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 precedence constraints and setup times

This paper presents different methods for solving parallel machine scheduling problems with precedence constraints and setup times between the jobs. Limited discrepancy search methods mixed with local search principles, dominance conditions and specific lower bounds are proposed. The proposed methods are evaluated on a set of randomly generated instances and compared with previous results from the literature and those obtained with an efficient commercial solver. We conclude that our propositions are quite competitive and our results even outperform other approaches in most cases

BEC: Bit-Level Static Analysis for Reliability against Soft Errors

Soft errors are a type of transient digital signal corruption that occurs in digital hardware components such as the internal flip-flops of CPU pipelines, the register file, memory cells, and even internal communication buses. Soft errors are caused by environmental radioactivity, magnetic interference, lasers, and temperature fluctuations, either unintentionally, or as part of a deliberate attempt to compromise a system and expose confidential data. We propose a bit-level error coalescing (BEC) static program analysis and its two use cases to understand and improve program reliability against soft errors. The BEC analysis tracks each bit corruption in the register file and classifies the effect of the corruption by its semantics at compile time. The usefulness of the proposed analysis is demonstrated in two scenarios, fault injection campaign pruning, and reliability-aware program transformation. Experimental results show that bit-level analysis pruned up to 30.04 % of exhaustive fault injection campaigns (13.71 % on average), without loss of accuracy. Program vulnerability was reduced by up to 13.11 % (4.94 % on average) through bit-level vulnerability-aware instruction scheduling. The analysis has been implemented within LLVM and evaluated on the RISC-V architecture. To the best of our knowledge, the proposed BEC analysis is the first bit-level compiler analysis for program reliability against soft errors. The proposed method is generic and not limited to a specific computer architecture.Comment: 13 pages, 4 figures, to be published in International Symposium on Code Generation and Optimization (CGO) 202

Schedule generation schemes for the job-shop problem with sequence-dependent setup times: dominance properties and computational analysis

We consider the job-shop problem with sequence-dependent setup times. We focus on the formal definition of schedule generation schemes (SGSs) based on the semi-active, active, and non-delay schedule categories. We study dominance properties of the sets of schedules obtainable with each SGS. We show how the proposed SGSs can be used within single-pass and multi-pass priority rule based heuristics. We study several priority rules for the problem and provide a comparative computational analysis of the different SGSs on sets of instances taken from the literature. The proposed SGSs significantly improve previously best-known results on a set of hard benchmark instances

Aligning barge and terminal operations using service-time profiles.

We consider a key issue in hinterland container navigation in ports, such as Rotterdam and Antwerp, namely the barge handling problem: how to optimize the alignment of barge and terminal operations in a port. We make a major step in solving the barge handling problem for practical settings. Specifically, we consider restricted opening times of terminals, unbalanced networks, the presence of sea vessels, and closing times of containers. Consequently, at a terminal a barge faces time dependency in: (1) the waiting time until the start of handling and (2) the handling time itself. The concept of waiting profiles which we introduced in an earlier paper only deals with (1). To deal with (1) and (2) together we introduce a more comprehensive concept, namely that of service-time profile. To establish how well our approach works, we evaluate the performance of our distributed planning approach extensively by means of simulation. We compare our results with those based on centralized planning by using an off-line benchmark resembling it. We show that the Multi-Agent system that we introduce enables barge and terminal operators to align their operations efficiently. Hence, it can be seen as a promising solution approach for solving the barge handling problem, since it enables (competing) companies to collaborate in a competitive way

ACO with automatic parameter selection for a scheduling problem with a group cumulative constraint

International audienceWe consider a RCPSP (resource constrained project scheduling problem), the goal of which is to schedule jobs on machines in order to minimise job tardiness. This problem comes from a real industrial application, and it requires an additional constraint which is a generalisation of the classical cumulative constraint: jobs are partitioned into groups, and the number of active groups must never exceeds a given capacity (where a group is active when some of its jobs have started while some others are not yet completed).We first study the complexity of this new constraint. Then, we describe an Ant Colony Optimisation algorithm to solve our problem, and we compare three different pheromone structures for it. We study the influence of parameters on the solving process, and show that it varies from an instance to another. Hence, we identify a subset of parameter settings with complementary strengths and weaknesses, and we use a per-instance algorithm selector in order to select the best setting for each new instance to solve. We experimentally compare our approach with a tabu search approach and an exact approach on a data set coming from our industrial application

