Showing papers on "Constraint programming published in 2021"

Constraint programming approach for multi-resource-constrained unrelated parallel machine scheduling problem with sequence-dependent setup times

Abstract: This paper studies the multi-resource-constrained unrelated parallel machine scheduling problem under various operational constraints with the objective of minimising maximum completion time among ...

Just-in-time two-dimensional bin packing

Abstract: This paper considers the on-time guillotine cutting of small rectangular items from large rectangular bins. Items assigned to a bin define the bins’ processing time. Consequently, an item inherits the completion time of its assigned bin. Any deviation of an item’s completion time from its due date causes either earliness or tardiness penalties. This just-in-time two-dimensional bin packing problem (JITBP) combines two difficult discrete optimization problems: Bin packing and total weighted earliness tardiness single machine scheduling. It is herein modeled as an integrated constraint program, augmented with two sets of logically redundant constraints that speed the search. The first set uses the concept of dual feasible functions. It focuses on bin packing feasibility. The second is the result of a linear program that schedules filled bins on a single machine. As an alternative to this integrated model, this paper proposes two decomposition cut-and-check approaches that define the master problem (MP) as a relaxation of JITBP where the items are reduced to dimensionless entities. They then reestablish the geometric feasibility of the MPs’ solutions by iteratively augmenting MP with Benders cuts generated from the subproblems. The two approaches are similar in concept except that one implements MP as a constraint program (CP) while the second implements it as a mixed-integer program (MIP). Because JITBP is computationally challenging, we test all approaches under a number of heuristic assumptions, which include a maximum runtime for the MIP and CP solvers. The results provide computational evidence that the integrated constraint programming approach performs relatively well, and outperforms the decomposition approach whose MP is a CP. However, both approaches are outperformed by the decomposition approach whose MP is a warm-started MIP.

Resource-Constrained Project Scheduling Problem under Multiple Time Constraints

Abstract: This paper introduces a new practical scheduling problem called the resource-constrained project scheduling problem under multiple time constraints, which involves a duration constraint of ...

Using CP/SMT Solvers for Scheduling and Routing of AGVs

Abstract: An improved method for solving conflict-free scheduling and routing of automated guided vehicles is proposed in this article, with promising results. This is achieved by reformulating the mathematical model of the problem, including several improvements and speedup strategies of an existing Benders decomposition method. A new heuristic is also presented that quickly yields high-quality solutions. Moreover, a real-large-scale industrial instance is solved using an open-source satisfiability module theories solver and a commercial constraint programming solver. According to the results, both of these general-purpose solvers can effectively solve the proposed models. Note to Practitioners —The problem of conflict-free routing and scheduling of automated guided vehicles (AGVs) in large-scale manufacturing systems has been an ever-present challenge for many AGV companies. Although these companies have developed rather efficient control policies and algorithms, retrofitting the existing heuristic to future’s denser, more complicated, and more demanding AGV layouts is not guaranteed to be easy. Furthermore, the installed system will not necessarily be as efficient as expected. Currently, it is common to use heuristics to allocate vehicles to orders and route them. There are also rules of thumbs to avoid collisions and deadlocks. However, with increasing demand for high-performance AGV solutions, it is of interest to employ optimization algorithms that handle the order allocation, scheduling, and routing in a more efficient way. In this article, we present an improved method to tackle this issue, with promising results. We have developed our work in collaboration with a Swedish AGV company, and we have investigated a real-large-scale industrial instance as our case study.

SeaPearl: A Constraint Programming Solver Guided by Reinforcement Learning

Abstract: The design of efficient and generic algorithms for solving combinatorial optimization problems has been an active field of research for many years. Standard exact solving approaches are based on a clever and complete enumeration of the solution set. A critical and non-trivial design choice with such methods is the branching strategy, directing how the search is performed. The last decade has shown an increasing interest in the design of machine learning-based heuristics to solve combinatorial optimization problems. The goal is to leverage knowledge from historical data to solve similar new instances of a problem. Used alone, such heuristics are only able to provide approximate solutions efficiently, but cannot prove optimality nor bounds on their solution. Recent works have shown that reinforcement learning can be successfully used for driving the search phase of constraint programming (CP) solvers. However, it has also been shown that this hybridization is challenging to build, as standard CP frameworks do not natively include machine learning mechanisms, leading to some sources of inefficiencies. This paper presents the proof of concept for SeaPearl, a new CP solver implemented in Julia, that supports machine learning routines in order to learn branching decisions using reinforcement learning. Support for modeling the learning component is also provided. We illustrate the modeling and solution performance of this new solver on two problems. Although not yet competitive with industrial solvers, SeaPearl aims to provide a flexible and open-source framework in order to facilitate future research in the hybridization of constraint programming and machine learning.

