM type, where the duration of each activity is a discrete, nonincreasing function of the amount of a single resource (money) committed to it. The objective is to construct the complete and efficient time/cost profile over the set of feasible project durations. The procedure uses a horizon-varying approach based on the iterative optimal solution of the problem of minimising the sum of the resource use over all activities subject to the activity precedence constraints and a project deadline. This optimal solution is derived using a branch-and-bound procedure which computes lower bounds by making convex piecewise linear underestimations of the discrete time/cost trade-off curves of the activities to be used as input for an adapted version of the Fulkerson labelling algorithm for the linear time/cost trade-off problem. Branching involves the selection of an activity in order to partition its set of execution modes into two subsets which are used to derive improved convex piecewise linear underestimations. The procedure has been programmed in Visual C ++ under Windows NT and has been validated using a factorial experiment on a large set of randomly generated problem instances.

Lee, H

doi: 10.1057/palgrave.jors.2600629pmid: N/A

AbstractWe investigate the potential of the parallelised Lagrangean approximation procedure (PLAP) for certain combinatorial optimisation problems in manufacturing systems. The framework of a PLAP is proposed for some combinatorial manufacturing problems which are decomposable into well-structured subproblems. The synchronous PLAP for the multistage dynamic lot-sizing problem is implemented on a parallel computer (Alliant FX/4) and its computational experience is reported as a promising application of vector-concurrent computing.

Karabati, S; Tan, B

doi: 10.1057/palgrave.jors.2600625pmid: N/A

AbstractIn this paper we address the stochastic cyclic scheduling problem in synchronous assembly and production lines. Synchronous lines are widely used in the production and assembly of various goods such as automobiles or household appliances. We consider cycle time minimisation (or throughput rate maximisation) as the objective of the scheduling problem with the assumption that the processing times are independent random variables. We first discuss the two-station case and present a lower bounding scheme and an approximate solution procedure for the scheduling problem. For the general case of the problem, two heuristic solution procedures are presented. An extension of the two-station lower bound to the general case of the problem is also discussed. The performance of the proposed heuristics on randomly generated problems is documented, and the impact of scheduling decisions on problems with different levels of variability in processing times are analysed. We also analyse the problem of sequence determination when the available information is limited to the expected values of individual processing times.

Portougal, V; Trietsch, D

doi: 10.1057/palgrave.jors.2600639pmid: N/A

AbstractThe ultimate goal of stochastic modelling in shop scheduling is to select the sequence with the best statistical distribution and use it to book capacity and quote delivery dates. For tractability reasons, stochastic models usually employ the expected value of the makespan as the criterion (instead of really looking at the whole distribution). In practice, this criterion is much harder to satisfy than solving for the (already strongly NP-hard) deterministic makespan. Therefore, other criteria have been proposed, and it is important to ask which one is best for long-term expected benefits. This paper analyses and compares several existing criteria for that purpose. We also suggest adding a variance minimisation objective, so that the quoted lead time required to satisfy a given service level will be minimised.

Kimms, A; Drexl, A

doi: 10.1057/palgrave.jors.2600632pmid: N/A

AbstractThis paper deals with proportional lot sizing and scheduling (PLSP) and gives some insights into the properties of this problem. Such insights may be useful for developing heuristic and/or exact solution procedures. The emphasis of this paper is on the multi-level, multi-machine case. We provide a mixed-integer programming model, relate it to other models that can be found in the literature, and discuss characteristics which make solving instances of the PLSP-model a hard task.

Zhou, Y-W; Lau, H-S

doi: 10.1057/palgrave.jors.2600626pmid: N/A

AbstractIn this paper, we develop an optimal manufactured lot-sizing model under the consideration of learning and shortages. Here the learning phenomenon conforms to the De Jong bounded learning curve. A one-dimensional search method is presented for determining the unique optimal production schedule, which minimises the sum of labour and material costs, fixed set-up costs, the holding cost and the shortage cost per unit time. A numerical example is used to illustrate the solution procedure; sensitivity analyses are also shown.