List scheduling revisited

We consider the problem of scheduling n jobs on m identical parallel machines to minimize a regular cost function. The standard list scheduling algorithm converts a list into a feasible schedule by focusing on the job start times. We prove that list schedules are dominant for this type of problem. Furthermore, we prove that an alternative list scheduling algorithm, focusing on the completion times rather than the start times, yields also dominant list schedules for problems with sequence dependent setup times

Parallel machine scheduling with release dates, due dates and family setup times

In manufacturing, there is a fundamental conflict between efficient production and delivery performance. Maximizing machine utilization by batching similar jobs may lead to poor delivery performance. Minimizing customers' dissatisfaction may lead to an inefficient use of the machines. In this paper, we consider the problem of scheduling n independent jobs with release dates, due dates, and family setup times on m parallel machines. The objective is to minimize the maximum lateness of any job. We present a branch-and-bound algorithm to solve this problem. This algorithm exploits the fact that an optimal schedule is contained in a specific subset of all feasible schedules. For lower bounding purposes, we see setup times as setup jobs with release dates, due dates and processing times. We present two lower bounds for the problem with setup jobs, one of which proceeds by allowing preemption

Hybrid job shop scheduling

We consider the problem of scheduling jobs in a hybrid job shop. We use the term\ud 'hybrid' to indicate that we consider a lot of extensions of the classic job shop, such as transportation times, multiple resources, and setup times. The Shifting Bottleneck procedure can be generalized to deal with those extensions. We test this approach for an assembly shop. In this shop, we study the influence of static and dynamic scheduling, setup times, batch sizes, and the arrival process of the jobs

Optimizing departure times in vehicle routes

Most solution methods for the vehicle routing problem with time\ud windows (VRPTW) develop routes from the earliest feasible departure time. However, in practice, temporal traffic congestions make\ud that such solutions are not optimal with respect to minimizing the\ud total duty time. Furthermore, VRPTW solutions do not account for\ud complex driving hours regulations, which severely restrict the daily\ud travel time available for a truck driver. To deal with these problems,\ud we consider the vehicle departure time optimization (VDO) problem\ud as a post-processing step of solving a VRPTW. We propose an ILP-formulation that minimizes the total duty time. The obtained solutions are feasible with respect to driving hours regulations and they\ud account for temporal traffic congestions by modeling time-dependent\ud travel times. For the latter, we assume a piecewise constant speed\ud function. Computational experiments show that problem instances\ud of realistic sizes can be solved to optimality within practical computation times. Furthermore, duty time reductions of 8 percent can\ud be achieved. Finally, the results show that ignoring time-dependent\ud travel times and driving hours regulations during the development of\ud vehicle routes leads to many infeasible vehicle routes. Therefore, vehicle routing methods should account for these real-life restrictions

Vehicle routing under time-dependent travel times: the impact of congestion avoidance

Daily traffic congestions form major problems for businesses such\ud as logistical service providers and distribution firms. They cause\ud late arrivals at customers and additional hiring costs for the truck\ud drivers. The additional costs of traffic congestions can be reduced\ud by taking into account and avoid well-predictable traffic congestions\ud within off-line vehicle route plans. In the literature, various strategies\ud are proposed to avoid traffic congestions, such as selecting alternative routes, changing the customer visit sequences, and changing the\ud vehicle-customer assignments. We investigate the impact of these and\ud other congestion avoidance strategies in off-line vehicle route plans on\ud the performance of these plans in reality. For this purpose, we develop\ud a set of VRP instances on real road networks, and a speed model that\ud inhabits the main characteristics of peak hour congestion. The instances are solved for different levels of congestion avoidance using a\ud modified Dijkstra algorithm and a restricted dynamic programming\ud heuristic. Computational experiments show that 99% of late arrivals\ud at customers can be eliminated if traffic congestions are accounted for\ud off-line. On top of that, almost 70% of the extra duty times caused by\ud the traffic congestions can be eliminated by clever avoidance strategies

Level of Repair Analysis: A Generic Model

Given a product design and a repair network, a level of repair analysis (lora) determines for each component in the product (1) whether it should be discarded or repaired upon failure and (2) at which echelon in the repair network to do this. The objective of the lora is to minimize the total (variable and fixed) costs. We propose an ip model that generalizes the existing models, based on cases that we have seen in practice. Analysis of our model reveals that the integrality constraints on a large number of binary variables can be relaxed without yielding a fractional solution. As a result, we are able to solve problem instances of a realistic size in a couple of seconds on average. Furthermore, we suggest some improvements to the lora analysis in the current literature

A Minimum Cost Flow model for Level of Repair Analysis

Given a product design and a repair network for capital goods, a level of repair analysis determines for each component in the product (1) whether it should be discarded or repaired upon failure and (2) at which location in the repair network to do this. In this paper, we show how the problem can be modelled as a minimum cost ow problem with side constraints. Advantages are that (1) solving our model requires less computational effort than solving existing models and (2) we achieve a high model exibility, i.e., many practical extensions can be added. Furthermore, we analyse the added value of modelling the exact structure of the repair network, instead of aggregating all data per echelon as is common in the literature. We show that in some cases, cost savings of over 7% can be achieved. We also show when it is sufficient to model the repair network by echelons only, which requires less input data

Time-constrained project scheduling

We study the Time-Constrained Project Scheduling Problem (TCPSP), in which the scheduling of activities is subject to strict deadlines. To be able to meet these deadlines, it is possible to work in overtime or hire additional capacity in regular time or overtime. For this problem, we develop a two stage heuristic. The key of our approach lies in the first stage in which we construct partial schedules with a randomized sampling technique. In these partial schedules, jobs may be scheduled for a shorter duration than required. The second stage uses an ILP formulation of the problem to turn a partial schedule into a feasible schedule, and to perform a neighbourhood search. The developed heuristic is quite flexible and, therefore, suitable for practice. We present experimental results on modified RCPSP benchmark instances. The two stage heuristic solves many instances to optimality, and if we substantially decrease the deadline, the rise in cost is only small

A Decision Support System for Ship Maintenance Capacity Planning

In this paper, the basic framework and algorithms of a decision support system are discussed, which enhance process and capacity planning at a large repair shop. The research is strongly motivated by experiences in a project carried out at a dockyard, which performs repair, overhaul and modification programs for various classes of navy ships. We outline the basic requirements placed upon order acceptance, process planning and capacity scheduling for large maintenance projects. In subsequent sections a number of procedures and algorithms to deal with these requirements, in particular a procedure for workload-based capacity planning, a database system to support process planning are developed, as well as a resource-constrained project scheduling system to support work planning at a more detailed level. The system has been designed to support decision making at the Navy Dockyard in particular, however, we believe that, due to its generic structure, it is applicable to a wide range of project-based manufacturing and maintenance environments