The quay crane scheduling problem (QCSP) is at the basis of a major logistic process in maritime container terminals: the process of discharging/loading containers from/on berthed vessels. Several groups of containers, laying in one or more stowage portions of a containership, have to be assigned to multiple cranes and discharge/loading operations have to be optimally sequenced, under some complicating constraints imposed by the practical working rules of quay cranes. The QCSP has been the object of a great deal of research work since the last decade and it is focused in this paper, with the aim of consolidating a promising solution approach based upon the combination of specialized branch & bound (B&B) and heuristic algorithms. A cost-effective solution technique that incorporates the local branching method within a refined B&B algorithm is proposed and its effectiveness is assessed by numerical comparisons against the latest algorithm available in literature.

In this paper, a supply chain management problem from a real case study is modeled and solved. A company in Pakistan wanted to outsource part of its warehousing activity to a third party logistics (3PL) provider. Consequently, the company had to decide on where to rent space in the 3PL warehouses. Knowing that such a strategic decision is affected by tactical and operational decisions, the problem is presented as a facility location problem integrating production, inventory, and distribution decisions. The problem is formulated as a mixed integer linear programming model which minimizes the total cost composed of location, distribution, production, and inventory costs. Several constraints specific to the situation and policy of the company were considered. A thorough analysis was done on the results obtained with respect to formulation efficiency, sensitivity analysis, and distribution of costs. In addition to the solution of the company problem, a set of 1215 problem instances was generated by varying five types of relevant costs in a full factorial manner. The solution of the generated problems always suggests to open in the same two locations and the integrality gaps averaged 0.062 % with a maximum of 0.102 %. On average, the major components of the total cost are production cost (96.6 %), transportation costs (2.7 %), and inventory holding costs (0.38 %). The total warehouse opening cost accounted for less than 0.05 % of the total costs.

Within the wide scope of logistics management,transportation plays a central role and is a crucialactivity in both production and service industry.Among others, it allows for the timely distributionof goods and services between suppliers, productionunits, warehouses, retailers, and final customers.More specifically, Vehicle Routing Problems(VRPs) deal with the design of a set of minimal costroutes that serve the demand for goods orservices of a set of geographically spread customers,satisfying a group of operational constraints.While it was traditionally a static problem, recenttechnological advances provide organizations withthe right tools to manage their vehicle fleet in realtime. Nonetheless, these new technologies alsointroduce more complexity in fleet managementtasks, unveiling the need for decision support systemsdedicated to dynamic vehicle routing. In thiscontext, the contributions of this Ph.D. thesis arethreefold : (i) it presents a comprehensive reviewof the literature on dynamic vehicle routing ; (ii)it introduces flexible optimization frameworks thatcan cope with a wide variety of dynamic vehiclerouting problems ; (iii) it defines a new vehicle routingproblem with numerous applications.

We establish strong NP-hardness and in-approximability of the so-called representatives selection problem, a tool selection problem in the area of robust optimization. Our results answer a recent question of Dolgui and Kovalev (4OR Q J Oper Res 10:181–192, 2012).

The capacitated \(p\)-median problem (CPMP) is one of the well-known facility-location problems. The objective of the problem is to minimize total cost of locating a set of capacitated service points and allocating a set of demand points to the located service points, while the total allocated demand for each service point is not be greater than its capacity limit. This paper presents an efficient heuristic algorithm based on the local branching and relaxation induced neighborhood search methods for the CPMP. The proposed algorithm is a heuristic technique that utilizes a general mixed integer programming solver to explore neighborhoods. The parameters of the proposed algorithm are tuned by design of experiments. The proposed method is tested on a large set of benchmark instances. The results show that the method outperforms the best method found in the literature.

We propose an extension of the FlowSort sorting method to the case when there is imprecision on the input data. Within multicriteria decision aid, a lot of attention has been paid to sorting problems where a set of actions has to be assigned to completely ordered categories. However, few methods suit when the data or the parameters of the model are not precisely defined. In this paper, instead of reducing the imprecise data to single values, we consider that the sorting parameters or the data are defined by intervals. We analyse the properties usually required for a sorting method and illustrate this extension on a practical example.

This paper deals with stability analysis in multi-objective combinatorial optimization problems. The stability radius of an efficient solution is defined as the maximal adjustment of the problem parameters such that this solution remains efficient. An algorithm based on inverse optimization is proposed to compute it. The adjustment is limited to the coefficients of the objective functions and measured by the Chebyshev norm. This approach is applied to randomly generated instances of the bi-objective knapsack problem and computational results are reported. Several illustrative examples are analyzed.

This is a summary of the PhD thesis defended by the author in September 2011 at the Ecole des Mines de Nantes (EMNantes). The thesis was advised by Narendra Jussien (EMNantes) and co-advised by Louis-Martin Rousseau (Ecole Polytechnique de Montreal—Poly), Christelle Gueret (EMNantes) and Andre Langevin (Poly). The manuscript is written in French and can be obtained from http://tel.archives-ouvertes. fr/tel-00630122/fr/ or by email from arnaud.malapert@unice.fr. The focus of the dissertation is to study the resolution of shop and batch scheduling problems derived from real-world applications using constraint programming. The remainder of this abstract summarizes the results of the dissertation. In shop problems, n jobs, consisting each of m tasks, must be processed on m machines. The processing orders of tasks which belong to a job can vary: global order (flow shop); order per job (job shop); no order (open shop). We consider the construction of non-preemptive schedules of minimal makespan, which is NP-Hard for m ≥ 3. The study and classification of models and search algorithms show that one of the major challenge of solving optimally these problems is to provide good solutions as quick as the metaheuristics. Therefore, we introduce a new algorithm characterized by its: simplicity, most of its components are available in constraint solvers; flexibility, two constraint models offer different trade-offs among propagation strength, speed and simplicity; genericity, it is applicable to any disjunctive scheduling problem. The algorithm relies on recent constraint filtering algorithm and (randomized) branching techniques with new upper bound heuristics. The algorithm starts with a randomized constructive heuristic (without propagation) which initializes the upper bound so that the selection and propagation of initial choices are improved. During the search, the filtering is based on the disjunctive graph model and disjunctive global constraints, whereas the branching is conducted by adding precedences to the disjunctive graph with the profile strategy. Besides, randomized restart policies combined with nogood recording allow to search diversification and learning from restarts. Indeed, nogoods