Combining target sampling with within field route-optimization to optimise on field yield estimation in viticulture

Abstract: This paper describes a new approach for yield sampling in viticulture. It combines approaches based on auxiliary information and path optimization to offer more consistent sampling strategies, integrating statistical approaches with computer methods. To achieve this, groups of potential sampling points, comparable according to their auxiliary data values are created. Then, an optimal path is constituted that passes through one point of each group of potential sampling points and minimizes the route distance. This part is performed using constraint programming, a programming paradigm offering tools to deal efficiently with combinatorial problems. The paper presents the formalization of the problem, as well as the tests performed on nine real fields were high resolution NDVI data and medium resolution yield data were available. In addition, tests on simulated data were performed to examine the sensitivity of the approach to field data characteristics such as the correlation between auxiliary data and yield, the spatial auto-correlation of the data among others. The approach does not alter much the results when compared to conventional approaches but greatly reduces sampling time. Results show that, for a given amount of time, combining model sampling and path optimization can give estimation error up to 30% lower for a given amount of time compared to previous methods.

A constraint programming approach and a hybrid of genetic and K-means algorithms to solve the p-hub location-allocation problems

Abstract: p-Hub location-allocation problem is one of the most interesting subjects in the location theory. Hubs act as switching points to reduce the transportation cost. In this study, two new solution met...

Human–Robot Task Allocation and Scheduling: Boeing 777 Case Study

Abstract: Motivated by Boeing 777 fully autonomous upright build project, orchestration of human and robotic agents are studied Tasks must be precisely allocated, sequenced, and coordinated among agents subject to temporal and spatial constraints The problem is formulated as a flexible job shop with sequence-dependent setup to capture heterogeneous agents and travel time Two exact central approaches are proposed: a mixed integer programming and a constraint programming, and tested for real-time perspective The computational study demonstrates the proposed method can generate optimal schedules up to 100 agents with 1000 subtasks that requires 10 subtasks per agent on average, within 183 seconds, a substantial improvement over all other benchmark approaches in the literature

Two-stage and one-group two-dimensional guillotine cutting problems with defects: a CP-based algorithm and ILP formulations

Abstract: We address two variants of the two-dimensional guillotine cutting problem that appear in different manufacturing settings that cut defective objects. Real-world applications include the production ...

Constrained optimization of landscape indices in conservation planning to support ecological restoration in New Caledonia

Abstract: 1. Curbing habitat loss, reducing fragmentation, and restoring connectivity are frequent concerns of conservation planning. In this respect, the incorporation of spatial constraints, fragmentation, and connectivity indices into optimization procedures is an important challenge for improving decision support. 2. Here we present a novel optimization approach developed to accurately represent a broad range of conservation planning questions with spatial constraints and landscape indices. Relying on constraint programming, a technique from artificial intelligence based on automatic reasoning, this approach provides both constraint satisfaction and optimality guarantees. 3. We applied this approach in a real case study to support managers of the “Cote Ou bliee – ‘Woen Vuu – Pwa Pereeu” provincial park project, in the biodiversity hotspot of New Caledonia. Under budget, accessibility, and equitable allocation constraints, we identified restorable areas optimal for reducing forest fragmentation and improving inter‐patch structural connectivity, respectively measured with the effective mesh size and the integral index of connectivity. 4. Synthesis and applications. Our work contributes to more effective and policy‐relevant conservation planning by providing a spatially‐explicit and problem‐focused optimization approach. By allowing an exact representation of spatial constraints and landscape indices, it can address new questions and ensure whether the solutions will be socio‐economically feasible, through optimality and satisfiability guarantees. Our approach is generic and flexible, thus applicable to a wide range of conservation planning problems such as ecological restoration planning, reserve or corridor design.

A new variable neighbourhood search with a constraint programming search strategy for the open shop scheduling problem with operation repetitions

Abstract: This article presents a new variant for the open shop scheduling problem, the open shop scheduling problem with repetitions (OSSPR), where the jobs can be processed on any machine more than once (o...

Heterogeneous Heuristic Optimisation and Scheduling for First-Order Theorem Proving.

