A Genetic Algorithm to Minimize the Makespan in a Two-Machine Cross-Docking Flow Shop Problem

We consider the problem of two-machine cross-docking flow shop scheduling where each job on the second machine cannot be processed unless a job or a set of jobs have been completed on the first machine.The aim is to find a feasible schedule that minimizes the makespan.As the problem is shown to be strongly NP-hard,we propose a genetic algorithm to solve small and large size problems.We test different types for each genetic operator where new ideas are introduced,which leads to propose six versions of the genetic algorithm.We then evaluate their effectiveness through an extensive computational experiments by using many instances generated randomly and by determining the percentage deviation from a lower bound from the literature.

Collaborative Optimization of Dock Door Assignment and Vehicle Scheduling in Cross-Docking

Cross-docking is a logistic strategy that can transport goods directly from suppliers or manufacturers to retailers or customers.In daily life,the requirements for timeliness of goods distribution have been continuously improved.Cross-docking can realize the rapid transshipment of goods and improve the process efficiency of distribution greatly.Meanwhile,during the cross-docking process,goods are deposited in the temporary storage area,which reduces the storage cost.This paper focuses on the analysis of reasonable vehicle scheduling and dock door allocation problems in cross-docking.The goal is to minimize the working time of cross-docks by the research on this combinatorial optimization problem.This paper proposes the genetic algorithm(GA)and the hybrid particle swarm optimization to solve the three-scale(small,medium and large)cross-docks.Optimal completion time,average completion time and average solution time are considered as factors to evaluate the efficiency of two algorithms on three scales.And then the concept of mixed-mode dock door is introduced.GA is used to conduct numerical experiments with mixed dock doors on different scales.Finally,by comparing the utilization rate of mixed dock doors,we can analyze the influence of mixed dock door on vehicles’waiting time.

A neural branch-and-price for truck scheduling in cross-docks

In this paper,we address the complex problem of dock-door assignment and truck scheduling within cross-docking operations.This is a problem that requires frequent resolution throughout the operational day,as disruptions often invalidate the optimal plan.Given the problem's highly combinatorial nature,finding an optimal solution demands significant computational time and resources.However,the distribution of data across problem instances over a lengthy planning horizon remains consistently stable,with minimal concern regarding distribution shift.These factors collectively establish the problem as an ideal candidate for a learn-to-optimize solution strategy.We propose a Dantzig-Wolfe reformulation,solving it via both a conventional branch-and-price approach and a neural branch-and-price approach,the latter of which employs imitation learning.Additionally,we introduce some classes of valid inequalities to enhance and refine the pricing problem through a branch-and-cut scheme.Our computational experiments demonstrate that this methodology is not only feasible but also presents a viable alternative to the traditional branch-and-price algorithms typically utilized for such challenges.

A New Method of Modeling Based on SCOR and BPMN Case Study： Platform of Cross Docking for a Car Manufacturer

In this paper, we develop a hybrid model based on SCOR and BPMN to model the operational processes of a platform of cross docking. The interest of the developed model is its dynamic capacity to describe the interactions between the logistic processes most faithfully possible, and on the other hand to propose an approach of evaluation of the performance. We called this ＂tool＂ ＂BPMPE＂ （business process modeling ＆ performance evaluation）. We used several constraints to estimate the robustness of the tool.

Multiple Cross-docks Scheduling with Multiple Doors using Fuzzy Approach and Metaheuristic Algorithms

The issue of supply chain in today's world is a major competitive advantage in reducing costs.Supply chain includes procurement,logistics and transportation,marketing,organizational behavior,networking,strategic management,information systems management and operations management.One of the most important practices in logistics is cross-docking which sets its goals as inventory reduction and customer satisfaction increase.Customers receive goods through docks.Docks are responsible to provide a place for goods before being delivered to the customers.Then,these materials are directly loaded into outbound trucks with little or no storage in between to send to customers in the shortest possible time.This paper is mainly aimed at introducing a mixed integer linear programming model to solve scheduling several cross-docking problems.The proposed model is highly facilitated to allocate the optimal destinations to storage doors and truck scheduling in docks while selecting the collection and delivery routes.Using optimization approaches at uncertainty conditions is also of great importance.Mathematical programming techniques vividly fail to solve transportation problems that include fuzzy objective function coefficients.A fuzzy multi-objective linear programming model is proposed to solve the transportation decision-making with fuzzy objective function coefficients in this paper.On the other hand,the existences of computational complexities lead this model to be categorized as a NP-Hard one.Therefore,we applied metaheuristic algorithms such as genetic and ant colony in order to solve our proposed problem.