Uncertain Optimization of Discrete Supply Networks with Order Delivery Disruption and Risk Preference in the Postepidemic Era

Epidemic blockade leads to increased uncertainty and dynamic supply network disruption. This study considers an uncertain optimization of dynamic supply networks with risk preference and order delivery disruption. Taking the subjective utility of downstream enterprises as a reference point for the utility measurement of order delivery disruption and risk preference, this study constructs a biobjective optimization model with the goal of maximizing the downstream firm’s subjective utility and minimizing the manufacturer’s cost. The influence of each parameter in the downstream firm’s subjective utility function on the integrated optimization was analysed. The research found that the uncertain optimization model with the risk preference of downstream firms for order delivery disruption better controls the actual manufacturer’s order allocation and distribution problems when considering the downstream firms’ behaviour preference characteristics under bounded rationality. When allocating orders, manufacturers should consider that changes in order delivery disruption will cause changes in the subjective utility of downstream enterprises. In the process of multiperiod cooperation between manufacturers and downstream firms, they can obtain downstream firm risk preferences through repeated investigations.

Crop Planning in Synchronized Crop-Demand Scenarios: A Biobjective Optimization Formulation

Crop planning problems have been extensively studied from different perspectives (profit maximization, optimizing available water use, sustainability, etc.). In this paper, a new approach is proposed that considers new forms of customer-producer relationship, involving long-term cooperation agreements where the product volumes are agreed, and the demand is guaranteed in advance. In this context, typical of manufacturing production systems, crop planning must guarantee a given production level on specific dates, thus becoming deterministic in nature. In that context, this paper introduces a lexicographic biobjective optimization approach that, in addition to cost minimization, aims at minimizing the risk of not meeting the agreed demands. The latter is done by maximizing the geographic dispersion of the crops so that weather risk is mitigated. A number of experiments have been carried out to test the proposed approach, showing the high complexity of the solution and opening the door to new solution procedures for a problem that results from interest given to the new type of relationships in the food logistics chain.

Simulation Optimization for the Multihoist Scheduling Problem

Although the Multihoist Scheduling Problem (MHSP) can be detailed as a job-shop configuration, the MHSP has additional constraints. Such constraints increase the difficulty and complexity of the schedule. Operation conditions in chemical processes are certainly different from other types of processes. Therefore, in order to model the real-world environment on a chemical production process, a simulation model is built and it emulates the feasibility requirements of such a production system. The results of the model, i.e., the makespan and the workload of the most loaded tank, are necessary for providing insights about which schedule on the shop floor should be implemented. A new biobjective optimization method is proposed, and it uses the results mentioned above in order to build new scenarios for the MHSP and to solve the aforementioned conflicting objectives. Various numerical experiments are shown to illustrate the performance of this new experimental technique, i.e., the simulation optimization approach. Based on the results, the proposed scheme tackles the inconvenience of the metaheuristics, i.e., lack of diversity of the solutions and poor ability of exploitation. In addition, the optimization approach is able to identify the best solutions by a distance-based ranking model and the solutions located in the first Pareto-front layer contributes to improve the search process of the aforementioned scheme, against other algorithms used in the comparison.

Biobjective Optimization of Well Placement: Algorithm, Validation, and Field Testing