Abstract: Good heuristics are essential for successful proof search in first-order automated theorem proving. As a result, state-of-the-art theorem provers offer a range of options for tuning the proof search process to specific problems. However, the vast configuration space makes it exceedingly challenging to construct effective heuristics. In this paper we present a new approach called HOS-ML, for automatically discovering new heuristics and mapping problems into optimised local schedules comprising of these heuristics. Our approach is based on interleaving Bayesian hyper-parameter optimisation for discovering promising heuristics and dynamic clustering to make optimisation efficient on heterogeneous problems. HOS-ML also use constraint programming to devise locally optimal schedules and machine learning for mapping unseen problems into such schedules. We evaluated HOS-ML on the theorem prover iProver and demonstrated that it can discover new heuristics that considerably improve performance and can solve problems that have not been solved previously by any other system.

New chance-constrained models for U-type stochastic assembly line balancing problem

Abstract: U-shaped assembly lines are widely encountered in contemporary JIT systems. Unlike presumptions of deterministic studies, task times may vary according to a probability distribution. In this study, a stochastic U-type assembly line balancing problem (ALBP) is considered. For this purpose, two new chance-constrained nonlinear models are proposed. While the first model belongs to the mixed-integer programming (MIP) category, the other is constraint programming (CP). The linearized chance-constrained counterparts are developed using a transformation approach to reduce the model complexity and solve the models linearly. Several numerical experiments are performed to test the effectiveness of the proposed models. The results are compared with the results of modified ant colony optimization and a piecewise-linear programming model. The numerical results demonstrate that the proposed CP and MIP models are more effective and successful in solving stochastic U-type ALBP.

Constraint programming approach for multi-objective two-sided assembly line balancing problem with multi-operator stations

Abstract: In recent years, the two-sided assembly line (TAL) has become popular since it offers several advantages, such as fewer workstations and movement of fewer workers inside the line. TAL assumes that ...

MiniCP: a lightweight solver for constraint programming

Abstract: This paper introduces MiniCP, a lightweight, open-source solver for constraint programming MiniCP is motivated by educational purposes and the desire to provide the core implementation of a constraint-programming solver for students in computer science and industrial engineering The design of MiniCP provides a one-to-one mapping between the theoretical and implementation concepts and its compositional abstractions favor extensibility and flexibility MiniCP obviously does not support all available constraint-programming features and implementation techniques, but these could be implemented as future extensions or exploratory projects MiniCP also comes with a full set of exercises, unit tests, and development projects

Vehicle and reliable driver scheduling for public bus transportation systems

Abstract: We propose the bus vehicle and reliable driver scheduling problem that is an integrated approach for the vehicle and the crew scheduling problems considering driver’s reliability information to reduce the number of no-covered trips along the day and thus improve the user’s satisfaction An exact constraint programming model is proposed and compared with a variable neighborhood search that incorporates the driver’s reliability and the trip’s importance The obtained trip-vehicle-driver assignments are evaluated on many scenarios with a Monte Carlo method to simulate the driver’s absenteeism Experimental results on randomly generated instances based on a real case study show our methodologies’ efficiency and the enormous gains in covered trips when the drivers’ reliability is considered

Control Performance Optimization for Application Integration on Automotive Architectures

Abstract: Automotive software implements different functionalities as multiple control applications sharing common platform resources. Although such applications are often developed independently, the control performance of the resulting system depends on how these applications are integrated. A key integration challenge is to efficiently schedule these applications on shared resources with minimal control performance degradation. We formulate this problem as that of scheduling multiple distributed periodic control tasks that communicate via messages with non-zero jitter. The optimization criterion used is a piecewise linear representation of the control performance degradation as a function of the end-to-end latency of the application. The three main contributions of this article are: 1) a constraint programming (CP) formulation to solve this integration problem optimally on time-triggered architectures; 2) an efficient heuristic called Flexi ; and 3) an experimental evaluation of the scalability and efficiency of the proposed approaches. In contrast to the CP formulation, which for many real-life problems might have unacceptably long running times, Flexi returns nearly optimal results (0.5 percent loss in control performance compared to optimal) for most problems with more acceptable running times.

A local search framework for industrial test laboratory scheduling

Abstract: In this paper we introduce a complex scheduling problem that arises in a real-world industrial test laboratory, where a large number of activities has to be performed using qualified personnel and specialized equipment, subject to time windows and several other constraints. The problem is an extension of the well-known Resource-Constrained Project Scheduling Problem and features multiple heterogeneous resources with very general availability restrictions, as well as a grouping phase, where the jobs have to be assembled from smaller units. We describe an instance generator for this problem and publicly available instance sets, both randomly generated and real-world data. Finally, we present and evaluate different metaheuristic approaches to solve the scheduling subproblem, where the assembled jobs are already provided. Our results show that Simulated Annealing can be used to achieve very good results, in particular for large instances, where it is able to consistently find better solutions than a state-of-the-art constraint programming solver within reasonable time.