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Modeling soft unloading constraints in the multi-drop container loading problem

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期308卷 336-352页

作者： Filella, Guillem Bonet Trivella, Alessio Corman, Francesco Swiss Fed Inst Technol Inst Transport Planning & Syst CH-8092 Zurich Switzerland Univ Twente Ind Engn & Business Informat Syst NL-7500 AE Enschede Netherlands

The multi-drop container loading problem (MDCLP) requires loading a truck so that boxes can be unloaded at each drop-off point without rearranging other boxes to deliver later. However, modeling such unloading constraints as hard constraints, as done in the literature, limits the flexibility to optimize the packing and utilize the vehicle capacity. We instead propose a more general approach that considers soft unloading constraints. Specifically, we penalize unnecessary relocations of boxes using penalty functions that depend on the volume and weight of the boxes to move as well as the type of move. Our goal is to maximize the value of the loaded cargo including penalties due to violations of the unloading constraints. We provide a mixed-integer linear programming formulation for the MDCLP with soft unloading constraints, which can solve to optimality small-scale instances but is intractable for larger ones. We thus propose a heuristic framework based on a randomized extreme-point constructive phase and a subsequent improvement phase. The latter phase iteratively destroys regions in the packing space where high penalties originate, and reconstructs them. Extensive numerical experiments involving different instances and penalties highlight the advantages of our method compared to a commercial optimization solver and a heuristic from the literature developed for a related problem. They also show that our approach significantly outperforms: (i) the hard unloading constraints approach, and (ii) a sequential heuristic that neglects unloading constraints first and evaluates the penalties afterwards. Our findings underscore the relevance of accounting for soft unloading constraints in the MDCLP.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Packing Container loading Multi -drop shipments mixed -integer programming Improvement heuristic

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Advancements in the computation of enclosures for multi-objective optimization problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期310卷 315-327页

作者： Eichfelder, Gabriele Warnow, Leo Tech Univ Ilmenau Inst Math PO 10 05 65 D-98684 Ilmenau Germany

A central goal for multi-objective optimization problems is to compute their nondominated sets. In most cases these sets consist of infinitely many points and it is not a practical approach to compute them exactly. One solution to overcome this problem is to compute an enclosure, a special kind of coverage, of the nondominated set. For that computation one often makes use of so-called local upper bounds. In this paper we present a generalization of this concept. For the first time, this allows to apply a warm start strategy to the computation of an enclosure. We also show how this generalized concept allows to remove empty areas of an enclosure by deleting certain parts of the lower and upper bound sets which has not been possible in the past. We demonstrate how to apply our ideas to the box approximation algorithm, a general framework to compute an enclosure, as recently used in the solver called BAMOP. We show how that framework can be simplified and improved significantly, especially concerning its practical numerical use. In fact, we show for selected numerical instances that our new approach is up to eight times faster than the original one. Hence, our new framework is not only of theoretical but also of practical use, for instance for continuous convex or mixed-integer quadratic optimization problems.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Multiple objective programming Nonlinear programming mixed -integer programming Enclosure Warm start strategies

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Integrated location and inventory planning in service parts logistics with last-mile delivery outsourcing

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 189卷

作者： Zhao, Quanwu Zuo, Yuxing Ai, Limin Liu, Hua Chongqing Univ Sch Econ & Business Adm Chongqing 400030 Peoples R China Chongqing Univ Chongqing Key Lab Logist Chongqing Peoples R China SANY HEAVY EQUIPMENT CO LTD Shenyang Peoples R China

Outsourcing last-mile delivery to express companies provides an innovative strategy for capital goods manufacturers to improve service parts logistics. Given delivery dilemma due to geographically dispersed customers and low-frequency uncertain demands, we develop an integrated location and inventory problem with last-mile delivery outsourcing in a two-echelon service parts logistics. The proposed non-linear mixed integer programming model captures important decisions involving (1) location of local warehouses, (2) customer allocation to opened local warehouses, (3) inventory levels at these local warehouses, (4) outsourcing or self-operating lastmile delivery. We then replace service level constraints that cause nonlinearity with two new sets of constraints, and construct a linear mixed integer formulation that could be solved by GUROBI solver for some small-scale problems. It is still a challenging problem for large scale case, for which we propose an adaptive large neighbourhood search (ALNS) algorithm. Our extensive computational experiments verify the effectiveness of our model and algorithm. Computational experiments' results indicate that outsourcing last-mile delivery amounting to around 12 % total cost savings on average, compared to self-operating in a real-world large instance from our research partner. Furthermore, we test some key parameters with computational experiments, and provide some managerial implications.

