Chefi Triki

Capacitated location of collection sites in an urban waste management system

Urban waste management is becoming an increasingly complex task, absorbing a huge amount of resources, and having a major environmental impact. The design of a waste management system consists in various activities, and one of these is related to the location of waste collection sites. In this paper, we propose an integer programming model that helps decision makers in choosing the sites where to locate the unsorted waste collection bins in a residential town, as well as the capacities of the bins to be located at each collection site. This model helps in assessing tactical decisions through constraints that force each collection area to be capacitated enough to fit the expected waste to be directed to that area, while taking into account Quality of Service constraints from the citizens point of view. Moreover, we propose an effective constructive heuristic approach whose aim is to provide a good solution quality in an extremely reduced computational time. Computational results on data related to the city of Nardò, in the south of Italy, show that both exact and heuristic approaches provide consistently better solutions than that currently implemented, resulting in a lower number of activated collection sites, and a lower number of bins to be used.

An Optimization-Simulation Approach for Groundwater Abstraction under Recharge Uncertainty

Droughts and climate variability cause uncertainties on water supply especially in arid regions and coastal aquifers’ over-exploitation causes seawater intrusion. Since the rate and extent of aquifer recharge is often very uncertain, determining the optimal groundwater abstraction is a challenging task. In this paper a framework is proposed for estimating the optimal abstraction of groundwater for urban supply under uncertainty and under complex conditions of water table fluctuations and seawater intrusion. It is based on a combination of several models: (i) a Monte-Carlo Simulation (MCS) to incorporate the uncertainties in groundwater recharge, (ii) a numerical groundwater flow model, MODFLOW to simulate the effects of abstractions on the water table fluctuations and seawater intrusion and (iii) a multi-objective optimization model to generate the set of Pareto optimal solutions for each recharging scenario. Maximizing the benefit to the water utility, minimizing the average groundwater table level fluctuations and minimizing the seawater intrusion are the objectives of the model. A fast multi-objective evolutionary algorithm is used to obtain the Pareto efficient solutions for each recharging scenario. Compromise programming (CP) is then used to select the closest solutions to the ideal. Finally, the amount of optimal reliable groundwater abstraction is estimated using a cumulative distribution function. The proposed methodology is applied to a coastal aquifer in the western part of Muscat metropolitan area, Oman. The results have shown that annual groundwater abstraction volume may range from 12.7 to 18.8xa0Mm3 compared to 6.8xa0Mm3 currently pumped. This would result in an economic benefit of

The stochastic bid generation problem in combinatorial transportation auctions

10.5 million to

Integrated production scheduling and vehicle routing problem with job splitting and delivery time windows

15.4 million/year. On the other hand the aquifer’s maximum annual mean drawdown would range from 0.7 to 0.9xa0m.

Location-based techniques for the synergy approximation in combinatorial transportation auctions

In this paper, we deal with the generation of bundles of loads to be submitted by carriers participating in combinatorial auctions in the context of long-haul full truckload transportation services. We develop a probabilistic optimization model that integrates the bid generation and pricing problems together with the routing of the carrier’s fleet. We propose two heuristic procedures that enable us to solve models with up to 400 auctioned loads.

Optimizing the performance of complex maintenance systems

Abstract In this paper, we study a production scheduling and vehicle routing problem with job splitting and delivery time windows in a company working in the metal packaging industry. In this problem, a set of jobs has to be processed on unrelated parallel machines with job splitting and sequence-dependent setup time (cost). Then the finished products are delivered in batches to several customers with heterogeneous vehicles, subject to delivery time windows. The objective of production is to minimize the total setup cost and the objective of distribution is to minimize the transportation cost. We propose mathematical models for decentralized scheduling problems, where a production schedule and a distribution plan are built consecutively. We develop a two-phase iterative heuristic to solve the integrated scheduling problem. We evaluate the benefits of coordination through numerical experiments.

An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers

The use of combinatorial auctions for the procurement of transportation services is investigated in this paper. We focus on the carrier viewpoint who is interested in submitting to the auction a selected bundle of loads that avoids some of the empty movement of the vehicles in his transportation network and that increases his profits. For this purpose we develop an optimization approach based on the use of the location techniques, usually used in the context of facility planning. Mathematically, this means maximizing the synergy among the bundle’s auctioned loads from one side and between the auctioned and the pre-existing loads from the other side. We show the validity of our approach by using first an illustrative example and then by applying it to solve a real-life problem related to a logistics company installed in the Arabic Gulf region.

Approximation neighborhood evaluation for the design of the logistics support of complex engineering systems

The scope of this paper is to optimize the performance of a multi-indenture maintenance engineering systems. We focus here on modelling and solving a Level of Repair Analysis (LORA) problem variant with simultaneous determination of resources capacity and spare parts. A solution approach based on optimization via simulation is developed. The simulation phase determines the performance of the whole system taking into account the dynamics of the maintenance processes. The optimization phase explores the search space efficiently through a local search method in order to define a new solution with better performance. Computational results collected while solving an illustrative test problem are reported and discussed.

Optimisation techniques for planning the petrol replenishment to retail stations over a multi-period horizon

Aquifer recharge rates and patterns are often uncertain, especially in arid areas due to sporadic and erratic rainfall. Therefore, determining the optimal groundwater abstraction using classical approaches such as Monte Carlo Simulation (MCS) requires a large number of groundwater simulations and exorbitant computational efforts. The problem becomes even more complex and time consuming for regional coastal aquifers whose domains must be discretized using high-resolution meshes. In fact, even fast evolutionary multi-objective optimization techniques generally require a large number of simulations to determine the Pareto-front among the objectives. This study explores the performance of a Decision Tree (DT) approach for the generation of the Pareto optimal solutions of groundwater extraction. This paper applies the DTs for the optimal management of the Al-Khoud coastal aquifer in Oman. The learning process of the developed DT-based model uses the output of a numerical simulation model to assess the aquifer response based on different abstraction policies. The trained DT network then utilizes the NSGA-II to determine the Pareto-optimal solutions. The simulation show that the general flux pattern in the study area is toward the sea and the hydraulic head following a similar pattern in both best and worst recharging scenarios downstream of the studied recharging dam. Statistical tests showed a good correlation between the DT-based and simulation-based results and demonstrate the capability of the DT approach to obtain high-quality solutions by incorporating a large number of recharge scenarios. Moreover, the required runtime of the DT-based approach is extremely low (5xa0min) compared to that of the simulation-based method (several days). This means that including additional Monte-Carlo simulations can be readily done in few minutes using the obtained DTs, instead of the long computational time needed by the simulation-based approach.

The Periodic Petrol Station Replenishment Problem: An Overview

This paper deals with the problem of designing the logistics support of complex multiindenture and multi-echelon engineering systems, with the aim of determining the spare parts stock and the maintenance resources capacity, as well as the level of repair. The problem is modeled as an integer program with a nonlinear probabilistic constraint on the expected availability, whose satisfaction can only be evaluated by means of very time-consuming simulation experiments. Thus, we use an optimization via simulation approach, in which the search space is eciently explored through an approximated neighborhood evaluation mechanism, which makes use of several parameters estimated by means of simulation. Experimental results on a number of instances show the eectiveness of the proposed approach.