IBM ILOG CP optimizer for scheduling

Laborie, Philippe; Rogerie, Jérôme; Shaw, Paul; Vilím, Petr

doi:10.1007/s10601-018-9281-x

Cited by 226 publications

(100 citation statements)

References 35 publications

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“…After formulating the service placement problem as CSP, we now choose a constraint solver to solve the problem. Constraint programming is often realized in imperative programming by software library, such as CPLEX CP Optimizer [19], JaCoP [20] and Choco [14]. We choose Choco as our constraint solver (many times awarded at international solver competitions MiniZinc 1 ).…”

Section: E Implementation Of Constraint Programming-based Service Plmentioning

confidence: 99%

“…Next, we push the experiments further by analyzing the model and observing the solving time of CP-SPP under the second experimental setting. 1) Comparison of the CP-SPP Model with Algorithms provided in [9]: This evaluation compares the CP-SPP model with (i) an Integer Linear Programming (ILP) algorithm implemented using IBM CPLEX [19]), (ii) First Fit heuristic (based on backtrack algorithm, that returns the first solution found (if any)), (iii) a metaheuristic Genetic Algorithm (GA) (based on refining a population (a set of placements) and continuously generates new placements and adds them into the population), and (iv) the heuristic "DAFNO-InitCO-DCO(0.3)" provided by Xia et al [9] that relies on a backtrack search algorithm accompanied by two heuristics: (1) naive search that order the fog nodes and applications components, and (2) search based on anchors to minimize average latency. Here, we propose (as considered in [9]) to deploy a single Smart Bell instance on the infrastructure graph depicted in Figure 2.…”

Section: B Performance Analysismentioning

confidence: 99%

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Service Placement in Fog Computing Using Constraint Programming

Salaht¹,

Desprez²,

Lèbre³

et al. 2019

2019 IEEE International Conference on Services Computing (SCC)

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This paper investigates whether the use of Constraint Programming (CP) could enable the development of a generic and easy-to-upgrade placement service for Fog Computing. Our contribution is a new formulation of the placement problem, an implementation of this model leveraging Chocosolver and an evaluation of its scalability in comparison to recent placement algorithms. To the best of our knowledge, our study is the first one to evaluate the relevance of CP approaches in comparison to heuristic ones in this context. CP interleaves inference and systematic exploration to search for solutions, letting users on what matters: the problem description. Thus, our service placement model not only can be easily enhanced (deployment constraints/objectives) but also shows a competitive tradeoff between resolution times and solutions quality.

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Section: E Implementation Of Constraint Programming-based Service Plmentioning

confidence: 99%

Section: B Performance Analysismentioning

confidence: 99%

Service Placement in Fog Computing Using Constraint Programming

Salaht¹,

Desprez²,

Lèbre³

et al. 2019

2019 IEEE International Conference on Services Computing (SCC)

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“…They proved that CP was very useful for a number of vehicle routing problem variants and the presence of synchronization constraints made this problem more convenient for a CP-based approach. Laborie et al [28] indicated that the CP optimizer is continuously improving and that they will continue to increase the performance of the search strategies.…”

Section: Proposed Constraint Programming Model (Cp-pdptw)mentioning

confidence: 99%

A constraint programming approach for the pickup and delivery problem with time windows

KÜÇÜK¹,

Yildiz²

2019

Pamukkale J Eng Sci

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ÖzThe pickup and delivery problem with time windows (PDPTW) is studied in this paper. It is referred to as the single-commodity capacitated vehicle routing problem with pickups and deliveries, in which a fleet of vehicles meet a group of customer demands. Each customer demand includes the usage of only one vehicle for both loading a specified quantity of one type of commodity at one place and delivering them to another place. All the demands of customers must be satisfied without exceeding the vehicle capacity and the pickup or delivery places time windows specified for each place. In this study, we introduce a novel Constraint Programming (CP) model for the PDPTW. CP is an exact solution approach that is popular for its performance to state complicated relationships and to achieve high-quality solutions within acceptable computational times for combinatorial optimization problems with complicated constraints such as the PDPTW. The performance of the proposed CP model is tested with well-known benchmark instances from literature. The results of computational analysis indicate that our CP model is very effective in finding highquality solutions for even large size problems. Bu makale zaman pencereli toplama ve dağıtım problemini (ZPTDP) ele almaktadır.Problem, müşteri taleplerinin bir araç filosu tarafından karşılandığı, tek ürünlü, toplama ve dağıtımlı araç rotalama problemi olarak adlandırılmaktadır. Her müşteri talebi belli miktardaki tek tip ürünün bir lokasyondan yüklenmesini ve başka bir lokasyona teslim edilmesini içermektedir. Müşteri talepleri araçların kapasitesi ve her bir lokasyon için belirlenmiş toplama ve dağıtım zaman pencereleri ihlal edilmeden karşılanmalıdır. Bu çalışmada, ZPTDP için yeni bir kısıt programlama (KP) modeli sunmaktayız. KP, ZPTDP gibi zor kısıtlı kombinatorik optimizasyon problemlerinin karmaşık ilişkilerinin tanımlanmasında ve kabul edilebilir hesaplama süresi içinde yüksek kaliteli çözümler bulmada yeterliliği iyi bilinen, kesin bir çözüm yaklaşımıdır. Önerilen KP modelini literatürde sıkça kullanılan karşılaştırma örneklerine uyguladık. Aldığımız sonuçlar KP modelimizin büyük boyutlu problemlerde bile yüksek kaliteli sonuçlar verebilecek kadar etkili olduğunu göstermiştir.

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“…Others include numerical features in more general CSP frameworks like Eclipse [18] or ILOG Solver C++ library [19].…”

Section: Interval Constraint Programmingmentioning

confidence: 99%

System Sizing with a Model-Based Approach: Application to the Optimization of a Power Transmission System

Yvars

Zimmer

2018

Mathematical Problems in Engineering

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We test the relevance of a model-based approach for sizing and optimizing complex systems. Classically a model-based approach is characterized by a clear partition between the problem description and the solving process. In the case of a design problem, we show that the sizing task could be systematically characterized and therefore could lead to a declarative model combining both system description and design requirements. Once translated into a constraint satisfaction problem, the resulting model can be solved with interval constraint programming methods and algorithms. Our first contribution to this approach is to precisely characterize the sizing task in design. The resulting terminology enables us to easily and systematically express the problem as a constraint satisfaction problem (CSP) which combines in the same model the system description and the design requirements. We have tested the approach on the optimal sizing problem of a power transmission system. Previous authors have described this scalable case study. They provide a mathematical formulation of the problem and solve it with an evolutionary algorithm. Starting from their description, we apply our methodology to model the problem as a CSP and then solve it with interval constraint programming algorithms. Our solutions are more adequate both in computational time and in optimization results than those published in the literature on the same problem. Moreover the declarative nature of constraint programming makes modifications or extensions easier than with evolutionary programming. The explanation of these results is our second contribution to the approach. However some important modelling issues remain to address in order to capture more and more complex system specifications. Further research is presented at the end of this paper.

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IBM ILOG CP optimizer for scheduling

Cited by 226 publications

References 35 publications

Service Placement in Fog Computing Using Constraint Programming

Service Placement in Fog Computing Using Constraint Programming

A constraint programming approach for the pickup and delivery problem with time windows

System Sizing with a Model-Based Approach: Application to the Optimization of a Power Transmission System

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