关键词： Multi-parts location and inventory problem Last -mile delivery Service parts logistics mixed -integer programming Adaptive large neighbourhood search

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Finding the most degree-central walks and paths in a graph: Exact and heuristic approaches

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第3期308卷 1021-1036页

作者： Matsypura, Dmytro Veremyev, Alexander Pasiliao, Eduardo L. Prokopyev, Oleg A. Univ Sydney Discipline Business Analyt Sydney NSW 2000 Australia Univ Cent Florida Dept Ind Engn & Management Syst 12800 Pegasus Dr Orlando FL 32816 USA Air Force Res Lab Munit Directorate 101 W Eglin Blvd Eglin Afb FL 32542 USA Univ Pittsburgh Dept Ind Engn 1031 Benedum Hall Pittsburgh PA 15261 USA

In network analysis, node centrality is used to quantify the importance of a node to the structure of the network. One of the most natural and widely used centrality measures is degree centrality, defined as the number of nodes adjacent to a given node. A simple generalization of this concept that arises in many real-life applications is to consider the centrality of node groups, including subgraphs with spe-cific connectivity properties. In this paper, we consider the problem of finding the most central walk in a network, where the centrality of the walk is given by the size of its immediate neighborhood. We begin with the problem of finding the most central shortest path and show that this problem can be solved in polynomial time. We then focus on finding other types of most central walks, such as general walks, trails, paths, and induced paths of some pre-defined length. We demonstrate that in contrast to the most central shortest path problem, these problems are NP-hard. We propose two types of linear MIP formulations to solve these problems exactly that rely on two interpretations of a walk: a sequence of visited nodes and a sequence of traversed edges. In addition, we develop two heuristic algorithms and demonstrate their effectiveness by comparing them with the exact solutions obtained using MIPs;we also exploit heuristic solutions to warm-start the MIP solver. Finally, we test our solution approaches using synthetic and real-life networks in an extensive computational study, which allows us to provide some interesting insights and observations.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Networks Degree centrality Group centrality Network analysis mixed -integer programming

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Design of a supply chain network for chemicals from biomass using green electrochemistry

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CLEANER LOGISTICS AND SUPPLY CHAIN 2024年 10卷

作者： Kashanian, Motahareh Ryan, Sarah M. Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA 50011 USA

Increasing concern about the environmental impact of industrial activities has prompted a shift to renewable energy sources and the development of environmentally conscious supply chains. In this regard, electrochemistry has shown promise for converting biomass into specialty chemicals in distributed facilities that exploit renewable energy resources. To examine the impact of electrochemistry technology on optimal supply chain configuration, we formulate a mixed-integer linear programming model to optimize the locations and capacities of distributed facilities for converting biomass to chemicals. The economic objective of the supply chain design model is to minimize the total annual cost of producing chemicals from biomass-derived glucose and delivering them to market. To analyze the trade-off between environmental and economic considerations, we also consider an environmental objective of minimizing greenhouse gas (GHG) emissions. The results of a US case study indicate that, while cost is minimized by constructing one large facility, GHG emissions are lowered by a distributed configuration. Varying the setting of a process design parameter expands the Pareto frontier along which decision-makers can choose a configuration according to their preferences between economic and environmental criteria.

关键词： Chemical supply chain Supply chain network design mixed -integer programming Electrochemistry

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Multiskilled personnel assignment with k-chaining considering the learning-forgetting phenomena

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2023年第1期265卷

作者： Henao, Cesar Augusto Mercado, Yessica Andrea Gonzalez, Virginia I. Luer-Villagra, Armin Univ Norte Barranquilla Colombia Univ Andres Bello Dept Engn Sci Santiago Chile

Multiskilling is a workforce flexibility strategy where companies educate workers to perform a set of task types effectively. When the multiskilling plans are structured using k-chaining policies, it is possible to obtain the maximum flexibility to match the uncertain workforce demand. This work evaluates the potential benefits of multiskilled workers using a k-chaining policy with k >= 2, considering the learning/forgetting phenomena to model a heterogeneous workforce. We propose a deterministic mixed-integer programming model to compute the level of required multiskilling. The mathematical formulation determines how many workers should be single-skilled and multiskilled, which task types they should be trained in, the allocation of working hours, and the expected productivity of each worker on each week of the planning horizon. We test our methodology on a case study using real, processed, and simulated data from a Chilean retail store. We perform three experiments, comparing them: zero multiskilling, k-chaining with k >= 2 and homogeneous workforce, and k-chaining with k >= 2 and heterogeneous workforce. We consider nine variability levels in the workforce demand for each experiment. The results show that modeling the workforce as homogeneous leads to underestimating the multiskilling level required to minimize understaffing. Incorporating heterogeneous workforce modeling through the learningforgetting phenomena suggests more multiskilling to compensate for the lower workers' productivity. We consider this solution is closer to the actual operation of the store. We also perform a sensitivity analysis on the learning rate parameter to evaluate the stability of the report solutions for each variability level.

