Ali Diabat

Supply chain risk management and its mitigation in a food industry

We create a model which analyses the various risks involved in a food supply chain with the help of interpretive structural modelling (ISM). The various types of risks were identified based on a review of the literature and in consultation with experts in the food industry. The types of risks are clustered into five categories and risk mitigation is discussed. The model developed is validated with the help of a case study involving a food products manufacturing firm.

Strategic Closed-Loop Facility Location Problem With Carbon Market Trading

The burgeoning environmental regulations are forcing companies to green their supply chains by integrating all of their business value-adding operations so as to minimize the impact on the environment. One dimension of greening the supply chain is extending the forward supply chain to collection and recovery of products in a closed-loop configuration. Remanufacturing is the basis of profit-oriented reverse logistics in which recovered products are restored to a marketable condition in order to be resold to the primary or secondary market. In this paper, we introduce a multiechelon multicommodity facility location problem with a trading price of carbon emissions and a cost of procurement. The company might either incur costs if the carbon cap, normally assigned by regulatory agencies, is lower than the total emissions, or gain profit if the carbon cap is higher than the total emissions. A numerical study is presented which studies the impact of different carbon prices on cost and configuration of supply chains.

An Integrated Quay Crane Assignment and Scheduling Problem

This paper investigates the integrated quay crane assignment and scheduling problem (QCASP). The problem requires determining the assignment of quay cranes to vessels and the scheduling of operations to be performed by each quay crane. Different practical aspects of the problem are considered including non-crossing and safety margin constraints. The resulting problem is NP-complete and, therefore, requires advanced techniques to solve it. For this purpose, we propose an exact method based on a branch-and-price algorithm. Computational experiments show that the proposed method can solve large-sized problems efficiently.

An improved Lagrangian relaxation-based heuristic for a joint location-inventory problem

We consider a multi-echelon joint inventory-location (MJIL) problem that makes location, order assignment, and inventory decisions simultaneously. The model deals with the distribution of a single commodity from a single manufacturer to a set of retailers through a set of sites where distribution centers can be located. The retailers face deterministic demand and hold working inventory. The distribution centers order a single commodity from the manufacturer at regular intervals and distribute the product to the retailers. The distribution centers also hold working inventory representing product that has been ordered from the manufacturer but has not been yet requested by any of the retailers. Lateral supply among the distribution centers is not allowed. The problem is formulated as a nonlinear mixed-integer program, which is shown to be NP-hard. This problem has recently attracted attention, and a number of different solution approaches have been proposed to solve it. In this paper, we present a Lagrangian relaxation-based heuristic that is capable of efficiently solving large-size instances of the problem. A computational study demonstrates that our heuristic solution procedure is efficient and yields optimal or near-optimal solutions.

A closed-loop location-inventory problem with spare parts consideration

A closed-loop location-inventory problem is considered. Forward supply chain consists of a single echelon where the distribution centers (DCs) have to distribute a single product to different retailers with random demands. Reverse supply chain also contains only one echelon where the remanufacturing centers (RCs) collect the returns from the retailers, remanufacture them as spare parts and then push them back to the retailers assigned to the DCs through the forward supply chain. The problem is to choose which DCs and RCs are to be opened and to associate the retailers with them. The problem is formulated using a mixed integer nonlinear location allocation model. An exact two-phase Lagrangian relaxation algorithm is developed to solve it. The computational results and sensitivity analysis are presented.

Hybrid algorithm for a vendor managed inventory system in a two-echelon supply chain

In this paper we address the issue of vendor managed inventory (VMI) by considering a two-echelon single vendor/multiple buyer supply chain network. We try to find the optimal sales quantity by maximizing profit, given as a nonlinear and non-convex objective function. For such complicated combinatorial optimization problems, exact algorithms and optimization commercial software such as LINGO are inefficient, especially on practical-size problems. In this paper we develop a hybrid genetic/simulated annealing algorithm to deal with this nonlinear problem. Our results demonstrate that the proposed hybrid algorithm outperforms previous methodologies and achieves more robust solutions.

A multi-vessel quay crane assignment and scheduling problem: Formulation and heuristic solution approach

This paper presents a new approach to analyze the integrated quay crane assignment and scheduling problem (QCASP). The problem determines the assignment of quay cranes to vessels and the sequence of tasks to be processed by each quay crane simultaneously, and accounts for important considerations such as safety margins between quay cranes (QCs), ordering conditions and vessel priority. Furthermore, QCs can travel from one vessel to another vessel whenever tasks are complete. The integrated problem is difficult to solve with exact methods due to its complexity. Therefore, a genetic algorithm (GA) is proposed to solve the integrated QCASP. Computational results validate the performance of the proposed GA.

A simulated annealing technique for multi-objective simulation optimization

In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.

An Integrated Supply Chain Problem with Environmental Considerations

The continuous rise in environmental awareness has affected several aspects of the global economy, including supply chain management. Traditionally, supply chains are designed and operated in a way that minimizes costs and increases profitability; however, this is not sufficient nowadays. It is of crucial importance to incorporate the target of emissions׳ reduction in the supply chain. Thus, in the current paper we address a joint location-inventory problem and extend it to account for the reduction of carbon emissions. The original problem consists of one plant, multiple distribution centers (DCs) and multiple retailers, with products flowing from a plant to DCs and from there to retailers. We also account for uncertainty by including a new variable that reflects the probability of different demand scenarios. In terms of the solution technique, we develop a Genetic Algorithm (GA) and justify our choice of this heuristic approach by the fact that the resulting model is high in complexity and requires solving within reasonable time. We test the GA with a thorough sensitivity analysis using several chromosome representations, different mutation and crossover probabilities, as well as different evaluation functions. Finally, we validate the accuracy of our GA on small instances that have been solved to optimality using GAMS.

An evolutionary programming approach for solving the capacitated facility location problem with risk pooling

In this paper, we propose a genetic algorithm as an alternative technique for solving the capacitated facility location problem with risk pooling (CLMRP). The CLMRP is a joint location-inventory problem involving a single supplier and multiple retailers that face stochastic demand. Due to the stochasticity of demand associated with each retailer, risk pooling may be achieved by allowing some retailers to serve as distribution centres (DCs). This is a combinatorial optimisation problem that has been shown to be NP-hard. A genetic algorithm that is computationally very efficient is developed to solve the problem. A computational experiment is conducted to test the performance of the developed technique and computational results are reported. The algorithm can easily find optimal or near optimal solutions for benchmark test problems from the literature, where the Lagrangian relaxation approach was used.