Summary We describe the development and validation of a novel algorithm for field-development optimization problems and document field-testing results. Our algorithm is founded on recent developments in bound-constrained multiobjective optimization of nonsmooth functions for problems in which the structure of the objective functions either cannot be exploited or are nonexistent. Such situations typically arise when the functions are computed as the result of numerical modeling, such as reservoir-flow simulation within the context of field-development planning and reservoir management. We propose an efficient implementation of a novel parallel algorithm, namely BiMADS++, for the biobjective optimization problem. Biobjective optimization is a special case of multiobjective optimization with the property that Pareto points may be ordered, which is extensively exploited by the BiMADS++ algorithm. The optimization algorithm generates an approximation of the Pareto front by solving a series of single-objective formulations of the biobjective optimization problem. These single-objective problems are solved using a new and more efficient implementation of the mesh adaptive direct search (MADS) algorithm, developed for nonsmooth optimization problems that arise within reservoir-simulation-based optimization workflows. The MADS algorithm is extensively benchmarked against alternative single-objective optimization techniques before the BiMADS++ implementation. Both the MADS optimization engine and the master BiMADS++ algorithm are implemented from the ground up by resorting to a distributed parallel computing paradigm using message passing interface (MPI) for efficiency in industrial-scaleproblems. BiMADS++ is validated and field tested on well-location optimization (WLO) problems. We first validate and benchmark the accuracy and computational performance of the MADS implementation against a number of alternative parallel optimizers [e.g., particle-swarm optimization (PSO), genetic algorithm (GA), and simultaneous perturbation and multivariate interpolation (SPMI)] within the context of single-objective optimization. We also validate the BiMADS++ implementation using a challenging analytical problem that gives rise to a discontinuous Pareto front. We then present BiMADS++ WLO applications on two simple, intuitive, and yet realistic problems, and a model for a real problem with known Pareto front. Finally, we discuss the results of the field-testing work on three real-field deepwater models. The BiMADS++ implementation enables the user to identify various compromise solutions of the WLO problem with a single optimization run without resorting to ad hoc adjustments of penalty weights in the objective function. Elimination of this “trial-and-error” procedure and distributed parallel implementation renders BiMADS++ easy to use and significantly more efficient in terms of computational speed needed to determine alternative compromise solutions of a given WLO problem at hand. In a field-testing example, BiMADS++ delivered a workflow speedup of greater than fourfold with a single biobjective optimization run over the weighted-sumsobjective-function approach, which requires multiple single-objective-function optimization runs.

One-exact approximate Pareto sets

Papadimitriou and Yannakakis (Proceedings of the 41st annual IEEE symposium on the Foundations of Computer Science (FOCS), pp 86–92, 2000) show that the polynomial-time solvability of a certain auxiliary problem determines the class of multiobjective optimization problems that admit a polynomial-time computable $$(1+\varepsilon , \dots , 1+\varepsilon )$$ ( 1 + ε , ⋯ , 1 + ε ) -approximate Pareto set (also called an $$\varepsilon $$ ε -Pareto set). Similarly, in this article, we characterize the class of multiobjective optimization problems having a polynomial-time computable approximate $$\varepsilon $$ ε -Pareto set that is exact in one objective by the efficient solvability of an appropriate auxiliary problem. This class includes important problems such as multiobjective shortest path and spanning tree, and the approximation guarantee we provide is, in general, best possible. Furthermore, for biobjective optimization problems from this class, we provide an algorithm that computes a one-exact $$\varepsilon $$ ε -Pareto set of cardinality at most twice the cardinality of a smallest such set and show that this factor of 2 is best possible. For three or more objective functions, however, we prove that no constant-factor approximation on the cardinality of the set can be obtained efficiently.

Hybrid self-adaptive biobjective optimization of multiple robot scheduling problem for mixed-model assembly lines considering energy savings

With the wide application of robots in the material distribution process on the assembly lines, single robot scheduling cannot meet the actual production needs. However, the high degree of mechanization also brings about environmental problems. Therefore, this article aims to develop a scheduling methodology to accomplish material supply tasks using multiple robots with energy consumption consideration. Meanwhile, a targeted mathematical model to minimize total weighted penalty costs and total energy consumption is developed. Due to the NP-hard nature of the problem, an adaptive hybrid mutation population extremal optimization multi-objective algorithm based on uniform distribution selection is proposed to solve multi-objective problems. Furthermore, a new coding method for initialization is designed to optimize the whole iterative process. The performance of the proposed algorithm is evaluated by comparing with three benchmark multi-objective algorithms. Computational experiments are represented to prove the validity and feasibility of the proposed algorithm.