关键词： Chaining Learning-forgetting phenomena Multiskilling mixed -integer programming Personnel scheduling Retail

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Energy-constrained multi-visit TSP with multiple drones considering non-customer rendezvous locations

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 210卷

作者： Mahmoudi, Bahare Eshghi, Kourosh Sharif Univ Technol Dept Ind Engn Tehran Iran

This paper introduces the energy-constrained multi-visit traveling salesman problem with multiple drones considering non-customer rendezvous locations (EM-TSPDs). A ground vehicle equipped with multiple drones that can serve multiple customers per trip performs deliveries. Before solving the EM-TSPDs, the non-customer rendezvous location subproblem (NRLP) is addressed to locate candidate non-customer rendezvous nodes so that all drone customers can be covered by drones. The importance of this full coverage is that all drone customers can reap the benefits of drone delivery by receiving fast delivery or being served in areas that are inaccessible by road network. We propose MILP formulations for the NRLP and EM-TSPDs that can be solved with standard MILP solvers. Due to the NP-hard nature of the problem, heuristic algorithms are provided to solve medium and large-scale instances in a time-efficient manner. Numerical results show that using multiple drones that can serve multiple customers per trip leads to a reduction in the makespan. Taking into account the battery energy con-sumption at different phases of the drone flight leads to more accurate and realistic modeling of the drone endurance. In addition, the impact of adding non-customer rendezvous locations is investigated.

关键词： Drone Traveling salesman problem Logistics Last-mile delivery mixed -integer programming Matheuristic approach

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The multi-factory two-stage assembly scheduling problem

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2024年 38卷

作者： Kazemi, Hamed Nourelfath, Mustapha Gendreau, Michel Laval Univ Dept Mech Engn Quebec City PQ Canada Interuniv Res Ctr Entreprise Networks Logist & Tra Montreal PQ Canada Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada

The recent notable focus on distributed production management in academic and industrial contexts has underscored the importance of scheduling across multiple factories. Accordingly, this study investigates a new multi-factory configuration in which non-identical factories produce different components of a final product in the first stage. Each factory is considered as a classical flow-shop, which can manufacture a unique component. These components are assembled into final products in the assembly factory, which is located in the second stage. Unlike other distributed scheduling problems, to determine a united production sequence in such a system, there is no need to find a suitable factory to assign a job since each factory is qualified for a particular task. In realworld applications, these systems encounter challenges that span from information architectures and negotiation mechanisms to the development of scheduling algorithms. The objective of this research is to schedule the jobs in each factory to minimize the makespan of the entire process. For this purpose, a mixed-integer programming model is developed to deal with small-size instances. Then, the lower bound is derived and incorporated to develop a branch and bound method. Furthermore, to deal with larger instances, five heuristic methods are developed, and the worst-case analysis is carried out. Computational experiments are conducted for different test classes to compare and to highlight the performance of the proposed solution procedures.

关键词： Multi -factory scheduling mixed -integer programming Branch and bound method Heuristic algorithms

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Logic-based Benders decomposition for order acceptance and scheduling in distributed manufacturing

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ADVANCED ENGINEERING INFORMATICS 2023年 58卷

作者： Chen, Jian Ma, Wenjing Ye, Xudong Zhao, Zhiheng Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing Peoples R China Minist Ind & Informat Technol Key Lab Intelligent Decis & Digital Operat Nanjing Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China

This paper studies an order acceptance and scheduling problem in distributed manufacturing to minimize the total costs consisting of rejection cost, production cost, transportation cost and tardiness cost. Two mixed-integer programming models are formulated, which are further improved by the proposed enhancement techniques. A logic-based Benders decomposition (LBBD) method and a branch and check search framework are developed in an attempt to realize the optimal and intelligent decisions of order acceptance, order assignment and scheduling. The proposed LBBD method following the principle of "divide and conquer" divides the original problem into determining the master problem of order acceptance and assignment, and determining the subproblem of order sequencing in each factory. A dynamic programming algorithm is also proposed to efficiently solve the subproblems. Extensive computational experiments are conducted, and the results demonstrate the effectiveness and efficiency of the enhancement techniques for the formulations, as well as the LBBD method. Besides, the value of distributed manufacturing is verified and the sensitivity of key cost factors is presented.

关键词： Logic -based Benders decomposition Order acceptance and scheduling Distributed manufacturing mixed -integer programming

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