Spatial Scheduling Algorithms for Production Planning Problems

Spatial resource allocation is an important consideration in shipbuilding and large-scale manufacturing industries. Spatial scheduling problems (SSP) involve the non-overlapping arrangement of jobs within a limited physical workspace such that some scheduling objective is optimized. Since jobs are heavy and occupy large areas, they cannot be moved once set up, requiring that the same contiguous units of space be assigned throughout the duration of their processing time. This adds an additional level of complexity to the general scheduling problem, due to which solving large instances of the problem becomes computationally intractable. The aim of this study is to gain a deeper understanding of the relationship between the spatial and temporal components of the problem. We exploit these acquired insights on problem characteristics to aid in devising solution procedures that perform well in practice. Much of the literature on SSP focuses on the objective of minimizing the makespan of the schedule. We concentrate our efforts towards the minimum sum of completion times objective and state several interesting results encountered in the pursuit of developing fast and reliable solution methods for this problem. Specifically, we develop mixed-integer programming models that identify groups of jobs (batches) that can be scheduled simultaneously. We identify scenarios where batching is useful and ones where batching jobs provides a solution with a worse objective function value. We present computational analysis on large instances and prove an approximation factor on the performance of this method, under certain conditions. We also provide greedy and list-scheduling heuristics for the problem and compare their objectives with the optimal solution. Based on the instances we tested for both batching and list-scheduling approaches, our assessment is that scheduling jobs similar in processing times within the same space yields good solutions. If processing times are sufficiently different, then grouping jobs together, although seemingly makes a more effective use of the space, does not necessarily result in a lower sum of completion times

The dynamic, resource-constrained shortest path problem on an acyclic graph with application in column generation and literature review on sequence-dependent scheduling

This dissertation discusses two independent topics: a resource-constrained shortest-path problem (RCSP) and a literature review on scheduling problems involving sequence-dependent setup (SDS) times (costs). RCSP is often used as a subproblem in column generation because it can be used to solve many practical problems. This dissertation studies RCSP with multiple resource constraints on an acyclic graph, because many applications involve this configuration, especially in column genetation formulations. In particular, this research focuses on a dynamic RCSP since, as a subproblem in column generation, objective function coefficients are updated using new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic graph with the goal of effectively reoptimizing RCSP in the context of column generation. This method uses a one-time âÂÂpreliminaryâ phase to transform RCSP into an unconstrained shortest path problem (SPP) and then solves the resulting SPP after new values of dual variables are used to update objective function coefficients (i.e., reduced costs) at each iteration. Network reduction techniques are considered to remove some nodes and/or arcs permanently in the preliminary phase. Specified techniques are explored to reoptimize when only several coefficients change and for dealing with forbidden and prescribed arcs in the context of a column generation/branch-and-bound approach. As a benchmark method, a label-setting algorithm is also proposed. Computational tests are designed to show the effectiveness of the proposed algorithms and procedures. This dissertation also gives a literature review related to the class of scheduling problems that involve SDS times (costs), an important consideration in many practical applications. It focuses on papers published within the last decade, addressing a variety of machine configurations - single machine, parallel machine, flow shop, and job shop - reviewing both optimizing and heuristic solution methods in each category. Since lot-sizing is so intimately related to scheduling, this dissertation reviews work that integrates these issues in relationship to each configuration. This dissertation provides a perspective of this line of research, gives conclusions, and discusses fertile research opportunities posed by this class of scheduling problems. since, as a subproblem in column generation, objective function coefficients are updated using new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic graph with the goal of effectively reoptimizing RCSP in the context of column generation. This method uses a one-tim